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#!/usr/bin/env python import glob import numpy as np import astropy.io.fits as fits import scipy.optimize as opt import matplotlib.pyplot as plt import matplotlib as mpl mpl.rcParams['font.family'] = 'Times New Roman' mpl.rcParams['font.size'] = '15' mpl.rcParams['mathtext.default'] = 'regular' #mpl.rcParams['xtick.top'] = 'True' #mpl.rcParams['ytick.right'] = 'True' mpl.rcParams['xtick.direction'] = 'in' mpl.rcParams['ytick.direction'] = 'in' #mpl.rcParams['axes.grid'] = 'True' mpl.rcParams['axes.xmargin'] = '.08' #'.05' mpl.rcParams['axes.ymargin'] = '.10' mpl.rcParams['savefig.facecolor'] = 'None' mpl.rcParams['savefig.edgecolor'] = 'None' mpl.rcParams['savefig.bbox'] = 'tight' numof_xrays_mpgrp = [] significance_n50 = [] significance_n25 = [] significance_n250 = [] for i in range(10): if i == 8: continue fitsfile = 'out/crabgrp/v181119_rand/input%02d/default/default.fits' % i if len(glob.glob(fitsfile)) == 0: continue print(fitsfile) hdu = fits.open(fitsfile) numof_xrays_mpgrp.append(hdu[1].header['NXMPGRP']/1e+6) for extnum in range(1,len(hdu)): peak_index = np.argmax(hdu[extnum].data['ALL_NORM_COUNTS']) peak_enhance = hdu[extnum].data['MPGRP_NORM_SUB'][peak_index] peak_enhance_error = hdu[extnum].data['MPGRP_NORM_SUB_ERROR'][peak_index] peak_enhance_significance = peak_enhance / peak_enhance_error print('%d: %.2e %.2e %.3e' % (extnum,peak_enhance,peak_enhance_error,peak_enhance_significance)) if hdu[extnum].name == 'PROFILE_N50': significance_n50.append(peak_enhance_significance) if hdu[extnum].name == 'PROFILE_N25': significance_n25.append(peak_enhance_significance) peak_index = np.argmax(hdu['PROFILE_N250'].data['ALL_NORM_COUNTS']) all_count_peak_sum = sum(hdu['PROFILE_N250'].data['ALL_COUNTS'][peak_index-1:peak_index+2]) mpgrp_count_peak_sum = sum(hdu['PROFILE_N250'].data['MPGRP_COUNTS'][peak_index-1:peak_index+2]) all_norm_peak_sum = float(all_count_peak_sum)/ float(hdu['PROFILE_N250'].header['NXALL']) mpgrp_norm_peak_sum = float(mpgrp_count_peak_sum) / float(hdu['PROFILE_N250'].header['NXMPGRP']) all_norm_peak_sum_error = np.sqrt(float(all_count_peak_sum))/ float(hdu['PROFILE_N250'].header['NXALL']) mpgrp_norm_peak_sum_error = np.sqrt(float(mpgrp_count_peak_sum)) / float(hdu['PROFILE_N250'].header['NXMPGRP']) peak_enhance = mpgrp_norm_peak_sum - all_norm_peak_sum peak_enhance_error = np.sqrt(all_norm_peak_sum_error**2+mpgrp_norm_peak_sum_error**2) peak_enhance_significance = peak_enhance / peak_enhance_error print(all_norm_peak_sum,mpgrp_norm_peak_sum,peak_enhance,peak_enhance_error,peak_enhance_significance) significance_n250.append(peak_enhance_significance) def func(x, k): return k * np.sqrt(x) # The actual curve fitting happens here optimizedParameters, pcov = opt.curve_fit(func, numof_xrays_mpgrp, significance_n250); # Use the optimized parameters to plot the best fit plt.clf() fig, axes = plt.subplots(1,1,figsize=(7.0,6.0)) #plt.plot(numof_xrays_mpgrp,significance_n250,'ro',markersize=8.0,label='0.04 x 3',zorder=4,edgecolor='black') #plt.plot(numof_xrays_mpgrp,significance_n50,'ys',markersize=8.0,label='0.02',zorder=3) #plt.plot(numof_xrays_mpgrp,significance_n25,'b^',markersize=8.0,label='0.04',zorder=2) plt.scatter(numof_xrays_mpgrp,significance_n250,marker='o',s=100.0,facecolor='r',label='0.004 x 3',zorder=4,edgecolor='black') plt.scatter(numof_xrays_mpgrp,significance_n50,marker='s',s=100.0,facecolor='y',label='0.02',zorder=3,edgecolor='black') plt.scatter(numof_xrays_mpgrp,significance_n25,marker='^',s=100.0,facecolor='b',label='0.04',zorder=2,edgecolor='black') x = np.arange(0,10,0.1) plt.plot(x,func(x, *optimizedParameters),'r',linestyle='--',zorder=1); axes.set_xlabel(r'Number of X-ray events coincidenced with MP-GRPs (10$^{6}$ photons)') axes.set_ylabel('Significance of the enhancement') axes.set_xlim(0.0,10.0) axes.set_ylim(0.0,6.0) plt.legend(loc='upper left',title='phase width') plt.savefig('out/crabgrp/v181119_rand/growth_curve_n50.pdf')
[ "scipy.optimize.curve_fit", "matplotlib.pyplot.savefig", "numpy.sqrt", "matplotlib.pyplot.legend", "matplotlib.pyplot.clf", "numpy.argmax", "glob.glob", "matplotlib.pyplot.scatter", "astropy.io.fits.open", "matplotlib.pyplot.subplots", "numpy.arange" ]
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# Importações import matplotlib.pyplot as plt import numpy as np # Acrescentar Sinais # Mensagem, Portadora, Subportadora # Descobrir o K tempo_maximo = 45000 frequencia_mensagem = 5 frequencia_portadora = 40 amplitude_portadora = 1 # Vai dividir cada valor do vetor criado para que se obtenha valores pequenos tempo = np.arange(tempo_maximo) / tempo_maximo mensagem = np.sin(2 * np.pi * frequencia_mensagem * tempo) portadora = np.multiply(amplitude_portadora, np.cos( 2 * np.pi * frequencia_portadora * tempo)) modulado = np.multiply(mensagem, portadora) # Gerar Graficos # Gráfico da Mensagem plt.subplot(2, 2, 1) plt.title('Mensagem') plt.plot(mensagem) plt.xlabel('Tempo') plt.ylabel('Amplitude') plt.grid(True) # Gráfico da Portadora plt.subplot(2, 2, 2) plt.title('Portadora') plt.plot(portadora) plt.xlabel('Tempo') plt.ylabel('Amplitude') plt.grid(True) # Gráfico do sinal final plt.subplot(2, 2, 3) plt.title('Sinal Modulado') plt.plot(modulado) plt.xlabel('Tempo') plt.ylabel('Amplitude') plt.grid(True) # Sinal Demodulado plt.subplot(2, 2, 4) plt.title('Sinal Demodulado') plt.plot(demodulado) plt.xlabel('Tempo') plt.ylabel('Amplitude') plt.grid(True) # Apresenta o Gráfico plt.show()
[ "numpy.multiply", "matplotlib.pyplot.grid", "matplotlib.pyplot.ylabel", "matplotlib.pyplot.xlabel", "matplotlib.pyplot.plot", "numpy.cos", "numpy.sin", "matplotlib.pyplot.title", "matplotlib.pyplot.subplot", "numpy.arange", "matplotlib.pyplot.show" ]
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import numpy as np import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers def f(x): return x[0]+x[1]*x[2]+np.sin(x[3]) def gettestdata(f,n=1000): x=np.random.normal(0,1,(n,4)) y=np.array([[f(xx)] for xx in x]) return x,y def traindensemodel(datax,datay,hidden,lr=0.001,batchsize=20,activation="relu",epochs=30): try: batchsize=int(batchsize) print("training model with",hidden,lr,batchsize,activation,epochs) # exit() # print("training model") inputs=keras.Input(shape=datax.shape[1:]) x=inputs for h in hidden: x=layers.Dense(h,activation=activation)(x) x=layers.Dense(datay.shape[-1])(x) model=keras.Model(inputs=inputs,outputs=x,name="super_simple_model") model.compile(loss="mse",optimizer=keras.optimizers.Adam(lr)) clen=int(datax.shape[0]) vlen=int(clen*0.2) history=model.fit(datax[:-vlen],datay[:-vlen],batch_size=batchsize,epochs=epochs,validation_split=0.2,verbose=0) score=model.evaluate(datax[-vlen:],datay[-vlen:],verbose=0) return score except: print("failed") return 1000.0 # return traindensemodel(datax,datay,hidden,lr,batchsize,activation,epochs) if __name__=="__main__": datax,datay=gettestdata(f) print(traindensemodel(datax,datay,[3,2])) import time t0=time.time() for i in range(10): print(traindensemodel(datax,datay,[3,2])) t1=time.time() print("!",(t1-t0)/10)
[ "numpy.random.normal", "tensorflow.keras.Model", "tensorflow.keras.optimizers.Adam", "tensorflow.keras.layers.Dense", "tensorflow.keras.Input", "numpy.sin", "time.time" ]
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# 相手の駒配置を予測 # これは不完全情報ゲームにおいて動作するようにする # 正体が不明な相手の駒をとりあえず-1としておく # board→14R24R34R44R15B25B35B45B41u31u21u11u40u30u20u10u # move import numpy as np import itertools import time from game import State # from pv_mcts import predict from pathlib import Path from tensorflow.keras.models import load_model from test import convert_func_use_in_guess # model_path = "models/10000.pth" default_gamma = 0.9 DN_INPUT_SHAPE = (6, 6, 4) # おそらく不完全情報ガイスター(のstateのみ？)を定義してそれを更新して管理した方がよさげ # 不完全情報ガイスターの盤面情報及びそれらの推測値 class II_State: # クラス変数で駒順を定義 piece_name = [ "h", "g", "f", "e", "d", "c", "b", "a", "A", "B", "C", "D", "E", "F", "G", "H", ] # 初期化 def __init__( self, real_my_piece_blue_set, real_enemy_piece_blue_set=None, see_through_piece_id=None, wrong_see_through_piece_id=None, all_piece=None, enemy_estimated_num=None, my_estimated_num=None, enemy_piece_list=None, my_piece_list=None, living_piece_color=None, ): # 全ての駒(hgfedcbaABCDEFGHの順になっている) # 敵駒0~7,自駒8~15 if all_piece == None: # numpyは基本的に型指定しない方が早い(指定すると裏で余計な処理するっぽい) self.all_piece = np.zeros(16, dtype=np.int16) # 初期配置を代入(各値は座標を示す)(脱出が88、死亡が99) # 0~7は敵駒, 8~15は自駒 self.all_piece[0] = 1 self.all_piece[1] = 2 self.all_piece[2] = 3 self.all_piece[3] = 4 self.all_piece[4] = 7 self.all_piece[5] = 8 self.all_piece[6] = 9 self.all_piece[7] = 10 self.all_piece[8] = 25 self.all_piece[9] = 26 self.all_piece[10] = 27 self.all_piece[11] = 28 self.all_piece[12] = 31 self.all_piece[13] = 32 self.all_piece[14] = 33 self.all_piece[15] = 34 else: self.all_piece = all_piece if enemy_piece_list == None: self.enemy_piece_list = [0, 1, 2, 3, 4, 5, 6, 7] else: self.enemy_piece_list = enemy_piece_list if my_piece_list == None: self.my_piece_list = [8, 9, 10, 11, 12, 13, 14, 15] else: self.my_piece_list = my_piece_list # real_my_piece_blue_setは自分の青駒のIDのセット(引数必須) self.real_my_piece_blue_set = set(real_my_piece_blue_set) self.real_my_piece_red_set = ( set(self.my_piece_list) - self.real_my_piece_blue_set ) # 敵の青駒のセット(デバッグ用) self.real_enemy_piece_blue_set = set(real_enemy_piece_blue_set) self.real_enemy_piece_red_set = ( set(self.enemy_piece_list) - self.real_enemy_piece_blue_set ) # {敵青, 敵赤, 自青,　自赤} if living_piece_color == None: self.living_piece_color = [4, 4, 4, 4] else: self.living_piece_color = living_piece_color # [[推測値A,(パターンAの青駒のtuple表現)],[推測値B,(パターンBの青駒のtuple表現),...] if enemy_estimated_num == None: # 盤面の推測値を作成(大きい程青らしく、小さい程赤らしい) self.enemy_estimated_num = [] for enemy_blue in itertools.combinations( set(self.enemy_piece_list), self.living_piece_color[0] ): self.enemy_estimated_num.append([0, enemy_blue]) else: self.enemy_estimated_num = enemy_estimated_num if my_estimated_num == None: # 盤面の推測値を作成(大きい程青らしく、小さい程赤らしい) self.my_estimated_num = [] for my_blue in itertools.combinations( set(self.my_piece_list), self.living_piece_color[0] ): self.my_estimated_num.append([0, my_blue]) else: self.my_estimated_num = my_estimated_num if see_through_piece_id == None and wrong_see_through_piece_id == None: self.see_through_piece_id = [] self.wrong_see_through_piece_id = [] elif wrong_see_through_piece_id == None: # 間違った推測のみnullだった場合 self.see_through_piece_id = see_through_piece_id self.wrong_see_through_piece_id = [] shave_impossible_board_from_see_through(self) # ありえない世界を初期化段階で消す elif see_through_piece_id == None: self.see_through_piece_id = [] self.wrong_see_through_piece_id = wrong_see_through_piece_id rebuilding_estimated_num( self, set(self.see_through_piece_id), set(self.wrong_see_through_piece_id), ) else: # どっちもnullでない self.see_through_piece_id = see_through_piece_id self.wrong_see_through_piece_id = wrong_see_through_piece_id rebuilding_estimated_num( self, set(self.see_through_piece_id), set(self.wrong_see_through_piece_id), ) # ボードの初期配置はこんな感じ(小文字が敵の駒で大文字が自分の駒) # 0 1 2 3 4 5 # 0 h g f e # 1 d c b a # 2 # 3 # 4 A B C D # 5 E F G H # 合法手のリストの取得 # NNはactionを与えると事前に学習した方策を返す。 # 赤のゴール(非合法なので知らない手)を与えると、そこを0にして返してくれるはず(エラーは吐かないはず？？？) def legal_actions(self): actions = [] # リストに自分の駒を全て追加 piece_coordinate_array = np.array([0] * 8) index = 0 for i in range(8, 16): piece_coordinate_array[index] = self.all_piece[i] index += 1 np.sort(piece_coordinate_array) # print("my:self.all_piece", piece_coordinate_array) for piece_coordinate in piece_coordinate_array: # 88以上は行動できないので省く(0~35) if piece_coordinate < 36: actions.extend( self.piece_coordinate_to_actions( piece_coordinate, piece_coordinate_array ) ) # 0と5はゴールの選択肢を追加(赤駒でも問答無用) if piece_coordinate == 0: actions.extend([2]) # 0*4 + 2 if piece_coordinate == 5: actions.extend([22]) # 5*4 + 2 return actions # 相手目線の合法手のリストを返す def enemy_legal_actions(self): actions = [] piece_coordinate_array = np.array([0] * 8) index = 0 for i in range(0, 8): if self.all_piece[i] < 36: piece_coordinate_array[index] = 35 - self.all_piece[i] else: piece_coordinate_array[index] = 99 index += 1 np.sort(piece_coordinate_array) # print("enemy:self.all_piece", piece_coordinate_array) for piece_coordinate in piece_coordinate_array: # 88以上は行動できないので省く(0~35) if piece_coordinate < 36: actions.extend( self.piece_coordinate_to_actions( piece_coordinate, piece_coordinate_array ) ) # 0と5はゴールの選択肢を追加(赤駒でも問答無用) if piece_coordinate == 0: actions.extend([2]) # 0*4 + 2 if piece_coordinate == 5: actions.extend([22]) # 5*4 + 2 return actions # 駒の移動元と移動方向を行動に変換 def position_to_action(self, position, direction): return position * 4 + direction def piece_coordinate_to_actions(self, piece_coordinate, piece_coordinate_array): actions = [] x = piece_coordinate % 6 y = int(piece_coordinate / 6) if y != 5 and not np.any(piece_coordinate_array == (piece_coordinate + 6)): # 下 actions.append(self.position_to_action(piece_coordinate, 0)) if x != 0 and not np.any(piece_coordinate_array == (piece_coordinate - 1)): # 左 actions.append(self.position_to_action(piece_coordinate, 1)) if y != 0 and not np.any(piece_coordinate_array == (piece_coordinate - 6)): # 上 actions.append(self.position_to_action(piece_coordinate, 2)) if x != 5 and not np.any(piece_coordinate_array == (piece_coordinate + 1)): # 右 actions.append(self.position_to_action(piece_coordinate, 3)) return actions # 駒ごと(駒1つに着目した)の合法手のリストの取得 def legal_actions_pos(self, position, piece_index_list): piece_list = [] for piece_index in piece_index_list: piece_list.append(self.all_piece[piece_index]) actions = [] x = position % 6 y = int(position / 6) # 下左上右の順に行動できるか検証し、できるならactionに追加 # ちなみにand演算子は左の値を評価して右の値を返すか決める(左の値がTrue系でなければ右の値は無視する)ので、はみ出し参照してIndexErrorにはならない(&だとなる) if y != 5 and (position + 6) not in piece_list: # 下端でない and 下に自分の駒がいない actions.append(self.position_to_action(position, 0)) if x != 0 and (position - 1) not in piece_list: # 左端でない and 左に自分の駒がいない actions.append(self.position_to_action(position, 1)) if y != 0 and (position - 6) not in piece_list: # 上端でない and 上に自分の駒がいない actions.append(self.position_to_action(position, 2)) if x != 5 and (position + 1) not in piece_list: # 右端でない and 右に自分の駒がいない actions.append(self.position_to_action(position, 3)) # 青駒のゴール行動の可否は1ターンに1度だけ判定すれば良いので、例外的にlegal_actionsで処理する(ここでは処理しない) return actions # 行動を受けて、次の状態に遷移 def next(self, action_num): coordinate_before, coordinate_after = action_to_coordinate(action_num) move_piece_index = np.where(self.all_piece == coordinate_before)[0][0] # 移動先に駒が存在する場合は殺す(味方の駒も殺してしまうが、そこは行動側で制御) if np.any(self.all_piece == coordinate_after): dead_piece_ID = np.where(self.all_piece == coordinate_after)[0][0] if dead_piece_ID < 8: # 死んだのが敵駒 # color_is_blue:死んだのが青駒かどうか color_is_blue = any( i == dead_piece_ID for i in self.real_enemy_piece_blue_set ) reduce_pattern(dead_piece_ID, color_is_blue, self) if self.wrong_see_through_piece_id != []: rebuilding_estimated_num( self, set(self.see_through_piece_id), set(self.wrong_see_through_piece_id), ) else: # 死んだのが味方の駒 color_is_blue = any( i == dead_piece_ID for i in self.real_my_piece_blue_set ) reduce_pattern(dead_piece_ID, color_is_blue, self) self.all_piece[move_piece_index] = coordinate_after # 駒の移動 # 推測値を返す(主にデバッグ用) def return_estimate_value(self): estimate_value = np.array([0] * 8, dtype="f4") for elem in self.enemy_estimated_num: id_matrix = [0] * 8 # 青駒IDのとこだけ1にする for blue_id in elem[1]: id_matrix[blue_id] = 1 estimate_value = estimate_value + ( np.array(id_matrix, dtype="f4") * elem[0] ) if False: print(self.enemy_estimated_num) print( "敵駒の住所", self.all_piece[0], self.all_piece[1], self.all_piece[2], self.all_piece[3], ) print( "味方駒の住所", self.all_piece[4], self.all_piece[5], self.all_piece[6], self.all_piece[7], ) return estimate_value # ボードの文字列表示 def __str__(self): row = "|{}|{}|{}|{}|{}|{}|" hr = "\n-------------------------------\n" # 1つのボードに味方の駒と敵の駒を集める board = [0] * 36 # 0~7が敵、8~15が自分 # 敵の駒 for enemy_piece_coo in self.all_piece[0:8]: if enemy_piece_coo < 36 and enemy_piece_coo >= 0: board[enemy_piece_coo] = -1 # 自分の駒 for blue_index in self.real_my_piece_blue_set: if self.all_piece[blue_index] < 36 and self.all_piece[blue_index] >= 0: board[self.all_piece[blue_index]] = 1 for red_index in self.real_my_piece_red_set: if self.all_piece[red_index] < 36 and self.all_piece[red_index] >= 0: board[self.all_piece[red_index]] = 2 board_essence = [] for i in board: if i == 1: board_essence.append("自青") elif i == 2: board_essence.append("自赤") elif i == -1: board_essence.append("敵駒") else: board_essence.append("　　") ii_str = ( hr + row + hr + row + hr + row + hr + row + hr + row + hr + row + hr ).format(*board_essence) ii_str += "\n" + str(self.living_piece_color) return ii_str # 盤面が確定しないような駒を選択する def create_see_through_piece(enemy_blue_piece_set, through_num): # 7個以上駒の色がわかるなら、全部わかるのと同意義 if through_num >= 7: return set({0, 1, 2, 3, 4, 5, 6, 7}) blue_piece_set = enemy_blue_piece_set.copy() red_piece_set = set({0, 1, 2, 3, 4, 5, 6, 7}) - blue_piece_set # 赤と青から1つ除外(これでパターンが確定しない) blue_piece_set.remove(random.choice(list(blue_piece_set))) red_piece_set.remove(random.choice(list(red_piece_set))) # セットの合成 see_thorugh_id_set = blue_piece_set | red_piece_set # through_numが少ない場合は見える駒を多く除外する for _ in range(6 - through_num): # 6は len(see_thorugh_id_set) see_thorugh_id_set.remove(random.choice(list(see_thorugh_id_set))) return see_thorugh_id_set # まちがった推測を含めて、破綻しない推測を作成 def create_wrong_and_see_through_piece( enemy_blue_piece_set: set, correct_through_num: int, wrong_through_num: int ): blue_piece_set = enemy_blue_piece_set.copy() red_piece_set = set({0, 1, 2, 3, 4, 5, 6, 7}) - blue_piece_set est_num = correct_through_num + wrong_through_num if est_num >= 9: print("普通にバグ") return if est_num >= 7: # 7個以上駒の色がわかるなら、全部わかるのと同意義 estimated_piece_set = set({0, 1, 2, 3, 4, 5, 6, 7}) else: # 赤と青から1つ除外(これでパターンが確定しない) blue_piece_set.remove(random.choice(list(blue_piece_set))) red_piece_set.remove(random.choice(list(red_piece_set))) # 赤と青から均等に推測駒を出す while len(blue_piece_set) + len(red_piece_set) > est_num: if len(blue_piece_set) > len(red_piece_set): blue_piece_set.remove(random.choice(list(blue_piece_set))) elif len(blue_piece_set) < len(red_piece_set): red_piece_set.remove(random.choice(list(red_piece_set))) else: # redとblueが同じ量の場合はランダムピック if random.randint(0, 1) == 0: blue_piece_set.remove(random.choice(list(blue_piece_set))) else: red_piece_set.remove(random.choice(list(red_piece_set))) wrong_piece_set = set() cp_wrong_through_num = wrong_through_num # wrong_through_numが奇数の場合 if cp_wrong_through_num % 2 == 1: cp_wrong_through_num -= 1 if len(blue_piece_set) > len(red_piece_set): piece = random.choice(list(blue_piece_set)) blue_piece_set.remove(piece) wrong_piece_set.add(piece) elif len(blue_piece_set) < len(red_piece_set): piece = random.choice(list(red_piece_set)) red_piece_set.remove(piece) wrong_piece_set.add(piece) else: # redとblueが同じ量の場合はランダムピック if random.randint(0, 1) == 0: piece = random.choice(list(blue_piece_set)) blue_piece_set.remove(piece) wrong_piece_set.add(piece) else: piece = random.choice(list(red_piece_set)) red_piece_set.remove(piece) wrong_piece_set.add(piece) # wrong_through_numの数だけ間違った推測駒を増やす for _ in range(cp_wrong_through_num // 2): piece = random.choice(list(blue_piece_set)) blue_piece_set.remove(piece) wrong_piece_set.add(piece) piece = random.choice(list(red_piece_set)) red_piece_set.remove(piece) wrong_piece_set.add(piece) correct_piece_set = blue_piece_set | red_piece_set return [correct_piece_set, wrong_piece_set] # stateの駒の色に応じたii_stateを作成する(初期のstateのみ使用可能) def create_ii_state_from_state( state, enemy_view=False, through_num=0, wrong_through_num=0 ): if enemy_view: # 敵視点でii_stateを作成 pieces = state.enemy_pieces enemy_pieces = state.pieces else: pieces = state.pieces enemy_pieces = state.enemy_pieces # 駒のIDと座標が紐づいたリストを手動作成(初期配置では座標番号25~28と31~34に駒が存在) piece_id_list = [0] * 36 for i in range(4): piece_id_list[25 + i] = 8 + i for i in range(4): piece_id_list[31 + i] = 12 + i blue_piece_set = set({}) for index, piece_color in enumerate(pieces): if piece_color == 1: blue_piece_set.add(piece_id_list[index]) # 敵駒の処理も同様にする enemy_piece_id_list = [0] * 36 for i in range(4): enemy_piece_id_list[25 + i] = 8 + i for i in range(4): enemy_piece_id_list[31 + i] = 12 + i enemy_blue_piece_set = set({}) for index, piece_color in enumerate(enemy_pieces): if piece_color == 1: enemy_blue_piece_set.add(enemy_piece_id_list[index]) # enemy_blue_piece_setの値を反転させ、推測の際に扱いやすいように変換する # (このままでは8~15の値をとるが、0~7の値に修正し扱う必要がある) rev_enemy_blue_piece_set = set({}) for piece_coo in enemy_blue_piece_set: rev_enemy_blue_piece_set.add(15 - piece_coo) if through_num == 0: ii_state = II_State(blue_piece_set, rev_enemy_blue_piece_set) elif wrong_through_num == 0: see_thorugh_id_set = create_see_through_piece( rev_enemy_blue_piece_set, through_num ) ii_state = II_State( blue_piece_set, rev_enemy_blue_piece_set, see_thorugh_id_set ) else: correct_wrong_piece_set = create_wrong_and_see_through_piece( blue_piece_set, through_num, wrong_through_num ) ii_state = II_State( blue_piece_set, rev_enemy_blue_piece_set, correct_wrong_piece_set[0], correct_wrong_piece_set[1], ) return ii_state def create_state_from_ii_state(ii_state, blue_set): pieces = [0] * 36 enemy_pieces = [0] * 36 # 0~7は敵の駒 for index, piece_coo in enumerate(ii_state.all_piece[:8]): if piece_coo < 36: if index in blue_set: enemy_pieces[35 - piece_coo] = 1 else: enemy_pieces[35 - piece_coo] = 2 for index, piece_coo in enumerate(ii_state.all_piece[8:]): if piece_coo < 36: if index + 8 in ii_state.real_my_piece_blue_set: pieces[piece_coo] = 1 else: pieces[piece_coo] = 2 state = State(pieces, enemy_pieces) return state ### ガイスターAI大会のプロトコル周り # プロトコルから相手の行動は送られず、更新されたボードが送られてくるそうなので、行動した駒の座標を求める # これは相手の行動のみ検知可能 def enemy_coordinate_checker(before_board, now_board): for i in range(len(before_board) // 2, len(before_board)): if before_board[i] != now_board[i]: break # iではなく(i//3)*3とすることで、座標と駒色(例:14R)の先頭インデックスが取れる(これしないと2文字目からとってくる恐れがある) beginningOfTheChanged = (i // 3) * 3 # 列番号+行番号*6でgame.pyで使ってる表現に直せる before_coordinate = ( int(before_board[beginningOfTheChanged]) + int(before_board[beginningOfTheChanged + 1]) * 6 ) now_coordinate = ( int(now_board[beginningOfTheChanged]) + int(now_board[beginningOfTheChanged + 1]) * 6 ) # 行動前と行動後の座標を返す return before_coordinate, now_coordinate # 行動番号を駒の移動元と移動方向に変換 def action_to_position(action_num): return (int(action_num / 4), action_num % 4) # position,direction # 行動番号を移動前の座標と移動後の座標に変換 def action_to_coordinate(action_num): coordinate_before, direction = action_to_position(action_num) if direction == 0: # 下 coordinate_after = coordinate_before + 6 elif direction == 1: # 左 coordinate_after = coordinate_before - 1 elif direction == 3: # 右 coordinate_after = coordinate_before + 1 elif direction == 2: # 上 if coordinate_before == 0 or coordinate_before == 5: # 0と5の上行動はゴール処理なので弾く coordinate_after = coordinate_before # coordinate_beforeを入れて駒の場所を動かさない(勝敗は決しているので下手に動かさない方が良い(多分)) else: coordinate_after = coordinate_before - 6 else: print("ERROR:action_to_coordinate(illegal action_num)") return coordinate_before, coordinate_after # 移動前の座標と方向番号から行動番号を算出 def position_to_action(position, direction): return position * 4 + direction # 移動前と移動後の座標から相手の行動番号を算出 def calculate_enemy_action_number_from_coordinate(before_coordinate, now_coordinate): enemy_looking_now_coordinate = 35 - now_coordinate enemy_looking_before_coordinate = 35 - before_coordinate difference = enemy_looking_now_coordinate - enemy_looking_before_coordinate if difference == 6: # 下 return position_to_action(enemy_looking_before_coordinate, 0) elif difference == 1: # 左 return position_to_action(enemy_looking_before_coordinate, 1) elif difference == -6: # 上 return position_to_action(enemy_looking_before_coordinate, 2) elif difference == -1: # 右 return position_to_action(enemy_looking_before_coordinate, 3) else: print("ERROR:find_enemy_action_number_from_coordinate(illegal move)") return -1 ### # 相手の行動を受けて、ガイスターの盤面を更新(駒が死んだ場合の処理もここで行う) def update_II_state(ii_state, before_coordinate, now_coordinate): kill = np.any(ii_state.all_piece == now_coordinate) # 敵駒がkillしていたら死んだ駒の処理を行う(99は死んだ駒) if kill: dead_piece_ID = np.where(ii_state.all_piece == now_coordinate)[0][0] color_is_blue = np.any(ii_state.real_my_piece_blue_set == dead_piece_ID) # print(dead_piece_ID, color_is_blue) reduce_pattern(dead_piece_ID, color_is_blue, ii_state) # 行動前の座標を行動後の座標に変更する ii_state.all_piece[ np.where(ii_state.all_piece == before_coordinate)[0][0] ] = now_coordinate # myの視点で状態を作成 def my_looking_create_state(ii_state, my_blue, my_red, enemy_blue, enemy_red): # プレイヤー毎のデュアルネットワークの入力の2次元配列の取得 def pieces_array_of(blue_piece_list, red_piece_list): table_list = [] blue_table = [0] * 36 table_list.append(blue_table) # ちなみにappendは参照渡し # blue_piece_listは駒のIDの値なので、ii_state.all_pieceでそのIDを参照してあげると座標が取れる for blue_piece in blue_piece_list: if ii_state.all_piece[blue_piece] < 36: # 死駒を除外 blue_table[ii_state.all_piece[blue_piece]] = 1 red_table = [0] * 36 table_list.append(red_table) for red_piece in red_piece_list: if ii_state.all_piece[red_piece] < 36: red_table[ii_state.all_piece[red_piece]] = 1 return table_list # デュアルネットワークの入力の2次元配列の取得(自分と敵両方) return [pieces_array_of(my_blue, my_red), pieces_array_of(enemy_blue, enemy_red)] # # 入力の順序はcreate # # enemyの視点から状態を作成 # def enemy_looking_create_state(ii_state, my_blue, my_red, enemy_blue, enemy_red): # # プレイヤー毎のデュアルネットワークの入力の2次元配列の取得 # def pieces_array_of(blue_piece_list, red_piece_list): # table_list = [] # blue_table = [0] * 36 # # blue_piece_listは駒のIDの値なので、ii_state.all_pieceでそのIDを参照してあげると座標が取れる # for blue_piece in blue_piece_list: # if ii_state.all_piece[blue_piece] < 36: # 死駒を除外 # blue_table[ii_state.all_piece[blue_piece]] = 1 # blue_table.reverse() # 逆視点にするために要素を反転 # table_list.append(blue_table) # red_table = [0] * 36 # for red_piece in red_piece_list: # if ii_state.all_piece[red_piece] < 36: # red_table[ii_state.all_piece[red_piece]] = 1 # red_table.reverse() # 逆視点にするために要素を反転 # table_list.append(red_table) # return table_list # # デュアルネットワークの入力の2次元配列の取得(自分と敵両方) # return [pieces_array_of(enemy_blue, enemy_red), pieces_array_of(my_blue, my_red)] # 諸々の情報からstateを作る def create_state_from_enemy_looking(ii_state, my_blue, my_red, enemy_blue, enemy_red): # 自分の駒を格納 my_table = [0] * 36 for my_b in my_blue: if ii_state.all_piece[my_b] < 36: my_table[ii_state.all_piece[my_b]] = 1 for my_r in my_red: if ii_state.all_piece[my_r] < 36: my_table[ii_state.all_piece[my_r]] = 2 # 敵の駒を格納 enemy_table = [0] * 36 for en_b in enemy_blue: if ii_state.all_piece[en_b] < 36: enemy_table[ii_state.all_piece[en_b]] = 1 for en_r in enemy_red: if ii_state.all_piece[en_r] < 36: enemy_table[ii_state.all_piece[en_r]] = 2 enemy_table.reverse() # このままでは敵の駒の座標が逆なので反転させて戻す # 敵視点でのstateを生成 state = State(enemy_table, my_table) return state # enemy→各駒の推測値を保存。推測のために70パターン想定するが、足し合わせるだけ(各盤面について保存はしない) # my→推測したい駒配置。 # 行動と推測盤面に対応した行動価値のリストを返す def my_ii_predict(model_path, ii_state): # 推論のための入力データのシェイプの変換 a, b, c = DN_INPUT_SHAPE # (6, 6, 4) # ii_stateから生きてる駒のリストを取得 my_piece_set = set(ii_state.my_piece_list) enemy_piece_set = set(ii_state.enemy_piece_list) # policies_list[パターン(0~最大69)][行動(盤面依存)] policies_list = [] legal_actions = list(ii_state.legal_actions()) # HandyRLで学習させた方策を取れる関数を定義 convert_func = convert_func_use_in_guess(model_path) for num_and_my_blue in ii_state.my_estimated_num: sum_np_policies = np.array([0] * len(legal_actions), dtype="f4") # 赤駒のインデックスをセット形式で獲得(青駒以外の駒は赤駒) my_red_set = my_piece_set - set(num_and_my_blue[1]) for num_and_enemy_blue in ii_state.enemy_estimated_num: # 同様に赤駒のインデックスを獲得 enemy_red_set = enemy_piece_set - set(num_and_enemy_blue[1]) ii_pieces_array = my_looking_create_state( ii_state, num_and_my_blue[1], my_red_set, num_and_enemy_blue[1], enemy_red_set, ) # HandyRLに適応 policies = convert_func(ii_pieces_array, legal_actions) # 行列演算するためにndarrayに変換 np_policies = np.array(policies, dtype="f4") # 自分のパターンは既存のpoliciesに足すだけ sum_np_policies = sum_np_policies + np_policies # value = y[1][0][0] # 価値の取得 policies_list.extend([sum_np_policies]) return policies_list # # 相手の行動前に、相手の目線で各パターンにおける各行動の価値を算出 # def enemy_ii_predict(model_path, ii_state): # a, b, c = DN_INPUT_SHAPE # (6, 6, 4) # my_piece_set = set(ii_state.my_piece_list) # enemy_piece_set = set(ii_state.enemy_piece_list) # policies_list = [] # enemy_legal_actions = sorted(list(ii_state.enemy_legal_actions()), key=lambda x: x) # convert_func = convert_func_use_in_guess(model_path) # for num_and_enemy_blue in ii_state.enemy_estimated_num: # enemyのパターンの確からしさを求めたい # # 赤駒のインデックスをセット形式で獲得(my_blueはタプル) # enemy_red_set = enemy_piece_set - set(num_and_enemy_blue[1]) # sum_np_policies = np.array([0] * len(enemy_legal_actions), dtype="f4") # for num_and_my_blue in ii_state.my_estimated_num: # my_red_set = my_piece_set - set(num_and_my_blue[1]) # # 要修正 # ii_pieces_array = enemy_looking_create_state( # ii_state, # num_and_my_blue[1], # my_red_set, # num_and_enemy_blue[1], # enemy_red_set, # ) # # HandyRLに適応 # policies = convert_func(ii_pieces_array, enemy_legal_actions) # # 行列演算するためにndarrayに変換 # np_policies = np.array(policies, dtype="f4") # # myのパターンは既存のpoliciesに足すだけ # sum_np_policies = sum_np_policies + np_policies # policies_list.extend([sum_np_policies]) # return policies_list from test import get_policies # 自分の駒配置を確定させて推測するパターン # 相手の行動前に、相手の目線で各パターンにおける各行動の価値を算出 def enemy_ii_predict(model_path, ii_state): my_piece_set = set(ii_state.my_piece_list) enemy_piece_set = set(ii_state.enemy_piece_list) policies_list = [] enemy_legal_actions = sorted(list(ii_state.enemy_legal_actions()), key=lambda x: x) # enemy_legal_actions = sorted(enemy_legal_actions, key=lambda x: x) # print("ii_legal", enemy_legal_actions) # convert_func = convert_func_use_in_guess(model_path) # print("盤面", ii_state) get_policies_func = get_policies(model_path) # enemyのパターンの確からしさ(蓋然性)を求める for num_and_enemy_blue in ii_state.enemy_estimated_num: # 赤駒のインデックスをセット形式で獲得(my_blueはタプル) enemy_red_set = enemy_piece_set - set(num_and_enemy_blue[1]) # 自分の駒配置は見抜かれているものとして相手の行動の価値を求める my_blue_set = ii_state.real_my_piece_blue_set my_red_set = my_piece_set - my_blue_set # 相手視点のstateを作成した上で方策を獲得 enemy_looking_state = create_state_from_enemy_looking( ii_state, my_blue_set, my_red_set, num_and_enemy_blue[1], enemy_red_set, ) ap_list = sorted(get_policies_func(enemy_looking_state), key=lambda x: x[0]) policies = [] for tup in ap_list: policies.append(tup[1]) # HandyRLに適応 # ii_pieces_array = enemy_looking_create_state( # ii_state, my_blue_set, my_red_set, num_and_enemy_blue[1], enemy_red_set, # ) # policies = convert_func(ii_pieces_array, enemy_legal_actions) # ndarrayに変換(自分の駒配置が確定でなかった際に、行列演算するためにnpに変換していた名残) np_policies = np.array(policies, dtype="f4") policies_list.extend([np_policies]) return policies_list # 相手の行動から推測値を更新 # state, enemy_ii_predictで作成した推測値の行列, 敵の行動番号 def update_predict_num_all( ii_state, beforehand_estimated_num, enemy_action_num, gamma=default_gamma ): # print(enemy_action_num) enemy_legal_actions = sorted(list(ii_state.enemy_legal_actions()), key=lambda x: x) enemy_action_index = enemy_legal_actions.index(enemy_action_num) for index, enemy_estimated_num in enumerate(ii_state.enemy_estimated_num): # ii_state.enemy_estimated_num[index][0] enemy_estimated_num[0] = ( enemy_estimated_num[0] * gamma ) + beforehand_estimated_num[index][enemy_action_index] # 相手の行動から推測値を更新 # 相手の取りうる指し手の中で最も価値の高い行動であれば、その盤面である可能性が高いと考える # 最も価値の高い行動であれば+1、そうでなければ+0 def update_predict_num_max_only( ii_state, beforehand_estimated_num, enemy_action_num, gamma=default_gamma ): enemy_legal_actions = sorted(list(ii_state.enemy_legal_actions()), key=lambda x: x) enemy_action_index = enemy_legal_actions.index(enemy_action_num) bf_estimated_num = beforehand_estimated_num # 未知の駒が0か5に存在している場合、未知の駒が赤駒である場合でも行動番号2や22を追加しないと配列への参照(beforehand_estimated_num[index][enemy_action_index])がズレる goal_actions = [] if 2 in enemy_legal_actions: goal_actions.append(2) if 22 in enemy_legal_actions: goal_actions.append(22) # ゴール行動が含まれていた場合 if goal_actions != []: legal_length = len(enemy_legal_actions) for goal in goal_actions: new_est_num = [] for bf_index, bf_est_num in enumerate(bf_estimated_num): # このやり方だと0,5の両方に赤駒がいた場合に対処できないが、ごく稀にしか起こらないためその場合の推測は割り切る if legal_length > len(bf_est_num): insert_index = enemy_legal_actions.index(goal) new_est_num.append( np.insert(bf_estimated_num[bf_index], insert_index, -999.9) ) else: new_est_num.append(bf_estimated_num[bf_index]) bf_estimated_num = new_est_num enemy_estimated_num = ii_state.enemy_estimated_num for index, en_est_num in enumerate(enemy_estimated_num): action_value = bf_estimated_num[index][enemy_action_index] # 相手の選択した行動が、相手の取りうる行動の中で最高の評価であった場合 if np.partition(bf_estimated_num[index].ravel(), -1)[-1] == action_value: en_est_num[0] = (en_est_num[0] * gamma) + 1 # 相手の行動から推測値を更新 # 方策の値を盤面ごとに正規化する def update_predict_num_normalize( ii_state, beforehand_estimated_num, enemy_action_num, gamma=default_gamma ): enemy_legal_actions = sorted(list(ii_state.enemy_legal_actions()), key=lambda x: x) enemy_action_index = enemy_legal_actions.index(enemy_action_num) bf_estimated_num = beforehand_estimated_num # 未知の駒が0か5に存在している場合、未知の駒が赤駒である場合でも行動番号2や22を追加しないと配列への参照(beforehand_estimated_num[index][enemy_action_index])がズレる goal_actions = [] if 2 in enemy_legal_actions: goal_actions.append(2) if 22 in enemy_legal_actions: goal_actions.append(22) # ゴール行動が含まれていた場合 if goal_actions != []: legal_length = len(enemy_legal_actions) for goal in goal_actions: new_est_num = [] for bf_index, bf_est_num in enumerate(bf_estimated_num): # このやり方だと0,5の両方に赤駒がいた場合に対処できないが、ごく稀にしか起こらないためその場合の推測は割り切る if legal_length > len(bf_est_num): # 最小値を探索 pol_min = np.amin(bf_est_num) # 非合法手の方策の値は最小値と同じにする insert_index = enemy_legal_actions.index(goal) new_est_num.append(np.insert(bf_est_num, insert_index, pol_min)) else: new_est_num.append(bf_estimated_num[bf_index]) bf_estimated_num = new_est_num # 最小値を0にする for bf_est_num in bf_estimated_num: bf_est_num -= np.amin(bf_est_num) # 最小値0、合計1に正規化する for bf_est_num in bf_estimated_num: if np.sum(bf_est_num) > 0.0: # 0除算対策(合計0の場合はそのまま扱う) bf_est_num /= np.sum(bf_est_num) for index, en_est_num in enumerate(ii_state.enemy_estimated_num): action_value = bf_estimated_num[index][enemy_action_index] # 実際に取られた行動の評価値を足すことで、推測値を更新 en_est_num[0] = (en_est_num[0] * gamma) + action_value # ありえないパターンを消す def shave_impossible_pattern(piece_ID: int, color_is_blue: bool, ii_state): if piece_ID < 8 and color_is_blue: # 敵駒 and 駒が青色 # piece_IDが含まれていないものを削除(piece_IDが青色で確定するため、それが青色の駒のリストに含まれていないとおかしい) # リストをそのままfor内で削除するとインデックスがバグるのでコピーしたものを参照 for enemy_estimated_num in ii_state.enemy_estimated_num[:]: if piece_ID not in enemy_estimated_num[1]: ii_state.enemy_estimated_num.remove(enemy_estimated_num) elif piece_ID < 8 and not color_is_blue: # 敵駒 and 駒が赤色 # piece_IDが含まれているものを削除 for enemy_estimated_num in ii_state.enemy_estimated_num[:]: if piece_ID in enemy_estimated_num[1]: ii_state.enemy_estimated_num.remove(enemy_estimated_num) elif piece_ID >= 8 and color_is_blue: # 自駒 and 駒が青色 for my_estimated_num in ii_state.my_estimated_num[:]: if piece_ID not in my_estimated_num[1]: ii_state.my_estimated_num.remove(my_estimated_num) elif piece_ID >= 8 and not color_is_blue: # 自駒 and 駒が赤色 for my_estimated_num in ii_state.my_estimated_num[:]: if piece_ID in my_estimated_num[1]: ii_state.my_estimated_num.remove(my_estimated_num) # 駒が死ぬたびに推測値を全て作り直して、死んだ駒と残った推測駒から新しい推測値を作成する def rebuilding_estimated_num( ii_state, correct_estimate_piece: list, wrong_estimate_piece: list ): # 生きてる駒のリストにcorrect_estimate_pieceとwrong_estimate_pieceが存在するかを確認 # enemy_piece_list = [0, 1, 2, 3, 4, 5, 6, 7] blue_pieces = [] red_pieces = [] for correct_piece in correct_estimate_piece: if correct_piece in ii_state.enemy_piece_list: # 生きてるか if correct_piece in ii_state.real_enemy_piece_blue_set: # 青駒かどうか blue_pieces.append(correct_piece) else: red_pieces.append(correct_piece) for wrong_piece in wrong_estimate_piece: if wrong_piece in ii_state.enemy_piece_list: # 生きてるか if ( wrong_piece in ii_state.real_enemy_piece_blue_set ): # 青駒かどうか(wrongは間違った推測なので赤青を反転させる) red_pieces.append(wrong_piece) else: blue_pieces.append(wrong_piece) # このままだと駒数が溢れている(青駒が5個などあり得ない比率になっている)可能性があるので、その際にはランダムピックで削除 if len(blue_pieces) > ii_state.living_piece_color[0]: while len(blue_pieces) > ii_state.living_piece_color[0]: blue_pieces.remove(random.choice(blue_pieces)) if len(red_pieces) > ii_state.living_piece_color[1]: while len(red_pieces) > ii_state.living_piece_color[1]: red_pieces.remove(random.choice(red_pieces)) # 盤面の推測値を新たに作成 enemy_estimated_num = [] for enemy_blue in itertools.combinations( set(ii_state.enemy_piece_list), ii_state.living_piece_color[0] ): # living_piece_color{敵青, 敵赤, 自青,　自赤} enemy_estimated_num.append([0, enemy_blue]) # 推測値を更新 ii_state.enemy_estimated_num = enemy_estimated_num # 推測の情報から状態を削減 for piece_id in blue_pieces: shave_impossible_pattern(piece_id, True, ii_state) for piece_id in red_pieces: shave_impossible_pattern(piece_id, False, ii_state) # 駒の死亡処理 # 既存のパターンから推測値を抜き出して新しい推測値を作成 def reduce_pattern(dead_piece_ID: int, color_is_blue: bool, ii_state): # 駒の色が確定するので、ありえないパターンを削ぐ shave_impossible_pattern(dead_piece_ID, color_is_blue, ii_state) # all_pieceから削除 ii_state.all_piece[dead_piece_ID] = 99 # **_piece_listから削除 if dead_piece_ID < 8: ii_state.enemy_piece_list.remove(dead_piece_ID) elif dead_piece_ID < 16: ii_state.my_piece_list.remove(dead_piece_ID) else: print("ERROR:reduce_pattern(**_piece_listから削除)") # living_piece_colorから削除 if dead_piece_ID < 8 and color_is_blue: # 敵駒 and 駒が青色 ii_state.living_piece_color[0] -= 1 elif dead_piece_ID < 8 and not color_is_blue: # 敵駒 and 駒が赤色 ii_state.living_piece_color[1] -= 1 elif dead_piece_ID >= 8 and color_is_blue: # 自駒 and 駒が青色 ii_state.living_piece_color[2] -= 1 elif dead_piece_ID >= 8 and not color_is_blue: # 自駒 and 駒が赤色 ii_state.living_piece_color[3] -= 1 else: print("ERROR:reduce_pattern(living_piece_colorから削除)") # 相手の推測値を使って無難な手を選択(元祖) # 価値が最大の行動番号を返す def action_decision_legacy(model_path, ii_state): a, b, c = DN_INPUT_SHAPE # (6, 6, 4) my_piece_set = set(ii_state.my_piece_list) enemy_piece_set = set(ii_state.enemy_piece_list) # 自分の駒配置を取得(確定) real_my_piece_blue_set = ii_state.real_my_piece_blue_set real_my_piece_red_set = ii_state.real_my_piece_red_set # legal_actions = list(ii_state.legal_actions()) #これがソートしてなかったせいでバグってた説ある legal_actions = sorted(list(ii_state.legal_actions()), key=lambda x: x) actions_value_sum_list = np.array([0] * len(legal_actions), dtype="f4") convert_func = convert_func_use_in_guess(model_path) # 相手の70パターンについてforループ(自分のパターンは確定で計算) for num_and_enemy_blue in ii_state.enemy_estimated_num: enemy_blue_set = set(num_and_enemy_blue[1]) enemy_red_set = enemy_piece_set - enemy_blue_set # 盤面を6*6*4次元の情報に変換 ii_pieces_array = my_looking_create_state( ii_state, real_my_piece_blue_set, real_my_piece_red_set, enemy_blue_set, enemy_red_set, ) policies = convert_func(ii_pieces_array, legal_actions) # 行列演算するためにndarrayに変換 np_policies = np.array(policies, dtype="f4") # パターンごとに「推測値を重みとして掛けた方策」を足し合わせる actions_value_sum_list = actions_value_sum_list + ( np_policies * num_and_enemy_blue[0] ) best_action_index = np.argmax(actions_value_sum_list) # 最大値のインデックスを取得 best_action = legal_actions[best_action_index] # 価値が最大の行動を取得 return best_action # 上位半分のみの世界しか考えない def half_action_decision(model_path, ii_state): a, b, c = DN_INPUT_SHAPE # (6, 6, 4) my_piece_set = set(ii_state.my_piece_list) enemy_piece_set = set(ii_state.enemy_piece_list) # 自分の駒配置を取得(確定) real_my_piece_blue_set = ii_state.real_my_piece_blue_set real_my_piece_red_set = ii_state.real_my_piece_red_set legal_actions = sorted(list(ii_state.legal_actions()), key=lambda x: x) actions_value_sum_list = np.array([0] * len(legal_actions), dtype="f4") convert_func = convert_func_use_in_guess(model_path) # 価値が上位の世界を抽出 en_est_num = ii_state.enemy_estimated_num.copy() sorted_en_est_num = sorted(en_est_num, reverse=True, key=lambda x: x[0]) sorted_en_est_num = sorted_en_est_num[0 : 1 + (len(sorted_en_est_num) // 4)] # 相手の70パターンについてforループ(自分のパターンは確定で計算) for num_and_enemy_blue in sorted_en_est_num: enemy_blue_set = set(num_and_enemy_blue[1]) enemy_red_set = enemy_piece_set - enemy_blue_set # 盤面を6*6*4次元の情報に変換 ii_pieces_array = my_looking_create_state( ii_state, real_my_piece_blue_set, real_my_piece_red_set, enemy_blue_set, enemy_red_set, ) policies = convert_func(ii_pieces_array, legal_actions) # 行列演算するためにndarrayに変換 np_policies = np.array(policies, dtype="f4") # パターンごとに「推測値を重みとして掛けた方策」を足し合わせる actions_value_sum_list = actions_value_sum_list + ( np_policies * num_and_enemy_blue[0] ) best_action_index = np.argmax(actions_value_sum_list) # 最大値のインデックスを取得 best_action = legal_actions[best_action_index] # 価値が最大の行動を取得 return best_action # 1位の世界しか考えないno1_ def no1_action_decision(model_path, ii_state): a, b, c = DN_INPUT_SHAPE # (6, 6, 4) my_piece_set = set(ii_state.my_piece_list) enemy_piece_set = set(ii_state.enemy_piece_list) # 自分の駒配置を取得(確定) real_my_piece_blue_set = ii_state.real_my_piece_blue_set real_my_piece_red_set = ii_state.real_my_piece_red_set legal_actions = sorted(list(ii_state.legal_actions()), key=lambda x: x) actions_value_sum_list = np.array([0] * len(legal_actions), dtype="f4") convert_func = convert_func_use_in_guess(model_path) # 価値が上位の世界を抽出 en_est_num = ii_state.enemy_estimated_num.copy() sorted_en_est_num = sorted(en_est_num, reverse=True, key=lambda x: x[0]) # 価値が1位の世界を抽出 sorted_en_est_num = sorted_en_est_num[0:2] # 相手の70パターンについてforループ(自分のパターンは確定で計算) for num_and_enemy_blue in sorted_en_est_num: enemy_blue_set = set(num_and_enemy_blue[1]) enemy_red_set = enemy_piece_set - enemy_blue_set # 盤面を6*6*4次元の情報に変換 ii_pieces_array = my_looking_create_state( ii_state, real_my_piece_blue_set, real_my_piece_red_set, enemy_blue_set, enemy_red_set, ) policies = convert_func(ii_pieces_array, legal_actions) # 行列演算するためにndarrayに変換 np_policies = np.array(policies, dtype="f4") # パターンごとに「推測値を重みとして掛けた方策」を足し合わせる actions_value_sum_list = actions_value_sum_list + ( np_policies * num_and_enemy_blue[0] ) best_action_index = np.argmax(actions_value_sum_list) # 最大値のインデックスを取得 best_action = legal_actions[best_action_index] # 価値が最大の行動を取得 return best_action # 上位の駒の共通項をくくるcommon_items_ def common_items_action_decision(model_path, ii_state): a, b, c = DN_INPUT_SHAPE # (6, 6, 4) my_piece_set = set(ii_state.my_piece_list) enemy_piece_set = set(ii_state.enemy_piece_list) # 自分の駒配置を取得(確定) real_my_piece_blue_set = ii_state.real_my_piece_blue_set real_my_piece_red_set = ii_state.real_my_piece_red_set legal_actions = sorted(list(ii_state.legal_actions()), key=lambda x: x) actions_value_sum_list = np.array([0] * len(legal_actions), dtype="f4") convert_func = convert_func_use_in_guess(model_path) # 価値が上位の世界を抽出 en_est_num = ii_state.enemy_estimated_num.copy() sorted_en_est_num = sorted(en_est_num, reverse=True, key=lambda x: x[0]) # 上位の駒をくくる piece_num = [0] * 8 # これだと全部くくってるので、適宜削る for est_num in sorted_en_est_num[0 : (len(sorted_en_est_num) // 2) + 1]: for piece_id in est_num[1]: # ここ+1じゃなくて推測値の方がええんかな piece_num[piece_id] += 1 value_and_id_list = [[1, []]] # 上位4つの駒を抽出 piece_num_top_four = sorted(piece_num, reverse=True)[:4] for p_value in piece_num_top_four: value_and_id_list[0][1].append(piece_num.index(p_value)) # いつもの for num_and_enemy_blue in value_and_id_list: enemy_blue_set = set(num_and_enemy_blue[1]) enemy_red_set = enemy_piece_set - enemy_blue_set # 盤面を6*6*4次元の情報に変換 ii_pieces_array = my_looking_create_state( ii_state, real_my_piece_blue_set, real_my_piece_red_set, enemy_blue_set, enemy_red_set, ) policies = convert_func(ii_pieces_array, legal_actions) # 行列演算するためにndarrayに変換 np_policies = np.array(policies, dtype="f4") # パターンごとに「推測値を重みとして掛けた方策」を足し合わせる actions_value_sum_list = actions_value_sum_list + ( np_policies * num_and_enemy_blue[0] ) best_action_index = np.argmax(actions_value_sum_list) # 最大値のインデックスを取得 best_action = legal_actions[best_action_index] # 価値が最大の行動を取得 return best_action # 各盤面について方策の正規化を行った上で無難な手を選択normalize_ def action_decision(model_path, ii_state): a, b, c = DN_INPUT_SHAPE # (6, 6, 4) my_piece_set = set(ii_state.my_piece_list) enemy_piece_set = set(ii_state.enemy_piece_list) # 自分の駒配置を取得(確定) real_my_piece_blue_set = ii_state.real_my_piece_blue_set real_my_piece_red_set = ii_state.real_my_piece_red_set legal_actions = sorted(list(ii_state.legal_actions()), key=lambda x: x) actions_value_sum_list = np.array([0] * len(legal_actions), dtype="f4") convert_func = convert_func_use_in_guess(model_path) en_est_num = ii_state.enemy_estimated_num.copy() sorted_en_est_num = sorted(en_est_num, reverse=True, key=lambda x: x[0]) # 価値が上位の世界を抽出 # sorted_en_est_num = sorted_en_est_num[0 : 1 + (len(sorted_en_est_num) // 4)] # 相手の70パターンについてforループ(自分のパターンは確定で計算) for num_and_enemy_blue in sorted_en_est_num: enemy_blue_set = set(num_and_enemy_blue[1]) enemy_red_set = enemy_piece_set - enemy_blue_set # 盤面を6*6*4次元の情報に変換 ii_pieces_array = my_looking_create_state( ii_state, real_my_piece_blue_set, real_my_piece_red_set, enemy_blue_set, enemy_red_set, ) policies = convert_func(ii_pieces_array, legal_actions) # 行列演算するためにndarrayに変換 np_policies = np.array(policies, dtype="f4") # 最小値を0にする np_policies -= np.amin(np_policies) # 最小値0、合計1に正規化する if np.sum(np_policies) > 0: # 0除算対策 np_policies /= np.sum(np_policies) # パターンごとに「推測値を重みとして掛けた方策」を足し合わせる actions_value_sum_list = actions_value_sum_list + ( np_policies * num_and_enemy_blue[0] ) best_action_index = np.argmax(actions_value_sum_list) # 最大値のインデックスを取得 best_action = legal_actions[best_action_index] # 価値が最大の行動を取得 return best_action from test import HandyAction # 1位の盤面に対してpolicyを用いたMCTSを実行し行動を決定mcts_ def mcts_action_decision(model_path, ii_state): a, b, c = DN_INPUT_SHAPE # (6, 6, 4) my_piece_set = set(ii_state.my_piece_list) enemy_piece_set = set(ii_state.enemy_piece_list) # 自分の駒配置を取得(確定) real_my_piece_blue_set = ii_state.real_my_piece_blue_set real_my_piece_red_set = ii_state.real_my_piece_red_set legal_actions = sorted(list(ii_state.legal_actions()), key=lambda x: x) actions_value_sum_list = np.array([0] * len(legal_actions), dtype="f4") # 価値が上位の世界を抽出 en_est_num = ii_state.enemy_estimated_num.copy() sorted_en_est_num = sorted(en_est_num, reverse=True, key=lambda x: x[0]) # 価値が1位の世界を抽出 sorted_en_est_num = sorted_en_est_num[0] # 価値が1位の盤面が、実際の盤面であると仮定しstateを取得 state = create_state_from_ii_state(ii_state, sorted_en_est_num[1]) # stateを利用しMCTS policy_action = HandyAction(model_path) best_action = predict_mcts_action(state, policy_action) return best_action import math # モンテカルロ木探索の行動選択 def predict_mcts_action(state, policy_action): # モンテカルロ木探索のノード class node: # 初期化 def __init__(self, state): self.state = state # 状態 self.w = 0 # 累計価値 self.n = 0 # 試行回数 self.child_nodes = None # 子ノード群 self.policy_action = policy_action # 評価 def evaluate(self): if self.state.is_done(): value = -1 if self.state.is_lose() else 0 # 負けは-1、引き分けは0 self.w += value self.n += 1 return value # 子ノードが存在しない時 if not self.child_nodes: # プレイアウトで価値を取得 value = policy_playout(self.state, self.policy_action) self.w += value self.n += 1 # 子ノードがない場合は初回探索で木を展開 self.expand() return value # 子ノードが存在する時 else: # UCB1が最大の子ノードの評価で価値を取得 value = -self.next_child_node().evaluate() # 累計価値と試行回数の更新 self.w += value self.n += 1 return value # 子ノードの展開 def expand(self): legal_actions = self.state.legal_actions() self.child_nodes = [] for action in legal_actions: self.child_nodes.append(node(self.state.next(action))) # UCB1が最大の子ノードを取得 def next_child_node(self): # 試行回数nが0の子ノードを返す for child_node in self.child_nodes: if child_node.n == 0: return child_node # UCB1の計算 t = 0 for c in self.child_nodes: t += c.n ucb1_values = [] for child_node in self.child_nodes: ucb1_values.append( -child_node.w / child_node.n + 2 * (2 * math.log(t) / child_node.n) ** 0.5 ) return self.child_nodes[argmax(ucb1_values)] # ルートノードの生成 root_node = node(state) root_node.expand() # ルートノードを評価 (rangeを変化させると評価回数を変化させられる) for _ in range(50): root_node.evaluate() # 試行回数の最大値を持つ行動を返す legal_actions = state.legal_actions() n_list = [] for c in root_node.child_nodes: n_list.append(c.n) return legal_actions[argmax(n_list)] # 方策を使ってゲームの終端までシミュレート def policy_playout(state, policy_action): if state.is_lose(): return -1 if state.is_draw(): return 0 return -policy_playout(state.next(policy_action(state)), policy_action) import random # あり得る世界からランダムに1つ選択し、その世界に対して最も適切な行動をとるrand_world_ def rand_world_action(model_path): policy_action = HandyAction(model_path) def rand_world_action(ii_state): # あり得る世界からランダムに1つ選出 possible_worlds_num = len(ii_state.enemy_estimated_num) world_piece_set = ii_state.enemy_estimated_num[ random.randrange(possible_worlds_num) ][1] # 選出した世界からstateを取得し、方策の値が最も高い行動を選択 state = create_state_from_ii_state(ii_state, world_piece_set) best_action = policy_action(state) return best_action return rand_world_action from test import PredictPolicy # あり得る世界からランダムにn通り選択し、その世界に対して最も適切な行動をとるrand_n_world_ def rand_n_world_action(model_path, n): policy_action = PredictPolicy(model_path) def rand_n_world_action(ii_state): # あり得る世界からランダムにいくつか選出 possible_worlds_num = len(ii_state.enemy_estimated_num) if possible_worlds_num < n: pic_num = possible_worlds_num else: pic_num = n # ランダムにn個の世界を選出 pic_world_list = random.sample(list(range(possible_worlds_num)), pic_num) world_piece_set_list = [] for pic_world in pic_world_list: world_piece_set_list.append(ii_state.enemy_estimated_num[pic_world][1]) legal_actions = sorted(ii_state.legal_actions(), key=lambda x: x) legal_num = len(legal_actions) actions_value_sum_list = np.array([0] * legal_num, dtype="f4") for world_piece_set in world_piece_set_list: # 選出した世界からstateを取得し、方策の値が最も高い行動を選択 state = create_state_from_ii_state(ii_state, world_piece_set) action_and_policy = policy_action(state) action_and_policy = sorted( [(a[0], a[1]) for a in action_and_policy], key=lambda x: x[0], ) policies = [0] * legal_num for index, ap in enumerate(action_and_policy): policies[index] = ap[1] # 行列演算するためにndarrayに変換 np_policies = np.array(policies, dtype="f4") # 最小値を0にする np_policies -= np.amin(np_policies) # 最小値0、合計1に正規化する if np.sum(np_policies) > 0: # 0除算対策 np_policies /= np.sum(np_policies) # パターンごとに「推測値を重みとして掛けた方策」を足し合わせる actions_value_sum_list = actions_value_sum_list + np_policies best_action = legal_actions[np.argmax(actions_value_sum_list)] return best_action return rand_n_world_action # 駒をテレポート(デバッグ用で破壊的)(敵駒の存在を想定していない) def teleport(ii_state, before, now): name = np.where(ii_state.all_piece == before)[0][0] ii_state.all_piece[name] = now # 相手の打った手の価値(方策)を盤面ごとに調査し、ランキング形式でプリントする関数 # 実際の盤面は何位なのか、トップの盤面の形などを出力すると良い？ def value_ranking_by_board(beforehand_estimated_num, action_num, ii_state): # 行動番号からインデックスを取得 enemy_legal_actions = sorted(list(ii_state.enemy_legal_actions()), key=lambda x: x) enemy_action_index = enemy_legal_actions.index(action_num) en_est_num = ii_state.enemy_estimated_num.copy() for index, (action_value_list, prenum_tuple) in enumerate( zip(beforehand_estimated_num, en_est_num) ): prenum_tuple[0] = action_value_list[enemy_action_index] # 価値の高い順にen_est_numをソートする sorted_en_est_num = sorted(en_est_num, reverse=True, key=lambda x: x[0]) real_rank = -1 for index, en_est in enumerate(sorted_en_est_num): if en_est[1] == tuple(ii_state.real_enemy_piece_blue_set): real_rank = index # print("real_set:", tuple(ii_state.real_enemy_piece_blue_set)) # print("action_num:", action_num) print("real_rank:", real_rank) # print(len(sorted_en_est_num)) print(sorted_en_est_num) # print(real_board_rank) # print(top_board) # 透視できている駒のidを用いて推測値から削ぐ def shave_impossible_board_from_see_through(ii_state): for piece_id in ii_state.see_through_piece_id: if piece_id in ii_state.real_enemy_piece_blue_set: shave_impossible_pattern(piece_id, True, ii_state) elif piece_id in ii_state.real_enemy_piece_red_set: shave_impossible_pattern(piece_id, False, ii_state) else: print("敵の駒のIDではありません") # 行動の一連の処理でii_stateを更新する def guess_enemy_piece_player_for_tcp( model_path, ii_state, before_tcp_str, now_tcp_str, gamma=default_gamma ): # 相手の盤面から全ての行動の推測値を計算しておく print("推測値を算出中") beforehand_estimated_num = enemy_ii_predict(model_path, ii_state) # 実際に取られた行動を取得 print("相手の行動番号を取得中") BeforeAndNow = enemy_coordinate_checker(before_tcp_str, now_tcp_str) print(BeforeAndNow) enemy_action_num = calculate_enemy_action_number_from_coordinate( BeforeAndNow[0], BeforeAndNow[1] ) print("敵の行動番号", enemy_action_num, sep=":") # 実際に取られた行動から推測値を更新 print("推測値を更新中") update_predict_num_all(ii_state, beforehand_estimated_num, enemy_action_num, gamma) # 相手の行動からボードを更新 print("ボード更新中") kill = update_II_state(ii_state, BeforeAndNow[0], BeforeAndNow[1]) # 相手の行動をボードに反映 # 行動を決定 print("行動を決定中") action_num = action_decision(model_path, ii_state) print("行動番号", action_num, sep=":") # 行動を受けて自分の推測値を更新 # beforehand_my_estimated_num = my_ii_predict(model, ii_state) # (未実装)update_my_predict_num_all(ii_state, beforehand_my_estimated_num, action_num) # 自分の決定した行動でii_stateを更新 ii_state.next(action_num) # 行動番号を返す return action_num # 推測をしない(推測値を更新しない) # 相手の行動からii_stateの更新->行動決定->自分の行動からii_stateを更新 def ii_state_action(rw_action, ii_state, just_before_enemy_action_num): if just_before_enemy_action_num != -1: # 相手の行動からボードを更新 before, now = action_to_coordinate(just_before_enemy_action_num) my_find_before = 35 - before # このままでは相手視点の座標なので、自分視点の座標に変換 my_find_now = 35 - now # 同様に変換 kill = update_II_state(ii_state, my_find_before, my_find_now) # 相手の行動をボードに反映 # 行動を決定 action_num = rw_action(ii_state) # 自分の決定した行動でii_stateを更新 ii_state.next(action_num) # 行動番号を返す return action_num # tcpを受けずに直接行動番号を受ける # 推測値の事前計算->推測値の更新->相手の行動からii_stateの更新->行動決定->自分の行動からii_stateを更新 def guess_enemy_piece_player(model_path, ii_state, just_before_enemy_action_num, gamma): # 相手の盤面から全ての行動の推測値を計算しておく # print("推測値を算出中") beforehand_estimated_num = enemy_ii_predict(model_path, ii_state) # print("敵の行動番号", just_before_enemy_action_num, sep=":") # value_ranking_by_board( # beforehand_estimated_num, just_before_enemy_action_num, ii_state # ) # print("盤面：", ii_state) # プリントデバッグ的なあれ # print("〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜〜") # print("実際の青駒：", ii_state.real_enemy_piece_blue_set) # en_est_num = ii_state.enemy_estimated_num.copy() # dead_piece = [] # for index, piece in enumerate(ii_state.all_piece): # if piece == 99: # dead_piece.append(index) # print("dead_piece:", dead_piece) # sorted_en_est_num = sorted(en_est_num, reverse=True, key=lambda x: x[0]) # print("推測値：", sorted_en_est_num) # print("beforehand_estimated_num:", beforehand_estimated_num) # value_ranking_by_board( # beforehand_estimated_num, just_before_enemy_action_num, ii_state # ) # 実際に取られた行動から推測値を更新 # update_predict_num_all( # ii_state, beforehand_estimated_num, just_before_enemy_action_num, gamma # ) # print("beforehand", beforehand_estimated_num) # update_predict_num_max_only( # ii_state, beforehand_estimated_num, just_before_enemy_action_num, gamma # ) update_predict_num_normalize( ii_state, beforehand_estimated_num, just_before_enemy_action_num, gamma ) # 相手の行動からボードを更新 # print("ボード更新中") before, now = action_to_coordinate(just_before_enemy_action_num) my_find_before = 35 - before # このままでは相手視点の座標なので、自分視点の座標に変換 my_find_now = 35 - now # 同様に変換 # print(my_find_before, my_find_now) kill = update_II_state(ii_state, my_find_before, my_find_now) # 相手の行動をボードに反映 # 行動を決定 # print("行動を決定中") action_num = action_decision(model_path, ii_state) # print("行動番号", action_num, sep=":") # 行動を受けて自分の推測値を更新 # beforehand_my_estimated_num = my_ii_predict(model_path, ii_state) # (未実装)update_my_predict_num_all(ii_state, beforehand_my_estimated_num, action_num) # 自分の決定した行動でii_stateを更新 ii_state.next(action_num) # 行動番号を返す return action_num # 動作確認 if __name__ == "__main__": start = time.time() # path = sorted(Path("./model").glob("*.h5"))[-1] # model = load_model(str(path)) # ii_state = II_State({8, 9, 10, 11}, {0, 1, 2, 3}) # ii_state = II_State({8, 9, 10, 11}, {0, 1, 2, 3}, {2, 3, 4, 5}) ii_state = II_State({8, 9, 10, 11}, {0, 1, 2, 3}, {2, 3, 4}, {0, 1, 6}) print("本当の青駒：", ii_state.real_enemy_piece_blue_set) print("初期値：", ii_state.enemy_estimated_num) reduce_pattern(4, False, ii_state) print("4赤(透視済)：", ii_state.enemy_estimated_num) reduce_pattern(1, True, ii_state) print("1青(未透視)：", ii_state.enemy_estimated_num) elapsed_time = time.time() - start print("elapsed_time:{0}".format(elapsed_time) + "[sec]")
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import itertools from os import path from pdb import set_trace import pickle from typing import Dict, List, Tuple from explicit import waiter, XPATH from selenium import webdriver import selenium from selenium.common.exceptions import NoSuchElementException from selenium.webdriver.chrome.options import Options from selenium.webdriver.common import action_chains from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.keys import Keys from selenium.webdriver.common.by import By from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC import time import traceback import urllib.parse import numpy.random as random import requests import yaml import pandas as pd from src.profile import AccountProfile, profile_parser, get_post_link SAVE_FILE_NAME = 'influencer_info.csv' COOKIES_PATH = './login.cache' class PrivateException(Exception): pass with open(r'config.yml') as file: account = yaml.full_load(file) class InstagramBot(): def __init__(self, account): options = Options() # options.add_argument('--headless') # options.add_argument('--no-sandbox') # options.add_argument('--disable-dev-shm-usage') options.add_argument("user-data-dir=selenium") self.driver = webdriver.Chrome(options=options) self.driver.implicitly_wait(5) self.http_base = requests.Session() self.username = account['username'] self.password = account['password'] self.info_list = [] self.visited = set() with open(r'filter.yml') as file: self.filter = yaml.full_load(file) ''' Helper function ''' def sleep(self, mu = 2, sigma = 0.5): time.sleep(self.wait(mu, sigma)) return def wait(self, mu = 2, sigma = 0.5): # random wait time to avoid being detected if sigma == 0: return mu else: t = random.normal(mu, sigma,1)[0] if t <= 0: return 0.1 else: return t ''' Low level API ''' def check_availability(self, username: str): """ Checking Status code, Taking number of posts, Privacy and followed by viewer Raise Error if the Profile is private and not following by viewer return: numbers of posts/followers/followings """ search = self.http_base.get(f'https://www.instagram.com/{username}/', params={'__a': 1}) search.raise_for_status() self.sleep(25) load_and_check = search.json() privacy = load_and_check.get('graphql').get('user').get('is_private') followed_by_viewer = load_and_check.get('graphql').get('user').get('followed_by_viewer') if privacy and not followed_by_viewer: raise PrivateException(f'[!] Account is private: {username}') return search.json() # def get_post_link(self, username: str) -> Tuple[List[str], List[str], List[str]]: # ''' # Get the links of first 12 posts(I don't where where did I put this stupid hard coded number) # that contains the detailed information of the posts. Also with the numbers of likes and comments # of this post. # The numbers of likes and comments will be rounded and formated in string. # ''' # url = f'https://www.instagram.com/{username}/' # self.driver.get(url) # action = ActionChains(self.driver) # mouse pointer driver # elements = self.driver.find_elements_by_xpath('//a[@href]') # post_links = [] # n_likes = [] # n_comments = [] # for elem in elements: # urls = elem.get_attribute('href') # if 'p' in urls.split('/'): # action.move_to_element(elem).perform() # move pointer to the post to get the likes and comments info # post_links.append(urls) # # get numbers of likes and comments # temp_likes, temp_comments = elem.find_element_by_class_name('qn-0x').text.split('\n') # n_likes.append(temp_likes) # n_comments.append(temp_comments) # return post_links, n_likes, n_comments def get_profile(self, username, keep_post_jsons=False) -> Dict: # Taking hrefs while scrolling down profile_graphql = self.check_availability(username) print(f"Analyzing account: {username}") user_profile = profile_parser(username, profile_graphql) self.sleep(0.5) self.driver.execute_script('window.scrollTo(0, document.body.scrollHeight);') self.sleep(0.5) if keep_post_jsons: ''' TODO: post basic infos can be directly parsed from profile_graphql, instead of doing http requests again and again ''' # post json string is stored in another link post_links, n_likes, n_comments = get_post_link(username) print(f'[*] extracting {len(post_links)} posts jsons string, please wait...'.title()) user_profile.new_links = [urllib.parse.urljoin(link, '?__a=1') for link in post_links] user_profile.post_jsons = [self.http_base.get(link.split()[0]).json() for link in user_profile.new_links] # here you can do anything to parse the detailed info of a post return user_profile # def get_followers(self, username: str, max_width: int = 500) -> List[str]: # ''' # Get a bunch of follwers of an account # ''' # url = f'https://www.instagram.com/{username}/' # self.driver.get(url) # # click on "followers" on main page # followersLink = self.driver.find_element_by_xpath("(//a[@class='-nal3 '])[2]") # followersLink.click() # self.sleep(1) # # find the popup follower tab # followersList = self.driver.find_element_by_xpath("//div[@role='dialog']") # num = len(followersList.find_elements_by_css_selector('li')) # actionChain = webdriver.ActionChains(self.driver) # while (num < max_width): # # followersList.click() # trick # self.sleep() # actionChain.key_down(Keys.SPACE).pause(self.wait(0.1,0.01)).key_up(Keys.SPACE).perform() # actionChain.reset_actions() # num = len(followersList.find_elements_by_css_selector('li')) # followers = [] # for user in followersList.find_elements_by_css_selector('li'): # userLink = user.find_element_by_css_selector('a').get_attribute('href') # followers.append(userLink.split('/')[-2]) # if (len(followers) >= max_width): # break # return followers def scrape_followers(self, username, max_width: int = 50): # Load account page self.driver.get("https://www.instagram.com/{0}/".format(username)) # Click the 'Follower(s)' link # driver.find_element_by_partial_link_text("follower").click self.sleep(25) self.driver.find_element_by_xpath("(//a[@class='-nal3 '])[2]").click() # Wait for the followers modal to load waiter.find_element(self.driver, "//div[@role='dialog']", by=XPATH) # allfoll = int(self.driver.find_element_by_xpath("//li[2]/a/span").text) # At this point a Followers modal pops open. If you immediately scroll to the bottom, # you hit a stopping point and a "See All Suggestions" link. If you fiddle with the # model by scrolling up and down, you can force it to load additional followers for # that person. # Now the modal will begin loading followers every time you scroll to the bottom. # Keep scrolling in a loop until you've hit the desired number of followers. # In this instance, I'm using a generator to return followers one-by-one followers = [] follower_css = "ul div li:nth-child({}) a.notranslate" # Taking advange of CSS's nth-child functionality for group in itertools.count(start=1, step=12): for follower_index in range(group, group + 12): if follower_index > max_width: return followers followers.append(waiter.find_element(self.driver, follower_css.format(follower_index)).text) # Instagram loads followers 12 at a time. Find the last follower element # and scroll it into view, forcing instagram to load another 12 # Even though we just found this elem in the previous for loop, there can # potentially be large amount of time between that call and this one, # and the element might have gone stale. Lets just re-acquire it to avoid # tha last_follower = waiter.find_element(self.driver, follower_css.format(group+11)) self.driver.execute_script("arguments[0].scrollIntoView();", last_follower) ''' High level API ''' def sign_in(self): if path.exists(COOKIES_PATH): print("Loading existing cookies") cookies = pickle.load(open(COOKIES_PATH, "rb")) # cookies = { # cookie['name']: cookie['value'] # for cookie in get_cookies # } # for cookie in get_cookies: # self.driver.add_cookie(cookie) else: self.driver.get('https://www.instagram.com/accounts/login/') try: WebDriverWait(self.driver, 20).until( EC.element_to_be_clickable((By.XPATH, "//input[@name='username']")) ) except Exception as e: traceback.print_exc() print(e) return self.driver.find_element_by_xpath("//input[@name='username']").send_keys(self.username) time.sleep(self.wait()) self.driver.find_element_by_xpath("//input[@name='password']").send_keys(self.password) time.sleep(self.wait(mu=0.6)) self.driver.find_element_by_xpath("//input[@name='password']").send_keys(Keys.ENTER) time.sleep(self.wait()) # Save Info or Not try: var_error = self.driver.find_element_by_xpath("//button[contains(.,'保存信息')]").click() time.sleep(self.wait()) except NoSuchElementException: pass """Check For Invalid Credentials""" try: var_error = self.driver.find_element_by_class_name('eiCW-').text raise ValueError('[!] Invalid Credentials') except NoSuchElementException: pass """Taking cookies""" get_cookies = self.driver.get_cookies() cookies = { cookie['name']: cookie['value'] for cookie in get_cookies } print(f"Generated new cookies and save it into {COOKIES_PATH}") pickle.dump(cookies, open(COOKIES_PATH,"wb")) self.http_base.cookies.update(cookies) def dive(self, username: str, max_depth=0, max_width=500, keep_post_jsons=False, depth=0): ''' Browse the TREE of an account (BFS) ''' if depth > max_depth: return if depth == 0: self.visited = set() elif username in self.visited: return try: user_profile = self.get_profile(username, keep_post_jsons) self.visited.add(username) except: traceback.print_exc() print(f"FAILED: got {username} profile") COND_FOLLOWERS = user_profile.n_followers > 2000 and user_profile.n_followers < 100000 COND_LIKES = True COND_COMMENTS = True if COND_FOLLOWERS and COND_LIKES and COND_COMMENTS: self.info_list.append(user_profile) info_df = pd.DataFrame([vars(user_profile)]) # save to csv file with open(SAVE_FILE_NAME, 'a') as f: info_df.to_csv(f, mode='a', index=False, header=f.tell()==0) followers = self.scrape_followers(username, max_width) print(f"Got followers: \n{followers}") for f in followers: try: self.dive(f, max_depth, max_width, keep_post_jsons=keep_post_jsons,depth = depth+1) except PrivateException: print(PrivateException) except Exception as e: print(e) else: print(f"BYPASS: account {username} condition not met") if __name__ == "__main__": # unit test root_username='laurensoyung' ib = InstagramBot(account) ib.sign_in() ib.dive(root_username, 2, 40, keep_post_jsons=False) # ib.get_profile(root_username)
[ "numpy.random.normal", "yaml.full_load", "selenium.webdriver.chrome.options.Options", "os.path.exists", "src.profile.get_post_link", "requests.Session", "selenium.webdriver.support.ui.WebDriverWait", "selenium.webdriver.Chrome", "explicit.waiter.find_element", "itertools.count", "traceback.print...
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# Copyright 2019-2020 ETH Zurich and the DaCe authors. All rights reserved. from __future__ import print_function import dace import numpy as np N = dace.symbol('N') @dace.program def dot(A, B, out): @dace.map def product(i: _[0:N]): a << A[i] b << B[i] o >> out(1, lambda x, y: x + y) o = a * b def test_dot(): n = 64 N.set(n) A = dace.ndarray([N], dtype=dace.float32) out_AA = dace.scalar(dace.float64) A[:] = np.random.rand(n).astype(dace.float32.type) out_AA[0] = dace.float64(0) dot(A, A, out_AA, N=n) diff_aa = np.linalg.norm(np.dot(A, A) - out_AA) / float(n) print("Difference:", diff_aa) assert diff_aa <= 1e-5 if __name__ == "__main__": test_dot()
[ "numpy.random.rand", "dace.symbol", "dace.scalar", "numpy.dot", "dace.ndarray", "dace.float64" ]
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"""Model training/evaluation base interface module. This module contains the interface required to train and/or evaluate a model based on different tasks. The trainers based on this interface are instantiated in launched sessions based on configuration dictionaries. """ import functools import json import logging import math import os import platform import random import time from abc import abstractmethod from copy import deepcopy from typing import Any, AnyStr, Optional # noqa: F401 import cv2 as cv import numpy as np import torch import torch.optim import thelper.typedefs as typ # noqa: F401 import thelper.utils logger = logging.getLogger(__name__) class Trainer: """Abstract trainer interface that defines basic session i/o and setup operations. This interface defines the general behavior of a training session which includes configuration parsing, tensorboard setup, metrics and goal setup, and loss/optimizer setup. It also provides utilities for uploading models and tensors on specific devices, and for saving the state of a session. This interface should be specialized for every task by implementing the ``train_epoch`` and ``eval_epoch`` functions in a derived class. See :class:`thelper.train.classif.ImageClassifTrainer` for an example. The main parameters that will be parsed by this interface from a configuration dictionary are the following: - ``epochs`` (mandatory if training): number of epochs to train for; one epoch is one iteration over all mini-batches. - ``optimization`` (mandatory if training): sub-dictionary containing types and extra parameters required for instantiating the loss, optimizer, and scheduler objects. See the code of each related loading function for more information on special parameters. - ``save_freq`` (optional, default=1): checkpoint save frequency (will save every epoch multiple of given number). - ``save_raw`` (optional, default=True): specifies whether to save raw types or thelper objects in checkpoints. - ``use_tbx`` (optional, default=False): defines whether to use tensorboardX writers for logging or not. - ``device`` (optional): specifies which device to train/evaluate the model on (default=all available). - ``metrics``: list of metrics to instantiate and update during training/evaluation; see related loading function for more information. - ``monitor``: specifies the name of the metric that should be monitored on the validation set for model improvement. Example configuration file:: # ... "trainer": { # type of trainer to instantiate (linked to task type) "type": "thelper.train.ImageClassifTrainer", # train for 40 epochs "epochs": 40, # save every 5 epochs "save_freq": 5, # monitor validation accuracy and save best model based on that "monitor": "accuracy", # optimization parameters block "optimization": { # all types & params below provided by PyTorch "loss": { "type": "torch.nn.CrossEntropyLoss" }, "optimizer": { "type": "torch.optim.SGD", "params": { "lr": 0.1, "momentum": 0.9, "weight_decay": 1e-06, "nesterov": true } }, "scheduler": { "type": "torch.optim.lr_scheduler.StepLR", "params": { "step_size": 10, "step_size": 0.1 } } }, # in this example, we use two consumers in total # (one metric for monitoring, and one for logging) "metrics": { "accuracy": { "type": "thelper.optim.Accuracy" }, "fullreport": { "type": "thelper.train.ClassifReport" } } } # ... Attributes: checkpoint_dir: session checkpoint output directory (located within the 'session directory'). config: session configuration dictionary holding all original settings, including trainer configuration. devices: list of (cuda) device IDs to upload the model/tensors to; can be empty if only the CPU is available. epochs: number of epochs to train the model for. logger: used to output debug/warning/error messages to session log. model: reference to the model being trained or used for evaluation/prediction. monitor: name of the training/validation metric that should be monitored for model improvement. name: name of the session, used for printing and creating log folders. optimization_config: dictionary of optim-related parameters, parsed at training time. output_paths: map of session output paths where training/evaluation results should be saved. save_freq: frequency of checkpoint saves while training (i.e. save every X epochs). save_raw: specifies whether to save raw types or thelper objects in checkpoints. skip_eval_iter: number of evaluation iterations to skip (useful for resuming a session). skip_tbx_histograms: flag used to skip the generation of graph histograms in tbx (useful for large models). task: reference to the object used to specialize the model and that holds task metainformation. tbx_histogram_freq: frequency of tbx histogram saves while training (i.e. save every X epochs). use_tbx: defines whether to use tensorboardX writers for logging or not. writers: map of tbx writers used to save training/evaluation events. TODO: move static utils to their related modules .. seealso:: | :class:`thelper.train.classif.ImageClassifTrainer` | :class:`thelper.train.segm.ImageSegmTrainer` | :class:`thelper.train.detect.ObjDetectTrainer` | :class:`thelper.train.regr.RegressionTrainer` | :func:`thelper.train.utils.create_trainer` """ def __init__(self, session_name, # type: AnyStr session_dir, # type: AnyStr model, # type: thelper.typedefs.ModelType task, # type: thelper.tasks.Task loaders, # type: thelper.typedefs.MultiLoaderType config, # type: thelper.typedefs.ConfigDict ckptdata=None # type: Optional[thelper.typedefs.CheckpointContentType] ): """Receives the trainer configuration dictionary, parses it, and sets up the session.""" assert isinstance(model, (thelper.nn.Module, torch.nn.Module)), "unknown model object type" assert isinstance(task, thelper.tasks.Task), "unknown task object type" assert isinstance(loaders, (list, tuple, np.ndarray)) and len(loaders) == 3, "invalid loaders array" assert isinstance(config, dict), "invalid config type" self.task = task self.model = model self.config = config # parse basic training config args trainer_config = thelper.utils.get_key("trainer", config, msg="session config dictionary missing 'trainer' field") os.makedirs(session_dir, exist_ok=True) logs_dir = os.path.join(session_dir, "logs") os.makedirs(logs_dir, exist_ok=True) thelper.utils.save_env_list(os.path.join(logs_dir, "packages.log")) train_logger_path = os.path.join(logs_dir, "trainer.log") train_logger_format = logging.Formatter("[%(asctime)s - %(process)s] %(levelname)s : %(message)s") train_logger_fh = logging.FileHandler(train_logger_path) train_logger_fh.setFormatter(train_logger_format) self.logger = thelper.utils.get_class_logger() self.logger.addHandler(train_logger_fh) self.logger.info(f"created training log for session '{session_name}'") self.logger.debug(f"session directory = {os.path.abspath(session_dir)}") self.logger.debug(f"logs directory = {os.path.abspath(logs_dir)}") logstamp = thelper.utils.get_log_stamp() repover = thelper.__version__ + ":" + thelper.utils.get_git_stamp() self.logger.debug(f"logstamp = {logstamp}") self.logger.debug(f"version = {repover}") self.name = session_name self.epochs = 1 self.save_freq = int(thelper.utils.get_key_def("save_freq", trainer_config, 1)) assert self.save_freq >= 1, "checkpoint save frequency should be strictly positive integer" self.save_raw = thelper.utils.str2bool(thelper.utils.get_key_def("save_raw", trainer_config, True)) self.checkpoint_dir = os.path.join(session_dir, "checkpoints") os.makedirs(self.checkpoint_dir, exist_ok=True) output_root_dir = thelper.utils.get_key_def("output_dir", trainer_config) if not output_root_dir: # append session name for cleaner TBX folder merging output_root_dir = os.path.join(session_dir, "output", self.name) assert isinstance(output_root_dir, str) and len(output_root_dir), "invalid output directory path" self.logger.debug(f"output directory = {os.path.abspath(output_root_dir)}") os.makedirs(output_root_dir, exist_ok=True) unique_output_dir = thelper.utils.get_key_def("unique_output_dir", trainer_config, True) assert isinstance(unique_output_dir, bool), "invalid unique_output_dir flag (should be bool)" self.logger.debug(f"output subdirectories {'will' if unique_output_dir else 'will not'} have unique names") devices_str = thelper.utils.get_key_def(["device", "devices", "train_device"], trainer_config, None) self.devices = self._load_devices(devices_str) self.skip_eval_iter = thelper.utils.get_key_def("skip_eval_iter", trainer_config, 0) # parse and prepare tbx stuff self.use_tbx = thelper.utils.str2bool(thelper.utils.get_key_def("use_tbx", trainer_config, False)) if self.use_tbx: import tensorboardX self.tbx = tensorboardX self.logger.debug(f"tensorboard init : tensorboard --logdir {output_root_dir} --port <your_port>") self.skip_tbx_histograms = thelper.utils.str2bool( thelper.utils.get_key_def("skip_tbx_histograms", trainer_config, False)) self.tbx_histogram_freq = int(thelper.utils.get_key_def("tbx_histogram_freq", trainer_config, 1)) assert self.tbx_histogram_freq >= 1, "histogram output frequency should be strictly positive integer" timestr = time.strftime("%Y%m%d-%H%M%S") self.writers, self.output_paths = {}, {} for cname, loader in zip(["train", "valid", "test"], loaders): if loader: folder_name = f"{cname}-{str(platform.node())}-{timestr}" if unique_output_dir else cname self.output_paths[cname] = os.path.join(output_root_dir, folder_name) self.logger.debug(f"output {cname} directory = {os.path.abspath(self.output_paths[cname])}") else: self.output_paths[cname] = None self.writers[cname] = None # will be instantiated only when needed based on above path # split loaders train_loader, valid_loader, test_loader = loaders assert (train_loader or valid_loader or test_loader), "must provide at least one loader with available data" self.train_loader, self.valid_loader, self.test_loader = train_loader, valid_loader, test_loader if train_loader: assert "epochs" in trainer_config and int(trainer_config["epochs"]) > 0, "bad trainer config epoch count" self.epochs = int(trainer_config["epochs"]) # loading optimization stuff later since model needs to be on correct device self.optimization_config = thelper.utils.get_key_def("optimization", trainer_config, {}) else: self.logger.info("no training data provided, will run a single epoch on valid/test data") # parse metrics assert "metrics" not in trainer_config or "base_metrics" not in trainer_config, \ "trainer config should have only one of 'metrics' and 'base_metrics'" metrics = {} if "metrics" in trainer_config: self.logger.debug("loading metrics defined in trainer config") metrics = thelper.train.create_consumers(trainer_config["metrics"]) elif "base_metrics" in trainer_config: self.logger.debug("loading base metrics defined in trainer config") metrics = thelper.train.create_consumers(trainer_config["base_metrics"]) self.train_metrics, self.valid_metrics, self.test_metrics = \ deepcopy(metrics), deepcopy(metrics), deepcopy(metrics) for skey, sval in zip(["train_metrics", "valid_metrics", "test_metrics"], [self.train_metrics, self.valid_metrics, self.test_metrics]): if skey in trainer_config: new_metrics = thelper.train.create_consumers(trainer_config[skey]) for mkey, mval in new_metrics.items(): assert mkey not in sval, f"metric name '{mkey}' duplicated in set '{skey}'" sval[mkey] = mval for mkey, mval in sval.items(): self.logger.info(f"parsed metric '{mkey}': {str(mval)}") # check for monitored metric self.monitor, self.monitor_best, self.monitor_best_epoch = None, None, -1 if "monitor" in trainer_config and trainer_config["monitor"]: self.monitor = trainer_config["monitor"] assert any([self.monitor in mset for mset in [self.train_metrics, self.valid_metrics]]), \ f"metric with name '{self.monitor}' could not be found in training/validation metrics" metric = self.valid_metrics[self.monitor] if self.monitor in self.valid_metrics \ else self.train_metrics[self.monitor] # makes no sense to search for it in test metrics... assert isinstance(metric, thelper.optim.metrics.Metric), \ "monitoring target should be an actual 'metric' class that returns a scalar!" assert metric.goal in [thelper.optim.Metric.minimize, thelper.optim.Metric.maximize], \ "monitored metric does not return proper optimization goal" self.monitor_goal = metric.goal self.monitor_best = thelper.optim.Metric.minimize if metric.goal == thelper.optim.Metric.maximize \ else thelper.optim.Metric.maximize self.logger.debug(f"will monitor metric '{self.monitor}' for best state checkpointing/early stopping") # parse checkpoint data from previous run (if available) ckptdata = {} if ckptdata is None else ckptdata self.monitor_best = thelper.utils.get_key_def("monitor_best", ckptdata, self.monitor_best) self.monitor_best_epoch = thelper.utils.get_key_def("monitor_best_epoch", ckptdata, -1) self.optimizer_state = thelper.utils.get_key_def("optimizer", ckptdata, None) self.scheduler_state = thelper.utils.get_key_def("scheduler", ckptdata, None) self.current_iter = thelper.utils.get_key_def("iter", ckptdata, 0) self.current_epoch = thelper.utils.get_key_def("epoch", ckptdata, 0) self.outputs = thelper.utils.get_key_def("outputs", ckptdata, {}) # parse callbacks (see ``thelper.typedefs.IterCallbackType`` and ``thelper.typedefs.IterCallbackParams`` definitions) for cname, mset in zip(["train", "valid", "test"], [self.train_metrics, self.valid_metrics, self.test_metrics]): # parse user (custom) callback # TODO: rewrite so that lists of callbacks can be supported @@@@ user_callback_keys = [f"{cname}_iter_callback", f"{cname}_callback", "callback"] user_callback = thelper.utils.get_key_def(user_callback_keys, trainer_config) # type: Optional[typ.IterCallbackType] user_callback_kwargs_keys = [f"{cname}_iter_callback_kwargs", f"{cname}_callback_kwargs", "callback_kwargs"] user_callback_kwargs = thelper.utils.get_key_def(user_callback_kwargs_keys, trainer_config, {}) if user_callback is not None: assert "user_callback" not in mset, "metrics set already had a 'user_callback' in it" mset["user_callback"] = thelper.train.utils.PredictionCallback(user_callback, user_callback_kwargs) # parse display callback display_flag_keys = [f"display_{cname}_preds", f"display_{cname}_predictions", f"display_{cname}", "display_preds", "display_predictions", "display"] display_flag = thelper.utils.get_key_def(display_flag_keys, trainer_config, False) display_kwargs_keys = [f"display_{cname}_preds_kwargs", f"display_{cname}_predictions_kwargs", f"display_{cname}_kwargs", "display_preds_kwargs", "display_predictions_kwargs", "display_kwargs"] display_kwargs = thelper.utils.get_key_def(display_kwargs_keys, trainer_config, {}) if display_flag: assert "display_callback" not in mset, "metrics set already had a 'display_callback' in it" display_kwargs["output_path"] = self.output_paths[cname] display_kwargs["save"] = thelper.utils.get_key_def(["save", "save_draw", "save_draw_output"], display_kwargs, False) mset["display_callback"] = thelper.train.utils.PredictionCallback("thelper.train.utils._draw_wrapper", display_kwargs) # add logging callback (will print to console and update iter metric evals) logging_kwargs = thelper.utils.get_key_def("logging_kwargs", trainer_config, {}) logging_kwargs["set_name"] = cname logging_kwargs["writers"] = self.writers # pass by ref, will be filled later display_kwargs["output_path"] = self.output_paths[cname] mset["logging_callback"] = thelper.train.utils.PredictionCallback(self._iter_logger_callback, logging_kwargs) def _init_writer(self, writer, path): if self.use_tbx and not writer: writer = self.tbx.SummaryWriter(path, comment=self.name) writer.add_text("config", json.dumps(self.config, indent=4, sort_keys=False, default=lambda x: str(x))) thelper.utils.save_config(self.config, os.path.join(path, "config.json")) return writer @staticmethod def _set_rng_state(seeds, epoch): if "torch" in seeds: torch.manual_seed(seeds["torch"] + epoch) torch.cuda.manual_seed_all(seeds["torch"] + epoch) if "numpy" in seeds: np.random.seed(seeds["numpy"] + epoch) if "random" in seeds: random.seed(seeds["random"] + epoch) @staticmethod def _upload_model(model, dev): """Uploads a model to a specific device, wrapping it in ``torch.nn.DataParallel`` if needed.""" if isinstance(dev, list): if len(dev) == 0: return model.cpu() elif len(dev) == 1: return model.cuda(dev[0]) else: return torch.nn.DataParallel(model, device_ids=dev).cuda(dev[0]) else: return model.to(dev) @staticmethod def _move_tensor(tensor, dev, detach=False): """Uploads a tensor to a specific device.""" if isinstance(tensor, (list, tuple)): return [Trainer._move_tensor(t, dev) for t in tensor] if isinstance(tensor, dict): return {k: Trainer._move_tensor(t, dev) for k, t in tensor.items()} if not isinstance(tensor, torch.Tensor): return tensor # ignored (cannot upload) if isinstance(dev, list): if len(dev) == 0: out = tensor.cpu() else: # no reason to have multiple devices if not cuda-enabled GPUs out = tensor.cuda(dev[0]) else: out = tensor.to(dev) return out.detach() if detach else out def _load_optimization(self, model, dev): """Instantiates and returns all optimization objects required for training the model.""" config = self.optimization_config # for abbrev only assert isinstance(config, dict), "optimization config should be provided as a dictionary" assert self.train_loader is not None and self.train_loader, "optimization only useful with training data" loss = None # can be omitted if using custom trainer if "loss" in config: uploader = functools.partial(self._move_tensor, dev=dev) loss = thelper.optim.create_loss_fn(config["loss"], model, self.train_loader, uploader) optimizer = None # can be omitted if using custom trainer if "optimizer" in config: optimizer = thelper.optim.create_optimizer(config["optimizer"], model) scheduler, scheduler_step_metric = None, None if "scheduler" in config and config["scheduler"]: # can always be omitted scheduler, scheduler_step_metric = thelper.optim.create_scheduler(config["scheduler"], optimizer) return loss, optimizer, scheduler, scheduler_step_metric def _load_devices(self, devices_str=None): """Validates and returns the list of CUDA devices available on the system.""" self.logger.debug("loading available devices") if devices_str is not None: devices = [] available_cuda_devices = None assert isinstance(devices_str, (str, list)), "unexpected device string type" if isinstance(devices_str, str): assert devices_str, "cannot specify empty device name, use 'None' to auto-detect" devices_str = devices_str.split(",") elif isinstance(devices_str, list): assert devices_str, "cannot specify empty device list, use 'None' to auto-detect" assert all([isinstance(dev_str, str) for dev_str in devices_str]), "unexpected type in dev list" for dev_idx, dev_str in enumerate(devices_str): assert "cuda" in dev_str or dev_str == "cpu", \ f"unknown device type '{dev_str}' (expecting 'cpu' or 'cuda:X')" if dev_str == "cpu": assert len(devices_str) == 1, "cannot combine cpu with other devices" return [] if dev_str == "cuda" or dev_str == "cuda:all": assert len(devices_str) == 1, "must specify device index (e.g. 'cuda:0') if combining devices" if available_cuda_devices is None: available_cuda_devices = thelper.utils.get_available_cuda_devices() assert available_cuda_devices, "could not find any available cuda devices" return available_cuda_devices assert "cuda:" in dev_str, "expecting cuda device format to be 'cuda:X' (where X is device index)" cuda_dev_idx = int(dev_str.rsplit(":", 1)[-1]) assert thelper.utils.test_cuda_device_availability(cuda_dev_idx), f"cuda device '{dev_str}' unavailable" devices.append(cuda_dev_idx) return devices else: return thelper.utils.get_available_cuda_devices() def train(self): """Starts the training process. This function will train the model until the required number of epochs is reached, and then evaluate it on the test data. The setup of loggers, tensorboard writers is done here, so is model improvement tracking via monitored metrics. However, the code related to loss computation and back propagation is implemented in a derived class via :func:`thelper.train.base.Trainer.train_epoch`. """ assert self.train_loader, "missing training data, invalid loader!" assert not isinstance(self.model, torch.jit.ScriptModule), "current impl cannot train model traces" # TODO self.logger.debug(f"uploading model to '{str(self.devices)}'...") model = self._upload_model(self.model, self.devices) loss, optimizer, scheduler, scheduler_step_metric = self._load_optimization(model, self.devices) if optimizer is not None and self.optimizer_state is not None: optimizer.load_state_dict(self.optimizer_state) self.optimizer_state = None if scheduler is not None and self.scheduler_state is not None: scheduler.load_state_dict(self.scheduler_state) self.scheduler_state = None self.logger.debug(f"loss: {str(loss)}") self.logger.debug(f"optimizer: {str(optimizer)}") latest_loss = math.inf while self.current_epoch < self.epochs: self.writers["train"] = self._init_writer(self.writers["train"], self.output_paths["train"]) self.logger.info(f"at epoch#{self.current_epoch} for '{self.name}' (dev={str(self.devices)})") if scheduler: if scheduler_step_metric: if scheduler_step_metric == "loss": # todo: use validation loss instead? more stable? scheduler.step(metrics=latest_loss, epoch=self.current_epoch) else: metric = None if self.valid_loader and scheduler_step_metric in self.valid_metrics: metric = self.valid_metrics[scheduler_step_metric] elif self.train_loader and scheduler_step_metric in self.train_metrics: metric = self.train_metrics[scheduler_step_metric] # note: makes no sense to look for it in test metrics assert metric is not None, f"cannot find metric '{scheduler_step_metric}' for scheduler step" assert isinstance(metric, thelper.optim.metrics.Metric), "monitoring consumer must be metric" metric_anti_goal = thelper.optim.Metric.maximize \ if metric.goal == thelper.optim.Metric.minimize \ else thelper.optim.Metric.minimize metric_val = metric.eval() if self.current_epoch > 0 else metric_anti_goal scheduler.step(metrics=metric_val, epoch=self.current_epoch) else: scheduler.step(epoch=self.current_epoch) if self.writers["train"] and not self.skip_tbx_histograms and \ (self.current_epoch % self.tbx_histogram_freq) == 0: for pname, param in model.named_parameters(): if "bn" in pname: continue # skip batch norm modules pname = pname.replace(".", "/") # for proper grouping if pname.startswith("module/"): pname = pname.replace("module/", "", 1) if pname.startswith("model/"): pname = pname.replace("model/", "", 1) data = param.data.cpu().numpy().flatten() self.writers["train"].add_histogram(pname, data, self.current_epoch) if param.grad is not None: grad = param.grad.data.cpu().numpy().flatten() self.writers["train"].add_histogram(pname + '/grad', grad, self.current_epoch) self.logger.debug(f"learning rate at {thelper.optim.get_lr(optimizer):.8f}") self._set_rng_state(self.train_loader.seeds, self.current_epoch) model.train() if hasattr(self.train_loader, "set_epoch") and callable(self.train_loader.set_epoch): self.train_loader.set_epoch(self.current_epoch) latest_loss = self.train_epoch(model, self.current_epoch, self.devices, loss, optimizer, self.train_loader, self.train_metrics) self._write_epoch_output(self.current_epoch, self.train_metrics, self.writers["train"], self.output_paths["train"], loss=latest_loss, optimizer=optimizer) train_metric_vals = {metric_name: metric.eval() for metric_name, metric in self.train_metrics.items() if isinstance(metric, thelper.optim.metrics.Metric)} result = {"train/loss": latest_loss, "train/metrics": train_metric_vals} monitor_type_key = "train/metrics" # if we cannot run validation, will monitor progression on training metrics if self.valid_loader: self._set_rng_state(self.valid_loader.seeds, self.current_epoch) model.eval() self.writers["valid"] = self._init_writer(self.writers["valid"], self.output_paths["valid"]) for metric in self.valid_metrics.values(): metric.reset() # force reset here, we always evaluate from a clean state if hasattr(self.valid_loader, "set_epoch") and callable(self.valid_loader.set_epoch): self.valid_loader.set_epoch(self.current_epoch) self.eval_epoch(model, self.current_epoch, self.devices, self.valid_loader, self.valid_metrics) self._write_epoch_output(self.current_epoch, self.valid_metrics, self.writers["valid"], self.output_paths["valid"]) valid_metric_vals = {metric_name: metric.eval() for metric_name, metric in self.valid_metrics.items() if isinstance(metric, thelper.optim.metrics.Metric)} result = {**result, "valid/metrics": valid_metric_vals} monitor_type_key = "valid/metrics" # since validation is available, use that to monitor progression new_best = False monitor_val = None for key, value in result.items(): if key == monitor_type_key and self.monitor is not None: assert self.monitor in value, f"not monitoring required variable '{self.monitor}' in metrics" monitor_val = value[self.monitor] if (self.monitor_goal == thelper.optim.Metric.minimize and monitor_val < self.monitor_best) or \ (self.monitor_goal == thelper.optim.Metric.maximize and monitor_val > self.monitor_best): self.monitor_best = monitor_val self.monitor_best_epoch = self.current_epoch new_best = True if not isinstance(value, dict): self.logger.debug(f" epoch#{self.current_epoch} result => {str(key)}: {value}") else: for subkey, subvalue in value.items(): self.logger.debug(f" epoch#{self.current_epoch} result => {str(key)}:{str(subkey)}: {subvalue}") if self.monitor is not None: assert monitor_val is not None, f"training/validation did not evaluate required metric '{self.monitor}'" if new_best: best_str = "(new best value)" else: best_str = f"(previous best = {self.monitor_best} @ epoch = {self.monitor_best_epoch})" self.logger.info(f"epoch {self.current_epoch}, monitored {self.monitor} = {monitor_val} {best_str}") self.outputs[self.current_epoch] = result if new_best or (self.current_epoch % self.save_freq) == 0: self.logger.info(f"saving checkpoint @ epoch#{self.current_epoch}") self._save(self.current_epoch, self.current_iter, optimizer, scheduler, save_best=new_best) self.current_epoch += 1 self.logger.info(f"training for session '{self.name}' done") return self.outputs def eval(self): """Starts the evaluation process. This function will evaluate the model using the test data (or the validation data, if no test data is available), and return the results. Note that the code related to the forwarding of samples inside the model itself is implemented in a derived class via :func:`thelper.train.base.Trainer.eval_epoch`. """ assert self.valid_loader or self.test_loader, "missing validation/test data, invalid loaders!" self.logger.debug(f"uploading model to '{str(self.devices)}'...") model = self._upload_model(self.model, self.devices) result = {} output_group = None, None if self.test_loader: self._set_rng_state(self.test_loader.seeds, self.current_epoch) model.eval() self.writers["test"] = self._init_writer(self.writers["test"], self.output_paths["test"]) for metric in self.test_metrics.values(): metric.reset() # force reset here, we always evaluate from a clean state if hasattr(self.test_loader, "set_epoch") and callable(self.test_loader.set_epoch): self.test_loader.set_epoch(self.current_epoch) self.eval_epoch(model, self.current_epoch, self.devices, self.test_loader, self.test_metrics) self._write_epoch_output(self.current_epoch, self.test_metrics, self.writers["test"], self.output_paths["test"], use_suffix=False) test_metric_vals = {metric_name: metric.eval() for metric_name, metric in self.test_metrics.items() if isinstance(metric, thelper.optim.metrics.Metric)} result = {**result, **test_metric_vals} output_group = "test/metrics" elif self.valid_loader: self._set_rng_state(self.valid_loader.seeds, self.current_epoch) model.eval() self.writers["valid"] = self._init_writer(self.writers["valid"], self.output_paths["valid"]) for metric in self.valid_metrics.values(): metric.reset() # force reset here, we always evaluate from a clean state if hasattr(self.valid_loader, "set_epoch") and callable(self.valid_loader.set_epoch): self.valid_loader.set_epoch(self.current_epoch) self.eval_epoch(model, self.current_epoch, self.devices, self.valid_loader, self.valid_metrics) self._write_epoch_output(self.current_epoch, self.valid_metrics, self.writers["valid"], self.output_paths["valid"], use_suffix=False) valid_metric_vals = {metric_name: metric.eval() for metric_name, metric in self.valid_metrics.items() if isinstance(metric, thelper.optim.metrics.Metric)} result = {**result, **valid_metric_vals} output_group = "valid/metrics" for key, value in result.items(): if not isinstance(value, dict): self.logger.debug(f" final result => {str(key)}: {value}") else: for subkey, subvalue in value.items(): self.logger.debug(f" final result => {str(key)}:{str(subkey)}: {subvalue}") if self.current_epoch not in self.outputs: self.outputs[self.current_epoch] = {} self.outputs[self.current_epoch][output_group] = result self.logger.info(f"evaluation for session '{self.name}' done") return self.outputs @abstractmethod def train_epoch(self, model, epoch, dev, loss, optimizer, loader, metrics): """Trains the model for a single epoch using the provided objects. Args: model: the model to train that is already uploaded to the target device(s). epoch: the epoch index we are training for (0-based). dev: the target device that tensors should be uploaded to. loss: the loss function used to evaluate model fidelity. optimizer: the optimizer used for back propagation. loader: the data loader used to get transformed training samples. metrics: the dictionary of metrics/consumers to update every iteration. """ raise NotImplementedError @abstractmethod def eval_epoch(self, model, epoch, dev, loader, metrics): """Evaluates the model using the provided objects. Args: model: the model to evaluate that is already uploaded to the target device(s). epoch: the epoch index we are training for (0-based). dev: the target device that tensors should be uploaded to. loader: the data loader used to get transformed valid/test samples. metrics: the dictionary of metrics/consumers to update every iteration. """ raise NotImplementedError def _iter_logger_callback(self, # see `thelper.typedefs.IterCallbackParams` for more info task, # type: thelper.tasks.utils.Task input, # type: thelper.typedefs.InputType pred, # type: thelper.typedefs.AnyPredictionType target, # type: thelper.typedefs.AnyTargetType sample, # type: thelper.typedefs.SampleType loss, # type: Optional[float] iter_idx, # type: int max_iters, # type: int epoch_idx, # type: int max_epochs, # type: int **kwargs, # type: Any ): # type: (...) -> None """Receives callback data for logging loss/monitored metric values each training/eval iteration.""" set_name = thelper.utils.get_key("set_name", kwargs, "missing set name in iter logger args") assert set_name in ["train", "valid", "test"], "unrecognized iter logger set name" metrics = self.train_metrics if set_name == "train" else self.valid_metrics if set_name == "valid" \ else self.test_metrics writers = thelper.utils.get_key("writers", kwargs, "missing writers dict in iter logger args") assert set_name in writers, "expected set name writer match in kwargs" writer = writers[set_name] monitor_val = None monitor_str = "" if self.monitor is not None and self.monitor in metrics: assert isinstance(metrics[self.monitor], thelper.optim.metrics.Metric), "unexpected metric type" if metrics[self.monitor].live_eval: monitor_val = metrics[self.monitor].eval() monitor_str = f" {self.monitor}: {monitor_val:.2f}" loss_str = "" if loss is not None: loss_str = f" loss: {loss:.6f}" assert self.current_epoch == epoch_idx, "something's messed up" self.logger.info( f"{set_name} epoch#{epoch_idx} (iter#{self.current_iter})" + f" batch: {iter_idx + 1}/{max_iters} ({((iter_idx + 1) / max_iters) * 100.0:.0f}%)" + f"{loss_str}{monitor_str}" ) if writer: if loss is not None: writer.add_scalar("iter/loss", loss, self.current_iter) for metric_name, metric in metrics.items(): if isinstance(metric, thelper.optim.metrics.Metric): if metric_name == self.monitor and monitor_val is not None: writer.add_scalar(f"iter/{self.monitor}", monitor_val, self.current_iter) elif metric.live_eval: # if live eval is not true, metric might be too heavy to compute at each iteration writer.add_scalar(f"iter/{metric_name}", metric.eval(), self.current_iter) if set_name == "train": self.current_iter += 1 def _write_epoch_output(self, epoch, metrics, tbx_writer, output_path, loss=None, optimizer=None, use_suffix=True): """Writes the cumulative evaluation result of all metrics using a specific writer.""" self.logger.debug(f"writing epoch metrics to {os.path.abspath(output_path)}") if not os.path.exists(output_path): os.makedirs(output_path) if tbx_writer is not None and loss is not None and optimizer is not None: tbx_writer.add_scalar("epoch/loss", loss, epoch) tbx_writer.add_scalar("epoch/lr", thelper.optim.get_lr(optimizer), epoch) for metric_name, metric in metrics.items(): if isinstance(metric, thelper.optim.metrics.Metric) and tbx_writer is not None: tbx_writer.add_scalar(f"epoch/{metric_name}", metric.eval(), epoch) if hasattr(metric, "render") and callable(metric.render): img = metric.render() if img is not None: if tbx_writer is not None: tbx_writer.add_image(metric_name, img, epoch, dataformats="HWC") raw_filename = f"{metric_name}{f'-{epoch:04d}' if use_suffix else ''}.png" raw_filepath = os.path.join(output_path, raw_filename) self.logger.debug(f"writing metric render output to {os.path.abspath(raw_filepath)}") cv.imwrite(raw_filepath, img[..., [2, 1, 0]]) txt = metric.report() if hasattr(metric, "report") and callable(metric.report) else None ext = getattr(metric, "ext", "txt") if not txt and isinstance(metric, thelper.optim.metrics.Metric): eval_res = metric.eval() if eval_res is not None: if isinstance(eval_res, float): txt = f"{eval_res:.4f}" # make sure we always have decent precision else: txt = str(eval_res) if txt: raw_filename = f"{metric_name}{f'-{epoch:04d}' if use_suffix else ''}.{ext}" raw_filepath = os.path.join(output_path, raw_filename) self.logger.debug(f"writing metric text output to '{os.path.abspath(raw_filepath)}'") with open(raw_filepath, "w") as fd: fd.write(txt) def _save(self, epoch, iter, optimizer, scheduler, save_best=False): """Saves a session checkpoint containing all the information required to resume training.""" # logically, this should only be called during training (i.e. with a valid optimizer) log_stamp = thelper.utils.get_log_stamp() # the saved state below should be kept compatible with the one in thelper.cli.export_model curr_state = { "name": self.name, "epoch": epoch, "iter": iter, "source": log_stamp, "git_sha1": thelper.utils.get_git_stamp(), "version": thelper.__version__, "task": str(self.task) if self.save_raw else self.task, "outputs": self.outputs, # we save model type/params here in case those are not in the current config "model": self.model.state_dict() if self.save_raw else self.model, "model_type": self.model.get_name(), "model_params": self.model.config if self.model.config else {}, "optimizer": optimizer.state_dict() if optimizer is not None else None, "scheduler": scheduler.state_dict() if (scheduler is not None and hasattr(scheduler, "state_dict")) else None, "monitor_best": self.monitor_best, "monitor_best_epoch": self.monitor_best_epoch, "config": self.config # note: this is the global app config } filename = f"ckpt.{epoch:04d}.{log_stamp}.pth" filename = os.path.join(self.checkpoint_dir, filename) self.logger.debug(f"writing checkpoint to {os.path.abspath(filename)}") torch.save(curr_state, filename) if save_best: filename_best = os.path.join(self.checkpoint_dir, "ckpt.best.pth") self.logger.debug(f"writing checkpoint to {os.path.abspath(filename_best)}") torch.save(curr_state, filename_best)
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from __future__ import print_function import os import time import json import numpy as np from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Lambda from keras.optimizers import Nadam as Trainer #from keras.optimizers import Adam as Trainer from keras.regularizers import WeightRegularizer from keras.callbacks import EarlyStopping, Callback, LearningRateScheduler from sklearn.preprocessing import MinMaxScaler from genomic_neuralnet.util import get_is_time_stats, get_should_plot TIMING_EPOCHS = 12000 class LossHistory(Callback): def on_train_begin(self, logs={}): self.losses = [] def on_epoch_end(self, epoch, logs={}): self.losses.append(logs.get('loss')) class NeuralNetContainer(object): def __init__(self): self.model = None self.learning_rate = None self.weight_decay = 0.0 self.dropout_prob = 0.0 self.epochs = 25 self.hidden_layers = (10,) self.verbose = False self.plot = False def clone(self): if not self.model is None: raise NotImplemented('Cannot clone container after building model') clone = NeuralNetContainer() clone.learning_rate = self.learning_rate clone.weight_decay = self.weight_decay clone.dropout_prob = self.dropout_prob clone.epochs = self.epochs clone.hidden_layers = self.hidden_layers clone.verbose = self.verbose clone.plot = self.plot return clone def _build_nn(net_container, n_features): model = Sequential() # Change scale from (-1, 1) to (0, 1) model.add(Lambda(lambda x: (x + 1) / 2, input_shape=(n_features,), output_shape=(n_features,))) if net_container.weight_decay > 0.0: weight_regularizer = WeightRegularizer(net_container.weight_decay) else: weight_regularizer = None last_dim = n_features for lidx, n_nodes in enumerate(net_container.hidden_layers): # Layer, activation, and dropout, in that order. model.add(Dense(output_dim=n_nodes, input_dim=last_dim, W_regularizer=weight_regularizer)) model.add(Activation('sigmoid')) if net_container.dropout_prob > 0.0: model.add(Dropout(net_container.dropout_prob)) last_dim = n_nodes model.add(Dense(output_dim=1, input_dim=last_dim, bias=False)) model.add(Activation('linear')) if not net_container.learning_rate is None: optimizer = Trainer(lr=net_container.learning_rate) else: #optimizer = Trainer(lr=0.0001) optimizer = Trainer() model.compile( optimizer=optimizer , loss='mean_squared_error' ) net_container.model = model def _train_net(container, X, y, override_epochs=None, is_check_train=False): """ Given a container, X (inputs), and y (outputs) train the network in the container. * If override_epochs is an integer, just run that many epochs. * The is_check_train parameter signifies that this training is a quick check to make sure that the network is properly initialized and that the output error is decreasing. The best "check trained" network will be passed in again for an additional full set of training epochs. """ model = container.model epochs = override_epochs if (not override_epochs is None) else container.epochs verbose = int(container.verbose) def rate_func(epoch): if epochs - epoch == 2000: # Settle down during last 2000 epochs. model.optimizer.lr.set_value(model.optimizer.lr.get_value()/4.0) if epochs - epoch == 500: # Go a bit further in last 500 epochs. model.optimizer.lr.set_value(model.optimizer.lr.get_value()/4.0) return float(model.optimizer.lr.get_value()) lr_scheduler = LearningRateScheduler(rate_func) loss_history = LossHistory() callbacks = [loss_history, lr_scheduler] model.fit( X, y, nb_epoch=epochs, batch_size=X.shape[0] / 4, verbose=verbose, callbacks=callbacks ) if (isinstance(override_epochs, int)) and (not is_check_train) and container.plot: # Plot, but only if this is not overriden epochs. import matplotlib.pyplot as plt plt.plot(range(len(loss_history.losses)), loss_history.losses) plt.show() return loss_history.losses[-1] def _predict(container, X): model = container.model return model.predict(X) _NET_TRIES = 2 def _get_initial_net(container, n_features, X, y): """ Create a few networks. Start the training process for a few epochs, then take the best one to continue training. This eliminates networks that are poorly initialized and will not converge. """ candidates = [] for _ in range(_NET_TRIES): cont = container.clone() _build_nn(cont, n_features) candidates.append(cont) losses = [] for candidate in candidates: # Train each candidate for 100 epochs. loss = _train_net(candidate, X, y, override_epochs=100, is_check_train=True) losses.append(loss) best_idx = np.argmin(losses) return candidates[best_idx] def get_net_prediction( train_data, train_truth, test_data, test_truth , hidden=(5,), weight_decay=0.0, dropout_prob=0.0 , learning_rate=None, epochs=25, verbose=False , iter_id=None ): container = NeuralNetContainer() container.learning_rate = learning_rate container.dropout_prob = dropout_prob container.weight_decay = weight_decay container.epochs = epochs container.hidden_layers = hidden container.verbose = verbose container.plot = get_should_plot() mms = MinMaxScaler(feature_range= (-1, 1)) # Scale output from -1 to 1. train_y = mms.fit_transform(train_truth[:,np.newaxis]) n_features = train_data.shape[1] collect_time_stats = get_is_time_stats() if collect_time_stats: start = time.time() # Find and return an effectively initialized network to start. container = _get_initial_net(container, n_features, train_data, train_y) # Train the network. if collect_time_stats: # Train a specific time, never terminating early. _train_net(container, train_data, train_y, override_epochs=TIMING_EPOCHS, is_check_train=False) else: # Normal training, enable all heuristics. _train_net(container, train_data, train_y) if collect_time_stats: end = time.time() print('Fitting took {} seconds'.format(end - start)) print(json.dumps({'seconds': end - start, 'hidden': container.hidden_layers})) # Unsupervised (test) dataset. predicted = _predict(container, test_data) predicted = mms.inverse_transform(predicted) return predicted.ravel()
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from __future__ import print_function import sys import cv2 import pdb import argparse import numpy as np import torch import torch.nn as nn import torch.nn.parallel import torch.backends.cudnn as cudnn import torch.optim as optim import torch.utils.data from torch.autograd import Variable import torch.nn.functional as F import time from utils.io import mkdir_p from utils.util_flow import write_flow, save_pfm from utils.flowlib import point_vec, warp_flow cudnn.benchmark = False parser = argparse.ArgumentParser(description='VCN+expansion') parser.add_argument('--dataset', default='2015', help='KITTI version') parser.add_argument('--datapath', default='/ssd/kitti_scene/training/', help='dataset path') parser.add_argument('--loadmodel', default=None, help='model path') parser.add_argument('--outdir', default='output', help='output dir') parser.add_argument('--testres', type=float, default=1, help='resolution') parser.add_argument('--maxdisp', type=int ,default=256, help='maxium disparity. Only affect the coarsest cost volume size') parser.add_argument('--fac', type=float ,default=1, help='controls the shape of search grid. Only affect the coarse cost volume size') args = parser.parse_args() # dataloader if args.dataset == '2015': from dataloader import kitti15list as DA maxw,maxh = [int(args.testres*1280), int(args.testres*384)] test_left_img, test_right_img ,_= DA.dataloader(args.datapath) elif args.dataset == '2015val': from dataloader import kitti15list_val as DA maxw,maxh = [int(args.testres*1280), int(args.testres*384)] test_left_img, test_right_img ,_= DA.dataloader(args.datapath) elif args.dataset == '2015vallidar': from dataloader import kitti15list_val_lidar as DA maxw,maxh = [int(args.testres*1280), int(args.testres*384)] test_left_img, test_right_img ,_= DA.dataloader(args.datapath) elif args.dataset == '2015test': from dataloader import kitti15list as DA maxw,maxh = [int(args.testres*1280), int(args.testres*384)] test_left_img, test_right_img ,_= DA.dataloader(args.datapath) elif args.dataset == 'seq': from dataloader import seqlist as DA maxw,maxh = [int(args.testres*1280), int(args.testres*384)] test_left_img, test_right_img ,_= DA.dataloader(args.datapath) elif args.dataset == 'sinteltest': from dataloader import sintellist as DA maxw,maxh = [int(args.testres*1024), int(args.testres*448)] test_left_img, test_right_img ,_= DA.dataloader(args.datapath) elif args.dataset == 'sintel': from dataloader import sintellist_val as DA maxw,maxh = [int(args.testres*1024), int(args.testres*448)] test_left_img, test_right_img ,_= DA.dataloader(args.datapath) max_h = int(maxh // 64 * 64) max_w = int(maxw // 64 * 64) if max_h < maxh: max_h += 64 if max_w < maxw: max_w += 64 maxh = max_h maxw = max_w mean_L = [[0.33,0.33,0.33]] mean_R = [[0.33,0.33,0.33]] # construct model, VCN-expansion from models.VCN_exp import VCN model = VCN([1, maxw, maxh], md=[int(4*(args.maxdisp/256)),4,4,4,4], fac=args.fac, exp_unc=('robust' in args.loadmodel)) # expansion uncertainty only in the new model model = nn.DataParallel(model, device_ids=[0]) model.cuda() if args.loadmodel is not None: pretrained_dict = torch.load(args.loadmodel) mean_L=pretrained_dict['mean_L'] mean_R=pretrained_dict['mean_R'] pretrained_dict['state_dict'] = {k:v for k,v in pretrained_dict['state_dict'].items()} model.load_state_dict(pretrained_dict['state_dict'],strict=False) else: print('dry run') print('Number of model parameters: {}'.format(sum([p.data.nelement() for p in model.parameters()]))) mkdir_p('%s/%s/'% (args.outdir, args.dataset)) def main(): model.eval() ttime_all = [] for inx in range(len(test_left_img)): print(test_left_img[inx]) imgL_o = cv2.imread(test_left_img[inx])[:,:,::-1] imgR_o = cv2.imread(test_right_img[inx])[:,:,::-1] # for gray input images if len(imgL_o.shape) == 2: imgL_o = np.tile(imgL_o[:,:,np.newaxis],(1,1,3)) imgR_o = np.tile(imgR_o[:,:,np.newaxis],(1,1,3)) # resize maxh = imgL_o.shape[0]*args.testres maxw = imgL_o.shape[1]*args.testres max_h = int(maxh // 64 * 64) max_w = int(maxw // 64 * 64) if max_h < maxh: max_h += 64 if max_w < maxw: max_w += 64 input_size = imgL_o.shape imgL = cv2.resize(imgL_o,(max_w, max_h)) imgR = cv2.resize(imgR_o,(max_w, max_h)) # flip channel, subtract mean imgL = imgL[:,:,::-1].copy() / 255. - np.asarray(mean_L).mean(0)[np.newaxis,np.newaxis,:] imgR = imgR[:,:,::-1].copy() / 255. - np.asarray(mean_R).mean(0)[np.newaxis,np.newaxis,:] imgL = np.transpose(imgL, [2,0,1])[np.newaxis] imgR = np.transpose(imgR, [2,0,1])[np.newaxis] # modify module according to inputs from models.VCN_exp import WarpModule, flow_reg for i in range(len(model.module.reg_modules)): model.module.reg_modules[i] = flow_reg([1,max_w//(2**(6-i)), max_h//(2**(6-i))], ent=getattr(model.module, 'flow_reg%d'%2**(6-i)).ent,\ maxdisp=getattr(model.module, 'flow_reg%d'%2**(6-i)).md,\ fac=getattr(model.module, 'flow_reg%d'%2**(6-i)).fac).cuda() for i in range(len(model.module.warp_modules)): model.module.warp_modules[i] = WarpModule([1,max_w//(2**(6-i)), max_h//(2**(6-i))]).cuda() # forward imgL = Variable(torch.FloatTensor(imgL).cuda()) imgR = Variable(torch.FloatTensor(imgR).cuda()) with torch.no_grad(): imgLR = torch.cat([imgL,imgR],0) model.eval() torch.cuda.synchronize() start_time = time.time() rts = model(imgLR) torch.cuda.synchronize() ttime = (time.time() - start_time); print('time = %.2f' % (ttime*1000) ) ttime_all.append(ttime) flow, occ, logmid, logexp = rts # upsampling occ = cv2.resize(occ.data.cpu().numpy(), (input_size[1],input_size[0]),interpolation=cv2.INTER_LINEAR) logexp = cv2.resize(logexp.cpu().numpy(), (input_size[1],input_size[0]),interpolation=cv2.INTER_LINEAR) logmid = cv2.resize(logmid.cpu().numpy(), (input_size[1],input_size[0]),interpolation=cv2.INTER_LINEAR) flow = torch.squeeze(flow).data.cpu().numpy() flow = np.concatenate( [cv2.resize(flow[0],(input_size[1],input_size[0]))[:,:,np.newaxis], cv2.resize(flow[1],(input_size[1],input_size[0]))[:,:,np.newaxis]],-1) flow[:,:,0] *= imgL_o.shape[1] / max_w flow[:,:,1] *= imgL_o.shape[0] / max_h flow = np.concatenate( (flow, np.ones([flow.shape[0],flow.shape[1],1])),-1) # save predictions idxname = test_left_img[inx].split('/')[-1] with open('%s/%s/flo-%s.pfm'% (args.outdir, args.dataset,idxname.split('.')[0]),'w') as f: save_pfm(f,flow[::-1].astype(np.float32)) flowvis = point_vec(imgL_o, flow) cv2.imwrite('%s/%s/visflo-%s.jpg'% (args.outdir, args.dataset,idxname),flowvis) imwarped = warp_flow(imgR_o, flow[:,:,:2]) cv2.imwrite('%s/%s/warp-%s.jpg'% (args.outdir, args.dataset,idxname),imwarped[:,:,::-1]) with open('%s/%s/occ-%s.pfm'% (args.outdir, args.dataset,idxname.split('.')[0]),'w') as f: save_pfm(f,occ[::-1].astype(np.float32)) with open('%s/%s/exp-%s.pfm'% (args.outdir, args.dataset,idxname.split('.')[0]),'w') as f: save_pfm(f,logexp[::-1].astype(np.float32)) with open('%s/%s/mid-%s.pfm'% (args.outdir, args.dataset,idxname.split('.')[0]),'w') as f: save_pfm(f,logmid[::-1].astype(np.float32)) torch.cuda.empty_cache() print(np.mean(ttime_all)) if __name__ == '__main__': main()
[ "dataloader.sintellist_val.dataloader", "torch.cuda.synchronize", "models.VCN_exp.WarpModule", "torch.squeeze", "utils.flowlib.point_vec", "numpy.mean", "argparse.ArgumentParser", "numpy.asarray", "numpy.tile", "numpy.ones", "utils.io.mkdir_p", "utils.flowlib.warp_flow", "cv2.resize", "tim...
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import numpy as np import keras import json from tqdm import tqdm import cv2 import random import matplotlib.pyplot as plt from keras.applications.vgg16 import preprocess_input from keras.preprocessing import image as keras_image import pickle def augment_patch(patch, augmentation): if augmentation=='H-Flip': augmented_patch = np.fliplr(patch) elif augmentation=='V-Flip': augmented_patch = np.flipud(patch) elif augmentation=='180': augmented_patch = np.rot90(patch, 2) elif augmentation=='90': augmented_patch = np.rot90(patch, 1) elif augmentation=='270': augmented_patch = np.rot90(patch, 3) else: augmented_patch = patch return augmented_patch def length(list_IDs, batch_size): 'Denotes the number of batches per epoch' return int(np.floor(len(list_IDs) / batch_size)) def load_image_paths(image_categories): with open('../train_pdfs.pkl', 'rb') as f: pdfs = pickle.load(f) with open('../from_scratch_classification.json', 'r') as f: cls_type = json.load(f) list_IDs = [] for doi in tqdm(pdfs): for panel,cls in cls_type[doi].items(): if cls in image_categories: path = '/nas/medifor/esabir/scientific_integrity/from_scratch/from_scratch_panels/'+doi+'/'+panel list_IDs += [path] return list_IDs def on_epoch_end(list_IDs): 'Updates indexes after each epoch' indexes = np.arange(len(list_IDs)) np.random.shuffle(indexes) return indexes def get_batch(indexes, index, list_IDs, batch_size, resize_dim, dim, n_channels, augmentation_list): indexes = indexes[index*batch_size:(index+1)*batch_size] # Find list of IDs list_IDs_temp = [list_IDs[k] for k in indexes] # Generate data X, y, y1, y2 = data_generation(list_IDs_temp, batch_size, resize_dim, dim, n_channels, augmentation_list) return X, y, y1, y2 def create_spliced_manipulation(img, resize_dim, augmentation_list): img = cv2.resize(img, resize_dim) h, w, ch = img.shape new_h, new_w = int(np.ceil(h/16.)*16), int(np.ceil(w/16.)*16) new_img = np.zeros((new_h, new_w, ch)) new_img[:h,:w,:] = img mask_img = np.zeros_like(new_img) duplicate = True if duplicate: dup1_r1 = random.randint(0,np.floor(0.75*new_h)) dup1_c1 = random.randint(0,np.floor(0.75*new_w)) dup1_r2 = random.randint(dup1_r1+10, dup1_r1+np.floor(0.25*new_h)) dup1_c2 = random.randint(dup1_c1+10, dup1_c1+np.floor(0.25*new_w)) assert np.floor(0.75*new_h)-dup1_r1>=0, 'Negative row for second patch!' assert np.floor(0.75*new_w)-dup1_c1>=0, 'Negative col for second patch!' augmentation = random.choice(augmentation_list) dup2_r1 = random.randint(0, np.floor(0.75*new_h)) dup2_c1 = random.randint(0, np.floor(0.75*new_w)) if augmentation in ['0', '180', 'H-Flip', 'V-Flip']: dup2_r2 = dup2_r1 + (dup1_r2-dup1_r1) dup2_c2 = dup2_c1 + (dup1_c2-dup1_c1) else: dup2_r2 = dup2_r1 + (dup1_c2-dup1_c1) dup2_c2 = dup2_c1 + (dup1_r2-dup1_r1) assert dup2_r2<=new_h, 'Second patch row out of bounds!' assert dup2_c2<=new_w, 'Second patch col out of bounds!' #if random.choice([True, False]): # patch = new_img[dup2_r1:dup2_r2,dup2_c1:dup2_c2,:] # augmented_patch = augment_patch(patch, augmentation) # new_img[dup1_r1:dup1_r2,dup1_c1:dup1_c2,:] = augmented_patch #else: patch = new_img[dup1_r1:dup1_r2,dup1_c1:dup1_c2,:] augmented_patch = augment_patch(patch, augmentation) new_img[dup2_r1:dup2_r2,dup2_c1:dup2_c2,:] = augmented_patch dup_coord1 = (dup1_r1,dup1_r2,dup1_c1,dup1_c2) dup_coord2 = (dup2_r1,dup2_r2,dup2_c1,dup2_c2) mask_img[dup1_r1:dup1_r2,dup1_c1:dup1_c2,1] = 1 mask_img[dup2_r1:dup2_r2,dup2_c1:dup2_c2,0] = 1 mask_img[:,:,2] = 1 tmp = mask_img[:,:,1] + mask_img[:,:,0] tmp[tmp>0] = 1 mask_img[:,:,2] = mask_img[:,:,2] - tmp simi_mask = np.concatenate((mask_img[:,:,:1]+mask_img[:,:,1:2], mask_img[:,:,2:3]), axis=-1) mani_mask = np.concatenate((mask_img[:,:,:1], mask_img[:,:,1:2]+mask_img[:,:,2:3]), axis=-1) return new_img, mask_img, simi_mask, mani_mask def unmanipulated(img, resize_dim): img = cv2.resize(img, resize_dim) gt_mask = np.zeros_like(img) gt_mask[:,:,2] = 1 return img, gt_mask, gt_mask[:,:,:2], gt_mask[:,:,:2] def create_manipulation(img, resize_dim, augmentation_list): choices = ['Pristine', 'Splice'] choice = random.choice(choices) if choice=='Pristine': img, gt_mask, gt_mask1, gt_mask2 = unmanipulated(img, resize_dim) elif choice=='Splice': img, gt_mask, gt_mask1, gt_mask2 = create_spliced_manipulation(img, resize_dim, augmentation_list) else: print('Invalid choice!') raise SystemExit img = img[:,:,::-1] return img, gt_mask, gt_mask1, gt_mask2 def data_generation(list_IDs_temp, batch_size, resize_dim, dim, n_channels, augmentation_list): 'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels) # Initialization X = np.empty((batch_size, dim[0], dim[1], n_channels)) y = np.empty((batch_size, dim[0], dim[1], 3)) y1 = np.empty((batch_size, dim[0], dim[1], 2)) y2 = np.empty((batch_size, dim[0], dim[1], 2)) # Generate data for i, ID in enumerate(list_IDs_temp): # Store sample img = cv2.imread(ID) X[i], y[i], y1[i], y2[i] = create_manipulation(img, resize_dim, augmentation_list) return X, y, y1, y2 def DataGenerator(image_categories, augmentation_list): 'Generates data for Keras' dim = (256,256) resize_dim = (256, 256) batch_size = 32 list_IDs = load_image_paths(image_categories) n_channels = 3 indexes = on_epoch_end(list_IDs) while True: for index in range(length(list_IDs, batch_size)): X, y, y1, y2 = get_batch(indexes, index, list_IDs, batch_size, resize_dim, dim, n_channels, augmentation_list) yield (X,y) indexes = on_epoch_end(list_IDs)
[ "numpy.ceil", "random.choice", "cv2.imread", "numpy.flipud", "numpy.fliplr", "tqdm.tqdm", "pickle.load", "numpy.floor", "numpy.zeros", "numpy.empty", "numpy.concatenate", "numpy.rot90", "json.load", "cv2.resize", "numpy.zeros_like", "numpy.random.shuffle" ]
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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon Feb 24 11:41:13 2020 @author: roopareddynagilla """ import numpy as np import sqlalchemy from sqlalchemy.ext.automap import automap_base from sqlalchemy.orm import Session from sqlalchemy import create_engine, func from flask import Flask, jsonify from datetime import date from datetime import datetime from datetime import timedelta as dtd ################################################# # Database Setup ################################################# engine = create_engine("sqlite:///Resources/hawaii.sqlite") # reflect an existing database into a new model Base = automap_base() # reflect the tables Base.prepare(engine, reflect=True) Base.classes.keys() # Save reference to the table Measurement = Base.classes.measurement Station = Base.classes.station # Create our session (link) from Python to the DB session = Session(engine) ################################################# # Flask Setup ################################################# app = Flask(__name__) ################################################# # Flask Routes ################################################# @app.route("/") def welcome(): """List all available api routes.""" return ( f"Available Routes:<br/>" f"/api/v1.0/precipitation:<br/>" f"/api/v1.0/stations <br/>" f"/api/v1.0/tobs <br/>" f"/api/v1.0/<start> <br/>" f"/api/v1.0/<start>/<end> <br/>" ) @app.route("/api/v1.0/precipitation") def precipitation(): # Create our session (link) from Python to the DB session = Session(engine) """Convert the query results to a Dictionary using date as the key and prcp as the value""" # Query all passengers results = session.query(Measurement.date, Measurement.prcp).all() session.close() dictionary = {} # Convert tuples into dictionary def Convert(tup, di): for a, b in tup: di.setdefault(a, []).append(b) return di #all_names = list(np.ravel(results)) Convert(results, dictionary) #Return the JSON representation of your dictionary return jsonify(dictionary) @app.route("/api/v1.0/stations") def stationList(): session = Session(engine) """Return a list of all stations""" station_list = session.query(Station.station).all() session.close() all_stations = list(np.ravel(station_list)) return jsonify(all_stations) @app.route("/api/v1.0/tobs") def tobsLastYear(): session = Session(engine) """Return a the latest date""" tobs_last_date = session.query(Measurement.date).order_by(Measurement.date.desc()).first() session.close() last_date = list(np.ravel(tobs_last_date)) import datetime as dt for item in last_date: Dyear, Dmon, Ddate = item.split('-') latest_date = dt.date(int(Dyear),int(Dmon),int(Ddate)) year_ago = latest_date - dt.timedelta(days=365) session = Session(engine) temperature_obs = session.query(Measurement.tobs).filter(Measurement.date >= year_ago).order_by(Measurement.date.desc()).all() temperature_obs_list = list(np.ravel(temperature_obs)) return jsonify(temperature_obs_list) @app.route("/api/v1.0/<start>") def tempStatsWithStartDate(start): def calc_temps(start, end_date): start_date = datetime.strptime(start, "%Y-%m-%d") end_date = session.query(func.max(Measurement.date)).all()[0][0] return session.query(func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)).\ filter(Measurement.date >= start_date).filter(Measurement.date <= end_date).all() enddate = session.query(func.max(Measurement.date)).all()[0][0] temps = calc_temps(start, enddate) temp_stats_list = list(np.ravel(temps)) return jsonify(temp_stats_list) @app.route("/api/v1.0/<start>/<end>") def tempStatsWithStartDateAndEndDate(start, end): def calc_temps_se(start, end): start_date = datetime.strptime(start, "%Y-%m-%d") end_date = datetime.strptime(end, "%Y-%m-%d") return session.query(func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)).\ filter(Measurement.date >= start_date).filter(Measurement.date <= end_date).all() temps_se = calc_temps_se(start, end) temp_stats_list_se = list(np.ravel(temps_se)) return jsonify(temp_stats_list_se) if __name__ == '__main__': app.run(debug=True)
[ "sqlalchemy.func.min", "flask.Flask", "datetime.datetime.strptime", "sqlalchemy.ext.automap.automap_base", "sqlalchemy.create_engine", "sqlalchemy.orm.Session", "sqlalchemy.func.max", "sqlalchemy.func.avg", "numpy.ravel", "datetime.timedelta", "flask.jsonify" ]
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import numpy as np import random def assign_trait_values(organism_type: str, organism_id: int) -> list: """ Function takes in the type of organism and returns a list of the various traits for that organism to be passed to the next list Parameters """ if organism_type == 'Producer': # All for producer # beta for producers should be 0.34 (Brose 2019 paper on body size) body_size = np.random.exponential(scale = 0.34, size = 1)[0] prey_limits = [None]*2 habitat_midpoint = round((np.random.uniform(low = 0, \ high = 1, \ size = 1)[0]), 3) habitat = [habitat_midpoint, 0, 0] if habitat_midpoint >= 0.2: habitat[1] = round((habitat_midpoint - 0.2), 3) else: habitat[1] = 0 if habitat_midpoint <= 0.8: habitat[2] = round((habitat_midpoint + 0.2), 3) else: habitat[2] = 1 else: # all for consumer # beta for non-producers = 800 (Brose 2019 paper on body size) body_size = np.random.exponential(scale = 800, size = 1)[0] prey_limits = [0.05*body_size, 0.65*body_size] habitat_midpoint = round((np.random.uniform(low = 0, \ high = 1, \ size = 1)[0]), 3) habitat = habitat = [habitat_midpoint, 0, 0] if habitat_midpoint >= 0.2: habitat[1] = round((habitat_midpoint - 0.2), 3) else: habitat[1] = 0 if habitat_midpoint <= 0.8: habitat[2] = round((habitat_midpoint + 0.2), 3) else: habitat[2] = 1 trait_list = [organism_id, \ organism_type, \ body_size, \ prey_limits, \ habitat, \ None] # biomass return trait_list def define_species_list(n: int) -> list: """ Function takes in the number of species and returns a list of lists with a sublist for each species with necessary locations for the pieces of data Parameters TO-DO: 1. Add in customization for web shape such that you can pass a shape (probably something like hourglass, top/bottom-heavy) and then that can be the thing that defines how many consumers there are etc """ # initialize species list sp_list = [None]*n i = 0 while (i < n): organism_type = random.sample(['Producer', \ 'Producer', \ 'Consumer'], 1)[0] sp_list[i] = assign_trait_values(organism_type = organism_type, \ organism_id = i) i += 1 # increment return sp_list test_species = define_species_list(n = 4) test_species[0][4][1] test_species[2][3] def determine_interaction(predator: int, prey: int, species_list:list) -> int: predator = 1; prey = 0; species_list = test_species pred_hab = species_list[predator][4] prey_hab = species_list[prey][4] prey_size = species_list[prey][2] pred_min = species_list[predator][3][0] pred_max = species_list[predator][3][1] if (predator == prey) or (species_list[predator][1] == 'Producer'): value = 0 # this is assuming no canibalism!!! elif (((prey_hab[1] >= pred_hab[1] and prey_hab[1] <= pred_hab[2]) or \ (prey_hab[2] >= pred_hab[1] and prey_hab[2] <= pred_hab[2])) and \ (prey_size <= pred_max and prey_size >= pred_min)): value = 1 else: value = 0 return value x = determine_interaction(1, 0, test_species) def create_interaction_matrix(species_list:list) -> np.array: """ This function takes in the species list and returns an interaction matrix that will form the basis of the networks to then be created """ j = 1; i = 0; species_list = test_species interaction_matrix = np.zeros(shape = (len(species_list), \ len(species_list)), \ dtype = np.int8) for i in range(interaction_matrix.shape[0]): # rows are prey for j in range(interaction_matrix.shape[1]): # cols are predators interaction_matrix[i][j] = \ determine_interaction(predator = j, \ prey = i, \ species_list = species_list) test_species interaction_matrix def initialize_biomass(species_list:list) -> list: """ This function takes in a species list, and based on body size values, assigns an initial biomass to each species for the purposes of having a starting value to simulate through. """ ### Important Assumptions # No canibalism # No variation in habitat association width
[ "random.sample", "numpy.random.exponential", "numpy.random.uniform" ]
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import matplotlib import numpy as np import matplotlib.pyplot as plt import seaborn as sns sns.set_style("dark") plt.rcParams['figure.figsize'] = 16, 12 import pandas as pd from tqdm import tqdm_notebook, tqdm import io from PIL import Image from glob import glob from collections import defaultdict import os import pickle from optparse import OptionParser from datetime import datetime import json import sys import time from shutil import copyfile import cv2 cv2.ocl.setUseOpenCL(True) import random import imgaug as ia from imgaug import augmenters as iaa import torch import torch.nn as nn import torch.optim as optim from torch.autograd import Variable import torch.nn.functional as F import torchvision import torchvision.transforms as transforms import torchvision.models as models from kaggle_camera_model_id_lib.utils import PechkaBot, ImageList, NpzFolder, NCrops, MultiDataset from kaggle_camera_model_id_lib.models import VggHead, StyleVggHead, IEEEfcn, ResNetFC, ResNetX, FatNet1 from kaggle_camera_model_id_lib.models import InceptionResNetV2fc, InceptionResNetV2fcSmall, InceptionResNetV2 from kaggle_camera_model_id_lib.models import ResNetDense, ResNetDenseFC from kaggle_camera_model_id_lib.utils import jpg_compress, equalize_v_hist, hsv_convert from kaggle_camera_model_id_lib.utils import scale_crop_pad, gamma_correction from kaggle_camera_model_id_lib.utils import patch_quality_dich, n_random_crops, n_pseudorandom_crops _bot = PechkaBot() def log(txt): print(txt) _bot.send_message(txt) def train_pass(train_loader, model, criterion, optimizer): loss_train_batch = 0 acc_train_batch = 0 for ix_batch, (X, Y) in tqdm( enumerate(train_loader), total=int(len(train_loader.dataset.imgs)/batch_size_train), desc='Train #%i' % ix_epoch): bs, ncrops, c, h, w = X.shape X = X.view(-1, c, h, w) Y = Y.view(ncrops*bs) X_var = Variable(X.cuda()) Y_var = Variable(Y.cuda()) log_p = model(X_var) loss = criterion(log_p, Y_var) optimizer.zero_grad() loss.backward() optimizer.step() loss_train_batch += loss.data[0] acc_train_batch += ((log_p.max(1)[1] == Y_var).float().sum()/Y_var.shape[0]).data[0] if options.is_debug and ix_batch > 50: break X_var = X_var.cpu() del(X_var) Y_var = Y_var.cpu() del(Y_var) loss_train_batch /= ix_batch + 1 acc_train_batch /= ix_batch + 1 return loss_train_batch, acc_train_batch def val_pass(val_loader, model, criterion): loss_val_batch = 0 acc_val_batch = 0 for ix_batch, (X, Y) in tqdm( enumerate(val_loader), total=int(len(val_loader.dataset.imgs)/batch_size_val), desc='Val #%i' % ix_epoch): bs, ncrops, c, h, w = X.shape X = X.view(-1, c, h, w) Y = Y.view(ncrops*bs) X_var = Variable(X.cuda(), volatile=True) Y_var = Variable(Y.cuda(), volatile=True) log_p = model(X_var) loss = criterion(log_p, Y_var) loss_val_batch += loss.data[0] acc_val_batch += ((log_p.max(1)[1] == Y_var).float().sum()/Y_var.shape[0]).data[0] if options.is_debug and ix_batch > 50: break X_var = X_var.cpu() del(X_var) Y_var = Y_var.cpu() del(Y_var) loss_val_batch /= ix_batch + 1 acc_val_batch /= ix_batch + 1 return loss_val_batch, acc_val_batch model_factory = { 'Vgg19Head_E_2b_bn': lambda n_classes: VggHead(num_classes=n_classes, vgg_key='E_2b', load_vgg_bn=True, batch_norm=True), 'Vgg19Head_E_3b_bn': lambda n_classes: VggHead(num_classes=n_classes, vgg_key='E_3b', load_vgg_bn=True, batch_norm=True), 'Vgg19Head_E_bn': lambda n_classes: VggHead(num_classes=n_classes, load_vgg_bn=True, vgg_key='E', batch_norm=True), 'Vgg11Head_A_bn': lambda n_classes: VggHead(num_classes=n_classes, load_vgg_bn=True, vgg_key='A', batch_norm=True), 'Vgg11Head_A': lambda n_classes: VggHead(num_classes=n_classes, load_vgg_bn=True, vgg_key='A', batch_norm=False), 'StyleVggHead_bn': lambda n_classes: StyleVggHead(num_classes=n_classes, load_vgg_bn=True), 'IEEEfcn': lambda n_classes: IEEEfcn(n_classes), 'resnet18fc_pretrained': lambda n_classes: ResNetFC( models.resnet.BasicBlock, [2, 2, 2, 2], num_classes=n_classes, load_resnet='resnet18'), 'resnet18fc': lambda n_classes: ResNetFC( models.resnet.BasicBlock, [2, 2, 2, 2], num_classes=n_classes, load_resnet=None), 'resnet18X_pretrained': lambda n_classes: ResNetX( models.resnet.BasicBlock, [2, 2, 2, 2], num_classes=n_classes, load_resnet='resnet18'), 'InceptionResNetV2fc_5_10_4': lambda n_classes: InceptionResNetV2fc( num_classes=n_classes, nun_block35=5, num_block17=10, num_block8=4), 'InceptionResNetV2fcSmall_5_10': lambda n_classes: InceptionResNetV2fcSmall( num_classes=n_classes, nun_block35=5, num_block17=10), 'resnet34fc_pretrained': lambda n_classes: ResNetFC( models.resnet.BasicBlock, [3, 4, 6, 3], num_classes=n_classes, load_resnet='resnet34'), 'resnet34fc_pretrained_maxpool': lambda n_classes: ResNetFC( models.resnet.BasicBlock, [3, 4, 6, 3], num_classes=n_classes, load_resnet='resnet34', pool_type='max'), 'resnet50fc_pretrained': lambda n_classes: ResNetFC( models.resnet.Bottleneck, [3, 4, 6, 3], num_classes=n_classes, load_resnet='resnet50'), 'FatNet1': lambda n_classes: FatNet1(n_classes), 'resnet34X_pretrained_maxpool': lambda n_classes: ResNetX( models.resnet.BasicBlock, [3, 4, 6, 3], num_classes=n_classes, load_resnet='resnet34', pool_type='max'), 'resnet50X_pretrained_maxpool': lambda n_classes: ResNetX( models.resnet.Bottleneck, [3, 4, 6, 3], num_classes=n_classes, load_resnet='resnet50', pool_type='max'), 'InceptionResNetV2': lambda n_classes: InceptionResNetV2(num_classes=n_classes), 'ResNetDense34_pretrained': lambda n_classes: ResNetDense( models.resnet.BasicBlock, [3, 4, 6, 3], num_classes=n_classes, load_resnet='resnet34'), 'ResNetDenseFC34_pretrained': lambda n_classes: ResNetDenseFC( models.resnet.BasicBlock, [3, 4, 6, 3], num_classes=n_classes, load_resnet='resnet34', zero_first_center=False), 'ResNetDenseFC34_pretrained_zfc': lambda n_classes: ResNetDenseFC( models.resnet.BasicBlock, [3, 4, 6, 3], num_classes=n_classes, load_resnet='resnet34', zero_first_center=True) } def create_CELoss(prms): if prms is None: return nn.CrossEntropyLoss() if 'weight' in prms: prms['weight'] = torch.FloatTensor(prms['weight']) return nn.CrossEntropyLoss(**prms) criterion_factory = { 'CrossEntropyLoss': lambda prms: create_CELoss(prms), 'MultiMarginLoss': lambda prms: nn.MultiMarginLoss(**prms) } if __name__ == '__main__': parser = OptionParser() parser.add_option('-c', '--config', dest='cfg_path', help='config path') parser.add_option('-d', '--debug', action="store_true", dest="is_debug") (options, args) = parser.parse_args() if options.cfg_path is None: sys.exit('cfg_path is not provided') if options.is_debug: log('DEBUG MODE ON') log('-----------\n\nStarting training process: \n %s\n %s' % (str(datetime.now()), __file__)) log('config: %s' % options.cfg_path) with open(options.cfg_path) as f: cfg = json.load(f) log('Config:') for k, v in cfg.items(): log(' %s = %s' % (k, v)) train_list_path = cfg['train_list_path'] val_path = cfg['val_path'] out_dir = cfg['out_dir'] model_path = cfg['model_path'] crop_size = cfg['crop_size'] step_crop_val = cfg['step_crop_val'] n_crops_train = cfg['n_crops_train'] batch_size_train = cfg['batch_size_train'] batch_size_val = cfg['batch_size_val'] workers = cfg['workers'] n_epoches = cfg['n_epoches'] model_type = cfg['model_type'] n_classes = cfg['n_classes'] learning_rate = cfg['learning_rate'] momentum = cfg['momentum'] lr_scheduler_step_size = cfg['lr_scheduler_step_size'] lr_scheduler_gamma = cfg['lr_scheduler_gamma'] weight_decay = cfg['weight_decay'] optim_type = cfg['optim_type'] crop_center_size = cfg['crop_center_size'] do_random_aug_kaggle = cfg['do_random_aug_kaggle'] p_random_aug_kaggle_train = cfg['p_random_aug_kaggle_train'] p_random_aug_kaggle_val = cfg['p_random_aug_kaggle_val'] do_hard_aug = cfg['do_hard_aug'] p_hard_aug_train = cfg['p_hard_aug_train'] p_hard_aug_val = cfg['p_hard_aug_val'] criterion_type = cfg['criterion_type'] criterion_params = cfg['criterion_params'] n_crops_search_train = cfg['n_crops_search_train'] train_list_pseudo_npz = cfg['train_list_pseudo_npz'] train_list_flickr = cfg['train_list_flickr'] to_tensor = transforms.ToTensor() normalize = transforms.Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] ) random_crop = transforms.RandomCrop(crop_size) center_crop = transforms.CenterCrop(crop_center_size) rvf = transforms.RandomVerticalFlip() rhf = transforms.RandomHorizontalFlip() random_flip = lambda img: rvf(rhf(img)) scale_05 = lambda img: scale_crop_pad(img, 0.5) scale_08 = lambda img: scale_crop_pad(img, 0.8) scale_15 = lambda img: scale_crop_pad(img, 1.5) scale_20 = lambda img: scale_crop_pad(img, 2.0) gamma_08 = lambda img: gamma_correction(img, 0.8) gamma_12 = lambda img: gamma_correction(img, 1.2) jpg_70 = lambda img: jpg_compress(img, (70, 71)) jpg_90 = lambda img: jpg_compress(img, (90, 91)) augs = [scale_05, scale_08, scale_15, scale_20, gamma_08, gamma_12, jpg_70, jpg_90] def random_aug_kaggle(img, p=0.5): if np.random.rand() < p: return random.choice(augs)(img) return img blur = iaa.GaussianBlur(sigma=(0, 2)) sharpen = iaa.Sharpen(alpha=(0, 1), lightness=(0.5, 2)) emboss = iaa.Emboss(alpha=(0, 1), strength=(0, 2)) contrast_normalization = iaa.ContrastNormalization(alpha=(0.7, 1.3)) hard_aug = iaa.OneOf([blur, sharpen, emboss, contrast_normalization]) sometimes_train = iaa.Sometimes(p_hard_aug_train, hard_aug) sometimes_val = iaa.Sometimes(p_hard_aug_val, hard_aug) def aug_train(img): #if min(img.size) > crop_center_size: # return random_flip(random_crop(center_crop(img))) #img_np = np.array(img) #if img_np.shape[0] < crop_center_size and img_np.shape[1] > crop_center_size: # n = np.random.randint(img_np.shape[1] - crop_center_size) # return random_flip(random_crop(Image.fromarray(img_np[:, n:(n + crop_center_size), :]))) #if img_np.shape[1] < crop_center_size and img_np.shape[0] > crop_center_size: # n = np.random.randint(img_np.shape[0] - crop_center_size) # return random_flip(random_crop(Image.fromarray(img_np[n:(n + crop_center_size), :, :]))) return random_flip(random_crop(img)) def aug_train_fscore(img): if min(img.size) > crop_center_size: img_np = np.array(center_crop(img)) else: img_np = np.array(img) if img_np.shape[0] < crop_center_size and img_np.shape[1] > crop_center_size: n = np.random.randint(img_np.shape[1] - crop_center_size) img_np = img_np[:, n:(n + crop_center_size), :] if img_np.shape[1] < crop_center_size and img_np.shape[0] > crop_center_size: n = np.random.randint(img_np.shape[0] - crop_center_size) img_np = img_np[n:(n + crop_center_size), :, :] crops = n_pseudorandom_crops(img_np, crop_size, n_crops_train, n_crops_search_train, patch_quality_dich) for img in crops: yield random_flip(random_crop(Image.fromarray(img))) def aug_optional_train(img): if do_hard_aug: img = Image.fromarray(sometimes_train.augment_image(np.array(img))) if do_random_aug_kaggle: img = random_aug_kaggle(img, p_random_aug_kaggle_train) return img def aug_optional_val(img): if do_hard_aug: img = Image.fromarray(sometimes_val.augment_image(np.array(img))) if do_random_aug_kaggle: img = random_aug_kaggle(img, p_random_aug_kaggle_val) return img if n_crops_search_train is None: log(' -> default transform_train is selected') transform_train = transforms.Compose([ transforms.Lambda(lambda img: [ aug_optional_train(aug_train(img)) for i in range(n_crops_train) ]), transforms.Lambda(lambda crops: torch.stack([normalize(to_tensor(crop)) for crop in crops])) ]) else: log(' -> dich fscore transform_train is selected') transform_train = transforms.Compose([ transforms.Lambda(lambda img: [ aug_optional_train(img) for img in aug_train_fscore(img) ]), transforms.Lambda(lambda crops: torch.stack([normalize(to_tensor(crop)) for crop in crops])) ]) ds_train = ImageList( train_list_path, transform=transform_train, target_transform=transforms.Compose([ transforms.Lambda(lambda y: [y]*n_crops_train), transforms.Lambda(lambda ylist: torch.LongTensor(ylist)) ])) train_ds_list = [] if train_list_pseudo_npz is not None: ds_train_pseudo = NpzFolder( train_list_pseudo_npz, transform=transforms.Compose([ transforms.Lambda(lambda img: [ aug_train(Image.fromarray(img)) for i in range(n_crops_train) ]), transforms.Lambda(lambda crops: torch.stack([normalize(to_tensor(crop)) for crop in crops])) ]), target_transform=transforms.Compose([ transforms.Lambda(lambda y: [y]*n_crops_train), transforms.Lambda(lambda ylist: torch.LongTensor(ylist)) ])) train_ds_list.append(ds_train_pseudo) log(' -> pseudo dataset is loaded') if train_list_flickr is not None: ds_train_flickr = ImageList( train_list_flickr, transform=transform_train, target_transform=transforms.Compose([ transforms.Lambda(lambda y: [y]*n_crops_train), transforms.Lambda(lambda ylist: torch.LongTensor(ylist)) ])) train_ds_list.append(ds_train_flickr) log(' -> flickr dataset is loaded') if len(train_ds_list) > 0: train_ds_list = [ds_train] + train_ds_list ds_train = MultiDataset(train_ds_list) log(' -> MultiDataset is created: %i' % len(train_ds_list)) #for ds in train_ds_list: # print('; '.join(['%s: %i' % (k, v) for (k, v) in sorted(ds.class_to_idx.items(), key=lambda t: t[1])])) #sys.exit('DEBUG EXIT') train_loader = torch.utils.data.DataLoader( ds_train, batch_size=batch_size_train, shuffle=True, num_workers=workers, pin_memory=True) log('train_loader.size: %i' % len(train_loader.dataset.imgs)) if val_path is not None: ds_val = NpzFolder( val_path, transform=transforms.Compose([ transforms.Lambda(lambda img: NCrops(img, crop_size=crop_size, step=step_crop_val)), transforms.Lambda(lambda crops: torch.stack([normalize(to_tensor(aug_optional_val(Image.fromarray(crop)))) for crop in crops])) ]), target_transform=transforms.Compose([ transforms.Lambda(lambda y: [y]*int(np.floor(1 + (512 - crop_size)/step_crop_val))**2), transforms.Lambda(lambda ylist: torch.LongTensor(ylist)) ])) val_loader = torch.utils.data.DataLoader( ds_val, batch_size=batch_size_val, shuffle=False, num_workers=workers, pin_memory=True) log('val_loader.size: %i' % len(val_loader.dataset.imgs)) model = model_factory[model_type](n_classes) if model_path is not None: checkpoint = torch.load(model_path) model.load_state_dict(checkpoint['model']) loss_train = checkpoint['loss_train'] acc_train = checkpoint['acc_train'] loss_val = checkpoint['loss_val'] acc_val = checkpoint['acc_val'] log('Last state:\n TLoss: %0.6f\n TAcc: %0.4f\n VLoss: %0.6f\n VAcc: %0.4f' % (loss_train[-1], acc_train[-1], loss_val[-1], acc_val[-1])) del(checkpoint) log('model loaded: %s' % model_path) model = model.cuda() criterion = criterion_factory[criterion_type](criterion_params) criterion = criterion.cuda() if optim_type == 'sgd': optimizer = optim.SGD( model.parameters(), lr=learning_rate, momentum=momentum, dampening=0, weight_decay=weight_decay, nesterov=True) elif optim_type == 'adam': optimizer = optim.Adam( model.parameters(), lr=learning_rate, betas=(0.9, 0.999), weight_decay=weight_decay) log('optimizer %s:\n' % str(type(optimizer)) + '\n'.join([('%s: %s' % (k, str(v))) for (k, v) in optimizer.param_groups[0].items() if k != 'params'])) lr_scheduler = torch.optim.lr_scheduler.StepLR( optimizer, step_size=lr_scheduler_step_size, gamma=lr_scheduler_gamma) loss_train = [] acc_train = [] loss_val = [] acc_val = [] epoch_time = [] for ix_epoch in range(n_epoches): start_time = time.time() lr_scheduler.step() model.train() loss_train_batch, acc_train_batch = train_pass(train_loader, model, criterion, optimizer) if val_path is not None: model.eval() loss_val_batch, acc_val_batch = val_pass(val_loader, model, criterion) else: loss_val_batch, acc_val_batch = 0.0, 0.0 loss_train.append(loss_train_batch) acc_train.append(acc_train_batch) loss_val.append(loss_val_batch) acc_val.append(acc_val_batch) epoch_time.append(time.time() - start_time) log('ix_epoch: %i' % ix_epoch) log(' Time: %0.2f\n TLoss: %0.6f\n TAcc: %0.4f\n VLoss: %0.6f\n VAcc: %0.4f' % (epoch_time[-1], loss_train[-1], acc_train[-1], loss_val[-1], acc_val[-1])) torch.save({ 'model': model.state_dict(), 'opt': optimizer.state_dict(), 'epoch': ix_epoch + 1, 'loss_train': loss_train, 'acc_train': acc_train, 'loss_val': loss_val, 'acc_val': acc_val, 'class_to_idx': train_loader.dataset.class_to_idx, 'cfg': cfg }, os.path.join(out_dir, 'checkpoint.tar')) if val_path is not None and acc_val[-1] == np.max(acc_val): log('Best found!') copyfile( os.path.join(out_dir, 'checkpoint.tar'), os.path.join(out_dir, 'best_model.tar')) if options.is_debug and ix_epoch == 1: log('END OF DEBUG') break
[ "torch.nn.CrossEntropyLoss", "numpy.random.rand", "imgaug.augmenters.GaussianBlur", "kaggle_camera_model_id_lib.models.InceptionResNetV2", "kaggle_camera_model_id_lib.utils.n_pseudorandom_crops", "kaggle_camera_model_id_lib.models.ResNetX", "torchvision.transforms.Lambda", "torch.LongTensor", "seabo...
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""" adaptation of part of <NAME>'s libvaxdata test suite for <NAME>'s PyVAX wrapper Authors ------- | <NAME> (<EMAIL>) <NAME> University of California, Los Angeles. """ import pyvax as pv import numpy as np from pytest import approx def func_i2(x): fstr = pv.from_vax_i2(x) print(np.frombuffer(fstr, dtype=np.int16, count=1)) return np.frombuffer(fstr, dtype=np.int16, count=1) def func_i4(x): fstr = pv.from_vax_i4(x) print(np.frombuffer(fstr, dtype=np.int32, count=1)) return np.frombuffer(fstr, dtype=np.int32, count=1) def func_f4(x): fstr = pv.from_vax_r4(x) print(np.frombuffer(fstr, dtype=np.float32, count=1)) return np.frombuffer(fstr, dtype=np.float32, count=1) def func_d8(x): fstr = pv.from_vax_d8(x) print(np.frombuffer(fstr, dtype=np.float64, count=1)) return np.frombuffer(fstr, dtype=np.float64, count=1) def func_g8(x): fstr = pv.from_vax_g8(x) print(np.frombuffer(fstr, dtype=np.float64, count=1)) return np.frombuffer(fstr, dtype=np.float64, count=1) def test_i2(): assert func_i2(b'\x01\x00') == 1 assert func_i2(b'\xFF\xFF') == -1 assert func_i2(b'\x00\x01') == 256 assert func_i2(b'\x00\xFF') == -256 assert func_i2(b'\x39\x30') == 12345 assert func_i2(b'\xC7\xCF') == -12345 def test_i4(): assert func_i4(b'\x01\x00\x00\x00') == 1 assert func_i4(b'\xFF\xFF\xFF\xFF') == -1 assert func_i4(b'\x00\x01\x00\x00') == 256 assert func_i4(b'\x00\xFF\xFF\xFF') == -256 assert func_i4(b'\x00\x00\x01\x00') == 65536 assert func_i4(b'\x00\x00\xFF\xFF') == -65536 assert func_i4(b'\x00\x00\x00\x01') == 16777216 assert func_i4(b'\x00\x00\x00\xFF') == -16777216 assert func_i4(b'\x15\xCD\x5B\x07') == 123456789 assert func_i4(b'\xEB\x32\xA4\xF8') == -123456789 def test_f4(): assert func_f4(b'\x80\x40\x00\x00') == approx(1.000000, rel=1e-7) assert func_f4(b'\x80\xC0\x00\x00') == approx(-1.000000, rel=1e-7) assert func_f4(b'\x60\x41\x00\x00') == approx(3.500000, rel=1e-7) assert func_f4(b'\x60\xC1\x00\x00') == approx(-3.500000, rel=1e-7) assert func_f4(b'\x49\x41\xD0\x0F') == approx(3.141590, rel=1e-7) assert func_f4(b'\x49\xC1\xD0\x0F') == approx(-3.141590, rel=1e-7) assert func_f4(b'\xF0\x7D\xC2\xBD') == approx(9.9999999E+36, rel=1e-7) assert func_f4(b'\xF0\xFD\xC2\xBD') == approx(-9.9999999E+36, rel=1e-7) assert func_f4(b'\x08\x03\xEA\x1C') == approx(9.9999999E-38, rel=1e-7) assert func_f4(b'\x08\x83\xEA\x1C') == approx(-9.9999999E-38, rel=1e-7) assert func_f4(b'\x9E\x40\x52\x06') == approx(1.234568, rel=1e-7) assert func_f4(b'\x9E\xC0\x52\x06') == approx(-1.234568, rel=1e-7) def test_d8(): assert func_d8(b'\x80\x40\x00\x00\x00\x00\x00\x00') == approx(1.000000000000000, rel=1e-14) assert func_d8(b'\x80\xC0\x00\x00\x00\x00\x00\x00') == approx(-1.000000000000000, rel=1e-14) assert func_d8(b'\x60\x41\x00\x00\x00\x00\x00\x00') == approx(3.500000000000000, rel=1e-14) assert func_d8(b'\x60\xC1\x00\x00\x00\x00\x00\x00') == approx(-3.500000000000000, rel=1e-14) assert func_d8(b'\x49\x41\xDA\x0F\x21\xA2\xBE\x68') == approx(3.141592653589793, rel=1e-14) assert func_d8(b'\x49\xC1\xDA\x0F\x21\xA2\xBE\x68') == approx(-3.141592653589793, rel=1e-14) assert func_d8(b'\xF0\x7D\xC2\xBD\xBB\x1A\xDB\x48') == approx(1.0000000000000000E+37, rel=1e-14) assert func_d8(b'\xF0\xFD\xC2\xBD\xBB\x1A\xDB\x48') == approx(-1.0000000000000000E+37, rel=1e-14) assert func_d8(b'\x08\x03\xEA\x1C\x54\x14\x75\x5C') == approx(9.9999999999999999E-38, rel=1e-14) assert func_d8(b'\x08\x83\xEA\x1C\x54\x14\x75\x5C') == approx(-9.9999999999999999E-38, rel=1e-14) assert func_d8(b'\x9E\x40\x52\x06\x62\x14\xE7\xCE') == approx(1.234567890123450, rel=1e-14) assert func_d8(b'\x9E\xC0\x52\x06\x62\x14\xE7\xCE') == approx(-1.234567890123450, rel=1e-14) def test_g8(): assert func_g8(b'\x10\x40\x00\x00\x00\x00\x00\x00') == approx(1.000000000000000, rel=1e-14) assert func_g8(b'\x10\xC0\x00\x00\x00\x00\x00\x00') == approx(-1.000000000000000, rel=1e-14) assert func_g8(b'\x2C\x40\x00\x00\x00\x00\x00\x00') == approx(3.500000000000000, rel=1e-14) assert func_g8(b'\x2C\xC0\x00\x00\x00\x00\x00\x00') == approx(-3.500000000000000, rel=1e-14) assert func_g8(b'\x29\x40\xFB\x21\x44\x54\x18\x2D') == approx(3.141592653589793, rel=1e-14) assert func_g8(b'\x29\xC0\xFB\x21\x44\x54\x18\x2D') == approx(-3.141592653589793, rel=1e-14) assert func_g8(b'\xBE\x47\xB8\x17\x57\x43\x1B\x69') == approx(1.0000000000000000E+37, rel=1e-14) assert func_g8(b'\xBE\xC7\xB8\x17\x57\x43\x1B\x69') == approx(-1.0000000000000000E+37, rel=1e-14) assert func_g8(b'\x61\x38\x9D\x03\x8A\x42\x8F\x8B') == approx(9.9999999999999999E-38, rel=1e-14) assert func_g8(b'\x61\xB8\x9D\x03\x8A\x42\x8F\x8B') == approx(-9.9999999999999999E-38, rel=1e-14) assert func_g8(b'\x13\x40\xCA\xC0\x8C\x42\xDD\x59') == approx(1.234567890123450, rel=1e-14) assert func_g8(b'\x13\xC0\xCA\xC0\x8C\x42\xDD\x59') == approx(-1.234567890123450, rel=1e-14)
[ "pytest.approx", "pyvax.from_vax_g8", "pyvax.from_vax_i2", "numpy.frombuffer", "pyvax.from_vax_d8", "pyvax.from_vax_r4", "pyvax.from_vax_i4" ]
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import csv from os import listdir from os.path import isfile, join import numpy as np import pandas as pd from scipy.io import arff from clustering_algorithms import CLARA, PAM, get_initial_points from data_loaders import load_data from timer import Timer from visualizers import plot_data DIRECTORY = "datasets/artificial" FILES = [ "insect.arff", "flame.arff", "zelnik3.arff", "zelnik5.arff", "R15.arff", "square3.arff", "sizes5.arff", "cure-t1-2000n-2D.arff", "xclara.arff", "s-set1.arff", ] if __name__ == "__main__": # files = [f for f in listdir(DIRECTORY) if isfile(join(DIRECTORY, f))] files = FILES for file in files: try: filepath = join(DIRECTORY, file) data = load_data(filepath) points = get_initial_points(data["df"], data["coordinates_columns"]) clara_time = [] pam_time = [] t = Timer() for iteration in range(10): # count clara time t.start() clara = CLARA(points, len(data["classes"]), labels=data["classes"]) clara.run() t.stop() clara_time.append(t.time) # # count pam time # t.start() # pam = PAM(points, len(data["classes"]), labels=data["classes"]) # pam.run() # t.stop() # pam_time.append(t.time) results = { "filename": file, "classes": len(data["classes"]), "coordinates_attributes": len(data["coordinates_columns"]), "points": len(points), "mean_time_clara": np.mean(clara_time), # "mean_time_pam": np.mean(pam_time), } with open("results_clara.csv", "a") as csvfile: writer = csv.DictWriter(csvfile, fieldnames=results.keys()) writer.writerow(results) except: pass
[ "clustering_algorithms.get_initial_points", "numpy.mean", "os.path.join", "data_loaders.load_data", "timer.Timer" ]
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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Jul 8 16:58:10 2020 @author: nephilim """ import keras import numpy as np import my_Im2col from matplotlib import pyplot,cm import skimage.transform import T_PowerGain import DataNormalized import Reshape2Encoder def GetPatch(Image,patch_size,slidingDis): blocks,idx=my_Im2col.my_im2col(Image,patch_size,slidingDis) return blocks,idx def CalculationSNR(Image,Noise): frac_up=np.sum(Image**2) frac_down=np.sum((Image-Noise)**2) SNR=10*np.log10(frac_up/frac_down) return SNR class AutoEncoder(): def __init__(self,ImageShape,filters,kernel_size,latent_dim): self.ImageShape=ImageShape self.filters=filters self.kernel_size=kernel_size self.latent_dim=latent_dim def Encoder(self): self.Encoder_Input=keras.Input(shape=self.ImageShape,name='Encoder_Input_2D') x=self.Encoder_Input for idx,_ in enumerate(self.filters): x=keras.layers.Conv2D(filters=self.filters[idx],kernel_size=self.kernel_size[idx],activation='relu',padding='same')(x) x=keras.layers.BatchNormalization()(x) x=keras.layers.MaxPool2D((2,2))(x) self.shape=keras.backend.int_shape(x) # print(self.shape) x=keras.layers.Flatten()(x) Encoder_Output=keras.layers.Dense(self.latent_dim,name='Encoder_Ouput_1D')(x) self.EncoderMode=keras.models.Model(inputs=self.Encoder_Input,outputs=Encoder_Output,name='EncoderPart') self.EncoderMode.summary() self.EncoderMode.compile(loss='mse',optimizer='adam') def Decoder(self): Decoder_Input=keras.Input(shape=(self.latent_dim,),name='Decoder_Input_1D') x=keras.layers.Dense(self.shape[1]*self.shape[2]*self.shape[3])(Decoder_Input) x=keras.layers.Reshape((self.shape[1],self.shape[2],self.shape[3]))(x) for idx,_ in enumerate(self.filters): x=keras.layers.Conv2DTranspose(filters=self.filters[len(self.filters)-idx-1],kernel_size=self.kernel_size[len(self.kernel_size)-idx-1],activation='relu',padding='same')(x) x=keras.layers.BatchNormalization()(x) x=keras.layers.UpSampling2D((2,2))(x) Decoder_Output=keras.layers.Conv2DTranspose(filters=1,kernel_size=5,activation='sigmoid',padding='same',name='Decoder_Output_1D')(x) self.DecoderMode=keras.models.Model(inputs=Decoder_Input,outputs=Decoder_Output) self.DecoderMode.summary() self.DecoderMode.compile(loss='mse',optimizer='adam') if __name__=='__main__': AutoEncoder_=AutoEncoder(ImageShape=(512,512,1),filters=[16,32,64],kernel_size=[5,5,5],latent_dim=256) AutoEncoder_.Encoder() patch_size=(512,512) slidingDis=64 Iteration=100 ProfileGain_train=[] for iteration in range(Iteration): Profile=np.load('./GPR_Modelling/Profile_TunnelLining/TunnelLining_Iter_%s.npy'%iteration) ProfileGain=T_PowerGain.tpowGain(Profile,np.arange(7000)/4,0.9) ProfileGain=skimage.transform.resize(ProfileGain,(512,512),mode='edge') ProfileGain=DataNormalized.DataNormalized(ProfileGain)/255 ProfileGain_Patch,_=GetPatch(ProfileGain,patch_size,slidingDis) ProfileGain_train.append(ProfileGain_Patch) Iteration=100 compare=1 for iteration in range(Iteration): Profile=np.load('./GPR_Modelling/ProfileAutoEncoder/%s_iter_record_%s_comp.npy'%(iteration,compare)) ProfileGain=T_PowerGain.tpowGain(Profile,np.arange(7000)/4,0.9) ProfileGain=skimage.transform.resize(ProfileGain,(512,512),mode='edge') ProfileGain=DataNormalized.DataNormalized(ProfileGain)/255 ProfileGain_Patch,_=GetPatch(ProfileGain,patch_size,slidingDis) ProfileGain_train.append(ProfileGain_Patch) ProfileGain_train=np.array(ProfileGain_train) Profile_train=Reshape2Encoder.ReshapeData2Encoder(ProfileGain_train,patch_size) del ProfileGain,ProfileGain_Patch,ProfileGain_train layer_outputs=[layer.output for layer in AutoEncoder_.EncoderMode.layers[1:None]] activation_model=keras.models.Model(inputs=AutoEncoder_.EncoderMode.input,outputs=layer_outputs) activations=activation_model.predict(Profile_train[2:3,:,:]) # pyplot.figure() # pyplot.imshow(ProfileGain) # pyplot.figure() # pyplot.imshow(activations[1][0,:,:,0]) # pyplot.figure() # pyplot.imshow(activations[3][0,:,:,0]) # pyplot.figure() # pyplot.imshow(activations[5][0,:,:,0]) # pyplot.figure() # pyplot.imshow(ProfileGain) # pyplot.figure() # pyplot.imshow(display_grid[0,:,:]) # pyplot.figure() # pyplot.imshow(display_grid[1,:,:]) # pyplot.figure() # pyplot.imshow(display_grid[2,:,:]) # pyplot.figure() # pyplot.imshow(Profile_train[1541,:,:,0]) # # pyplot.figure() # # pyplot.imshow(activations[1][0,:,:,0]) # # pyplot.figure() # # pyplot.imshow(activations[3][0,:,:,0]) # # pyplot.figure() # # pyplot.imshow(activations[5][0,:,:,0]) # image_per_row=16 # for layer_activation in activations: # n_features=layer_activation.shape[-1] # size=layer_activation.shape[1] # n_cols=n_features//image_per_row # display_grid=np.zeros((size*n_cols,image_per_row*size)) # for col in range(n_cols): # for row in range(image_per_row): # channel_image=layer_activation[0,:,:,col*image_per_row+row] # display_grid[col*size:(col+1)*size,row*size:(row+1)*size]=channel_image # pyplot.figure() # pyplot.imshow(display_grid[:,:]) # pyplot.axis('off') pyplot.figure() data=Profile_train[2:3,:,:] data=data[0,:,:,0] pyplot.imshow(data,cmap=cm.seismic) pyplot.axis('off') pyplot.savefig('OriginalInput.png',dpi=1000) FirstConv2DLayer=activations[2] image_per_row=4 n_features=FirstConv2DLayer.shape[-1] size=FirstConv2DLayer.shape[1] n_cols=n_features//image_per_row display_grid=np.zeros((n_cols*size,image_per_row*size)) for col in range(n_cols): for row in range(image_per_row): channel_image=FirstConv2DLayer[0,:,:,col*image_per_row+row] display_grid[col*size:(col+1)*size,row*size:(row+1)*size]=channel_image pyplot.figure() pyplot.imshow(display_grid[:,:],vmin=-0.2,vmax=0.4,cmap=cm.seismic) pyplot.axis('off') pyplot.savefig('FirstConv2D.png',dpi=1000) FirstConv2DLayer=activations[5] image_per_row=8 n_features=FirstConv2DLayer.shape[-1] size=FirstConv2DLayer.shape[1] n_cols=n_features//image_per_row display_grid=np.zeros((n_cols*size,image_per_row*size)) for col in range(n_cols): for row in range(image_per_row): channel_image=FirstConv2DLayer[0,:,:,col*image_per_row+row] display_grid[col*size:(col+1)*size,row*size:(row+1)*size]=channel_image pyplot.figure() pyplot.imshow(display_grid[:,:],vmin=-0.2,vmax=0.4,cmap=cm.seismic) pyplot.axis('off') pyplot.savefig('SecondConv2D.png',dpi=1000) FirstConv2DLayer=activations[8] image_per_row=8 n_features=FirstConv2DLayer.shape[-1] size=FirstConv2DLayer.shape[1] n_cols=n_features//image_per_row display_grid=np.zeros((n_cols*size,image_per_row*size)) for col in range(n_cols): for row in range(image_per_row): channel_image=FirstConv2DLayer[0,:,:,col*image_per_row+row] display_grid[col*size:(col+1)*size,row*size:(row+1)*size]=channel_image pyplot.figure() pyplot.imshow(display_grid[:,:],vmin=-0.2,vmax=0.4,cmap=cm.seismic) pyplot.axis('off') pyplot.savefig('ThirdConv2D.png',dpi=1000)
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import sys import numpy as np np.set_printoptions(precision=2, threshold=sys.maxsize) from scipy.linalg import block_diag from qpsolvers import solve_qp from util import util from pnc.data_saver import DataSaver class IHWBC(object): """ Implicit Hierarchy Whole Body Control ------------------ Usage: update_setting --> solve """ def __init__(self, sf, sa, sv, data_save=False): self._n_q_dot = sa.shape[1] self._n_active = sa.shape[0] self._n_passive = sv.shape[0] self._sf = sf self._snf = np.concatenate( (np.zeros((self._n_active + self._n_passive, 6)), np.eye(self._n_active + self._n_passive)), axis=1) self._sa = sa self._sv = sv self._trq_limit = None self._lambda_q_ddot = 0. self._lambda_rf = 0. self._w_rf = 0. self._w_hierarchy = 0. self._b_data_save = data_save if self._b_data_save: self._data_saver = DataSaver() @property def trq_limit(self): return self._trq_limit @property def lambda_q_ddot(self): return self._lambda_q_ddot @property def lambda_rf(self): return self._lambda_rf @property def w_hierarchy(self): return self._w_hierarchy @property def w_rf(self): return self._w_rf @trq_limit.setter def trq_limit(self, val): assert val.shape[0] == self._n_active self._trq_limit = np.copy(val) @lambda_q_ddot.setter def lambda_q_ddot(self, val): self._lambda_q_ddot = val @lambda_rf.setter def lambda_rf(self, val): self._lambda_rf = val @w_hierarchy.setter def w_hierarchy(self, val): self._w_hierarchy = val @w_hierarchy.setter def w_rf(self, val): self._w_rf = val def update_setting(self, mass_matrix, mass_matrix_inv, coriolis, gravity): self._mass_matrix = np.copy(mass_matrix) self._mass_matrix_inv = np.copy(mass_matrix_inv) self._coriolis = np.copy(coriolis) self._gravity = np.copy(gravity) def solve(self, task_list, contact_list, internal_constraint_list, rf_des=None, verbose=False): """ Parameters ---------- task_list (list of Task): Task list contact_list (list of Contact): Contact list internal_constraint_list (list of InternalConstraint): Internal constraint list rf_des (np.ndarray): Reaction force desired verbose (bool): Printing option Returns ------- joint_trq_cmd (np.array): Joint trq cmd joint_acc_cmd (np.array): Joint acc cmd sol_rf (np.array): Reaction force """ # ====================================================================== # Internal Constraint # Set ni, jit_lmd_jidot_qdot, sa_ni_trc_bar_tr, and b_internal_constraint # ====================================================================== if len(internal_constraint_list) > 0: ji = np.concatenate( [ic.jacobian for ic in internal_constraint_list], axis=0) jidot_qdot = np.concatenate( [ic.jacobian_dot_q_dot for ic in internal_constraint_list], axis=0) lmd = np.linalg.pinv( np.dot(np.dot(ji, self._mass_matrix_inv), ji.transpose())) ji_bar = np.dot(np.dot(self._mass_matrix_inv, ji.transpose()), lmd) ni = np.eye(self._n_q_dot) - np.dot(ji_bar, ji) jit_lmd_jidot_qdot = np.squeeze( np.dot(np.dot(ji.transpose(), lmd), jidot_qdot)) sa_ni_trc = np.dot(self._sa, ni)[:, 6:] sa_ni_trc_bar = util.weighted_pinv(sa_ni_trc, self._mass_matrix_inv[6:, 6:]) sa_ni_trc_bar_tr = sa_ni_trc_bar.transpose() b_internal_constraint = True else: ni = np.eye(self._n_q_dot) jit_lmd_jidot_qdot = np.zeros(self._n_q_dot) sa_ni_trc_bar = np.eye(self._n_active) sa_ni_trc_bar_tr = sa_ni_trc_bar.transpose() b_internal_constraint = False # print("ni") # print(ni) # print("jit_lmd_jidot_qdot") # print(jit_lmd_jidot_qdot) # print("sa_ni_trc_bar_tr") # print(sa_ni_trc_bar_tr) # exit() # ====================================================================== # Cost # ====================================================================== cost_t_mat = np.zeros((self._n_q_dot, self._n_q_dot)) cost_t_vec = np.zeros(self._n_q_dot) for i, task in enumerate(task_list): j = task.jacobian j_dot_q_dot = task.jacobian_dot_q_dot x_ddot = task.op_cmd if verbose: print("====================") print(task.target_id, " task") task.debug() cost_t_mat += self._w_hierarchy[i] * np.dot(j.transpose(), j) cost_t_vec += self._w_hierarchy[i] * np.dot( (j_dot_q_dot - x_ddot).transpose(), j) # cost_t_mat += self._lambda_q_ddot * np.eye(self._n_q_dot) cost_t_mat += self._lambda_q_ddot * self._mass_matrix if contact_list is not None: uf_mat = np.array( block_diag( *[contact.cone_constraint_mat for contact in contact_list])) uf_vec = np.concatenate( [contact.cone_constraint_vec for contact in contact_list]) contact_jacobian = np.concatenate( [contact.jacobian for contact in contact_list], axis=0) assert uf_mat.shape[0] == uf_vec.shape[0] assert uf_mat.shape[1] == contact_jacobian.shape[0] dim_cone_constraint, dim_contacts = uf_mat.shape cost_rf_mat = (self._lambda_rf + self._w_rf) * np.eye(dim_contacts) if rf_des is None: rf_des = np.zeros(dim_contacts) cost_rf_vec = -self._w_rf * np.copy(rf_des) cost_mat = np.array(block_diag( cost_t_mat, cost_rf_mat)) # (nqdot+nc, nqdot+nc) cost_vec = np.concatenate([cost_t_vec, cost_rf_vec]) # (nqdot+nc,) else: dim_contacts = dim_cone_constraint = 0 cost_mat = np.copy(cost_t_mat) cost_vec = np.copy(cost_t_vec) # if verbose: # print("==================================") # np.set_printoptions(precision=4) # print("cost_t_mat") # print(cost_t_mat) # print("cost_t_vec") # print(cost_t_vec) # print("cost_rf_mat") # print(cost_rf_mat) # print("cost_rf_vec") # print(cost_rf_vec) # print("cost_mat") # print(cost_mat) # print("cost_vec") # print(cost_vec) # ====================================================================== # Equality Constraint # ====================================================================== if contact_list is not None: eq_floating_mat = np.concatenate( (np.dot(self._sf, self._mass_matrix), -np.dot(self._sf, np.dot(contact_jacobian, ni).transpose())), axis=1) # (6, nqdot+nc) if b_internal_constraint: eq_int_mat = np.concatenate( (ji, np.zeros((ji.shape[0], dim_contacts))), axis=1) # (2, nqdot+nc) eq_int_vec = np.zeros(ji.shape[0]) else: eq_floating_mat = np.dot(self._sf, self._mass_matrix) if b_internal_constraint: eq_int_mat = np.copy(ji) eq_int_vec = np.zeros(ji.shape[0]) eq_floating_vec = -np.dot(self._sf, np.dot(ni.transpose(), (self._coriolis + self._gravity))) if b_internal_constraint: eq_mat = np.concatenate((eq_floating_mat, eq_int_mat), axis=0) eq_vec = np.concatenate((eq_floating_vec, eq_int_vec), axis=0) else: eq_mat = np.copy(eq_floating_mat) eq_vec = np.copy(eq_floating_vec) # ====================================================================== # Inequality Constraint # ====================================================================== if self._trq_limit is None: if contact_list is not None: ineq_mat = np.concatenate( (np.zeros((dim_cone_constraint, self._n_q_dot)), -uf_mat), axis=1) ineq_vec = -uf_vec else: ineq_mat = None ineq_vec = None else: if contact_list is not None: ineq_mat = np.concatenate( (np.concatenate( (np.zeros((dim_cone_constraint, self._n_q_dot)), -np.dot(sa_ni_trc_bar_tr, np.dot(self._snf, self._mass_matrix)), np.dot(sa_ni_trc_bar_tr, np.dot(self._snf, self._mass_matrix))), axis=0), np.concatenate( (-uf_mat, np.dot( np.dot(sa_ni_trc_bar_tr, self._snf), np.dot(contact_jacobian, ni).transpose()), -np.dot( np.dot(sa_ni_trc_bar_tr, self._snf), np.dot(contact_jacobian, ni).transpose())), axis=0)), axis=1) ineq_vec = np.concatenate(( -uf_vec, np.dot( np.dot(sa_ni_trc_bar_tr, self._snf), np.dot(ni.transpose(), (self._coriolis + self._gravity))) + np.dot( np.dot(sa_ni_trc_bar_tr, self._snf), jit_lmd_jidot_qdot) - self._trq_limit[:, 0], -np.dot( np.dot(sa_ni_trc_bar_tr, self._snf), np.dot(ni.transpose(), (self._coriolis + self._gravity))) - np.dot( np.dot(sa_ni_trc_bar_tr, self._snf), jit_lmd_jidot_qdot) + self._trq_limit[:, 1])) else: ineq_mat = np.concatenate( (-np.dot( np.dot(sa_ni_trc_bar_tr, self._snf), self._mass_matrix), np.dot( np.dot(sa_ni_trc_bar_tr, self._snf), self._mass_matrix)), axis=0) ineq_vec = np.concatenate( (np.dot( np.dot(sa_ni_trc_bar_tr, self._snf), np.dot(ni.transpose(), (self._coriolis + self._gravity))) + np.dot( np.dot(sa_ni_trc_bar_tr, self._snf), jit_lmd_jidot_qdot) - self._trq_limit[:, 0], -np.dot( np.dot(sa_ni_trc_bar_tr, self._snf), np.dot(ni.transpose(), (self._coriolis + self._gravity))) - np.dot( np.dot(sa_ni_trc_bar_tr, self._snf), jit_lmd_jidot_qdot) + self._trq_limit[:, 1])) # if verbose: # print("eq_mat") # print(eq_mat) # print("eq_vec") # print(eq_vec) # print("ineq_mat") # print(ineq_mat) # print("ineq_vec") # print(ineq_vec) sol = solve_qp( cost_mat, cost_vec, ineq_mat, ineq_vec, eq_mat, eq_vec, solver="quadprog", verbose=True) if contact_list is not None: sol_q_ddot, sol_rf = sol[:self._n_q_dot], sol[self._n_q_dot:] else: sol_q_ddot, sol_rf = sol, None if contact_list is not None: joint_trq_cmd = np.dot( np.dot(sa_ni_trc_bar_tr, self._snf), np.dot(self._mass_matrix, sol_q_ddot) + np.dot(ni.transpose(), (self._coriolis + self._gravity)) - np.dot(np.dot(contact_jacobian, ni).transpose(), sol_rf)) else: joint_trq_cmd = np.dot( np.dot(sa_ni_trc_bar_tr, self._snf), np.dot(self._mass_matrix, sol_q_ddot) + np.dot(ni, (self._coriolis + self._gravity))) joint_acc_cmd = np.dot(self._sa, sol_q_ddot) if verbose: print("joint_trq_cmd: ", joint_trq_cmd) print("sol_q_ddot: ", sol_q_ddot) print("sol_rf: ", sol_rf) for i, task in enumerate(task_list): j = task.jacobian j_dot_q_dot = task.jacobian_dot_q_dot x_ddot = task.op_cmd print(task.target_id, " task") print("des x ddot: ", x_ddot) print("j*qddot_sol + Jdot*qdot: ", np.dot(j, sol_q_ddot) + j_dot_q_dot) if self._b_data_save: self._data_saver.add('joint_trq_cmd', joint_trq_cmd) self._data_saver.add('joint_acc_cmd', joint_acc_cmd) self._data_saver.add('rf_cmd', sol_rf) return joint_trq_cmd, joint_acc_cmd, sol_rf
[ "numpy.copy", "numpy.eye", "pnc.data_saver.DataSaver", "qpsolvers.solve_qp", "numpy.dot", "numpy.zeros", "numpy.concatenate", "scipy.linalg.block_diag", "util.util.weighted_pinv", "numpy.set_printoptions" ]
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import os import sys import unittest import HtmlTestRunner import cv2.cv2 as cv2 import numpy as np current_path = os.path.dirname(os.path.abspath(__file__)) list_paths = [os.sep.join([current_path, os.pardir, 'src']), os.sep.join([current_path, os.pardir])] for path in list_paths: if path not in sys.path: sys.path.insert(0, path) from TrafficDetector import TrafficDetector from MapSlicer import MapSlicer test_data_dir = os.path.join(current_path, '../data/') class Test(unittest.TestCase): @classmethod def setUpClass(cls): cls.MS = MapSlicer() def test_coordinates_to_pixel(self): """ Test pixel from given coordinates. Pixel must be in accepted region [(382, 187), (437, 274)] """ coordinates = (30.218193, -97.7859167) image = os.sep.join([test_data_dir, "austin1.tif"]) gt_pixel = (1133, 4722) h, w = self.MS.get_pixel_from_coordinates(image, coordinates) str_error = f'Pixel ({w, h})out of bounds {gt_pixel} for given coordenates: ' \ f'{coordinates}' self.assertEqual(h, gt_pixel[0], str_error) self.assertEqual(w, gt_pixel[1], str_error) def test_street_coordenates(self): """ Test MapSlicer returns any coordinates given street name. """ street_name = "L<NAME>, austin" coordenates = self.MS.get_street_box_in_coordinates(street_name) self.assertGreater(len(coordenates), 0) def test_number_cars(self): """ Test for checking the number of cars detected by TrafficDetector. """ image = cv2.imread(test_data_dir + 'image21.png') TD = TrafficDetector(image) cars_list = TD.get_cars_from_image() # Pass: +-1 cars self.assertGreater(len(cars_list), 6) self.assertLess(len(cars_list), 12) def test_street_surface(self): """ Test for checking """ image_complete = test_data_dir + 'street-surface-unmask.png' image_mask = test_data_dir + 'street-surface-masked.png' img_compl = cv2.imread(image_complete) img_mask = cv2.imread(image_mask) surface_gt = len(np.nonzero(img_mask[:, :, 0])[0]) # surface = 58705 surface = self.MS.get_street_surface(img_compl) # Pass: surface +-5000 pixels self.assertGreater(surface, surface_gt - 5000, 'Surface detected for street is too small') self.assertLess(surface, surface_gt + 5000, 'Surface detected for street is too big') if __name__ == '__main__': # Test sets test_results = unittest.TestSuite() test_results.addTest(Test('test_coordinates_to_pixel')) test_results.addTest(Test('test_street_coordenates')) test_results.addTest(Test('test_number_cars')) test_results.addTest(Test('test_street_surface')) # Test launch unittest.TextTestRunner().run(test_results) unittest.main(testRunner=HtmlTestRunner.HTMLTestRunner(output='./results'))
[ "HtmlTestRunner.HTMLTestRunner", "unittest.TestSuite", "sys.path.insert", "cv2.cv2.imread", "os.path.join", "MapSlicer.MapSlicer", "os.sep.join", "TrafficDetector.TrafficDetector", "numpy.nonzero", "os.path.abspath", "unittest.TextTestRunner" ]
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#!/usr/bin/env python """ Hmm maybe its impossible to get anywhere with integral without first fixing the BetaInverse In [22]: pw.subs(b, 1.55) Out[22]: Piecewise((Max(0, 0.542862145685886*e - 3.9544951109741), (e > 7.294) & (e < 7.75)), (0, True)) In [23]: pw.subs(b, 1.) Out[23]: Piecewise((Max(0, 0.225957188630962*e - 1.06222481205998), (e > 7.294) & (e < 7.75)), (0, True)) """ import numpy as np import sympy as sym ri = np.array([ [ 1.55 , 1.478], [ 1.795, 1.48 ], [ 2.105, 1.484], [ 2.271, 1.486], [ 2.551, 1.492], [ 2.845, 1.496], [ 3.064, 1.499], [ 4.133, 1.526], [ 6.2 , 1.619], [ 6.526, 1.618], [ 6.889, 1.527], [ 7.294, 1.554], [ 7.75 , 1.793], [ 8.267, 1.783], [ 8.857, 1.664], [ 9.538, 1.554], [10.33 , 1.454], [15.5 , 1.454] ]) if __name__ == '__main__': e, b = sym.symbols("e b") i = 11 e0, r0 = ri[i] e1, r1 = ri[i+1] em = (e0 + e1)/2. v0 = ( 1 - b/r0 ) * ( 1 + b/r0 ) v1 = ( 1 - b/r1 ) * ( 1 + b/r1 ) fr = (e-e0)/(e1-e0) pt = ( sym.Max(v0*(1-fr) + v1*fr,0), (e > e0) & (e < e1) ) ot = (0, True ) pw = sym.Piecewise( pt, ot ) v = pw.subs(b, 1.55).subs(e, em) print(v)
[ "sympy.symbols", "numpy.array", "sympy.Piecewise", "sympy.Max" ]
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import numpy as np #--------------------------------------------------------- #Read in transition counts between cells (N_alpha_beta) #--------------------------------------------------------- def compute_Nab_Ta(trajectory_crossings,N,i): ''' Compute N_a_b and T_a for one individual trajectory at one individual cell -------------------------------------------------------------------------- trajectory_crossings : time vs crossings time series N : number of milestones = number of cells + 1 i : cell index ''' N_a_b = np.zeros((N-1,N-1)) T_a = np.zeros(N-1) l = trajectory_crossings right_crossings = np.sum(l[:,1]) left_crossings = len(l) - right_crossings if i==0 : N_a_b[i,i+1] = right_crossings elif i==N-2 : N_a_b[i,i-1] = left_crossings else : N_a_b[i,i-1] = left_crossings N_a_b[i,i+1] = right_crossings T_a[i] = l[-1,0] return N_a_b, T_a #----------------------------------------------------------- #Compute probabilities and free energy per cell #----------------------------------------------------------- #compute transition flux matrix (k_alpha_beta) def compute_k_a_b(N_a_b, T_a, i, milestones): ''' Compute transition flux matrix (k_alpha_beta) for one individual cell and one individual trajectory trace ''' N = len(N_a_b) + 1 k_a_b = np.zeros((N-1,N-1)) k_a_b[i] += (N_a_b[i]/T_a[i]) return k_a_b #----- Perform self consistent iterations to calculate stationar state ----# def probability(k_a_b,Niter=10000): N = len(k_a_b) + 1 #initial guess p_init = np.ones(N-1) p_init /= len(p_init) p = p_init for j in range(Niter): p_new = np.zeros(N-1) for i in range(N-1): if i==0: p_new[i] = p[i+1]*k_a_b[i+1,i]/k_a_b[i,i+1] elif i==N-2: p_new[i] = p[i-1]*k_a_b[i-1,i]/k_a_b[i,i-1] else : p_new[i] = (p[i-1]*k_a_b[i-1,i] + p[i+1]*k_a_b[i+1,i])/(k_a_b[i,i-1] + k_a_b[i,i+1]) p_new /= np.sum(p_new) p = p_new return p #----------------------------------------------------------------------------- #Construct the Q matrix for kinetics calculation #----------------------------------------------------------------------------- def compute_Nij_Ri(trajectory_crossings,p,i,N): '''Construct Nij and Ri for an individual trajectory in one individual cell ''' N_i_j = np.zeros((N,N)) R_i = np.zeros(N) #for i in range(N-1): l = trajectory_crossings T_a = l[-1,0] #compute N_i_j for j in range(1,len(l)): #hitting a milestone coming from a different milestone if l[j-1,1] == 0 and l[j,1] == 1 : N_i_j[i,i+1] += 1.0 * p[i]/T_a elif l[j-1,1] == 1 and l[j,1] == 0 : N_i_j[i+1,i] += 1.0 * p[i]/T_a #compute R_i for j in range(1,len(l)): if l[j-1,1] == 0: R_i[i] += (l[j,0] - l[j-1,0]) * p[i]/T_a elif l[j-1,1] == 1: R_i[i+1] += (l[j,0] - l[j-1,0]) * p[i]/T_a return N_i_j, R_i #Construct the matrix of number of hitting points for one trajectory trace def compute_Nhit(trajectory_crossings,i,N): Nhit = np.zeros((N,N)) l = trajectory_crossings for j in range(1,len(l)): #hitting a milestone coming from a different milestone if l[j-1,1] == 0 and l[j,1] == 1 : Nhit[i,i+1] += 1.0 elif l[j-1,1] == 1 and l[j,1] == 0 : Nhit[i+1,i] += 1.0 return Nhit def Q_matrix(N_i_j,R_i,N): ''' Construct the Q matrix for kinetics calculation ''' Q = np.zeros((N,N)) for i in range(N): for j in range(N): if R_i[i] != 0: Q[i,j] = N_i_j[i,j]/R_i[i] for i in range(N): Q[i,i] = -np.sum(Q[i]) return Q def Q_matrix_rev(N_i_j,R_i,N): ''' Construct the reverse Q matrix for kinetics calculation of the backward transition Replacing i with N-1-i so that indices change linke this 0 -> N-1 1 -> N-2 ... N-1 -> 0 ''' Q = np.zeros((N,N)) for i in range(N): for j in range(N): if R_i[N-1-i] != 0: Q[i,j] = N_i_j[N-1-i,N-1-j]/R_i[N-1-i] for i in range(N): Q[i,i] = -np.sum(Q[i]) return Q #------------------------------------------------------------------------------- #Construct the truncated rate matrix and compute MFPT #------------------------------------------------------------------------------- def MFPT(Q,start,end): ''' Construct the truncated rate matrix and compute MFPT ''' #dimention of rate matrix M = end - start Q_rate = np.zeros((M,M)) I1 = np.ones(M) #print(I1) for i in range(M): for j in range(M): Q_rate[i,j] = Q[start+i,start+j] MFPTs = -np.linalg.solve(Q_rate,I1) return MFPTs def Monte_Carlo_bootstrapping(N_total,K,t,Nhit,interval): '''Perform nonreversible element shift Monte Carlo to sample rate matrices Input ----- N_total : Total number of MC moves (accepted and rejected) K : Rate Matrix t : lifetime vector (R_i) Nhit : Matrix containing number of hitting points interval : After how many MC moves a matrix is sampled Returns ------- Q_list : (n,N,N) dim array where n is the number of sampled rate matrices ''' N = len(t) Q_list = [] for k in range(N_total): Q = K.copy() #choose one of the non-zero elements to change r1 = np.random.randint(0,N-1) if r1 == 0: Q_ab = Q[r1,r1+1] N_ab = Nhit[r1,r1-1] else : r2 = np.random.randint(0,1) if r2 == 0: Q_ab = Q[r1,r1-1] N_ab = Nhit[r1,r1-1] else : Q_ab = Q[r1,r1+1] N_ab = Nhit[r1,r1+1] delta = np.random.exponential(scale=Q_ab) - Q_ab log_pacc = N_ab*np.log((Q_ab + delta)/Q_ab) - delta * t[r1]*np.sum(Nhit[r1]) r = np.random.uniform(low=0.0,high=1.0) if np.log(r) < log_pacc : #accept Q[r1,r1] -= delta if r1 == 0: Q[r1,r1+1] += delta else : if r2 == 0 : Q[r1,r1-1] += delta else : Q[r1,r1+1] += delta #only include after "interval" steps if (k+1)%interval == 0: Q_list.append(Q) #convert from list to array before returning return np.array(Q_list)
[ "numpy.linalg.solve", "numpy.ones", "numpy.log", "numpy.random.exponential", "numpy.array", "numpy.sum", "numpy.zeros", "numpy.random.randint", "numpy.random.uniform" ]
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#!/usr/bin/env python # -*- coding: utf-8 -*- r"""Provide the wrench task. The wrench task tries to generate a wrench near the desired one by minimizing: .. math:: || w - k (w_{des} - w_t) ||^2 where :math:`w = [f \tau] \in \mathbb{R}^6` is the wrench vector being optimized, :math:`k` is a proportional gain, :math:`w_{des}` is the desired wrench vector, and :math:`w_t` is the current wrench vector. The above formulation is equivalent to the QP objective function :math:`||Ax - b||^2`, by setting :math:`A = I`, :math:`x = w`, and :math:`b = k (w_{des} - w_t)`. The implementation of this class is inspired by [1] (which is licensed under the LGPLv2). References: - [1] "OpenSoT: A whole-body control library for the compliant humanoid robot COMAN", Rocchi et al., 2015 """ import numpy as np from pyrobolearn.priorities.tasks import ForceTask __author__ = "<NAME>" __copyright__ = "Copyright 2019, PyRoboLearn" __credits__ = ["<NAME> (C++)", "<NAME> (Python + doc)"] __license__ = "GNU GPLv3" __version__ = "1.0.0" __maintainer__ = "<NAME>" __email__ = "<EMAIL>" __status__ = "Development" class WrenchTask(ForceTask): # TODO: improve this class by considering only forces or torques + using links r"""Wrench Task The wrench task tries to generate a wrench near the desired one by minimizing: .. math:: || w - k (w_{des} - w_t) ||^2 where :math:`w = [f \tau] \in \mathbb{R}^6` is the wrench vector being optimized, :math:`k` is a proportional gain, :math:`w_{des}` is the desired wrench vector, and :math:`w_t` is the current wrench vector. The above formulation is equivalent to the QP objective function :math:`||Ax - b||^2`, by setting :math:`A = I`, :math:`x = w`, and :math:`b = k (w_{des} - w_t)`. The implementation of this class is inspired by [1] (which is licensed under the LGPLv2). References: - [1] "OpenSoT: A whole-body control library for the compliant humanoid robot COMAN", Rocchi et al., 2015 """ def __init__(self, model, desired_wrenches, wrenches, kp=1., weight=1., constraints=[]): """ Initialize the task. Args: model (ModelInterface): model interface. desired_wrenches (list[np.array[float[6]]]): list of desired wrenches. wrenches (list[np.array[float[6]]]): list of current wrenches that are usually read from F/T sensors. This has to be of the same size as the desired wrenches. weight (float, np.array[float[M*6,M*6]]): weight scalar or matrix associated to the task. constraints (list[Constraint]): list of constraints associated with the task. """ super(WrenchTask, self).__init__(model=model, weight=weight, constraints=constraints) # set variables self.desired_wrenches = desired_wrenches self.wrenches = wrenches self.kp = kp # first update self.update() ############## # Properties # ############## @property def desired_wrenches(self): """Get the desired wrenches.""" return self._desired_wrenches @desired_wrenches.setter def desired_wrenches(self, wrenches): """Set the desired wrenches.""" if not isinstance(wrenches, (list, tuple, np.ndarray)): raise TypeError("Expecting the given 'desired_wrenches' to be a tuple/list of np.array, or a np.array, " "but got instead: {}".format(type(wrenches))) self._desired_wrenches = np.asarray(wrenches).reshape(-1) # (N*6,) or (N*3,) # enable / disable the tasks based on the number of contact links if len(self._desired_wrenches) == 0: self.disable() else: self.enable() # set A matrix self._A = np.identity(len(self._desired_wrenches)) @property def wrenches(self): """Get the current wrenches.""" return self._wrenches @wrenches.setter def wrenches(self, wrenches): """Set the current wrenches.""" if not isinstance(wrenches, (list, tuple, np.ndarray)): raise TypeError("Expecting the given 'desired_wrenches' to be a tuple/list of np.array, or a np.array, " "but got instead: {}".format(type(wrenches))) self._wrenches = np.asarray(wrenches).reshape(-1) # (N*6,) or (N*3,) @property def kp(self): """Return the proportional gain.""" return self._kp @kp.setter def kp(self, kp): """Set the proportional gain.""" if kp is None: kp = 1. if not isinstance(kp, (float, int, np.ndarray)): raise TypeError("Expecting the given proportional gain kp to be an int, float, np.array, instead " "got: {}".format(type(kp))) x_size = len(self.desired_wrenches) if isinstance(kp, np.ndarray) and kp.shape != (x_size, x_size): raise ValueError("Expecting the given proportional gain matrix kp to be of shape {}, but instead " "got shape: {}".format((x_size, x_size), kp.shape)) self._kp = kp ########### # Methods # ########### def _update(self, x=None): """ Update the task by computing the A matrix and b vector that will be used by the task solver. """ self._b = self._kp * (self._desired_wrenches - self._wrenches)
[ "numpy.asarray" ]
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import numpy as np def trans(): print("Transpose of matrix:") #Enter rows and columns row=int(input("Enter number of rows:")) column=int(input("Enter number of columns:")) print("Enter the elements of Matrix:") matrix_a= [[int(input()) for i in range(column)] for i in range(row)] print("Matrix is: ") for n in matrix_a: print(n) matrix_a=np.array(matrix_a) print("Transpose of matrix is:") tran=matrix_a.transpose() print(tran)
[ "numpy.array" ]
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import numpy as np import matplotlib.pyplot as plt x=np.linspace(0,2*np.pi,101) s=np.sin(x) c=np.cos(x) plt.close('all') plt.subplot(2,2,1) plt.plot(x,s,'b+',x,c,'r+') plt.axis('tight') plt.subplot(2,2,2) plt.plot(x,s) plt.grid() plt.xlabel('radians') plt.ylabel('amplitudes') plt.title('sin(x)') plt.axis('tight') plt.savefig('myplot.png')
[ "matplotlib.pyplot.grid", "matplotlib.pyplot.title", "matplotlib.pyplot.savefig", "matplotlib.pyplot.ylabel", "matplotlib.pyplot.xlabel", "matplotlib.pyplot.plot", "matplotlib.pyplot.close", "numpy.linspace", "numpy.cos", "numpy.sin", "matplotlib.pyplot.axis", "matplotlib.pyplot.subplot" ]
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import torch import torchvision import torchvision.transforms as transforms import torch.nn.functional as F from torch import nn import os import matplotlib.pyplot as plt import sys import numpy as np sys.path.append('./') from args import opt sys.path.append('./lib/') from dataset import FruitFlyNeuronDataset from cv2transforms import composed_transforms, RandomExtract from utils import show_img, show_patches, initialize_weights patch_h = int(opt.patch_height) patch_w = int(opt.patch_width) N_patches = int(opt.N_subimgs) root_dir = opt.root_dir training_dir = {_: os.path.join(root_dir, _) for _ in ['images', '1st_manual', 'mask']} compose = composed_transforms() # imgs_dataset = FruitFlyNeuronDataset(root_dir=training_dir, transforms=compose) # comp_imgs_dataset = FruitFlyNeuronDataset(root_dir=training_dir) # for i in range(len(imgs_dataset)): # show_img(imgs_dataset[i]) # show_img(comp_imgs_dataset[i]) # if i == 3: # plt.show() # break training_dataset = FruitFlyNeuronDataset( root_dir=training_dir, transforms=compose) full_imgs = np.empty((20, 584, 565)) full_masks = np.empty((20, 584, 565)) for i in range(len(training_dataset)): full_imgs[i] = training_dataset[i]['images'] full_masks[i] = training_dataset[i]['mask'] full_imgs = np.reshape(full_imgs, (20, 584, 565, 1)).transpose((0, 3, 1, 2)) full_masks = np.reshape(full_masks, (20, 584, 565, 1)).transpose((0, 3, 1, 2)) rx = RandomExtract(patch_h=patch_h, patch_w=patch_w, N_patches=N_patches) patches, patches_masks = rx(full_imgs=full_imgs, full_masks=full_masks) show_patches(patches, patches_masks) class _EncoderBlock(nn.Module): def __init__(self, in_channels, out_channels, dropout=False): super(_EncoderBlock, self).__init__() layers = [ nn.Conv2d(in_channels, out_channels, kernel_size=3), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), nn.Conv2d(out_channels, out_channels, kernel_size=3), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), ] if dropout: layers.append(nn.Dropout()) layers.append(nn.MaxPool2d(kernel_size=2, stride=2)) self.encode = nn.Sequential(*layers) def forward(self, x): return self.encode(x) class _DecoderBlock(nn.Module): def __init__(self, in_channels, middle_channels, out_channels): super(_DecoderBlock, self).__init__() self.decode = nn.Sequential( nn.Conv2d(in_channels, middle_channels, kernel_size=3), nn.BatchNorm2d(middle_channels), nn.ReLU(inplace=True), nn.Conv2d(middle_channels, middle_channels, kernel_size=3), nn.BatchNorm2d(middle_channels), nn.ReLU(inplace=True), nn.ConvTranspose2d(middle_channels, out_channels, kernel_size=2, stride=2), ) def forward(self, x): return self.decode(x) # U-Net model class UNet(nn.Module): def __init__(self, num_classes): super(UNet, self).__init__() self.enc1 = _EncoderBlock(3, 64) self.enc2 = _EncoderBlock(64, 128) self.enc3 = _EncoderBlock(128, 256) self.enc4 = _EncoderBlock(256, 512, dropout=True) self.center = _DecoderBlock(512, 1024, 512) self.dec4 = _DecoderBlock(1024, 512, 256) self.dec3 = _DecoderBlock(512, 256, 128) self.dec2 = _DecoderBlock(256, 128, 64) self.dec1 = nn.Sequential( nn.Conv2d(128, 64, kernel_size=3), nn.BatchNorm2d(64), nn.ReLU(inplace=True), nn.Conv2d(64, 64, kernel_size=3), nn.BatchNorm2d(64), nn.ReLU(inplace=True), ) self.final = nn.Conv2d(64, num_classes, kernel_size=1) initialize_weights(self) def forward(self, x): enc1 = self.enc1(x) enc2 = self.enc2(enc1) enc3 = self.enc3(enc2) enc4 = self.enc4(enc3) center = self.center(enc4) dec4 = self.dec4( torch.cat([center, F.upsample(enc4, center.size()[2:], mode='bilinear')], 1)) dec3 = self.dec3( torch.cat([dec4, F.upsample(enc3, dec4.size()[2:], mode='bilinear')], 1)) dec2 = self.dec2( torch.cat([dec3, F.upsample(enc2, dec3.size()[2:], mode='bilinear')], 1)) dec1 = self.dec1( torch.cat([dec2, F.upsample(enc1, dec2.size()[2:], mode='bilinear')], 1)) final = self.final(dec1) return F.upsample(final, x.size()[2:], mode='bilinear') net = UNet(10) print(net)
[ "torch.nn.ConvTranspose2d", "torch.nn.BatchNorm2d", "torch.nn.ReLU", "cv2transforms.composed_transforms", "numpy.reshape", "torch.nn.Dropout", "torch.nn.Sequential", "utils.initialize_weights", "utils.show_patches", "os.path.join", "torch.nn.Conv2d", "dataset.FruitFlyNeuronDataset", "torch.n...
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import pdb import copy import numpy as np import os import scipy import math import torch import torch from pytorch3d.transforms import euler_angles_to_matrix, matrix_to_euler_angles def read_total_poses(cam_file_path): with open(cam_file_path) as f: lines = f.readlines() index_list = np.array(list(range(len(lines)))) index_poses = np.where((index_list % 5) == 0) index = index_list[index_poses] total_poses = [] for i in index: pose = np.empty([4, 4]).astype(np.float32) pose[0, :] = np.array(lines[i + 1].rstrip().split(' ')[:4], dtype=np.float32) pose[1, :] = np.array(lines[i + 2].rstrip().split(' ')[:4], dtype=np.float32) pose[2, :] = np.array(lines[i + 3].rstrip().split(' ')[:4], dtype=np.float32) pose[3, :] = np.array(lines[i + 4].rstrip().split(' ')[:4], dtype=np.float32) pose_new = pose[:3, :4] # pose_new = np.linalg.inv(pose) # pose_new = np.matmul(trans_mat_inv,pose_new)[:3,:4] total_poses.append(pose_new) return total_poses def readCameraRTK_as_np_tanks(cameraPO_file, datasetName): with open(cameraPO_file) as f: lines = f.readlines() cameraRTO = np.empty((3, 4)).astype(np.float64) cameraRTO[0, :] = np.array(lines[1].rstrip().split(' ')[:4], dtype=np.float64) cameraRTO[1, :] = np.array(lines[2].rstrip().split(' ')[:4], dtype=np.float64) cameraRTO[2, :] = np.array(lines[3].rstrip().split(' ')[:4], dtype=np.float64) cameraKO = np.empty((3, 3)).astype(np.float64) cameraKO[0, :] = np.array(lines[7].rstrip().split(' ')[:3], dtype=np.float64) cameraKO[1, :] = np.array(lines[8].rstrip().split(' ')[:3], dtype=np.float64) cameraKO[2, :] = np.array(lines[9].rstrip().split(' ')[:3], dtype=np.float64) if datasetName == 'DTU': cameraPO = np.dot(cameraKO, cameraRTO) elif datasetName == 'tanks_COLMAP': cameraPO = np.dot(cameraKO, cameraRTO) elif datasetName == 'blendedMVS': cameraPO = np.dot(cameraKO, cameraRTO) elif datasetName == 'giga_ours': cameraPO = np.dot(cameraKO, cameraRTO) return cameraRTO, cameraKO def readCameraP0_as_np_tanks(cameraPO_file, datasetName, ): with open(cameraPO_file) as f: lines = f.readlines() cameraRTO = np.empty((3, 4)).astype(np.float64) cameraRTO[0, :] = np.array(lines[1].rstrip().split(' ')[:4], dtype=np.float64) cameraRTO[1, :] = np.array(lines[2].rstrip().split(' ')[:4], dtype=np.float64) cameraRTO[2, :] = np.array(lines[3].rstrip().split(' ')[:4], dtype=np.float64) cameraKO = np.empty((3, 3)).astype(np.float64) cameraKO[0, :] = np.array(lines[7].rstrip().split(' ')[:3], dtype=np.float64) cameraKO[1, :] = np.array(lines[8].rstrip().split(' ')[:3], dtype=np.float64) cameraKO[2, :] = np.array(lines[9].rstrip().split(' ')[:3], dtype=np.float64) if datasetName == 'DTU': cameraPO = np.dot(cameraKO, cameraRTO) elif datasetName == 'tanks_COLMAP': cameraPO = np.dot(cameraKO, cameraRTO) elif datasetName == 'blendedMVS': cameraPO = np.dot(cameraKO, cameraRTO) elif datasetName == 'giga_ours': cameraPO = np.dot(cameraKO, cameraRTO) return cameraPO def __readCameraPO_as_np_DTU__(cameraPO_file): """ only load a camera PO in the file ------------ inputs: cameraPO_file: the camera pose file of a specific view outputs: cameraPO: np.float64 (3,4) ------------ usage: >>> p = __readCameraPO_as_np_DTU__(cameraPO_file = './test/cameraPO/pos_060.txt') >>> p # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE array([[ 1.67373847e+03, -2.15171320e+03, 1.26963515e+03, ... 6.58552305e+02]]) """ cameraPO = np.loadtxt(cameraPO_file, dtype=np.float64, delimiter=' ') return cameraPO def __readCameraPOs_as_np_Middlebury__(cameraPO_file, viewList): """ load camera POs of multiple views in one file ------------ inputs: cameraPO_file: the camera pose file of a specific view viewList: view list outputs: cameraPO: np.float64 (N_views,3,4) ------------ usage: >>> p = __readCameraPOs_as_np_Middlebury__(cameraPO_file = './test/cameraPO/dinoSR_par.txt', viewList=[3,8]) >>> p # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE array([[[ -1.22933223e+03, 3.08329199e+03, 2.02784015e+02, ... 6.41227584e-01]]]) """ with open(cameraPO_file) as f: lines = f.readlines() cameraPOs = np.empty((len(lines), 3, 4)).astype(np.float64) for _n, _l in enumerate(lines): if _n == 0: continue _params = np.array(_l.strip().split(' ')[1:], dtype=np.float64) _K = _params[:9].reshape((3, 3)) _R = _params[9:18].reshape((3, 3)) _t = _params[18:].reshape((3, 1)) cameraPOs[_n] = np.dot(_K, np.c_[_R, _t]) return cameraPOs[viewList] def readCameraPOs_as_np( datasetFolder, datasetName, poseNamePattern, # model, viewList, model=None, ): """ inputs: datasetFolder: 'x/x/x/middlebury' datasetName: 'DTU' / 'Middlebury' #model: 1..128 / 'dinoxx' viewList: [3,8,21,...] output: cameraPOs (N_views,3,4) np.flost64 """ cameraPOs = np.empty((len(viewList), 3, 4), dtype=np.float64) cameraRTOs = np.empty((len(viewList), 3, 4), dtype=np.float64) cameraKOs = np.empty((len(viewList), 3, 3), dtype=np.float64) if 'Middlebury' in datasetName: cameraPOs = self.__readCameraPOs_as_np_Middlebury__( cameraPO_file=os.path.join(datasetFolder, poseNamePattern), viewList=viewList) elif datasetName == 'tanks': for _i, _view in enumerate(viewList): _cameraPO = readCameraP0_as_np_tanks(cameraPO_file=os.path.join(datasetFolder, poseNamePattern.replace('#', '{:03}'.format( _view)).replace( '@', '{}'.format(_view)))) # _cameraPO = readCameraP0_as_np_tanks(cameraPO_file = datasetFolder+poseNamePattern.replace('#', '{:03}'.format(_view)).replace('@', '{}'.format(_view))) cameraPOs[_i] = _cameraPO _cameraRT, _cameraK = readCameraRTK_as_np_tanks(cameraPO_file=os.path.join(datasetFolder, poseNamePattern.replace('#', '{:03}'.format( _view)).replace( '@', '{}'.format(_view)))) cameraRTOs[_i] = _cameraRT cameraKOs[_i] = _cameraK elif datasetName == 'tanks_COLMAP': # zhiwei for _i, _view in enumerate(viewList): _cameraPO = readCameraP0_as_np_tanks(cameraPO_file=os.path.join(datasetFolder, poseNamePattern.replace('#', '{:03}'.format( _view))), datasetName=datasetName) # _cameraPO = readCameraP0_as_np_tanks(cameraPO_file = datasetFolder+poseNamePattern.replace('#', '{:03}'.format(_view)).replace('@', '{}'.format(_view))) cameraPOs[_i] = _cameraPO _cameraRT, _cameraK = readCameraRTK_as_np_tanks(cameraPO_file=os.path.join(datasetFolder, poseNamePattern.replace('#', '{:03}'.format( _view))), datasetName=datasetName) cameraRTOs[_i] = _cameraRT cameraKOs[_i] = _cameraK elif datasetName == 'blendedMVS': # zhiwei for _i, _view in enumerate(viewList): _cameraPO = readCameraP0_as_np_tanks(cameraPO_file=os.path.join(datasetFolder, poseNamePattern.replace('#', '{:03}'.format( _view))), datasetName=datasetName) # _cameraPO = readCameraP0_as_np_tanks(cameraPO_file = datasetFolder+poseNamePattern.replace('#', '{:03}'.format(_view)).replace('@', '{}'.format(_view))) cameraPOs[_i] = _cameraPO _cameraRT, _cameraK = readCameraRTK_as_np_tanks(cameraPO_file=os.path.join(datasetFolder, poseNamePattern.replace('#', '{:03}'.format( _view))), datasetName=datasetName) cameraRTOs[_i] = _cameraRT cameraKOs[_i] = _cameraK elif datasetName == 'giga_ours': # zhiwei for _i, _view in enumerate(viewList): _cameraPO = readCameraP0_as_np_tanks(cameraPO_file=os.path.join(datasetFolder, poseNamePattern.replace('#', '{:03}'.format( _view))), datasetName=datasetName) # _cameraPO = readCameraP0_as_np_tanks(cameraPO_file = datasetFolder+poseNamePattern.replace('#', '{:03}'.format(_view)).replace('@', '{}'.format(_view))) cameraPOs[_i] = _cameraPO _cameraRT, _cameraK = readCameraRTK_as_np_tanks(cameraPO_file=os.path.join(datasetFolder, poseNamePattern.replace('#', '{:03}'.format( _view))), datasetName=datasetName) cameraRTOs[_i] = _cameraRT cameraKOs[_i] = _cameraK else: # cameraPOs are stored in different files # tran_mat_path = os.path.join(datasetFolder,transMatPattern) for _i, _view in enumerate(viewList): # if 'DTU' in datasetName: # _cameraPO = __readCameraPO_as_np_DTU__(cameraPO_file=os.path.join(datasetFolder, # poseNamePattern.replace('#', # '{:03}'.format( # _view)).replace( # '@', '{}'.format(_view)))) _cameraPO = readCameraP0_as_np_tanks(cameraPO_file=os.path.join(datasetFolder, poseNamePattern.replace('#', '{:03}'.format( _view - 1)).replace( '@', '{}'.format(_view - 1))), datasetName=datasetName) cameraPOs[_i] = _cameraPO _cameraRT, _cameraK = readCameraRTK_as_np_tanks(cameraPO_file=os.path.join(datasetFolder, poseNamePattern.replace('#', '{:03}'.format( _view - 1)).replace( '@', '{}'.format(_view - 1))), datasetName=datasetName) cameraRTOs[_i] = _cameraRT cameraKOs[_i] = _cameraK # print('cameraPOs', cameraPOs) return cameraPOs, cameraRTOs, cameraKOs def readCameraP0s_np_allModel(datasetFolder, datasetName, poseNamePatternModels, modelList, viewList, transMatPattern=None ): cameraPs = [] cameraP4s = [] cameraRTs = [] cameraKs = [] for i in modelList: if datasetName == 'tanks': ##########TODO################### cameraPOs, cameraRTOs, cameraKOs = readCameraPOs_as_np(datasetFolder, datasetName, poseNamePattern, viewList, ) ones = np.repeat(np.array([[[0, 0, 0, 1]]]), repeats=cameraPOs.shape[0], axis=0) cameraPOs, cameraRTOs, cameraKOs = np.concatenate((cameraPOs, ones), axis=1) elif datasetName == 'DTU': cameraPOs, cameraRTOs, cameraKOs = readCameraPOs_as_np(datasetFolder, datasetName, poseNamePatternModels, viewList, ) ones = np.repeat(np.array([[[0, 0, 0, 1]]]), repeats=cameraPOs.shape[0], axis=0) cameraP0s = np.concatenate((cameraPOs, ones), axis=1) elif datasetName == 'tanks_COLMAP': # zhiwei cameraPOs, cameraRTOs, cameraKOs = readCameraPOs_as_np(datasetFolder, datasetName, poseNamePatternModels.replace('$', str(i)), viewList, ) ones = np.repeat(np.array([[[0, 0, 0, 1]]]), repeats=cameraPOs.shape[0], axis=0) cameraP0s = np.concatenate((cameraPOs, ones), axis=1) elif datasetName == 'blendedMVS': # zhiwei cameraPOs, cameraRTOs, cameraKOs = readCameraPOs_as_np(datasetFolder, datasetName, poseNamePatternModels.replace('$', str(i)), viewList, ) ones = np.repeat(np.array([[[0, 0, 0, 1]]]), repeats=cameraPOs.shape[0], axis=0) cameraP0s = np.concatenate((cameraPOs, ones), axis=1) elif datasetName == 'giga_ours': # zhiwei cameraPOs, cameraRTOs, cameraKOs = readCameraPOs_as_np(datasetFolder, datasetName, poseNamePatternModels.replace('$', str(i)), viewList, ) ones = np.repeat(np.array([[[0, 0, 0, 1]]]), repeats=cameraPOs.shape[0], axis=0) cameraP0s = np.concatenate((cameraPOs, ones), axis=1) cameraPs.append(cameraPOs) cameraP4s.append(cameraP0s) cameraRTs.append(cameraRTOs) cameraKs.append(cameraKOs) return (cameraPs, np.array(cameraP4s), np.array(cameraRTs), np.array(cameraKs)) def __cameraP2T__(cameraPO): """ cameraPO: (3,4) return camera center in the world coords: cameraT (3,0) >>> P = np.array([[798.693916, -2438.153488, 1568.674338, -542599.034996], \ [-44.838945, 1433.912029, 2576.399630, -1176685.647358], \ [-0.840873, -0.344537, 0.417405, 382.793511]]) >>> t = np.array([555.64348632032, 191.10837560939, 360.02470478273]) >>> np.allclose(__cameraP2T__(P), t) True """ homo4D = np.array([np.linalg.det(cameraPO[:, [1, 2, 3]]), -1 * np.linalg.det(cameraPO[:, [0, 2, 3]]), np.linalg.det(cameraPO[:, [0, 1, 3]]), -1 * np.linalg.det(cameraPO[:, [0, 1, 2]])]) # print('homo4D', homo4D) cameraT = homo4D[:3] / homo4D[3] return cameraT def cameraPs2Ts_all(cameraPOs_all): """ """ model_num = len(cameraPOs_all) # pdb.set_trace() cameraT_all = np.zeros((model_num, cameraPOs_all[0].shape[0], 3)) for i in range(model_num): cameraT_all[i] = cameraPs2Ts(cameraPOs_all[i]) return cameraT_all def cameraPs2Ts(cameraPOs): """ convert multiple POs to Ts. ---------- input: cameraPOs: list / numpy output: cameraTs: list / numpy """ if type(cameraPOs) is list: N = len(cameraPOs) else: N = cameraPOs.shape[0] cameraT_list = [] for _cameraPO in cameraPOs: cameraT_list.append(__cameraP2T__(_cameraPO)) return cameraT_list if type(cameraPOs) is list else np.stack(cameraT_list) def inverse_camera_matrix(cameraP0s): N_Ms = cameraP0s.shape[0] projection_new = np.zeros((N_Ms, 4, 4)) projection_new[:, 0:3, :] = cameraP0s projection_new[:, 3, :] = np.array(([[0, 0, 0, 1]])) projection_new = np.linalg.inv(projection_new) return projection_new def calculate_angle_p1_p2_p3(p1, p2, p3, return_angle=True, return_cosine=True): """ calculate angle <p1,p2,p3>, which is the angle between the vectors p2p1 and p2p3 Parameters ---------- p1/p2/p3: numpy with shape (3,) return_angle: return the radian angle return_cosine: return the cosine value Returns ------- angle, cosine Examples -------- """ unit_vector = lambda v: v / np.linalg.norm(v) angle = lambda v1, v2: np.arccos(np.clip(np.dot(unit_vector(v1), unit_vector(v2)), -1.0, 1.0)) cos_angle = lambda v1, v2: np.clip(np.dot(unit_vector(v1), unit_vector(v2)), -1.0, 1.0) vect_p2p1 = p1 - p2 vect_p2p3 = p3 - p2 return angle(vect_p2p1, vect_p2p3) if return_angle else None, \ cos_angle(vect_p2p1, vect_p2p3) if return_cosine else None def k_combination_np(iterable, k=2): """ list all the k-combination along the output rows: input: [2,5,8], list 2-combination to a numpy array output: np.array([[2,5],[2,8],[5,8]]) ---------- usages: >>> k_combination_np([2,5,8]) array([[2, 5], [2, 8], [5, 8]]) >>> k_combination_np([2,5,8]).dtype dtype('int64') >>> k_combination_np([2.2,5.5,8.8,9.9], k=3) array([[ 2.2, 5.5, 8.8], [ 2.2, 5.5, 9.9], [ 2.2, 8.8, 9.9], [ 5.5, 8.8, 9.9]]) """ combinations = [] for _combination in itertools.combinations(iterable, k): combinations.append(_combination) return np.asarray(combinations) def viewPairAngles_wrt_pts(cameraTs, pts_xyz): """ given a set of camera positions and a set of points coordinates, output the angle between camera pairs w.r.t. each 3D point. ----------- inputs: cameraTs: (N_views, 3) camera positions pts_xyz: (N_pts, 3) 3D points' coordinates ----------- outputs: viewPairAngle_wrt_pts: (N_pts, N_viewPairs) angle ----------- usages: >>> pts_xyz = np.array([[0,0,0],[1,1,1]], dtype=np.float32) # p1 / p2 >>> cameraTs = np.array([[0,0,1], [0,1,1], [1,0,1]], dtype=np.float32) # c1/2/3 >>> viewPairAngles_wrt_pts(cameraTs, pts_xyz) * 180 / math.pi # output[i]: [<c1,pi,c2>, <c1,pi,c3>, <c2,pi,c3>] array([[ 45., 45., 60.], [ 45., 45., 90.]], dtype=float32) """ unitize_array = lambda array, axis: array / np.linalg.norm(array, axis=axis, ord=2, keepdims=True) calc_arccos = lambda cos_values: np.arccos(np.clip(cos_values, -1.0, 1.0)) # TODO does it need clip ? N_views = cameraTs.shape[0] vector_pts2cameras = pts_xyz[:, None, :] - cameraTs[ None, ...] # (N_pts, 1, 3) - (1, N_views, 3) ==> (N_pts, N_views, 3) unit_vector_pts2cameras = unitize_array(vector_pts2cameras, axis=-1) # (N_pts, N_views, 3) unit vector along axis=-1 # do the matrix multiplication for the (N_pats,) tack of (N_views, 3) matrixs ## (N_pts, N_views, 3) * (N_pts, 3, N_views) ==> (N_pts, N_views, N_views) # viewPairCosine_wrt_pts = np.matmul(unit_vector_pts2cameras, unit_vector_pts2cameras.transpose((0,2,1))) viewPairs = self.k_combination_np(range(N_views), k=2) # (N_combinations, 2) viewPairCosine_wrt_pts = np.sum( np.multiply(unit_vector_pts2cameras[:, viewPairs[:, 0]], unit_vector_pts2cameras[:, viewPairs[:, 1]]), axis=-1) # (N_pts, N_combinations, 3) elementwise multiplication --> (N_pts, N_combinations) sum over the last axis viewPairAngle_wrt_pts = calc_arccos(viewPairCosine_wrt_pts) # (N_pts, N_combinations) return viewPairAngle_wrt_pts # def viewPairAngles_p0s_pts(self, projection_M, ) def viewPairAngles_wrt_groupView(cameraTs, group_cameraTs, xyz_3D): ''' :param cameraTs: shape: (N_views,3) :param group_cameraTs: shape:(N_bool_views,3) :param xyz_3D: shape:(3) :return: angle_total: the angle of group T and camera T shape: (N_bool_views, N_views) ''' cameraTs_array = (cameraTs - xyz_3D)[None, :, :, None] # (N_views,3)->(1,N_views, 3,1) group_cameraTs_array = (group_cameraTs - xyz_3D)[:, None, None, :] # (N_bool_views,3)->(N_bool_views,1,3,1) dot_two = np.matmul(group_cameraTs_array, cameraTs_array)[:, :, 0, 0] # (N_bool_views, N_views) len_cameraTs = np.linalg.norm(cameraTs - xyz_3D, axis=1)[None, :] # (1, N_views) len_group_cameraTs = np.linalg.norm(group_cameraTs - xyz_3D, axis=1)[:, None] # (N_bool_views, 1) len_total = len_cameraTs * len_group_cameraTs # (N_bool_views, N_views) cos_total = dot_two / (len_total + 1e-10) # (N_bool_views, N_views) angle_total = np.arccos(np.clip(cos_total, -1.0, 1.0)) return (angle_total) def select_group_pairs(projection_M, cameraTs, group_cameraTs, xyz_3D, cube_length, image_shape, angle_thres, group_pair_num_max, group_pair_num_min, group_pair_index): ''' given group view number, select groupviews :param projection_M: the shape:(N_views, 3,4) :param cameraTs: shape:(N_views, 3) :param group_cameraTs: shape:(N_boole_views, 3) :param xyz_3D: shape:(3) :param cube_length: float: the length of the cube :param image_shape: (img_h, img_w) :param angle_thres: float ses params.in_group_angle :param group_pair_num_max/min: int see params.group_pair_num_max/min :param group_pair_index list of int pair: see params.group_pair_index :return: view_pair_list: list of view_pair index element in list: (group_left, group_right, (group_id_left, group_id_right)) group_left/right: numpy 1d array of view pair number e.g. [(array([ 6, 16, 4, 2, 6]), array([33, 24, 16, 14, 24]), (0, 2)), (array([ 3, 15, 20, 4, 33]), array([ 7, 36, 5, 19, 4]), (1, 3)), (array([33, 24, 16, 14, 24]), array([24, 15, 22, 34, 15]), (2, 4)), (array([ 7, 36, 5, 19, 4]), array([24, 43, 34, 42, 14]), (3, 5)), (array([24, 15, 22, 34, 15]), array([42, 34, 38, 18, 37]), (4, 6)), (array([24, 43, 34, 42, 14]), array([43, 42, 33, 15, 35]), (5, 7))] ''' view_in_flag = judge_cubic_center_in_view(projection_M, xyz_3D, cube_length, image_shape, ) angle_total = viewPairAngles_wrt_groupView(cameraTs, group_cameraTs, xyz_3D) group_pair_flag = view_in_flag[None, :] * (angle_total < angle_thres) # print('group_pair_flag', group_pair_flag.shape) view_list = np.repeat((np.arange(group_pair_flag.shape[1]))[None, :], axis=0, repeats=group_pair_flag.shape[0]) # print(group_pair_flag) view_num_list = [] for i in range(group_pair_flag.shape[0]): view_num_i = view_list[i, group_pair_flag[i, :]] if (view_num_i.shape[0] >= group_pair_num_max): view_num_i = np.random.choice(view_num_i, group_pair_num_max, replace=False) view_num_list.append(view_num_i) view_pair_list = [] for (i, j) in (group_pair_index): if ((view_num_list[i].shape[0] >= group_pair_num_min) and (view_num_list[j].shape[0] >= group_pair_num_min)): view_pair_list.append((view_num_list[i], view_num_list[j], (i, j))) # print('view_pair_list',view_pair_list) return view_pair_list def select_group(projection_M, cameraTs, group_cameraTs, xyz_3D, cube_length, image_shape, angle_thres, group_pair_num_max, group_pair_num_min): ''' given group view number, select groupviews :param projection_M: the shape:(N_views, 3,4) :param cameraTs: shape:(N_views, 3) :param group_cameraTs: shape:(N_boole_views, 3) :param xyz_3D: shape:(3) :param cube_length: float: the length of the cube :param image_shape: (img_h, img_w) :param angle_thres: float ses params.in_group_angle :param group_pair_num_max/min: int see params.group_pair_num_max/min :return: view_list: list of view index element in list: group: numpy 1d array of view number ''' # view_in_flag = judge_cubic_center_in_view(projection_M , # xyz_3D , # cube_length, # image_shape, # ) view_in_flag = np.ones((projection_M.shape[0]), dtype=np.bool) angle_total = viewPairAngles_wrt_groupView(cameraTs, group_cameraTs, xyz_3D) group_pair_flag = view_in_flag[None, :] * (angle_total < angle_thres) # print('group_pair_flag', group_pair_flag.shape) view_list = np.repeat((np.arange(group_pair_flag.shape[1]))[None, :], axis=0, repeats=group_pair_flag.shape[0]) # print(group_pair_flag) view_num_list = [] for i in range(group_pair_flag.shape[0]): view_num_i = view_list[i, group_pair_flag[i, :]] if (view_num_i.shape[0] >= group_pair_num_max): view_num_i = np.sort(np.random.choice(view_num_i, group_pair_num_max, replace=False), axis=0) # view_num_i = (np.random.choice(view_num_i, group_pair_num_max, replace = False)) # pdb.set_trace() if (view_num_i.shape[0] >= group_pair_num_min): view_num_list.append(view_num_i) return view_num_list def perspectiveProj( projection_M, xyz_3D, return_int_hw=True, return_depth=False): """ perform perspective projection from 3D points to 2D points given projection matrix(es) support multiple projection_matrixes and multiple 3D vectors notice: [matlabx,matlaby] = [width, height] ---------- inputs: projection_M: numpy with shape (3,4) / (N_Ms, 3,4), during calculation (3,4) will --> (1,3,4) xyz_3D: numpy with shape (3,) / (N_pts, 3), during calculation (3,) will --> (1,3) return_int_hw: bool, round results to integer when True. ---------- outputs: img_h, img_w: (N_pts,) / (N_Ms, N_pts) ---------- usages: inputs: (N_Ms, 3,4) & (N_pts, 3), return_int_hw = False/True >>> np.random.seed(201611) >>> Ms = np.random.rand(2,3,4) >>> pts_3D = np.random.rand(2,3) >>> pts_2Dh, pts_2Dw = perspectiveProj(Ms, pts_3D, return_int_hw = False) >>> np.allclose(pts_2Dw, np.array([[ 1.35860185, 0.9878389 ], ... [ 0.64522543, 0.76079278 ]])) True >>> pts_2Dh_int, pts_2Dw_int = perspectiveProj(Ms, pts_3D, return_int_hw = True) >>> np.allclose(pts_2Dw_int, np.array([[1, 1], [1, 1]])) True inputs: (3,4) & (3,) >>> np.allclose( ... np.r_[perspectiveProj(Ms[1], pts_3D[0], return_int_hw = False)], ... np.stack((pts_2Dh, pts_2Dw))[:,1,0]) True """ if projection_M.shape[-2:] != (3, 4): raise ValueError( "perspectiveProj needs projection_M with shape (3,4), however got {}".format(projection_M.shape)) if xyz_3D.ndim == 1: xyz_3D = xyz_3D[None, :] if xyz_3D.shape[1] != 3 or xyz_3D.ndim != 2: raise ValueError( "perspectiveProj needs xyz_3D with shape (3,) or (N_pts, 3), however got {}".format(xyz_3D.shape)) # perspective projection N_pts = xyz_3D.shape[0] xyz1 = np.c_[xyz_3D, np.ones((N_pts, 1))].astype(np.float64) # (N_pts, 3) ==> (N_pts, 4) pts_3D = np.matmul(projection_M, xyz1.T) # (3, 4)/(N_Ms, 3, 4) * (4, N_pts) ==> (3, N_pts)/(N_Ms,3,N_pts) # the result is vector: [w,h,1], w is the first dim!!! (matlab's x/y/1') pts_2D = pts_3D[..., :2, :] # self.pts_3D = pts_3D pts_2D /= pts_3D[..., 2:3, :] # (2, N_pts) /= (1, N_pts) | (N_Ms, 2, N_pts) /= (N_Ms, 1, N_pts) # self.pts_2D = pts_2D # print(self.pts_2D) if return_int_hw: pts_2D = pts_2D.round().astype(np.int64) # (2, N_pts) / (N_Ms, 2, N_pts) img_w, img_h = pts_2D[..., 0, :], pts_2D[..., 1, :] # (N_pts,) / (N_Ms, N_pts) if return_depth: depth = pts_3D[..., 2, :] return img_h, img_w, depth return img_h, img_w def perspectiveProj_cubesCorner(projection_M, cube_xyz_min, cube_D_mm, return_int_hw=True, return_depth=False): """ perform perspective projection from 3D points to 2D points given projection matrix(es) support multiple projection_matrixes and multiple 3D vectors notice: [matlabx,matlaby] = [width, height] ---------- inputs: projection_M: numpy with shape (3,4) / (N_Ms, 3,4), during calculation (3,4) will --> (1,3,4) cube_xyz_min: numpy with shape (3,) / (N_pts, 3), during calculation (3,) will --> (1,3) cube_D_mm: cube with shape D^3 return_int_hw: bool, round results to integer when True. return_depth: bool ---------- outputs: img_h, img_w: (N_Ms, N_pts, 8) ---------- usages: inputs: (N_Ms, 3, 4) & (N_pts, 3), return_int_hw = False/True, outputs (N_Ms, N_pts, 8) >>> np.random.seed(201611) >>> Ms = np.random.rand(2,3,4) >>> pts_3D = np.random.rand(2,3) >>> pts_2Dh, pts_2Dw = perspectiveProj_cubesCorner(Ms, pts_3D, cube_D_mm = 1, return_int_hw = False) >>> np.allclose(pts_2Dw[:,:,0], np.array([[ 1.35860185, 0.9878389 ], ... [ 0.64522543, 0.76079278 ]])) True >>> pts_2Dh_int, pts_2Dw_int = perspectiveProj_cubesCorner(Ms, pts_3D, cube_D_mm = 1, return_int_hw = True) >>> np.allclose(pts_2Dw_int[:,:,0], np.array([[1, 1], [1, 1]])) True inputs: (3,4) & (3,), outputs (1,1,8) >>> np.allclose( ... perspectiveProj_cubesCorner(Ms[1], pts_3D[0], cube_D_mm = 1, return_int_hw = False)[0], ... pts_2Dh[1,0]) # (1,1,8) True """ if projection_M.shape[-2:] != (3, 4): raise ValueError( "perspectiveProj needs projection_M with shape (3,4), however got {}".format(projection_M.shape)) if cube_xyz_min.ndim == 1: cube_xyz_min = cube_xyz_min[None, :] # (3,) --> (N_pts, 3) if cube_xyz_min.shape[1] != 3 or cube_xyz_min.ndim != 2: raise ValueError("perspectiveProj needs cube_xyz_min with shape (3,) or (N_pts, 3), however got {}".format( cube_xyz_min.shape)) N_pts = cube_xyz_min.shape[0] cubeCorner_shift = np.indices((2, 2, 2)).reshape((3, -1)).T[None, :, :] * cube_D_mm # (3,2,2,2) --> (1,8,3) cubeCorner = cube_xyz_min[:, None, :] + cubeCorner_shift # (N_pts, 1, 3) + (1,8,3) --> (N_pts, 8, 3) img_h, img_w = perspectiveProj(projection_M=projection_M, xyz_3D=cubeCorner.reshape((N_pts * 8, 3)), return_int_hw=return_int_hw, return_depth=return_depth) # img_w/h: (N_Ms, N_pts*8) img_w = img_w.reshape((-1, N_pts, 8)) img_h = img_h.reshape((-1, N_pts, 8)) return img_h, img_w def image_compress_coef(projection_M, cube_xyz_min, cube_D_mm, _cube_D_, image_compress_multiple, compress_ratio=1.0 ): img_h, img_w = perspectiveProj_cubesCorner(projection_M, cube_xyz_min, cube_D_mm, return_int_hw=True, return_depth=False) img_h_max = np.max(img_h, axis=2) # (N_Ms, N_pts) img_w_max = np.max(img_w, axis=2) img_h_min = np.min(img_h, axis=2) img_w_min = np.min(img_w, axis=2) img_h_resol = (img_h_max - img_h_min + 0.0) / _cube_D_ img_w_resol = (img_w_max - img_w_min + 0.0) / _cube_D_ compress_h = compress_ratio * img_h_resol.mean() / image_compress_multiple compress_w = compress_ratio * img_w_resol.mean() / image_compress_multiple return ((compress_h), (compress_w)) # def resize_matrix(projection_M, compress_h_new, compress_w_new): # transform_matrix = np.array([[[1 / compress_w_new, 0, 0], [0, 1 / compress_h_new, 0], [0, 0, 1]]]) # projection_M_new = np.matmul(transform_matrix, projection_M) # # cameraTs = cameraPs2Ts(projection_M) # cameraTs_new = cameraPs2Ts(projection_M_new) # trans_vector = (cameraTs - cameraTs_new)[:, :, None] # identical_matrix = np.repeat(np.array([[[1, 0, 0], [0, 1, 0], [0, 0, 1]]]), cameraTs.shape[0], axis=0) # bottom_matrix = np.repeat(np.array([[[0, 0, 0, 1]]]), cameraTs.shape[0], axis=0) # transform_matrix2 = np.concatenate((identical_matrix, trans_vector), axis=2) # transform_matrix2 = np.concatenate((transform_matrix2, bottom_matrix), axis=1) # projection_M_new_f = np.concatenate((projection_M_new, bottom_matrix), axis=1) # # projection_M_new = np.matmul(transform_matrix2, projection_M_new_f) # projection_M_new = projection_M_new[:, :3, :] # return projection_M_new def resize_image_and_matrix(images, projection_M, cube_xyz_min, cube_D_mm, _cube_D_, image_compress_multiple, return_list=False, compress_ratio=1.0): ''' compress image and garantee the camera position is not changing :param images: all images of one model type:list or None if list list element: image array shape: (img_h,img_w, 3) :param projection_M: camera matrix shape: (N_views, 3, 4) :param cube_xyz_min: min xyz coordinate shape: (3,) / (N_pts, 3) usually it is (3,) because we only sample one cubic to judge the resize term :param cube_D_mm: cubic length float :param _cube_D_: cubic size int :param image_compress_multiple: same as param.image_compress_multiple :param return_list: bool if False return the numpy array :param compress_ratio see self.params.compress_ratio :return: if image is not None images_resized:resized image shape:(N_view, img_h_new, img_w_new)resize_image_and_matrix projection_M_new: new cameraP shape:(N_view,3,4) (compress_h_new,compress_w_new):(float,float) elif image is None: only change the matrix projection_M_new: new cameraP shape:(N_view,3,4) (compress_h_new,compress_w_new):(float,float) ''' (compress_h, compress_w) = image_compress_coef(projection_M, cube_xyz_min, cube_D_mm, _cube_D_, image_compress_multiple, compress_ratio=compress_ratio) resized_h = int(image_compress_multiple * (images[0].shape[0] // (compress_h * image_compress_multiple))) resized_w = int(image_compress_multiple * (images[0].shape[1] // (compress_w * image_compress_multiple))) compress_h_new = images[0].shape[0] / (resized_h + 0.0) compress_w_new = images[0].shape[1] / (resized_w + 0.0) transform_matrix = np.array([[[1 / compress_w_new, 0, 0], [0, 1 / compress_h_new, 0], [0, 0, 1]]]) projection_M_new = np.matmul(transform_matrix, projection_M) cameraTs = cameraPs2Ts(projection_M) cameraTs_new = cameraPs2Ts(projection_M_new) trans_vector = (cameraTs - cameraTs_new)[:, :, None] identical_matrix = np.repeat(np.array([[[1, 0, 0], [0, 1, 0], [0, 0, 1]]]), cameraTs.shape[0], axis=0) bottom_matrix = np.repeat(np.array([[[0, 0, 0, 1]]]), cameraTs.shape[0], axis=0) transform_matrix2 = np.concatenate((identical_matrix, trans_vector), axis=2) transform_matrix2 = np.concatenate((transform_matrix2, bottom_matrix), axis=1) projection_M_new_f = np.concatenate((projection_M_new, bottom_matrix), axis=1) projection_M_new = np.matmul(transform_matrix2, projection_M_new_f) projection_M_new = projection_M_new[:, :3, :] image_resized_list = [] if (images is not None): for image in images: image_resized = scipy.misc.imresize(image, size=(resized_h, resized_w), interp='bicubic') image_resized = image_resized / 256.0 - 0.5 image_resized_list.append(image_resized) images_resized = image_resized_list if return_list else np.stack(image_resized_list) return (images_resized, projection_M_new, (compress_h_new, compress_w_new)) else: return (None, projection_M_new, (compress_h_new, compress_w_new)) # def resize_multistage_image_and_matrix(images, # projection_M, # cube_xyz_min, # cube_D_mm, # _cube_D_, # image_compress_multiple, # image_compress_stage, # return_list=False, # compress_ratio=1.0): # ''' # compress image and garantee the camera position is not changing # :param images: all images of one model # type:list or None # if list # list element: image array # shape: (img_h,img_w, 3) # # :param projection_M: camera matrix # shape: (N_views, 3, 4) # :param cube_xyz_min: min xyz coordinate # shape: (3,) / (N_pts, 3) usually it is (3,) because we only sample one cubic to judge the resize term # :param cube_D_mm: # cubic length float # :param _cube_D_: # cubic size int # :param image_compress_multiple: # same as param.image_compress_multiple # :param image_compress_stage # same as param.image_compress_stage # :param return_list: bool # if False return the numpy array # :param compress_ratio # see self.params.compress_ratio # :return: # if image is not None # image_resized_stage_list:multistage of resized image # length : = image_compress_stage # ele in each list: # shape:(N_view, img_h_new//2**iter, img_w_new//2**iter) # projection_M_new: new cameraP # shape:(N_view,3,4) # (compress_h_new,compress_w_new):(float,float) # elif image is None: only change the matrix # projection_M_new: new cameraP # shape:(N_view,3,4) # (compress_h_new,compress_w_new):(float,float) # ''' # # (compress_h, compress_w) = image_compress_coef(projection_M, # # cube_xyz_min, # # cube_D_mm, # # _cube_D_, # # 1, # # compress_ratio = compress_ratio) # # # print('compress_h', compress_h, compress_w) # compress_h = compress_ratio # compress_w = compress_ratio # resized_h = int(image_compress_multiple * (images[0].shape[0] // (compress_h * image_compress_multiple))) # resized_w = int(image_compress_multiple * (images[0].shape[1] // (compress_w * image_compress_multiple))) # # # pdb.set_trace() # compress_h_new = images[0].shape[0] / (resized_h + 0.0) # compress_w_new = images[0].shape[1] / (resized_w + 0.0) # transform_matrix = np.array([[[1 / compress_w_new, 0, 0], [0, 1 / compress_h_new, 0], [0, 0, 1]]]) # projection_M_new = np.matmul(transform_matrix, projection_M) # # cameraTs = cameraPs2Ts(projection_M) # cameraTs_new = cameraPs2Ts(projection_M_new) # trans_vector = (cameraTs - cameraTs_new)[:, :, None] # identical_matrix = np.repeat(np.array([[[1, 0, 0], [0, 1, 0], [0, 0, 1]]]), cameraTs.shape[0], axis=0) # bottom_matrix = np.repeat(np.array([[[0, 0, 0, 1]]]), cameraTs.shape[0], axis=0) # transform_matrix2 = np.concatenate((identical_matrix, trans_vector), axis=2) # transform_matrix2 = np.concatenate((transform_matrix2, bottom_matrix), axis=1) # projection_M_new_f = np.concatenate((projection_M_new, bottom_matrix), axis=1) # # projection_M_new = np.matmul(transform_matrix2, projection_M_new_f) # projection_M_new = projection_M_new[:, :3, :] # # if (images is not None): # image_resized_stage_list = [] # for iter in range(image_compress_stage): # image_resized_list = [] # for image in images: # # print('resized image shape',resized_h, resized_w) # image_resized = scipy.misc.imresize(image, # size=(int(resized_h // (2 ** iter)), int(resized_w // (2 ** iter))), # interp='bicubic') # image_resized = image_resized / 256.0 - 0.5 # image_resized_list.append(image_resized) # images_resized = image_resized_list if return_list else np.stack(image_resized_list) # image_resized_stage_list.append(images_resized) # return (image_resized_stage_list, projection_M_new, (compress_h_new, compress_w_new)) # else: # return (None, projection_M_new, (compress_h_new, compress_w_new)) def judge_cubic_center_in_view(projection_M, xyz_3D, cube_length, image_shape, ): ''' 'the bool flag of each view can see the center of cubic:' :param projection_M: shape:(N_views, 3, 4) :param xyz_3D: shape:(3) :param cube_length float :param image_shape: (img_h,img_w) :return: view_in_flag: bool array shape: (N_views) ''' img_h_new, img_w_new = perspectiveProj( projection_M=projection_M, xyz_3D=xyz_3D, ) img_h_100, img_w_100 = perspectiveProj( projection_M=projection_M, xyz_3D=xyz_3D + np.array((cube_length, 0, 0)), ) img_h_010, img_w_010 = perspectiveProj( projection_M=projection_M, xyz_3D=xyz_3D + np.array((0, cube_length, 0)), ) img_h_001, img_w_001 = perspectiveProj( projection_M=projection_M, xyz_3D=xyz_3D + np.array((0, 0, cube_length)), ) img_h_011, img_w_011 = perspectiveProj( projection_M=projection_M, xyz_3D=xyz_3D + np.array((0, cube_length, cube_length)), ) img_h_101, img_w_101 = perspectiveProj( projection_M=projection_M, xyz_3D=xyz_3D + np.array((cube_length, 0, cube_length)), ) img_h_110, img_w_110 = perspectiveProj( projection_M=projection_M, xyz_3D=xyz_3D + np.array((cube_length, cube_length, 0)), ) img_h_111, img_w_111 = perspectiveProj( projection_M=projection_M, xyz_3D=xyz_3D + cube_length, ) img_h_bool = (img_h_new < image_shape[0]) * (img_h_new > 0) img_w_bool = (img_w_new < image_shape[1]) * (img_w_new > 0) img_h_bool_001 = (img_h_001 < image_shape[0]) * (img_h_001 > 0) img_w_bool_001 = (img_w_001 < image_shape[1]) * (img_w_001 > 0) img_h_bool_010 = (img_h_010 < image_shape[0]) * (img_h_010 > 0) img_w_bool_010 = (img_w_010 < image_shape[1]) * (img_w_010 > 0) img_h_bool_100 = (img_h_100 < image_shape[0]) * (img_h_100 > 0) img_w_bool_100 = (img_w_100 < image_shape[1]) * (img_w_100 > 0) img_h_bool_011 = (img_h_011 < image_shape[0]) * (img_h_011 > 0) img_w_bool_011 = (img_w_011 < image_shape[1]) * (img_w_011 > 0) img_h_bool_110 = (img_h_110 < image_shape[0]) * (img_h_110 > 0) img_w_bool_110 = (img_w_110 < image_shape[1]) * (img_w_110 > 0) img_h_bool_101 = (img_h_101 < image_shape[0]) * (img_h_101 > 0) img_w_bool_101 = (img_w_101 < image_shape[1]) * (img_w_101 > 0) img_h_bool_111 = (img_h_111 < image_shape[0]) * (img_h_111 > 0) img_w_bool_111 = (img_w_111 < image_shape[1]) * (img_w_111 > 0) view_in_flag = img_h_bool * img_w_bool * img_h_bool_001 * img_w_bool_001 * img_h_bool_010 * img_w_bool_010 * img_h_bool_100 * img_w_bool_100 * img_h_bool_110 * img_w_bool_110 * img_h_bool_101 * img_w_bool_101 * img_h_bool_011 * img_w_bool_011 * img_h_bool_111 * img_w_bool_111 print('the bool flag of each view can see the center of cubic:', view_in_flag.sum()) return view_in_flag[:, 0] def count_gx_gy(projection_M, h_length=1, w_length=1): projection_M_inverse = inverse_camera_matrix(projection_M) N_view = projection_M_inverse.shape[0] vector_101 = np.array(([w_length, 0, 1, 1]))[None, :, None] vector_011 = np.array(([0, h_length, 1, 1]))[None, :, None] vector_001 = np.array(([0, 0, 1, 1]))[None, :, None] global_101 = np.matmul(projection_M_inverse, vector_101)[:, :3, 0] # shape: (N_view, 4,1)->(N_view, 3) global_011 = np.matmul(projection_M_inverse, vector_011)[:, :3, 0] global_001 = np.matmul(projection_M_inverse, vector_001)[:, :3, 0] gx = np.linalg.norm(global_101 - global_001, axis=1) # shape: (N_views) gy = np.linalg.norm(global_011 - global_001, axis=1) return (gx, gy) def generateMetaVector_old( projection_M, cube_xyz_min, cameraTs, cube_D_resol, _cube_D_, ): ''' :param projection_M: shape:(N_views, 3, 4) :param cube_xyz_min: shape:(,3) :param cameraTs: shape:(N_views, 3) :param cube_D_resol: resolution of each voxel float :param _cube_D_: length of cube int :return: meta_vector: the array of each vector represent camera position shape: (N_views, _cube_D_, _cube_D_, _cube_D_, 10) wrapping_vector: the map from each voxel to image shape: (N_views, _cube_D_, _cube_D_, _cube_D_, 3) ''' x = np.arange(0, _cube_D_, 1.0) y = np.arange(0, _cube_D_, 1.0) z = np.arange(0, _cube_D_, 1.0) if not (x.shape[0] == _cube_D_): print('shape of Meta vector went wrong') raise TypeError xx, yy, zz = np.meshgrid(x, y, z) XYZ = np.array([yy.flatten(), xx.flatten(), zz.flatten()]).reshape(3, _cube_D_, _cube_D_, _cube_D_) XYZ = np.moveaxis(XYZ, 0, 3) if not (list(XYZ[0, 1, 3, :]) == [0.0, 1.0, 3.0]): print('index of Meta vector went wrong') raise TypeError cube_xyz = cube_xyz_min[None, None, None, :] + XYZ * cube_D_resol # shape:(_cube_D_, _cube_D_, _cube_D_, 3) ones = np.ones((_cube_D_, _cube_D_, _cube_D_, 1)) cube_xyz_matmul = np.concatenate((cube_xyz, ones), axis=3)[None, :, :, :, :, None] # shape:(1, _cube_D_, _cube_D_, _cube_D_, 4, 1) projection_M_matmul = projection_M[:, None, None, None, :, :] # shape:(N_view, 1, 1, 1, 3, 4) project_cube_xyz = np.matmul(projection_M_matmul, cube_xyz_matmul) # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 3, 1) (gx, gy) = count_gx_gy(projection_M) Z = project_cube_xyz[:, :, :, :, 2, 0] # the depth of each cubic points shape:(N_view, _cube_D_, _cube_D_, _cube_D_) alpha_x = (Z * gx[:, None, None, None] / cube_D_resol)[:, :, :, :, None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 1) alpha_y = (Z * gy[:, None, None, None] / cube_D_resol)[:, :, :, :, None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 1) print('the average pixel a cubic can get on x axis', alpha_x.mean()) print('the average pixel a cubic can get on y axis', alpha_y.mean()) tau = project_cube_xyz[:, :, :, :, :, 0] / np.linalg.norm(project_cube_xyz[:, :, :, :, :, 0], axis=4)[:, :, :, :, None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 3) vector_xyz = cube_xyz[None, :, :, :, :] - cameraTs[:, None, None, None, :] # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 3) theta = vector_xyz / np.linalg.norm(vector_xyz, axis=4)[:, :, :, :, None] # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 3) YX = project_cube_xyz[:, :, :, :, :2, 0] / project_cube_xyz[:, :, :, :, 2, 0][:, :, :, :, None] H = YX[:, :, :, :, 1][:, :, :, :, None] # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1) W = YX[:, :, :, :, 0][:, :, :, :, None] # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1) D = np.zeros(np.shape(H)) X = H - np.floor(H) # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1) Y = W - np.floor(W) # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1) meta_vector = np.concatenate((alpha_x, alpha_y, tau, theta, X, Y), axis=4) wrapping_vector = np.concatenate((D, H, W), axis=4) return (meta_vector, wrapping_vector) def generateMetaVector( projection_M, compress, cube_xyz_min, cameraTs, cube_D_resol, _cube_D_, ): ''' :param projection_M: shape:(N_views, 3, 4) :param compress turple: (compress_h, compress_w) :param cube_xyz_min: shape:(,3) :param cameraTs: shape:(N_views, 3) :param cube_D_resol: resolution of each voxel float :param _cube_D_: length of cube int :return: meta_vector: the array of each vector represent camera position shape: (N_views, _cube_D_, _cube_D_, _cube_D_, 10) wrapping_vector: the map from each voxel to image shape: (N_views, _cube_D_, _cube_D_, _cube_D_, 3) ''' compress_h, compress_w = compress x = np.arange(0, _cube_D_, 1.0) y = np.arange(0, _cube_D_, 1.0) z = np.arange(0, _cube_D_, 1.0) if not (x.shape[0] == _cube_D_): print('shape of Meta vector went wrong') raise TypeError xx, yy, zz = np.meshgrid(x, y, z) XYZ = np.array([yy.flatten(), xx.flatten(), zz.flatten()]).reshape(3, _cube_D_, _cube_D_, _cube_D_) XYZ = np.moveaxis(XYZ, 0, 3) if not (list(XYZ[0, 1, 3, :]) == [0.0, 1.0, 3.0]): print('index of Meta vector went wrong') raise TypeError cube_xyz = cube_xyz_min[None, None, None, :] + XYZ * cube_D_resol # shape:(_cube_D_, _cube_D_, _cube_D_, 3) # print('cube_xyz_min[None, None, None, :]', cube_xyz_min[None, None, None, :]) # print('@(*#@!#!@(*$&!@(*') # print('cube_xyz[2,3,1,:]', cube_xyz[2,3,1,:]) # print('cube_xyz[2,3,2,:]', cube_xyz[2, 3, 2, :]) # print('cube_xyz[2,4,1,:]', cube_xyz[2, 4, 1, :]) ones = np.ones((_cube_D_, _cube_D_, _cube_D_, 1)) cube_xyz_matmul = np.concatenate((cube_xyz, ones), axis=3)[None, :, :, :, :, None] # shape:(1, _cube_D_, _cube_D_, _cube_D_, 4, 1) projection_M_matmul = projection_M[:, None, None, None, :, :] # shape:(N_view, 1, 1, 1, 3, 4) project_cube_xyz = np.matmul(projection_M_matmul, cube_xyz_matmul) # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 3, 1) # print('@(*#@!#!@(*$&!@(*') # print(project_cube_xyz.shape) # print('project_cube_xyz[2,3,1,:]', project_cube_xyz[44, 2, 3, 1, :]) # print('project_cube_xyz[2,3,2,:]', project_cube_xyz[44, 2, 3, 2, :]) # print('project_cube_xyz[2,4,1,:]', project_cube_xyz[44, 2, 4, 1, :]) (gx, gy) = count_gx_gy(projection_M, h_length=compress_h, w_length=compress_w) Z = project_cube_xyz[:, :, :, :, 2, 0] # the depth of each cubic points shape:(N_view, _cube_D_, _cube_D_, _cube_D_) alpha_x = (Z * gx[:, None, None, None] / cube_D_resol)[:, :, :, :, None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 1) alpha_y = (Z * gy[:, None, None, None] / cube_D_resol)[:, :, :, :, None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 1) print('the average pixel a cubic can get on x axis', alpha_x.mean()) print('the average pixel a cubic can get on y axis', alpha_y.mean()) tau = project_cube_xyz[:, :, :, :, :, 0] / np.linalg.norm(project_cube_xyz[:, :, :, :, :, 0], axis=4)[:, :, :, :, None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 3) vector_xyz = cube_xyz[None, :, :, :, :] - cameraTs[:, None, None, None, :] # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 3) theta = vector_xyz / np.linalg.norm(vector_xyz, axis=4)[:, :, :, :, None] # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 3) YX = project_cube_xyz[:, :, :, :, :2, 0] / project_cube_xyz[:, :, :, :, 2, 0][:, :, :, :, None] H = YX[:, :, :, :, 1][:, :, :, :, None] / compress_h # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1) W = YX[:, :, :, :, 0][:, :, :, :, None] / compress_w # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1) D = np.zeros(np.shape(H)) X = H - np.floor(H) # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1) Y = W - np.floor(W) # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1) meta_vector = np.concatenate((alpha_x, alpha_y, tau, theta, X, Y), axis=4) wrapping_vector = np.concatenate((W, H, D), axis=4) # To avoid confusion in notation, let’s note that x corresponds to the width dimension IW, y corresponds to the height dimension IH and z corresponds to the depth dimension ID. return (meta_vector, wrapping_vector) def generate_sparseMetaVector( projection_M, compress, cube_xyz_min, stage_num, cameraTs, cube_D_resol, _cube_D_, info_list=None ): ''' :param projection_M: shape:(N_views, 3, 4) :param compress turple: (compress_h, compress_w) :param cube_xyz_min: shape:(,3) :param stage_num int :param cameraTs: shape:(N_views, 3) :param cube_D_resol: resolution of each voxel float :param _cube_D_: length of cube int :param info_list :return: meta_list: list of meta\wrapping vector len: stage_num ele: (meta_vector, wrapping_vector) output_list: list of output vector len: stage_num ele: (q_final, xyz_final, rgb_final, n_final) ''' meta_list = [] input_list = [] output_list = [] resol_new = cube_D_resol xyz_3D_new = copy.copy(cube_xyz_min) cube_D_new = _cube_D_ for i in range(stage_num): cubes_gt_np = info_list[i] if (i == (stage_num - 1)): use_dense = True else: use_dense = False (xyz_global_final, xyz_final, rgb_final, n_final, q_final, sort_index) = generate_sparse( cube_xyz_min=xyz_3D_new, cube_D_resol=resol_new, _cube_D_=cube_D_new, cubes_gt_np=cubes_gt_np, use_dense=use_dense ) (meta_vector, wrapping_vector) = generateMeta_from_xyz(projection_M=projection_M, compress=compress, cameraTs=cameraTs, cube_D_resol=resol_new, _cube_D_=cube_D_new, pts_xyz=xyz_global_final ) meta_list.append((meta_vector, wrapping_vector)) output_list.append((q_final, xyz_final, rgb_final, n_final, xyz_global_final, sort_index)) xyz_3D_new += (resol_new / 2) resol_new *= 2 cube_D_new /= 2 compress = (compress[0] * 2, compress[1] * 2) return meta_list, output_list def generate_sparse( cube_xyz_min, cube_D_resol, _cube_D_, cubes_gt_np=None, use_dense=False ): ''' :param cube_xyz_min: shape:(,3) :param cube_D_resol: resolution of each voxel float :param _cube_D_: length of cube int :param cubes_gt_np :return: xyz_global_final : the location of input voxel shape: (N,3) xyz_final: the relative location of output voxel shape: (N,3) rgb_final: output voxel shape: (N,3) n_final: output voxel shape: (N,3) q_final: output voxel shape: bool (N,1) sort_index: the sort index of the ground truth used for point up convolution shape: int (N_points,) ''' # cubes_gt_np_sort = np.sort(cubes_gt_np, order = 'ijk_id') x = np.arange(0, _cube_D_, 1.0) y = np.arange(0, _cube_D_, 1.0) z = np.arange(0, _cube_D_, 1.0) if not (x.shape[0] == _cube_D_): print('shape of Meta vector went wrong') raise TypeError xx, yy, zz = np.meshgrid(x, y, z) XYZ = np.array([yy.flatten(), xx.flatten(), zz.flatten()]).T XYZ_id = 8 * ((_cube_D_ / 2) * (_cube_D_ / 2) * (XYZ[:, 0] // 2) + (_cube_D_ / 2) * (XYZ[:, 1] // 2) + XYZ[:, 2] // 2) + ( 4 * (XYZ[:, 0] % 2) + 2 * (XYZ[:, 1] % 2) + XYZ[:, 2] % 2) XYZ_id_s = (_cube_D_ * _cube_D_ * XYZ[:, 0] + _cube_D_ * XYZ[:, 1] + XYZ[:, 2]) XYZ_np = np.empty((XYZ.shape[0],), dtype=[('ijk', np.uint32, (3,)), ('ijk_id', np.uint32), ('ijk_id_s', np.uint32)]) XYZ_np['ijk'] = XYZ XYZ_np['ijk_id'] = XYZ_id XYZ_np['ijk_id_s'] = XYZ_id_s XYZ_sort_np = np.sort(XYZ_np, order='ijk_id') XYZ_sort = XYZ_sort_np['ijk'] # xyz_global = np.zeros((XYZ.shape[0], 3)) xyz = np.zeros((XYZ.shape[0], 3)) rgb = np.zeros((XYZ.shape[0], 3)) n = np.zeros((XYZ.shape[0], 3)) q = np.zeros((XYZ.shape[0], 1), dtype=np.bool) # xyz_global[cubes_gt_np['ijk_id'], :] = cubes_gt_np['xyz_global'] xyz_global = XYZ_sort * cube_D_resol + cube_xyz_min xyz[cubes_gt_np['ijk_id'], :] = cubes_gt_np['xyz'] rgb[cubes_gt_np['ijk_id'], :] = cubes_gt_np['rgb'] n[cubes_gt_np['ijk_id'], :] = cubes_gt_np['normals'] q[cubes_gt_np['ijk_id'], :] = True XYZ_big_num = int(XYZ.shape[0] // 8) xyz_global_new = xyz_global.reshape((XYZ_big_num, 8, 3)) xyz_new = xyz.reshape((XYZ_big_num, 8, 3)) rgb_new = rgb.reshape((XYZ_big_num, 8, 3)) n_new = n.reshape((XYZ_big_num, 8, 3)) q_new = q.reshape((XYZ_big_num, 8, 1)) ijk_id_s_new = XYZ_sort_np['ijk_id_s'].reshape((XYZ_big_num, 8, 1)) if (use_dense): xyz_global_final = xyz_global_new.reshape((-1, 3)) xyz_final = xyz_new.reshape((-1, 3)) rgb_final = rgb_new.reshape((-1, 3)) n_final = n_new.reshape((-1, 3)) q_final = q_new.reshape((-1, 1)) ijk_id_s_final = ijk_id_s_new.reshape((-1)) else: cubes_gt_id_big = np.unique(cubes_gt_np['ijk_id'] // 8) xyz_global_final = xyz_global_new[cubes_gt_id_big, :, :].reshape((-1, 3)) xyz_final = xyz_new[cubes_gt_id_big, :, :].reshape((-1, 3)) rgb_final = rgb_new[cubes_gt_id_big, :, :].reshape((-1, 3)) n_final = n_new[cubes_gt_id_big, :, :].reshape((-1, 3)) q_final = q_new[cubes_gt_id_big, :, :].reshape((-1, 1)) ijk_id_s_final = ijk_id_s_new[cubes_gt_id_big, :, :].reshape((-1)) sort_index = np.argsort(ijk_id_s_final[q_final[:, 0]]) return (xyz_global_final, xyz_final, rgb_final, n_final, q_final, sort_index) def generateMeta_from_xyz(projection_M, compress, cameraTs, cube_D_resol, _cube_D_, pts_xyz ): ''' :param projection_M: shape:(N_views, 3, 4) :param compress turple: (compress_h, compress_w) :param cameraTs: shape:(N_views, 3) :param cube_D_resol: resolution of each voxel float :param _cube_D_: length of cube int :param pts_xyz: points of voxel shape: (N_points, 3) :return: meta_vector: the array of each vector represent camera position shape: (N_views, N_points, 10) wrapping_vector: the map from each voxel to image shape: (N_views, N_points, 3) ''' compress_h, compress_w = compress N_points = pts_xyz.shape[0] ones = np.ones((N_points, 1)) cube_xyz_matmul = np.concatenate((pts_xyz, ones), axis=1)[None, :, :, None] # shape:(1, N_points, 4, 1) projection_M_matmul = projection_M[:, None, :, :] # shape:(N_view, 1, 3, 4) project_cube_xyz = np.matmul(projection_M_matmul, cube_xyz_matmul) # shape:(N_view, N_points, 3, 1) (gx, gy) = count_gx_gy(projection_M, h_length=compress_h, w_length=compress_w) Z = project_cube_xyz[:, :, 2, 0] # the depth of each cubic points shape:(N_view, N_points,) alpha_x = (Z * gx[:, None] / cube_D_resol)[:, :, None] # shape:(N_view, N_points, 1) alpha_y = (Z * gy[:, None] / cube_D_resol)[:, :, None] # shape:(N_view, N_points, 1) print('the average pixel a cubic can get on x axis', alpha_x.mean()) print('the average pixel a cubic can get on y axis', alpha_y.mean()) tau = project_cube_xyz[:, :, :, 0] / np.linalg.norm(project_cube_xyz[:, :, :, 0], axis=2)[:, :, None] # shape:(N_view, N_points, 3) vector_xyz = pts_xyz[None, :, :] - cameraTs[:, None, :] # shape: (N_view, N_points, 3) theta = vector_xyz / np.linalg.norm(vector_xyz, axis=2)[:, :, None] # shape: (N_view, N_points, 3) YX = project_cube_xyz[:, :, :2, 0] / project_cube_xyz[:, :, 2, 0][:, :, None] # shape: (N_view, N_points, 2) H = YX[:, :, 1][:, :, None] / compress_h # shape: (N_view, N_points, 1) W = YX[:, :, 0][:, :, None] / compress_w # shape: (N_view, N_points, 1) D = np.zeros(np.shape(H)) X = H - np.floor(H) # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1) Y = W - np.floor(W) # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1) meta_vector = np.concatenate((alpha_x, alpha_y, tau, theta, X, Y), axis=2) wrapping_vector = np.concatenate((W, H, D), axis=2) # To avoid confusion in notation, let’s note that x corresponds to the width dimension IW, y corresponds to the height dimension IH and z corresponds to the depth dimension ID. return (meta_vector, wrapping_vector) def generateMetaVector( projection_M, compress, cube_xyz_min, cameraTs, cube_D_resol, _cube_D_, ): ''' :param projection_M: shape:(N_views, 3, 4) :param compress turple: (compress_h, compress_w) :param cube_xyz_min: shape:(,3) :param cameraTs: shape:(N_views, 3) :param cube_D_resol: resolution of each voxel float :param _cube_D_: length of cube int :return: meta_vector: the array of each vector represent camera position shape: (N_views, _cube_D_, _cube_D_, _cube_D_, 10) wrapping_vector: the map from each voxel to image shape: (N_views, _cube_D_, _cube_D_, _cube_D_, 3) ''' compress_h, compress_w = compress x = np.arange(0, _cube_D_, 1.0) y = np.arange(0, _cube_D_, 1.0) z = np.arange(0, _cube_D_, 1.0) if not (x.shape[0] == _cube_D_): print('shape of Meta vector went wrong') raise TypeError xx, yy, zz = np.meshgrid(x, y, z) XYZ = np.array([yy.flatten(), xx.flatten(), zz.flatten()]).reshape(3, _cube_D_, _cube_D_, _cube_D_) XYZ = np.moveaxis(XYZ, 0, 3) if not (list(XYZ[0, 1, 3, :]) == [0.0, 1.0, 3.0]): print('index of Meta vector went wrong') raise TypeError cube_xyz = cube_xyz_min[None, None, None, :] + XYZ * cube_D_resol # shape:(_cube_D_, _cube_D_, _cube_D_, 3) # print('cube_xyz_min[None, None, None, :]', cube_xyz_min[None, None, None, :]) # print('@(*#@!#!@(*$&!@(*') # print('cube_xyz[2,3,1,:]', cube_xyz[2,3,1,:]) # print('cube_xyz[2,3,2,:]', cube_xyz[2, 3, 2, :]) # print('cube_xyz[2,4,1,:]', cube_xyz[2, 4, 1, :]) ones = np.ones((_cube_D_, _cube_D_, _cube_D_, 1)) cube_xyz_matmul = np.concatenate((cube_xyz, ones), axis=3)[None, :, :, :, :, None] # shape:(1, _cube_D_, _cube_D_, _cube_D_, 4, 1) projection_M_matmul = projection_M[:, None, None, None, :, :] # shape:(N_view, 1, 1, 1, 3, 4) project_cube_xyz = np.matmul(projection_M_matmul, cube_xyz_matmul) # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 3, 1) # print('@(*#@!#!@(*$&!@(*') # print(project_cube_xyz.shape) # print('project_cube_xyz[2,3,1,:]', project_cube_xyz[44, 2, 3, 1, :]) # print('project_cube_xyz[2,3,2,:]', project_cube_xyz[44, 2, 3, 2, :]) # print('project_cube_xyz[2,4,1,:]', project_cube_xyz[44, 2, 4, 1, :]) (gx, gy) = count_gx_gy(projection_M, h_length=compress_h, w_length=compress_w) Z = project_cube_xyz[:, :, :, :, 2, 0] # the depth of each cubic points shape:(N_view, _cube_D_, _cube_D_, _cube_D_) alpha_x = (Z * gx[:, None, None, None] / cube_D_resol)[:, :, :, :, None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 1) alpha_y = (Z * gy[:, None, None, None] / cube_D_resol)[:, :, :, :, None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 1) print('the average pixel a cubic can get on x axis', alpha_x.mean()) print('the average pixel a cubic can get on y axis', alpha_y.mean()) tau = project_cube_xyz[:, :, :, :, :, 0] / np.linalg.norm(project_cube_xyz[:, :, :, :, :, 0], axis=4)[:, :, :, :, None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 3) vector_xyz = cube_xyz[None, :, :, :, :] - cameraTs[:, None, None, None, :] # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 3) theta = vector_xyz / np.linalg.norm(vector_xyz, axis=4)[:, :, :, :, None] # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 3) YX = project_cube_xyz[:, :, :, :, :2, 0] / project_cube_xyz[:, :, :, :, 2, 0][:, :, :, :, None] H = YX[:, :, :, :, 1][:, :, :, :, None] / compress_h # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1) W = YX[:, :, :, :, 0][:, :, :, :, None] / compress_w # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1) D = np.zeros(np.shape(H)) X = H - np.floor(H) # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1) Y = W - np.floor(W) # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1) meta_vector = np.concatenate((alpha_x, alpha_y, tau, theta, X, Y), axis=4) wrapping_vector = np.concatenate((W, H, D), axis=4) # To avoid confusion in notation, let’s note that x corresponds to the width dimension IW, y corresponds to the height dimension IH and z corresponds to the depth dimension ID. return (meta_vector, wrapping_vector) def generate_multiImageMetaVector( projection_M, compress, xyz_3D, stage_num, cameraTs, images_resized, angles, Ts, ): ''' :param projection_M: shape:(N_views, 3, 4) :param compress turple: (compress_h, compress_w) :param stage_num int :param cameraTs: shape:(N_views, 3) :param images_resized:resized images list :return: meta_list: list of meta\wrapping vector len: stage_num ele: (vector_image, cameraTs) ''' meta_list = [] for i in range(stage_num): (vector_image) = generateImageMetaVector( projection_M, compress, cameraTs, image_size=images_resized[i].shape[1:3] ) # direction_transfer = generateDirectionMetaVector(vector_image, # cameraTs, # xyz_3D, # angles, # Ts, # ) meta_list.append(vector_image) # meta_list.append(direction_transfer) compress = (compress[0] * 2, compress[1] * 2) return meta_list def generate_matrix( angles, ts ): (alpha, beta, gamma) = angles ratio = 180 / 3.14159 alpha /= ratio beta /= ratio gamma /= ratio (t_x, t_y, t_z) = ts R_z = np.array([[np.cos(gamma), -np.sin(gamma), 0], [np.sin(gamma), np.cos(gamma), 0], [0, 0, 1]]) R_x = np.array([[1, 0, 0], [0, np.cos(alpha), -np.sin(alpha)], [0, np.sin(alpha), np.cos(alpha)]]) R_y = np.array([[np.cos(beta), 0, np.sin(beta)], [0, 1, 0], [-np.sin(beta), 0, np.cos(beta)]]) R_rotate = np.matmul(R_x, np.matmul(R_y, R_z)) t_total = np.array([[t_x], [t_y], [t_z]]) RT = np.concatenate((R_rotate, t_total), axis=1) return R_rotate def generateDirectionMetaVector(vector_image, cameraTs, BB_middle, angles, ts ): R_rotate = generate_matrix(angles, ts) s = BB_middle[None, :] - cameraTs # shape:(N_views,3) d_origin = 1 v_length = (s[:, :, None, None] * vector_image).sum(axis=1)[:, None, :, :] # shape:(N_views,1,img_w,img_h) direction = v_length * vector_image - s[:, :, None, None] # shape:(N_views,3,img_w,img_h) # R_rotate_inverse = np.linalg.inv(R_rotate) #shape(3,3) direction_rotate = np.matmul(R_rotate[None, None, None, ...], np.moveaxis(direction, 1, -1)[..., None]) direction_rotate = np.moveaxis(direction_rotate[:, :, :, :, 0], -1, 1) # shape:(N_views,3,img_w,img_h) td_length = (np.array(ts)[None, :, None, None] * direction_rotate).sum(axis=1)[:, None, :, :] dd_length = (direction_rotate * direction_rotate).sum(axis=1)[:, None, :, :] direction_transfer = (1 + td_length / (dd_length + 1e-8)) * direction_rotate return direction_transfer # pdb.set_trace() def generateImageMetaVector( projection_M, compress, cameraTs, image_size ): ''' :param projection_M: shape:(N_views, 3, 4) :param compress turple: (compress_h, compress_w) :param cameraTs: shape:(N_views, 3) :param image_size turple :return: meta_vector: the array of each vector represent camera position shape: (N_views, _cube_D_, _cube_D_, _cube_D_, 10) wrapping_vector: the map from each voxel to image shape: (N_views, _cube_D_, _cube_D_, _cube_D_, 3) ''' compress_h, compress_w = compress img_h, img_w = image_size x = np.arange(0, img_w, 1.0) * compress_w y = np.arange(0, img_h, 1.0) * compress_h xx, yy = np.meshgrid(x, y) XY = np.array([yy.flatten(), xx.flatten()]).T.reshape(img_h, img_w, 2) XY = np.moveaxis(XY, 2, 0) Z = np.ones((1, img_h, img_w)) XYZ = np.concatenate((XY, Z), axis=0) # shape:(3,img_h, img_w) image_vector_inverse = inverseImageVector(projection_M, XYZ) vector_image = image_vector_inverse - cameraTs[..., None, None] vector_image = vector_image / (1e-8 + np.linalg.norm(vector_image, axis=1)[:, None, :, :]) vector_image = np.repeat(XY[None, ...] / 1600, cameraTs.shape[0], axis=0) # return (vector_image, cameraTs) return (vector_image) # ones = np.ones((_cube_D_, _cube_D_, _cube_D_, 1)) # cube_xyz_matmul = np.concatenate((cube_xyz, ones), axis = 3)[None, :,:,:,:,None] #shape:(1, _cube_D_, _cube_D_, _cube_D_, 4, 1) # projection_M_matmul = projection_M[:,None,None,None,:,:] #shape:(N_view, 1, 1, 1, 3, 4) # project_cube_xyz = np.matmul(projection_M_matmul, cube_xyz_matmul) #shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 3, 1) # (gx, gy) = count_gx_gy(projection_M, h_length = compress_h, w_length = compress_w) # Z = project_cube_xyz[:,:,:,:,2,0] #the depth of each cubic points shape:(N_view, _cube_D_, _cube_D_, _cube_D_) # alpha_x = (Z * gx[:,None,None,None] / cube_D_resol)[:, :, :, :, None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 1) # alpha_y = (Z * gy[:,None,None,None] / cube_D_resol)[:, :, :, :, None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 1) # print('the average pixel a cubic can get on x axis', alpha_x.mean()) # print('the average pixel a cubic can get on y axis', alpha_y.mean()) # tau = project_cube_xyz[:, :, :, :, :, 0] / np.linalg.norm(project_cube_xyz[:, :, :, :, :, 0], axis = 4)[:, :, :, :, None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 3) # vector_xyz = cube_xyz[None, :,:,:,:] - cameraTs[:,None,None,None,:] #shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 3) # theta = vector_xyz / np.linalg.norm(vector_xyz, axis = 4)[:, :, :, :, None] #shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 3) # YX = project_cube_xyz[:,:,:,:,:2,0] / project_cube_xyz[:,:,:,:,2,0][:,:,:,:,None] # H = YX[:,:,:,:,1][:,:,:,:,None] / compress_h #shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1) # W = YX[:, :, :, :, 0][:,:,:,:,None] / compress_w #shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1) # D = np.zeros(np.shape(H)) # X = H - np.floor(H) #shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1) # Y = W - np.floor(W) #shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1) # meta_vector = np.concatenate((alpha_x, alpha_y, tau, theta, X, Y), axis = 4) # wrapping_vector = np.concatenate((W, H, D), axis = 4) #To avoid confusion in notation, let’s note that x corresponds to the width dimension IW, y corresponds to the height dimension IH and z corresponds to the depth dimension ID. # return (meta_vector, wrapping_vector) def rotateImageVector(self, pts_3D, image_vector): ''' pts_3D: shape:(M_view * N_points * 3)/(N_points * 3) image_vector: shape:(M_view * N_points * 3 * img_h * img_w)/(N_points * 3 * img_h * img_w) ---------------------------------------------------- pts_3D = np.random.rand(1,2,3) image_vector = np.zeros((1,2,3,4,5)) image_vector[...,:,:] = pts_3D[...,None,None] camera_test = Camera() q = camera_test.rotateImageVector(pts_3D, image_vector) print(q.shape) print(q) ''' if (len(image_vector.shape) == 4): image_vector = np.moveaxis(image_vector, 1, -1) N, img_h, img_w, _ = image_vector.shape matrix_x = np.zeros((N, img_h, img_w, 3, 3)) matrix_yz = np.zeros((N, img_h, img_w, 3, 3)) elif (len(image_vector.shape) == 5): image_vector = np.moveaxis(image_vector, 2, -1) M, N, img_h, img_w, _ = image_vector.shape matrix_x = np.zeros((M, N, img_h, img_w, 3, 3)) matrix_yz = np.zeros((M, N, img_h, img_w, 3, 3)) else: raise ValueError('inputs shape is wrong') a = pts_3D[..., 0] b = pts_3D[..., 1] c = pts_3D[..., 2] # print(pts_3D[...,1:]) norm_bc = np.linalg.norm(pts_3D[..., 1:], axis=-1) norm_abc = np.linalg.norm(pts_3D[..., :], axis=-1) matrix_x[..., 0, 0] = 1.0 matrix_x[..., 1, 1] = (c / norm_bc)[..., None, None] matrix_x[..., 1, 2] = (b / norm_bc)[..., None, None] matrix_x[..., 2, 1] = (-b / norm_bc)[..., None, None] matrix_x[..., 2, 2] = (c / norm_bc)[..., None, None] matrix_yz[..., 1, 1] = 1.0 matrix_yz[..., 0, 0] = (norm_bc / norm_abc)[..., None, None] matrix_yz[..., 0, 2] = (a / norm_abc)[..., None, None] matrix_yz[..., 2, 0] = (-a / norm_abc)[..., None, None] matrix_yz[..., 2, 2] = (norm_bc / norm_abc)[..., None, None] self.matrix_R = np.matmul(matrix_x, matrix_yz) image_vector = np.matmul(image_vector[..., None, :], self.matrix_R) image_vector = image_vector[..., 0, :] image_vector = np.moveaxis(image_vector, -1, -3) return (image_vector) def inverseImageVector( projection_M, image_vector): ''' projection_M: shape:(N_views, 3, 4) image_vector: shape:(3 * img_h * img_w) :return image_vector_inverse shape:(N_views,3,img_h, img_w) ---------------------------------------------------- ''' image_vector = np.moveaxis(image_vector, 0, -1) N_Ms = projection_M.shape[0] img_h, img_w, _ = image_vector.shape image_vector_new = np.ones((1, img_h, img_w, 4)) image_vector_new[..., 0:3] = image_vector projection_new = np.zeros((N_Ms, 4, 4)) projection_new[:, 0:3, :] = projection_M projection_new[:, 3, :] = np.array(([[0, 0, 0, 1]])) projection_new = np.linalg.inv(projection_new)[:, None, None, :, :] # shape:(N_views,img_h, img_w, 4, 4) image_vector_inverse = np.matmul(projection_new, image_vector_new[..., None]) # shape:(N_views,img_h, img_w, 4, 1) image_vector_inverse = image_vector_inverse[..., 0:3, 0] image_vector_inverse = np.moveaxis(image_vector_inverse, -1, -3) # shape:(N_views,3,img_h, img_w) return (image_vector_inverse) def K_partition(cameraKO, compress_ratio_h=4.0, compress_ratio_w=4.0, img_size=(1200, 1600)): cx = cameraKO[0][2] cy = cameraKO[1][2] principal_coords_list = [] partition_num = int(compress_ratio_h * compress_ratio_w) h = int(img_size[0] / compress_ratio_h) w = int(img_size[1] / compress_ratio_w) for i in range(compress_ratio_h): for j in range(compress_ratio_w): cxx = cx - j * w cyy = cy - i * h principal_coord = (cxx, cyy) principal_coords_list.append(principal_coord) cameraKOs = np.empty((partition_num, 3, 3), dtype=np.float64) for k in range(partition_num): cameraKOs[k] = cameraKO cameraKOs[k][0][2] = principal_coords_list[k][0] cameraKOs[k][1][2] = principal_coords_list[k][1] return cameraKOs def partition_image_and_matrix(images, cameraPO4s_model, cameraRTO4s_model, cameraKO4s_model, # image_compress_stage, # return_list=False, compress_ratio_h=4.0, compress_ratio_w=4.0): ''' Args: images, list, (N_view, 3, img_h, img_w) cameraPO4s_model, (N_view, 4, 4) compress_ratio_h: compress_ratio for the H dimension. compress_ratio_w: compress_ratio for the W dimension. Outputs: parti_imgs, (N_view, N_partition, 3, parti_h, parti_w) _cameraP04s, (N_view, N_partition, 4, 4). ''' num_view = len(images) num_partition = int(compress_ratio_w * compress_ratio_h) parti_h = int(images[0].shape[1] / compress_ratio_h) parti_w = int(images[0].shape[2] / compress_ratio_w) # parti_imgs = [] parti_imgs = np.empty((num_view, num_partition, 3, parti_h, parti_w), dtype=np.float64) # cameraPO4s = np.empty((num_view, num_partition, 4, 4), dtype=np.float64) # print('images.shape: ', images[0].shape) for view_i in range(num_view): for partition_j in range(num_partition): start_h_idx = math.floor(partition_j / compress_ratio_w) start_w_idx = (partition_j % compress_ratio_w) start_h_pix = start_h_idx * parti_h start_w_pix = start_w_idx * parti_w final_h_pix = start_h_pix + parti_h final_w_pix = start_w_pix + parti_w # parti_imgs.append(images[view_i][start_h_pix: final_h_pix, start_w_pix: final_w_pix, :]) parti_imgs[view_i, partition_j, :, :, :] = images[view_i][:, start_h_pix: final_h_pix, start_w_pix: final_w_pix] # print('^^^^^^^^^^', parti_imgs.shape) _cameraP04s, _, _ = CameraPOs_as_torch_partitioned(cameraPO4s_model, cameraRTO4s_model, cameraKO4s_model, compress_ratio_h=compress_ratio_h, compress_ratio_w=compress_ratio_w, img_size=(images[0].shape[1], images[0].shape[2])) return parti_imgs, _cameraP04s def CameraPOs_as_torch_partitioned(cameraPO4s_model, cameraRTO4s_model, cameraKO4s_model, compress_ratio_h=4.0, compress_ratio_w=4.0, img_size=(1200, 1600)): ''' Args: cameraKO4s_models: (N_view, 3, 3) outputs: cameraP04s_: (N_view, N_partition, 4, 4). ''' # num_model = len(cameraKO4s_models) num_view = cameraPO4s_model.shape[0] num_partition = int(compress_ratio_w * compress_ratio_h) # modify the dimension from (3,4) to (4,4) cameraPO4s = np.empty((num_view, num_partition, 3, 4), dtype=np.float64) cameraRTO4s = np.empty((num_view, num_partition, 3, 4), dtype=np.float64) cameraKO4s = np.empty((num_view, num_partition, 3, 3), dtype=np.float64) # for i in range(num_model): for j in range(num_view): cameraK0 = cameraKO4s_model[j] cameraK0s = K_partition(cameraK0, compress_ratio_h, compress_ratio_w, img_size) # (num_partition, 3, 3) for k in range(num_partition): cameraKO4s[j][k] = cameraK0s[k] cameraRTO4s[j][k] = cameraRTO4s_model[j] cameraPO4s[j][k] = np.dot(cameraK0s[k], cameraRTO4s_model[j]) # concatenation for PO4, from (..., 3, 4) to (..., 4, 4). ones1 = np.repeat(np.array([[[0, 0, 0, 1]]]), repeats=num_partition, axis=0) ones2 = np.repeat(np.expand_dims(ones1, axis=0), repeats=num_view, axis=0) # ones3 = np.repeat(np.expand_dims(ones2, axis=0), repeats=num_model, axis=0) cameraP04s = np.concatenate((cameraPO4s, ones2), axis=2) #print('cameraP04s shape: ', cameraP04s.shape) # total_num = num_partition * num_view * num_model # ones = np.repeat(np.array([[[[[0, 0, 0, 1]]]]]), repeats=total_num, axis=0) # cameraP04s = np.concatenate((cameraPO4s, ones), axis=3) _cameraP04s = torch.from_numpy(cameraP04s).type(torch.FloatTensor) _cameraRTO4s = torch.from_numpy(cameraRTO4s).type(torch.FloatTensor) _cameraKO4s = torch.from_numpy(cameraKO4s).type(torch.FloatTensor) return _cameraP04s, _cameraRTO4s, _cameraKO4s def resize_multistage_image_and_matrix(images, projection_M, intrinsic_K, cube_xyz_min, cube_D_mm, _cube_D_, image_compress_multiple, image_compress_stage, return_list=False, compress_ratio=1.0): # input intrinsic_K (N_views, 3, 3). ''' compress image and garantee the camera position is not changing :param images: all images of one model type:list or None if list list element: image array shape: (img_h,img_w, 3) :param projection_M: camera matrix shape: (N_views, 3, 4) :param intrinsic_K: intrinsic matrix shape: (N_views, 3, 3) :param extrinsic_RT: extrinsic matrix shape: (N_views, 3, 4) :param cube_xyz_min: min xyz coordinate shape: (3,) / (N_pts, 3) usually it is (3,) because we only sample one cubic to judge the resize term :param cube_D_mm: cubic length float :param _cube_D_: cubic size int :param image_compress_multiple: same as param.image_compress_multiple :param image_compress_stage same as param.image_compress_stage :param return_list: bool if False return the numpy array :param compress_ratio see self.params.compress_ratio :return: if image is not None image_resized_stage_list:multistage of resized image length : = image_compress_stage ele in each list: shape:(N_view, img_h_new//2**iter, img_w_new//2**iter) projection_M_new: new cameraP shape:(N_view,3,4) (compress_h_new,compress_w_new):(float,float) elif image is None: only change the matrix projection_M_new: new cameraP shape:(N_view,3,4) (compress_h_new,compress_w_new):(float,float) ''' compress_h = compress_ratio compress_w = compress_ratio resized_h = int(image_compress_multiple * (images[0].shape[0] // (compress_h * image_compress_multiple))) resized_w = int(image_compress_multiple * (images[0].shape[1] // (compress_w * image_compress_multiple))) # pdb.set_trace() compress_h_new = images[0].shape[0] / (resized_h + 0.0) compress_w_new = images[0].shape[1] / (resized_w + 0.0) transform_matrix = np.array([[[1 / compress_w_new, 0, 0], [0, 1 / compress_h_new, 0], [0, 0, 1]]]) projection_M_new = np.matmul(transform_matrix, projection_M) # calculate the K after resizing. intrinsic_K_new = np.matmul(transform_matrix, intrinsic_K) cameraTs = cameraPs2Ts(projection_M) cameraTs_new = cameraPs2Ts(projection_M_new) trans_vector = (cameraTs - cameraTs_new)[:, :, None] identical_matrix = np.repeat(np.array([[[1, 0, 0], [0, 1, 0], [0, 0, 1]]]), cameraTs.shape[0], axis=0) bottom_matrix = np.repeat(np.array([[[0, 0, 0, 1]]]), cameraTs.shape[0], axis=0) transform_matrix2 = np.concatenate((identical_matrix, trans_vector), axis=2) transform_matrix2 = np.concatenate((transform_matrix2, bottom_matrix), axis=1) projection_M_new_f = np.concatenate((projection_M_new, bottom_matrix), axis=1) projection_M_new = np.matmul(transform_matrix2, projection_M_new_f) projection_M_new = projection_M_new[:, :3, :] if (images is not None): image_resized_stage_list = [] for iter in range(image_compress_stage): image_resized_list = [] for image in images: # print('resized image shape',resized_h, resized_w) image_resized = scipy.misc.imresize(image, size=(int(resized_h // (2 ** iter)), int(resized_w // (2 ** iter))), interp='bicubic') image_resized = image_resized / 256.0 - 0.5 image_resized_list.append(image_resized) images_resized = image_resized_list if return_list else np.stack(image_resized_list) image_resized_stage_list.append(images_resized) return (image_resized_stage_list, projection_M_new, intrinsic_K_new, (compress_h_new, compress_w_new)) else: return (None, projection_M_new, intrinsic_K_new, (compress_h_new, compress_w_new)) def resize_matrix(projection_M, intrinsic_K, compress_ratio_total): """ input: projection_M, (N_view, 3, 4). output: projection_M_new: (N_view, 3, 4) intrinsic_K_new: (N_view, 3, 3). """ compress_w_new = compress_ratio_total compress_h_new = compress_ratio_total transform_matrix = np.array([[[1 / compress_w_new, 0, 0], [0, 1 / compress_h_new, 0], [0, 0, 1]]]) projection_M_new = np.matmul(transform_matrix, projection_M) # calculate the K after resizing. intrinsic_K_new = np.matmul(transform_matrix, intrinsic_K) cameraTs = cameraPs2Ts(projection_M) cameraTs_new = cameraPs2Ts(projection_M_new) trans_vector = (cameraTs - cameraTs_new)[:, :, None] identical_matrix = np.repeat(np.array([[[1, 0, 0], [0, 1, 0], [0, 0, 1]]]), cameraTs.shape[0], axis=0) bottom_matrix = np.repeat(np.array([[[0, 0, 0, 1]]]), cameraTs.shape[0], axis=0) transform_matrix2 = np.concatenate((identical_matrix, trans_vector), axis=2) transform_matrix2 = np.concatenate((transform_matrix2, bottom_matrix), axis=1) projection_M_new_f = np.concatenate((projection_M_new, bottom_matrix), axis=1) projection_M_new = np.matmul(transform_matrix2, projection_M_new_f) projection_M_new = projection_M_new[:, :3, :] return projection_M_new, intrinsic_K_new def interpolate_cameras(cameraRTO4s_models, cameraKO4s_models, chooose_list, rate_list, interpolate_num=4, direction=1, zoomin_flag=False): if zoomin_flag: cameraRT_ch1 = torch.Tensor( np.array([[0.7404845135614985, 0.2325005573497136, 0.6305753991446678, -357.5776160575932], [-0.6113867548639025, 0.6226553427405186, 0.48837052366865125, -224.67534057994519], [-0.2790849063168464, -0.747156625483257, 0.6032149168942565, -314.37583021393465]])) cameraRT_ch2 = torch.Tensor( np.array([[0.7818430657776811, -0.5105525697800951, 0.3578509652459924, -236.34755864003523], [0.011400854998416882, 0.5855732302942881, 0.8105398357749803, -432.3902637782938], [-0.6233711434370777, -0.6296345988675118, 0.46364696910912734, -86.72248020694681]])) for i in range(len(chooose_list)): if chooose_list[i] == 1: cameraRT_new = zoomin_camera(cameraRTO4s_models[0, i], cameraKO4s_models[0, i], rate_list[i]) cameraRTO4s_models[0, i] = cameraRT_new cameraRTO4s_models[0, 2] = cameraRT_ch1 cameraRTO4s_models[0, 3] = cameraRT_ch2 # cameraK_new = expand_camera(cameraRTO4s_models[0,1], cameraKO4s_models[0,1], 0.5) # cameraKO4s_models[0,1] = cameraK_new camera_angles = matrix_to_euler_angles(cameraRTO4s_models[:, :, :, :3], convention="XYZ") # shape:(N_models, N_view, 3) camera_ts = cameraRTO4s_models[:, :, :, 3:4] # shape:(N_models, N_view, 3, 1) camera_angles_begin_expand = camera_angles[:, 0:-1, :][:, :, None, :].expand(-1, -1, interpolate_num, -1) # shape:(N_models, N_view - 1, interpolate_num, 3) camera_angles_end_expand = camera_angles[:, 1:, :][:, :, None, :].expand(-1, -1, interpolate_num, -1) # shape:(N_models, N_view - 1, interpolate_num, 3) camera_ts_begin_expand = camera_ts[:, 0:-1, ...][:, :, None, ...].expand(-1, -1, interpolate_num, -1, -1) # shape:(N_models, N_view - 1, interpolate_num, 3, 1) camera_ts_end_expand = camera_ts[:, 1:, ...][:, :, None, ...].expand(-1, -1, interpolate_num, -1, -1) # shape:(N_models, N_view - 1, interpolate_num, 3, 1) if (direction == 1): interpolate_alpha = torch.arange(0, 1, 1.0 / interpolate_num) # shape : (interpolate_num) else: interpolate_alpha = 1 - 1.0 / interpolate_num - torch.arange(0, 1, 1.0 / interpolate_num) # shape : (interpolate_num) camera_angles_new_expand = interpolate_alpha[None, None, :, None] * camera_angles_begin_expand + ( 1 - interpolate_alpha[None, None, :, None]) * camera_angles_end_expand # shape:(N_models, N_view - 1, interpolate_num, 3) # camera_angles_new_expand = camera_angles_begin_expand camera_ts_new_expand = interpolate_alpha[None, None, :, None, None] * camera_ts_begin_expand + ( 1 - interpolate_alpha[None, None, :, None, None]) * camera_ts_end_expand # shape:(N_models, N_view - 1, interpolate_num, 3, 1) # camera_ts_new_expand = camera_ts_begin_expand camera_rs_new_expand = euler_angles_to_matrix(camera_angles_new_expand, convention="XYZ") # shape:(N_models, N_view - 1, interpolate_num, 3, 3) camera_rt_new_expand = torch.cat((camera_rs_new_expand, camera_ts_new_expand), dim=4) camera_rt_new = camera_rt_new_expand.reshape(camera_rt_new_expand.shape[0], -1, 3, 4) # shape:(N_models, N_view_new, 3, 4) camera_ks_expand = cameraKO4s_models[:, 0:-1, None, :, :].expand(-1, -1, interpolate_num, -1, -1) # shape:(N_models, N_view - 1, interpolate_num, 3, 3) camera_ks_new = camera_ks_expand.reshape(camera_rt_new_expand.shape[0], -1, 3, 3) # shape:(N_models, N_view_new, 3, 3) # pdb.set_trace() camera_p0s_new = torch.matmul(camera_ks_new, camera_rt_new) # shape:(N_models, N_view_new, 3, 4) camera_ts_new = torch.from_numpy(cameraPs2Ts_all(camera_p0s_new.numpy())).type(camera_p0s_new.dtype) ones = torch.tensor([0, 0, 0, 1]).type(camera_rt_new.dtype)[None, None, None, :].expand(camera_rt_new.shape[0], camera_rt_new.shape[1], 1, -1) # shape:(N_models, N_view_new, 1, 4) camera_p04s_new = torch.cat((camera_p0s_new, ones), dim=2) # shape:(N_models, N_view_new, 4, 4) # pdb.set_trace() # return more camera parameters. return camera_p04s_new, camera_p0s_new, camera_ks_new, camera_rt_new, camera_ts_new
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import torch import torch.nn as nn import numpy as np import torch.nn.functional as F __all__ = ['FixupResNet', 'fixup_resnet18', 'fixup_resnet34', 'fixup_resnet50', 'fixup_resnet101', 'fixup_resnet152'] def conv3x3(in_planes, out_planes, stride=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) def conv5x5(in_planes, out_planes, stride=1): """5x5 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=5, stride=stride, padding=2, bias=False) def conv7x7(in_planes, out_planes, stride=1): """7x7 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=7, stride=stride, padding=3, bias=False) def conv1x1(in_planes, out_planes, stride=1): """1x1 convolution""" return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) class FixupBasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None, conv_create=conv3x3): super(FixupBasicBlock, self).__init__() # Both self.conv1 and self.downsample layers downsample the input when stride != 1 self.bias1a = nn.Parameter(torch.zeros(1)) self.conv1 = conv_create(inplanes, planes, stride) self.bias1b = nn.Parameter(torch.zeros(1)) self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) self.bias2a = nn.Parameter(torch.zeros(1)) self.conv2 = conv_create(planes, planes) self.scale = nn.Parameter(torch.ones(1)) self.bias2b = nn.Parameter(torch.zeros(1)) self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv1(x + self.bias1a) out = self.lrelu(out + self.bias1b) out = self.conv2(out + self.bias2a) out = out * self.scale + self.bias2b if self.downsample is not None: identity = self.downsample(x + self.bias1a) out += identity out = self.lrelu(out) return out class FixupResNet(nn.Module): def __init__(self, block, layers, upscale_applications=2, num_filters=64, inject_noise=False): super(FixupResNet, self).__init__() self.inject_noise = inject_noise self.num_layers = sum(layers) + layers[-1] * (upscale_applications - 1) # The last layer is applied repeatedly to achieve high level SR. self.inplanes = num_filters self.upscale_applications = upscale_applications # Part 1 - Process raw input image. Most denoising should appear here and this should be the most complicated # part of the block. self.conv1 = nn.Conv2d(3, num_filters, kernel_size=5, stride=1, padding=2, bias=False) self.bias1 = nn.Parameter(torch.zeros(1)) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) self.layer1 = self._make_layer(block, num_filters, layers[0], stride=1) self.skip1 = nn.Conv2d(num_filters, 3, kernel_size=5, stride=1, padding=2, bias=False) self.skip1_bias = nn.Parameter(torch.zeros(1)) # Part 2 - This is the upsampler core. It consists of a normal multiplicative conv followed by several residual # convs which are intended to repair artifacts caused by 2x interpolation. # This core layer should by itself accomplish 2x super-resolution. We use it in repeat to do the # requested SR. self.nf2 = int(num_filters/4) # This part isn't repeated. It de-filters the output from the previous step to fit the filter size used in the # upsampler-conv. self.upsampler_conv = nn.Conv2d(num_filters, self.nf2, kernel_size=3, stride=1, padding=1, bias=False) self.uc_bias = nn.Parameter(torch.zeros(1)) self.inplanes = self.nf2 if layers[1] > 0: # This is the repeated part. self.layer2 = self._make_layer(block, int(self.nf2), layers[1], stride=1, conv_type=conv5x5) self.skip2 = nn.Conv2d(self.nf2, 3, kernel_size=5, stride=1, padding=2, bias=False) self.skip2_bias = nn.Parameter(torch.zeros(1)) self.final_defilter = nn.Conv2d(self.nf2, 3, kernel_size=5, stride=1, padding=2, bias=True) self.bias2 = nn.Parameter(torch.zeros(1)) for m in self.modules(): if isinstance(m, FixupBasicBlock): nn.init.normal_(m.conv1.weight, mean=0, std=np.sqrt(2 / (m.conv1.weight.shape[0] * np.prod(m.conv1.weight.shape[2:]))) * self.num_layers ** (-0.5)) nn.init.constant_(m.conv2.weight, 0) if m.downsample is not None: nn.init.normal_(m.downsample.weight, mean=0, std=np.sqrt(2 / (m.downsample.weight.shape[0] * np.prod(m.downsample.weight.shape[2:])))) def _make_layer(self, block, planes, blocks, stride=1, conv_type=conv3x3): defilter = None if self.inplanes != planes * block.expansion: defilter = conv1x1(self.inplanes, planes * block.expansion, stride) layers = [] layers.append(block(self.inplanes, planes, stride, defilter)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes, conv_create=conv_type)) return nn.Sequential(*layers) def forward(self, x): if self.inject_noise: rand_feature = torch.randn_like(x) x = x + rand_feature * .1 x = self.conv1(x) x = self.lrelu(x + self.bias1) x = self.layer1(x) skip_lo = self.skip1(x) + self.skip1_bias x = self.lrelu(self.upsampler_conv(x) + self.uc_bias) if self.upscale_applications > 0: x = F.interpolate(x, scale_factor=2.0, mode='nearest') x = self.layer2(x) skip_med = self.skip2(x) + self.skip2_bias else: skip_med = skip_lo if self.upscale_applications > 1: x = F.interpolate(x, scale_factor=2.0, mode='nearest') x = self.layer2(x) x = self.final_defilter(x) + self.bias2 return x, skip_med, skip_lo class FixupResNetV2(FixupResNet): def __init__(self, **kwargs): super(FixupResNetV2, self).__init__(**kwargs) # Use one unified filter-to-image stack, not the previous skip stacks. self.skip1 = None self.skip1_bias = None self.skip2 = None self.skip2_bias = None # The new filter-to-image stack will be 2 conv layers deep, not 1. self.final_process = nn.Conv2d(self.nf2, self.nf2, kernel_size=5, stride=1, padding=2, bias=True) self.bias2 = nn.Parameter(torch.zeros(1)) self.fp_bn = nn.BatchNorm2d(self.nf2) self.final_defilter = nn.Conv2d(self.nf2, 3, kernel_size=3, stride=1, padding=1, bias=True) self.bias3 = nn.Parameter(torch.zeros(1)) def filter_to_image(self, filter): x = self.final_process(filter) + self.bias2 x = self.lrelu(self.fp_bn(x)) x = self.final_defilter(x) + self.bias3 return x def forward(self, x): if self.inject_noise: rand_feature = torch.randn_like(x) x = x + rand_feature * .1 x = self.conv1(x) x = self.lrelu(x + self.bias1) x = self.layer1(x) x = self.lrelu(self.upsampler_conv(x) + self.uc_bias) skip_lo = self.filter_to_image(x) if self.upscale_applications > 0: x = F.interpolate(x, scale_factor=2.0, mode='nearest') x = self.layer2(x) skip_med = self.filter_to_image(x) if self.upscale_applications > 1: x = F.interpolate(x, scale_factor=2.0, mode='nearest') x = self.layer2(x) if self.upscale_applications == 2: x = self.filter_to_image(x) elif self.upscale_applications == 1: x = skip_med skip_med = skip_lo skip_lo = None elif self.upscale_applications == 0: x = skip_lo skip_lo = None skip_med = None return x, skip_med, skip_lo def fixup_resnet34(nb_denoiser=20, nb_upsampler=10, **kwargs): """Constructs a Fixup-ResNet-34 model. """ model = FixupResNet(FixupBasicBlock, [nb_denoiser, nb_upsampler], **kwargs) return model def fixup_resnet34_v2(nb_denoiser=20, nb_upsampler=10, **kwargs): """Constructs a Fixup-ResNet-34 model. """ kwargs['block'] = FixupBasicBlock kwargs['layers'] = [nb_denoiser, nb_upsampler] model = FixupResNetV2(**kwargs) return model __all__ = ['FixupResNet', 'fixup_resnet34', 'fixup_resnet34_v2']
[ "torch.nn.BatchNorm2d", "numpy.prod", "torch.nn.LeakyReLU", "torch.nn.init.constant_", "torch.nn.Sequential", "torch.nn.Conv2d", "torch.randn_like", "torch.nn.functional.interpolate", "torch.zeros", "torch.ones" ]
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# Copyright 2021 The FLAN Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Utility functions for FLAN.""" import abc import re from typing import Optional import numpy as np from flan import templates def is_classification(flan_pattern_name: str): """Returns if the task is a classification task.""" # ReCoRD task has variable length options, so it is not called options in # the input pattern. But it is classification. if flan_pattern_name == 'record': return True input_patterns = [p[0] for p in templates.PATTERNS[flan_pattern_name]] return np.any(['{options_}' in pattern for pattern in input_patterns]) class SeqioTaskName(metaclass=abc.ABCMeta): """Abstract class for seqio task name.""" @abc.abstractclassmethod def get(cls, *args): """Returns task name.""" raise NotImplementedError @abc.abstractclassmethod def parse(cls, task_name: str): """Returns task name.""" raise NotImplementedError @abc.abstractclassmethod def match(cls, task_name: str) -> Optional[re.Match]: """Returns the match object if `task_name` matches the name pattern.""" raise NotImplementedError class ZeroshotEvalTaskName(SeqioTaskName): """Task name for zeroshot eval.""" @classmethod def get(cls, t_name: str, template_id: int) -> str: return f'{t_name}_type_{template_id}' @classmethod def parse(cls, task_name): match = cls.match(task_name) return match[1], int(match[2]) @classmethod def match(cls, task_name) -> Optional[re.Match]: return re.fullmatch(r'^(.+)_type_(\d+)$', task_name) class ZeroshotScoreEvalTaskName(SeqioTaskName): """Task name for zeroshot scoring eval.""" @classmethod def get(cls, t_name: str, template_id: int) -> str: return f'{t_name}_type_{template_id}_scoring_eval' @classmethod def parse(cls, task_name): match = cls.match(task_name) return match[1], int(match[2]) @classmethod def match(cls, task_name) -> Optional[re.Match]: return re.fullmatch(r'^(.+)_type_(\d+)_scoring_eval$', task_name) class ZeroshotScoreEvalNoOptionTaskName(SeqioTaskName): """Task name for zeroshot scoring eval without options.""" @classmethod def get(cls, t_name: str, template_id: int) -> str: return f'{t_name}_type_{template_id}_score_eval_no_options' @classmethod def parse(cls, task_name): match = cls.match(task_name) return match[1], int(match[2]) @classmethod def match(cls, task_name) -> Optional[re.Match]: return re.fullmatch(r'^(.+)_type_(\d+)_score_eval_no_options$', task_name) class ZeroshotScoreFLANNoOptionTaskName(SeqioTaskName): """Task name for zeroshot scoring eval without options.""" @classmethod def get(cls, t_name: str, template_id: int) -> str: return f'{t_name}_type_{template_id}_score_flan_no_options' @classmethod def parse(cls, task_name): match = cls.match(task_name) return match[1], int(match[2]) @classmethod def match(cls, task_name) -> Optional[re.Match]: return re.fullmatch(r'^(.+)_type_(\d+)_score_flan_no_options$', task_name) class AllPromptsTaskName(SeqioTaskName): """Task name for the training job realized from all prompts.""" @classmethod def get(cls, t_name: str) -> str: return f'{t_name}_all_prompts' @classmethod def parse(cls, task_name): match = cls.match(task_name) return match[1] @classmethod def match(cls, task_name) -> Optional[re.Match]: return re.fullmatch(r'^(.+)_all_prompts', task_name) class ZeroshotTemplatedTaskName(SeqioTaskName): """Zeroshot task name with number of realized templates.""" @classmethod def get(cls, t_name: str, num_templates: int) -> str: return f'{t_name}_{num_templates}templates' @classmethod def parse(cls, task_name): match = cls.match(task_name) return match[1], int(match[2]) @classmethod def match(cls, task_name) -> Optional[re.Match]: return re.fullmatch(r'^(.+)_(\d+)templates$', task_name) class XshotTemplatedTaskName(SeqioTaskName): """Zeroshot task name with number of realized templates.""" @classmethod def get(cls, t_name: str, num_templates: int, num_shot: str) -> str: return f'{t_name}_{num_templates}templates_{num_shot}_shot' @classmethod def parse(cls, task_name): match = cls.match(task_name) return match[1], int(match[2]), match[3] @classmethod def match(cls, task_name) -> Optional[re.Match]: return re.fullmatch(r'^(.+)_(\d+)templates_([a-z]+)_shot$', task_name) def remove_input_patterns_options(input_pattern: str) -> str: """Remove options from the input pattern.""" no_options_pattern = input_pattern.replace('{options_}', '') no_options_pattern = no_options_pattern.replace('{options_str}', '').strip() return no_options_pattern def t_name_to_flan_pattern_name(t_name: str) -> str: """Converts `t_name` to flan `PATTERN` key. Some seqio tasks use the same flan patterns. Args: t_name: Task config name. Returns: a key for `PATTERNS`. """ if 'para_crawl' in t_name: return 'para_crawl' elif 'wmt16_translate' in t_name: return 'wmt16_translate' elif t_name in {'arc_challenge', 'arc_easy'}: return 'arc' elif t_name in {'anli_r1', 'anli_r2', 'anli_r3'}: return 'anli' elif t_name in {'mnli_matched', 'mnli_mismatched'}: return 'mnli' return t_name def get_eval_dir_basename(task: str, split: str) -> str: """Returns the basename for eval directory. Args: task: a seqio eval task name. split: split name. """ return f'eval_{task}_{split}'
[ "re.fullmatch", "numpy.any" ]
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""" Implements modification of human attributes at different levels. ###################### Quarantining / Behavior change logic ######################## Following orders takes care of the person faced with multiple quarantining triggers (each trigger has a suggested duration for quarantine) - (i) (not app-based) QUARANTINE_DUE_TO_POSITIVE_TEST_RESULT, QUARANTINE_UNTIL_TEST_RESULT. In event of positive result, person is quarantined for 14 days from the day that test was taken. If negative result, person is quarantined until the test results come out (ii) (non-app based) SELF_DIAGNOSIS (iii) (app based) RISK_LEVEL_UPDATE: x->MAX LEVEL Dropout enables non-adherence to quarantine at any time. To consider household quarantine, residents are divided into two groups: (i) index cases - they have a quarantine trigger i.e. a reason to believe that they should quarantine (ii) secondary cases - rest of the residents non-app quarantining for index cases - * A trigger higher in precedence overwrites other triggers i.e. quarantining duration is changed based on the trigger * `human` might already be quarantining at the time of this trigger, so the duration is changed only if trigger requirements require so. * if there are no non-app triggers, app-based triggers are checked every `human.time_slot` and behavior levels are adjusted accordingly non-app quarantining for secondary cases - * All of them quarantine for the same duration unless someone is converted to an index case, in which case, they quarantine and influence household quarantine according to their triggers. * this duration is defined by the index case who has maximum quarantining restrictions. app-based recommendations - Behavior changes for non-app recommendation for household members - * if there are no non-app quarantining triggers, humans are put on app recommendation * if MAKE_HOUSEHOLD_BEHAVE_SAME_AS_MAX_RISK_RESIDENT is True, other residents follow the same behavior as the max risk individual in the house ######################################################################## """ import numpy as np import warnings import datetime from covid19sim.locations.hospital import Hospital, ICU from covid19sim.utils.constants import SECONDS_PER_DAY from covid19sim.utils.constants import TEST_TAKEN, SELF_DIAGNOSIS, RISK_LEVEL_UPDATE from covid19sim.utils.constants import NEGATIVE_TEST_RESULT, POSITIVE_TEST_RESULT from covid19sim.utils.constants import QUARANTINE_UNTIL_TEST_RESULT, QUARANTINE_DUE_TO_POSITIVE_TEST_RESULT, QUARANTINE_DUE_TO_SELF_DIAGNOSIS from covid19sim.utils.constants import UNSET_QUARANTINE, QUARANTINE_HOUSEHOLD from covid19sim.utils.constants import INITIALIZED_BEHAVIOR, INTERVENTION_START_BEHAVIOR, IS_IMMUNE_BEHAVIOR def convert_intervention_to_behavior_level(intervention_level): """ Maps `human._intervention_level` to `IntervenedBehavior.behavior_level` """ return intervention_level + 1 if intervention_level >= 0 else -1 class Quarantine(object): """ Contains logic to handle different combinations of non-app quarantine triggers. Args: human (covid19sim.human.Human): `human` whose behavior needs to be changed env (simpy.Environment): environment to schedule events conf (dict): yaml configuration of the experiment """ def __init__(self, intervened_behavior, human, env, conf): self.human = human self.intervened_behavior = intervened_behavior self.env = env self.conf = conf self.start_timestamp = None self.end_timestamp = None self.reasons = [] self.quarantine_idx = self.intervened_behavior.quarantine_idx self.baseline_behavior_idx = self.intervened_behavior.baseline_behavior_idx self.human_no_longer_needs_quarantining = False # once human has recovered (infered from 14 days after positive test), human no longer quarantines def update(self, trigger): """ Updates quarantine start and end timestamp based on the new `trigger` and previous triggers. Note 1: `human_no_longer_needs_quarantining` is set in `reset_quarantine`. if its True, all calls to this function are ignored. Note 2: Test results are treated to have conclusive and ultimate say on the duration of quarantine. Note 3: There can be quarantining due to several reasons, so all those combinations are treated in this function through rules described in Quaranining Logic at the top. Args: trigger (str): reason for quarantine trigger. """ if self.human_no_longer_needs_quarantining: return # if `human` is already quarantining due to TEST_TAKEN, then do not change anything if ( QUARANTINE_UNTIL_TEST_RESULT in self.reasons or QUARANTINE_DUE_TO_POSITIVE_TEST_RESULT in self.reasons ): return if ( trigger == QUARANTINE_HOUSEHOLD and self.end_timestamp is not None and self.end_timestamp >= self.human.household.quarantine_end_timestamp ): return # self.reasons.append(trigger) if self.start_timestamp is None: self.start_timestamp = self.env.timestamp # set end timestamp and behavior levels accordingly # negative test result - quarantine until the test result if trigger == QUARANTINE_UNTIL_TEST_RESULT: duration = self.human.time_to_test_result * SECONDS_PER_DAY self.end_timestamp = self.env.timestamp + datetime.timedelta(seconds=duration) self._set_quarantine_behavior(self.reasons, test_recommended=False) if self.conf['QUARANTINE_HOUSEHOLD_UPON_INDIVIDUAL_TEST_TAKEN']: self.human.household.add_to_index_case(self.human, trigger) # positive test result - quarantine until max duration elif trigger == QUARANTINE_DUE_TO_POSITIVE_TEST_RESULT: duration = self.conf['QUARANTINE_DAYS_ON_POSITIVE_TEST'] * SECONDS_PER_DAY self.end_timestamp = self.start_timestamp + datetime.timedelta(seconds=duration) self._set_quarantine_behavior(self.reasons, test_recommended=False) if self.conf['QUARANTINE_HOUSEHOLD_UPON_INDIVIDUAL_POSITIVE_TEST']: self.human.household.add_to_index_case(self.human, trigger) elif trigger == QUARANTINE_DUE_TO_SELF_DIAGNOSIS: assert False, NotImplementedError(f"{trigger} quarantine not implemented") elif trigger == QUARANTINE_HOUSEHOLD: self.end_timestamp = self.human.household.quarantine_end_timestamp self._set_quarantine_behavior(self.reasons, test_recommended=False) else: raise ValueError(f"Unknown trigger for quarantine: {trigger}") def _set_quarantine_behavior(self, reasons, test_recommended): """ Sets behavior level for quarantining and whether a test is recommended or not. Check Quarantine.update for more. Note: It is to be called from `Quarantine.update` Args: reasons (list): reasons for quarantining. test_recommended (bool): whether `human` should get a test or not. """ self.intervened_behavior.set_behavior(level=self.quarantine_idx, reasons=reasons) self.human._test_recommended = test_recommended def _unset_quarantine_behavior(self, to_level): """ Resets `human` from `quarantine_idx` to `to_level`. Note: It is to be called from `Quarantine.update` Args: to_level (int): the level to which `human`s behavior level should be reset to. """ assert to_level != self.quarantine_idx, "unsetting the quarantine to quarantine_level. Something is wrong." self.intervened_behavior.set_behavior(level=to_level, reasons=[UNSET_QUARANTINE, f"{UNSET_QUARANTINE}: {self.intervened_behavior._behavior_level}->{to_level}"]) self.human._test_recommended = False def reset_quarantine(self): """ Resets quarantine related attributes and puts `human` into a relevant behavior level. Note 1: Specific to non-binary risk tracing, reset doesn't work if the recommendation is still to quarantine. Note 2: It also sets the flag for no more need to quarantine once the test results are positive. """ assert self.start_timestamp is not None, "unsetting quarantine twice not allowed" assert not self.human_no_longer_needs_quarantining, f"{self.human} was quarantined while it shouldn't have" last_reason = self.reasons[-1] # if ( not self.human_no_longer_needs_quarantining and ( self.human.has_had_positive_test or last_reason == QUARANTINE_DUE_TO_POSITIVE_TEST_RESULT or self.human.test_result == POSITIVE_TEST_RESULT ) ): self.human_no_longer_needs_quarantining = True self.start_timestamp = None self.end_timestamp = None self.reasons = [] self._unset_quarantine_behavior(self.baseline_behavior_idx) self.human.household.reset_index_case(self.human) def reset_if_its_time(self): """ Resets `timestamp`s. It is called everytime a new activity is to be decided or a trigger is added. """ if self.start_timestamp is not None: if self.end_timestamp <= self.env.timestamp: self.reset_quarantine() class IntervenedBehavior(object): """ A base class to implement intervened behavior. Args: human (covid19sim.human.Human): `human` whose behavior needs to be changed env (simpy.Environment): environment to schedule events conf (dict): yaml configuration of the experiment """ def __init__(self, human, env, conf): self.human = human self.env = env self.conf = conf self.rng = human.rng assert conf['N_BEHAVIOR_LEVELS'] >= 2, "At least 2 behavior levels are required to model behavior changes" # we reserve 0-index self.n_behavior_levels = conf['N_BEHAVIOR_LEVELS'] + 1 self.quarantine_idx = self.n_behavior_levels - 1 self.baseline_behavior_idx = 1 self._behavior_level = 0 # true behavior level self.behavior_level = 0 # its a property.setter # start filling the reduction levels from the end reduction_levels = { "HOUSEHOLD": np.zeros(self.n_behavior_levels), "WORKPLACE": np.zeros(self.n_behavior_levels), "OTHER": np.zeros(self.n_behavior_levels), "SCHOOL": np.zeros(self.n_behavior_levels), } reduction_levels["HOUSEHOLD"][-1] = 1.0 reduction_levels["WORKPLACE"][-1] = 1.0 reduction_levels["OTHER"][-1] = 1.0 reduction_levels["SCHOOL"][-1] = 1.0 last_filled_index = self.quarantine_idx # if number of behavior levels is 2 and interpolation is with respect to lockdown contacts, it is a Lockdown scenario if conf['INTERPOLATE_CONTACTS_USING_LOCKDOWN_CONTACTS']: reduction_levels["HOUSEHOLD"][-2] = conf['FRACTION_LOCKDOWN_INTERPOLATION'] * conf['LOCKDOWN_FRACTION_REDUCTION_IN_CONTACTS_AT_HOUSEHOLD'] reduction_levels["WORKPLACE"][-2] = conf['FRACTION_LOCKDOWN_INTERPOLATION'] * conf['LOCKDOWN_FRACTION_REDUCTION_IN_CONTACTS_AT_WORKPLACE'] reduction_levels["OTHER"][-2] = conf['FRACTION_LOCKDOWN_INTERPOLATION'] * conf['LOCKDOWN_FRACTION_REDUCTION_IN_CONTACTS_AT_OTHER'] reduction_levels["SCHOOL"][-2] = conf['FRACTION_LOCKDOWN_INTERPOLATION'] * conf['LOCKDOWN_FRACTION_REDUCTION_IN_CONTACTS_AT_SCHOOL'] last_filled_index -= 1 else: # if its a non-tracing scenario, and lockdown is not desired, its an unmitigated scenario with 0% reduction in the first level if conf["RISK_MODEL"] == "" and conf['N_BEHAVIOR_LEVELS'] == 2: last_filled_index -= 1 assert last_filled_index == self.baseline_behavior_idx, "unmitigated scenario should not have non-zero reduction in baseline_behavior" # in a non-tracing scenario, baseline_behavior is not defined so we populate levels until baseline_behavior while last_filled_index > self.baseline_behavior_idx: to_fill_index = last_filled_index - 1 for location_type in ["HOUSEHOLD", "WORKPLACE", "OTHER", "SCHOOL"]: reduction_levels[location_type][to_fill_index] = reduction_levels[location_type][last_filled_index] / 2 last_filled_index = to_fill_index self.reduction_levels = reduction_levels # start everyone at the zero level by default (unmitigated scenario i.e. no reduction in contacts) self.quarantine = Quarantine(self, self.human, self.env, self.conf) self.set_behavior(level=0, reasons=[INITIALIZED_BEHAVIOR]) # dropout self._follow_recommendation_today = None self.last_date_to_decide_dropout = None # self.intervention_started = False self.pay_no_attention_to_triggers = False def initialize(self, check_has_app=False): """ Sets up a baseline behavior on the day intervention starts. Args: check_has_app (bool): whether to initialize a baseline beahvior only for humans with the app """ assert self.conf['INTERVENTION_DAY'] >= 0, "negative intervention day and yet intialization is called." assert self.n_behavior_levels >= 2, "with 2 behavior levels and a risk model, behavior level 1 will quarantine everyone" if check_has_app and self.human.has_app: warnings.warn("An unrealistic scenario - initilization of baseline behavior is only for humans with an app") self.set_behavior(level=self.baseline_behavior_idx, reasons=[INTERVENTION_START_BEHAVIOR]) return self.set_behavior(level=self.baseline_behavior_idx, reasons=[INTERVENTION_START_BEHAVIOR]) self.intervention_started = True def update_and_get_true_behavior_level(self): """ Returns the true underlying behavior of human. Updates the underlying behavior level if this function is called past the `quarantine.end_timestamp`. (WIP) A true behavior of such kind can only be achieved by using Quarantine as a simpy.Event. Note: if `human` uses the app and follows recommendation of someone else in the hosuehold, _behavior_level will be different then what behavior_level is. Returns: (int): Behavior level of `human` that determines the number of interactions that a human can have """ self.quarantine.reset_if_its_time() return self._behavior_level @property def behavior_level(self): """ Returns appropriate behavior according to which `human` is supposed to act. Dropout, not used here, can further affect this level. Note: It updates the underlying behavior level if this function is called past the `quarantine.end_timestamp`. It can not be considered as a side effect. A true behavior of such kind can only be achieved by using Quarantine as a simpy.Event. Returns: (int): Behavior level of `human` that determines the number of interactions that a human can have """ if self.human.is_dead: return -1 # if currently someone in the house is following app Rx (someone in the house has to have an app) if ( self.quarantine.start_timestamp is None # currently no non-app quarantining and not self.pay_no_attention_to_triggers # hasn't had a positive test in the past and self.conf['MAKE_HOUSEHOLD_BEHAVE_SAME_AS_MAX_RISK_RESIDENT'] ): # Note: some `human`s in recovery phase who haven't reset their test_results yet will also come here return max(resident.intervened_behavior.update_and_get_true_behavior_level() for resident in self.human.household.residents) return self.update_and_get_true_behavior_level() @behavior_level.setter def behavior_level(self, val): self._behavior_level = val @property def follow_recommendation_today(self): """ Determines whether `human` follows the restrictions today or not """ last_date = self.last_date_to_decide_dropout current_date = self.env.timestamp.date() if ( last_date is None or (current_date - last_date).days > 0 ): dropout = _get_dropout_rate(self.current_behavior_reason, self.conf) self.last_date_to_decide_dropout = current_date self._follow_recommendation_today = self.rng.rand() < (1 - dropout) return self._follow_recommendation_today @property def is_under_quarantine(self): """ Returns True if `human` is under quarantine restrictions. It doesn't account for dropout. """ return self.behavior_level == self.quarantine_idx def is_quarantining(self): """ Returns True if `human` is currently quarantining. It accounts for dropout (non-adherence). """ self.quarantine.reset_if_its_time() if self.is_under_quarantine: if self.follow_recommendation_today: return True return False def daily_interaction_reduction_factor(self, location): """ Returns fraction of contacts that are reduced from the unmitigated scneraio. Args: location (covid19sim.locations.location.Location): location where `human` is currently at Returns: (float): fraction by which unmiitgated contacts should be reduced. 1.0 means 0 interactions, and 0.0 means interactions under unmitigated scenario. """ if ( self.intervention_started and isinstance(location, (Hospital, ICU)) and self.conf['ASSUME_SAFE_HOSPITAL_DAILY_INTERACTIONS_AFTER_INTERVENTION_START'] ): return 1.0 # if `human` is not following any recommendations today, then set the number of interactions to level 0 if not self.follow_recommendation_today: return 0.0 location_type = _get_location_type(self.human, location) return self.reduction_levels[location_type][self.behavior_level] def set_behavior(self, level, reasons): """ Sets `self.behavior_level` to level for duration `until`. Args: level (int): behvaior level to put `human` on reasons (list): reasons for this level. """ assert reasons is not None and type(reasons) == list, f"reasons: {reasons} is None or it is not a list." self.behavior_level = level self.current_behavior_reason = reasons # (debug) # if self.human.name in ["human:71", "human:77", "human:34"]: # print(self.env.timestamp, "set behavior level of", self.human, f"to {level}", "because", self.current_behavior_reason, self.quarantine.start_timestamp, self.quarantine.end_timestamp) def set_recommended_behavior(self, level): """ All app-based behavior changes happen through here. It sets _test_recommended attribute of human according to the behavior level. Args: level (int): behvaior level to put `human` on """ if level == self.quarantine_idx: self.human._test_recommended = True # TODO - P - how should this affect score at the test facility elif ( level != self.quarantine_idx and self._behavior_level == self.quarantine_idx ): self.human._test_recommended = False self.set_behavior(level=level, reasons=[RISK_LEVEL_UPDATE, f"{RISK_LEVEL_UPDATE}: {self._behavior_level}->{level}"]) def trigger_intervention(self, reason): """ Changes the necessary attributes in `human`, `self.quarantine`, and `self` depending on the reason. Args: reason (str): reason for the change in behavior of human """ # if `human` knows about immunity, there is no need to follow any recommendations/quarantining if self.pay_no_attention_to_triggers: return if ( not self.pay_no_attention_to_triggers and self.human.has_had_positive_test and self.quarantine.start_timestamp is None ): self.pay_no_attention_to_triggers = True self.set_behavior(level=self.baseline_behavior_idx, reasons=[IS_IMMUNE_BEHAVIOR]) return # (no app required) # If someone went for a test, they need to quarantine if reason == TEST_TAKEN: result = self.human.hidden_test_result if result == NEGATIVE_TEST_RESULT: self.quarantine.update(QUARANTINE_UNTIL_TEST_RESULT) elif result == POSITIVE_TEST_RESULT: self.quarantine.update(QUARANTINE_DUE_TO_POSITIVE_TEST_RESULT) else: raise ValueError(f"Unknown test result:{result}") # (no app required) elif reason == SELF_DIAGNOSIS: assert self.conf['QUARANTINE_SELF_REPORTED_INDIVIDUALS'], "configs do not allow for quarantining self-reported individuals" self.quarantine.update(QUARANTINE_DUE_TO_SELF_DIAGNOSIS) # (app required) tracing based behavioral changes elif reason == RISK_LEVEL_UPDATE: assert self.conf['RISK_MODEL'] != "", "risk model is empty but behavior change due to risk changes is being called." assert self.human.has_app, "human doesn't have an app, but the behavior changes are being called." # if currently quarantining because of non-app triggers, don't do anything self.quarantine.reset_if_its_time() if self.quarantine.start_timestamp is not None: return # determine recommendation of the app normalized_model = False if self.human.city.daily_rec_level_mapping is None: intervention_level = self.human.rec_level else: # QKFIX: There are 4 recommendation levels, the value is hard-coded here probas = self.human.city.daily_rec_level_mapping[self.human.rec_level] intervention_level = self.rng.choice(4, p=probas) normalized_model = True self.human._intervention_level = intervention_level # map intervention level to behavior levels by shifting them by 1 (because 1st index is reserved for no reduction in contacts) behavior_level = convert_intervention_to_behavior_level(intervention_level) assert 0 < behavior_level < self.n_behavior_levels, f"behavior_level: {behavior_level} can't be outside the range [1,{self.n_behavior_levels}]. Total number of levels:{self.n_behavior_levels}" # if there is no change in the recommendation, don't do anything if ( RISK_LEVEL_UPDATE in self.current_behavior_reason and self._behavior_level == behavior_level ): return # (debug) # if self.human.name == "human:71" and self._behavior_level==1 and behavior_level==4: # breakpoint() self.set_recommended_behavior(level=behavior_level) else: raise ValueError(f"Unknown reason for intervention:{reason}") def __repr__(self): return f"IntervenedBehavior for {self.human}" def _get_location_type(human, location): """ Returns the location type to use for contact reduction depending on location and human's attributes Args: human (covid19sim.human.Human): `human` for whom this factor need to be determined location (covid19sim.locations.location.Location): `location` at which human is currently Returns: (str): location type that should be considered for evaluation number of contacts """ if ( location == human.workplace and location.location_type != "SCHOOL" ): return "WORKPLACE" elif ( location == human.workplace and location.location_type == "SCHOOL" ): return "SCHOOL" elif location == human.household: return "HOUSEHOLD" else: return "OTHER" def _get_dropout_rate(reasons, conf): """ Returns a probability of not following an intervention due to `reasons` Args: reasons (list): list of strings that define the current behavior conf (dict): yaml configuration of the experiment Returns: (float): dropout rate for the current behavior """ _reason = reasons[-1] _reason = UNSET_QUARANTINE if UNSET_QUARANTINE in _reason else _reason _reason = RISK_LEVEL_UPDATE if RISK_LEVEL_UPDATE in _reason else _reason if _reason in [INITIALIZED_BEHAVIOR, INTERVENTION_START_BEHAVIOR, UNSET_QUARANTINE, IS_IMMUNE_BEHAVIOR]: return 0.0 if _reason in [QUARANTINE_UNTIL_TEST_RESULT, QUARANTINE_DUE_TO_POSITIVE_TEST_RESULT]: return conf['QUARANTINE_DROPOUT_TEST'] elif _reason == QUARANTINE_DUE_TO_SELF_DIAGNOSIS: return conf['QUARANTINE_DROPOUT_SELF_REPORTED_SYMPTOMS'] elif _reason == QUARANTINE_HOUSEHOLD: return conf['QUARANTINE_DROPOUT_HOUSEHOLD'] elif _reason == RISK_LEVEL_UPDATE: return conf['ALL_LEVELS_DROPOUT'] else: raise ValueError(f"Unknown value:{reasons}")
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import numpy import random from solution import solution def elitism(population, scores, bestIndividual, bestScore): """ This melitism operator of the population """ # get the worst individual worstFitnessId = selectWorstIndividual(scores) # replace worst cromosome with best one from previous generation if its fitness is less than the other if scores[worstFitnessId] > bestScore: population[worstFitnessId] = numpy.copy(bestIndividual) scores[worstFitnessId] = numpy.copy(bestScore) def selectWorstIndividual(scores): """ It is used to get the worst individual in a population based n the fitness value """ maxFitnessId = numpy.where(scores == numpy.max(scores)) maxFitnessId = maxFitnessId[0][0] return maxFitnessId class GA(solution): def __init__(self, objf, sol_shift, lb, ub, dim, PopSize, EvlNum): self.cp = 1 # crossover Probability self.mp = 0.01 # Mutation Probability self.keep = 2 # elitism parameter: how many of the best individuals to keep from one generation to the next self.dim = dim self.popnum = PopSize self.maxiers = int(EvlNum / PopSize) self.optimizer = "GA" self.objfname = objf.__name__ self.objf = objf self.sol_shift = sol_shift # convert lb, ub to array self.lb = numpy.array([lb for _ in range(dim)]) self.ub = numpy.array([ub for _ in range(dim)]) self.best = float("inf") # initialize population self.solutions = [] self.solutions_new = numpy.zeros((self.popnum,self.dim)) for p in range(PopSize): sol = [] for d in range(dim): d_val = random.uniform(self.lb[d], self.ub[d]) sol.append(d_val) self.solutions.append(sol) self.solutions = numpy.array(self.solutions) self.population_fitness = [] # calculate fitness for all the population for i in range(PopSize): fitness = objf(self.solutions[i, :]-self.sol_shift) self.population_fitness += [fitness] if fitness < self.best: self.best = fitness self.bestIndividual = self.solutions[i, :] self.population_fitness = numpy.array(self.population_fitness) def update(self, iter_id): if iter_id < self.maxiers: # The crossover of all individuals # initialize a new population newPopulation = numpy.empty_like(self.solutions) newPopulation[0:self.keep] = self.solutions[0:self.keep] # Create pairs of parents. The number of pairs equals the number of individuals divided by 2 for i in range(self.keep, self.popnum, 2): # pair of parents selection ##reverse score because minimum value should have more chance of selection reverse = max(self.population_fitness) + min(self.population_fitness) reverseScores = reverse - self.population_fitness.copy() sumScores = sum(reverseScores) pick = random.uniform(0, sumScores) current = 0 for individualId in range(self.popnum): current += reverseScores[individualId] if current > pick: parent1Id = individualId break pick = random.uniform(0, sumScores) current = 0 for individualId in range(self.popnum): current += reverseScores[individualId] if current > pick: parent2Id = individualId break parent1 = self.solutions[parent1Id].copy() parent2 = self.solutions[parent2Id].copy() crossoverLength = min(len(parent1), len(parent2)) parentsCrossoverProbability = random.uniform(0.0, 1.0) if parentsCrossoverProbability < self.cp: # The point at which crossover takes place between two parents. crossover_point = random.randint(0, crossoverLength - 1) # The new offspring will have its first half of its genes taken from the first parent and second half of its genes taken from the second parent. offspring1 = numpy.concatenate( [parent1[0:crossover_point], parent2[crossover_point:]] ) # The new offspring will have its first half of its genes taken from the second parent and second half of its genes taken from the first parent. offspring2 = numpy.concatenate( [parent2[0:crossover_point], parent1[crossover_point:]] ) else: offspring1 = parent1.copy() offspring2 = parent2.copy() # Add offsprings to population newPopulation[i] = numpy.copy(offspring1) newPopulation[i + 1] = numpy.copy(offspring2) # mutation for i in range(self.keep, self.popnum): # Mutation offspringMutationProbability = random.uniform(0.0, 1.0) if offspringMutationProbability < self.mp: mutationIndex = random.randint(0, self.dim - 1) mutationValue = random.uniform(self.lb[mutationIndex], self.ub[mutationIndex]) newPopulation[i, mutationIndex] = mutationValue # ga = clearDups(ga, lb, ub) newPopulation_unique = numpy.unique(newPopulation, axis=0) oldLen = len(newPopulation) newLen = len(newPopulation_unique) if newLen < oldLen: nDuplicates = oldLen - newLen newPopulation_unique = numpy.append( newPopulation_unique, numpy.random.uniform(0, 1, (nDuplicates, self.dim)) * (numpy.array(self.ub) - numpy.array(self.lb)) + numpy.array(self.lb), axis=0, ) # Loop through individuals in population for i in range(self.popnum): # Return back the search agents that go beyond the boundaries of the search space newPopulation_unique[i] = numpy.clip(newPopulation_unique[i], self.lb, self.ub) # calculate fitness for all the population for i in range(self.popnum): fitness = self.objf(newPopulation_unique[i, :]-self.sol_shift) if fitness < self.population_fitness[i]: self.population_fitness[i] = fitness self.solutions[i] = newPopulation_unique[i].copy() if fitness < self.best: self.best = fitness self.bestIndividual = self.solutions[i] # Sort from best to worst sortedIndices = self.population_fitness.argsort() self.solutions = self.solutions[sortedIndices] self.population_fitness = self.population_fitness[sortedIndices]
[ "numpy.clip", "numpy.copy", "random.uniform", "numpy.unique", "numpy.max", "numpy.array", "numpy.zeros", "numpy.empty_like", "numpy.concatenate", "numpy.random.uniform", "random.randint" ]
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# Copyright (c) 2018, Xilinx # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # 3. Neither the name of the <organization> nor the # names of its contributors may be used to endorse or promote products # derived from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> BE LIABLE FOR ANY # DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import numpy as np import math from im2col import im2col_indices from abc import ABCMeta, abstractproperty class Layer: __metaclass__ = ABCMeta @abstractproperty def __init__(self): pass @abstractproperty def execute(self): pass # @abstractproperty # def updateBitwidths(self, inBitWidth): ## pass def get_type(self): return self.__class__.__name__ def get_stride(self): if hasattr(self, 'stride'): return self.stride return 1 def get_in_dim(self): if hasattr(self, 'padded_idim'): return self.padded_idim elif hasattr(self, 'in_dim'): return self.in_dim elif hasattr(self, 'idim'): return self.idim return -1 def get_pad(self): if hasattr(self, 'pad'): return self.pad return 0 def getOutputSize(self): return (self.outsize) def get_filter_dim(self): if hasattr(self, 'kernel'): return self.kernel return 0 def getInputSize(self): return (self.insize) def get_parallel(self): if hasattr(self, 'parallel'): return self.parallel return 1 def get_out_dim(self): #print "using baseclass def: ", self.get_in_dim(), self.get_filter_dim(), self.get_stride() return int(math.floor(float(self.get_in_dim() - self.get_filter_dim())/self.get_stride()+1)) # Why was it this? math.ceil(self.get_in_dim() + ( 2 * self.get_pad() - self.get_filter_dim() + 1/self.get_stride())) def __repr__(self): return self.__class__.__name__ # device-independent FINN layer types class DummyLayer(Layer): def __init__(self): self.ibits = 32 self.obits = 32 def execute(self): pass def updateBitwidths(self, inBitWidth): self.ibits = inBitWidth self.obits = self.ibits return self.obits class ExternalExecutionLayer(Layer): "Call an executable to process the data. I/O is done via npy files." def __init__(self, execmd): import tempfile self.execmd = execmd self.ifilename = tempfile.mktemp() + ".npy" self.ofilename = tempfile.mktemp() + ".npy" def execute(self, v): from subprocess import check_call np.save(self.ifilename, v.astype(np.float32)) check_call([self.execmd, self.ifilename, self.ofilename]) return np.load(self.ofilename) class MatrixThresholdLayer(Layer): "Fused layer type for a matrix operation followed by thresholding" def __init__(self, name, mlayer, tlayer): self.name = name self.mlayer = mlayer self.kernel = mlayer.kernel self.tlayer = tlayer self.ibits = self.mlayer.ibits self.wbits = self.mlayer.wbits self.obits = self.tlayer.obits self.outsize = self.mlayer.getOutputSize() self.insize = self.mlayer.getInputSize() def getNumOps(self): return self.mlayer.getNumOps() def execute(self, v): return self.tlayer.execute(self.mlayer.execute(v)) def updateBitwidths(self, inBitWidth): self.tlayer.updateBitwidths(self.mlayer.updateBitwidths(inBitWidth)) self.ibits = self.mlayer.ibits self.obits = self.tlayer.obits return self.obits class SoftmaxLayer(Layer): "Compute softmax values for each sets of scores." def __init__(self): self.ibits = 32 self.obits = 32 def execute(selv, v): e_x = np.exp(v - np.max(v)) return e_x / e_x.sum() def updateBitwidths(self, inBitWidth): return self.obits class ReLULayer(Layer): "Apply elementwise ReLU to the vector." def __init__(self): self.ibits = 32 self.obits = 32 def execute(self, v): return np.asarray(map(lambda x: x if x>0 else 0, v)) def updateBitwidths(self, inBitWidth): # strictly speaking, ReLU can actually reduce the output # bitwidth since everything below 0 becomes a 0 self.ibits = inBitWidth self.obits = self.ibits return self.obits class LinearLayer(Layer): "Using vectors A and B, apply Ax+B to incoming x." def __init__(self, A, B): if A.shape != B.shape: raise Exception("LinearLayer A and B shapes do not match") self.A = A self.B = B self.ibits = 32 self.wbits = 32 self.obits = 32 # TODO this is not always correct -- actual size must be propagated from # previous layer. the param shape used here can be much smaller than # the actual incoming image size (in which case the param is repeated/ # broadcasted) self.insize = A.shape[0] self.outsize = A.shape[0] def execute(self, v): # the outermost dimension is the channel dimension # reshape as inner dimension to apply transform vr = v.reshape((self.A.shape[0], -1)).transpose() return (self.A*vr+self.B).transpose().flatten() def updateBitwidths(self, inBitWidth): self.ibits = inBitWidth # for now, we assume the LinearLayer always performs 32-bit float math return self.obits class ThresholdingLayer(Layer): "Given a set of thresholds, return the number of thresholds crossed." def __init__(self, thresholds): # we expect the thresholds array in the following format: # thresholds = [levels][channels] if thresholds.ndim == 1: self.thresholds = thresholds.reshape((len(thresholds),-1)) elif thresholds.ndim == 2: self.thresholds = thresholds else: raise Exception("Thresholds array must be 1- or 2-dimensional") self.ibits = 32 self.obits = int(math.ceil(math.log(self.thresholds.shape[0]+1, 2))) def execute(self, v): # interpret as multi-channel image, where the number of channels is # decided as the number of threshold channels vr = v.reshape((self.thresholds.shape[1], -1)) ret = np.zeros(vr.shape, dtype=np.int) for t in self.thresholds: for c in range(self.thresholds.shape[1]): ret[c] += map(lambda x: 1 if x == True else 0, vr[c] >= t[c]) return ret.flatten() def updateBitwidths(self, inBitWidth): self.ibits = inBitWidth # output bit width stays unchanged for ThresholdingLayer return self.obits class BipolarThresholdingLayer(ThresholdingLayer): "A 1-level ThresholdingLayer that returns -1 and +1 instead of 0 and 1." def __init__(self, thresholds): super(BipolarThresholdingLayer, self).__init__(thresholds) if self.thresholds.shape[0] != 1: raise Exception("BipolarThresholdingLayer can only have one level") def execute(self, v): # just the base implementation, but scaled by 2x-1 such that the output # is -1, +1 instead of 0, 1. this could have been done with a following # LinearLayer, but that LinearLayer may disappear as a result of # streamlining. we have an interest in keeping the bipolar thresholding # intact since there are special XNOR primitives for it. ret = super(BipolarThresholdingLayer, self).execute(v) return 2*ret - 1 # TODO add a LookupTableLayer for nonlinear quantization support class FullyConnectedLayer(Layer): """ A layer that implements fully-connected network layers. Note that bias is not implemented, this can be done by adding a LinearLayer following the FullyConnectedLayer. """ def __init__(self, W, wbits, ibits, obits): self.kernel = 1 self.wbits = wbits self.ibits = ibits self.obits = obits self.W = W self.outsize = W.shape[0] self.insize = W.shape[1] def execute(self, v): return np.dot(self.W, v) def updateBitwidths(self, inBitWidth): self.ibits = inBitWidth if self.ibits == 32 or self.wbits == 32: # produce float outputs for float inputs (since 32bits means # float at the moment) self.obits = 32 else: # find the number of bits necessary to represent the largest possible # sum for a result element. assume maximum valued weight and input: maxWVal = (1 << self.wbits) - 1 maxIVal = (1 << self.ibits) - 1 # assume every single input is maximum: self.obits = int(self.insize*maxWVal*maxIVal).bit_length() return self.obits def getParamSize(self): return self.W.size def getNumOps(self): return self.W.size * 2 def getInputSize(self): """in_channels""" return (self.insize) def getOutputSize(self): return (self.outsize) def getTotalParamBits(self): return self.wbits * self.getParamSize() def getTotalInputBits(self): return self.ibits * np.prod(self.getInputSize()) def getTotalOutputBits(self): return self.obits * np.prod(self.getOutputSize()) class ChanInterleaveLayer(Layer): """ Interleaves multichannel image data passing though the layer. For instance, a typical RGB image may be normally laid out as three single-channel images (R, G, B) such that we have img[chan][row][col]. After passing through this layer, it will be converted to a single image of RGB pixels, such that it is laid out as img[row][col][chan]. """ def __init__(self, inDim, inChans): self.dim = inDim self.chans = inChans self.ibits = 32 self.obits = 32 def execute(self, v): # first, convert the incoming flattened vector into a multidim array img = v.reshape((self.chans, self.dim, self.dim)) # tranpose axes, flatten and return return img.transpose((1, 2, 0)).flatten() def updateBitwidths(self, inBitWidth): self.ibits = inBitWidth self.obits = self.ibits return self.obits class ChanDeinterleaveLayer(Layer): "Does the inverse of ChanInterleaveLayer, see explanation there." def __init__(self, inDim, inChans): self.dim = inDim self.chans = inChans def execute(self, v): # first, convert the incoming flattened vector into a multidim array img = v.reshape((self.dim, self.dim, self.chans)) # tranpose axes, flatten and return return img.transpose((2, 0, 1)).flatten() class PaddingLayer(Layer): "A layer that adds padding around the edges of the image." def __init__(self, inDim, inChans, padCount, padVal): self.dim = inDim self.chans = inChans self.padCount = padCount self.padVal = padVal def execute(self, v): img = v.reshape((self.chans, self.dim, self.dim)) padCounts = ((0, 0), (self.padCount, self.padCount), (self.padCount, self.padCount)) img = np.pad(img, padCounts, "constant", constant_values=self.padVal) return img.flatten() class SlidingWindowLayer(Layer): "Slide a window over a multichannel image (im2col)" def __init__(self, inDim, inChans, windowDim, stride=1): self.idim = inDim self.chans = inChans self.k = windowDim self.s = stride def execute(self, v): # reshape the input vector into a 2D image img = v.reshape((1, self.chans, self.idim, self.idim)) # call im2col to get the sliding window result res = im2col_indices(img, self.k, self.k, padding=0, stride_y=self.s, stride_x=self.s) return res.flatten() class ConvolutionLayer(Layer): "Convolution via im2col and matrix-matrix multiplication" def __init__(self, W, inDim, pad, stride, wbits, ibits, obits, padVal=0): self.wbits = wbits self.ibits = ibits self.obits = obits self.ofm = W.shape[0] self.ifm = W.shape[1] self.kernel = W.shape[2] self.idim = inDim self.padded_idim = inDim + 2*pad self.odim =int(math.floor((float(self.padded_idim - self.kernel) / stride) +1)) self.in_dim = inDim self.out_dim = self.odim self.stride = stride self.pad = pad self.padVal = padVal if(W.shape[2] != W.shape[3]): raise Exception("Only square conv filters supported for now") # instantiate internal layer components self.layers = [] if pad != 0: self.layers += [PaddingLayer(self.idim, self.ifm, pad, padVal)] self.layers += [SlidingWindowLayer(self.padded_idim, self.ifm, self.kernel, self.stride)] self.W = W.reshape((self.ofm, self.ifm*self.kernel*self.kernel)) self.outsize = self.ofm * self.odim * self.odim def execute(self, v): # execute internal padding/sliding window layers first vn = v for l in self.layers: vn = l.execute(vn) # reconstruct image matrix vn = vn.reshape((self.ifm*self.kernel*self.kernel, self.odim*self.odim)) # matrix-matrix multiply res = np.dot(self.W, vn) return res.flatten() def get_filter_dim(self): return self.kernel def get_in_dim(self): return self.padded_idim def getParamSize(self): return self.W.size def getNumOps(self): if hasattr(self, 'parallel'): return self.W.size * self.odim * self.odim * 2 * self.parallel return self.W.size * self.odim * self.odim * 2 def getInputSize(self): return (self.ifm, self.idim, self.idim)[0] def getOutputSize(self): return (self.ofm, self.odim, self.odim)[0] def getTotalParamBits(self): return self.wbits * self.getParamSize() def getTotalInputBits(self): return self.ibits * np.prod(self.getInputSize()) def getTotalOutputBits(self): return self.obits * np.prod(self.getOutputSize()) def updateBitwidths(self, inBitWidth): self.ibits = inBitWidth if self.ibits == 32 or self.wbits == 32: # produce float outputs for float inputs (since 32bits means # float at the moment) self.obits = 32 else: # find the number of bits necessary to represent the largest possible # sum for a result element. assume maximum valued weight and input: maxWVal = (1 << self.wbits) - 1 maxIVal = (1 << self.ibits) - 1 # assume every single input is maximum: self.obits = int(self.W.shape[1]*maxWVal*maxIVal).bit_length() return self.obits class PoolingLayer(Layer): "Perform pooling" def __init__(self, inDim, inChans, poolSize, strideSize, poolFxn = "max"): self.ibits = 32 self.obits = 32 self.idim = inDim self.chans = inChans self.k = poolSize self.s = strideSize self.odim = math.ceil((float(self.idim - self.k) / float(self.s))+1) self.poolFxn = poolFxn self.outsize = (self.chans, self.odim, self.odim)[0] self.insize = self.idim * self.idim * self.chans def execute(self, v): img = v.reshape((self.chans, self.idim, self.idim)) out_img = np.zeros((self.chans, self.odim*self.odim), dtype=np.float32) for c in range(self.chans): chan_img = img[c].reshape((1, 1, self.idim, self.idim)) # extract parts of image with sliding window wnd = im2col_indices(chan_img, self.k, self.k, padding=0, stride_y=self.s, stride_x=self.s) # each window is a column -- get the reduction along columns if self.poolFxn == "MAX": out_img[c]=wnd.max(axis = 0).flatten() elif self.poolFxn == "AVE": out_img[c]=wnd.mean(axis = 0).flatten() else: raise Exception("Unsupported pooling function") return out_img.flatten() def updateBitwidths(self, inBitWidth): self.ibits = inBitWidth self.obits = self.ibits return self.obits class MonitorLayer(Layer): "A layer that prints the numpy array data passing through." def __init__(self, tag): self.tag = tag self.i = 0 def execute(self, v): print("\n\nMonitorLayer %s at execution %d:" % (self.tag, self.i)) o = np.get_printoptions() np.set_printoptions(threshold=np.nan) print(np.array_repr(v)) np.set_printoptions(**o) self.i += 1 return v def isLinearLayer(layer): lname = layer.__class__.__name__ return (lname == "LinearLayer") def isScalarLinearLayer(layer): if isLinearLayer(layer): return layer.A.shape == (1,) else: return False def isMatrixLayer(layer): lname = layer.__class__.__name__ return lname == "FullyConnectedLayer" or lname == "ConvolutionLayer" def isThresholdLayer(layer): return isinstance(layer, ThresholdingLayer) def isMatrixThresholdLayer(layer): return isinstance(layer, MatrixThresholdLayer) def isPoolingLayer(layer): return isinstance(layer, PoolingLayer) def isMaxPoolingLayer(layer): if isPoolingLayer(layer): return layer.poolFxn == "MAX" else: return False def isFCLayer(layer): return isinstance(layer, FullyConnectedLayer) def isConvLayer(layer): return isinstance(layer, ConvolutionLayer) def isReLULayer(layer): return isinstance(layer, ReLULayer) def isSoftmaxLayer(layer): return isinstance(layer, SoftmaxLayer)
[ "subprocess.check_call", "numpy.get_printoptions", "numpy.array_repr", "im2col.im2col_indices", "numpy.max", "tempfile.mktemp", "math.log", "numpy.dot", "numpy.zeros", "numpy.pad", "numpy.load", "numpy.set_printoptions" ]
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import matplotlib.pyplot as plt import numpy as np import dynpy bn = dynpy.bn.BooleanNetwork(rules=dynpy.sample_nets.budding_yeast_bn) initState = np.zeros(bn.num_vars, 'uint8') initState[ [1,3,6] ] = 1 plt.spy(bn.get_trajectory(start_state=initState, max_time=15)) plt.xlabel('Node') plt.ylabel('Time')
[ "numpy.zeros", "matplotlib.pyplot.xlabel", "dynpy.bn.BooleanNetwork", "matplotlib.pyplot.ylabel" ]
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from keras.models import load_model, Model import numpy as np import os from src.data.shl_data import shl_min, shl_max, mean, std from tqdm import tqdm import seaborn as sns import shutil from loguru import logger x_min = shl_min()[np.newaxis, np.newaxis, :] x_max = shl_max()[np.newaxis, np.newaxis, :] x_mean = mean() x_std = std() base = "data/interim/hips/data" logger.add("duplex_fe_cv.log", format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}") logger.info("CV feature extraction has started.") sensors = ["accel", "gyro", "mag"] sources = [0]*3 + [1]*3 + [2]*3 destinations = [0, 1, 2]*3 modalities = list(zip(sources, destinations)) model_names = [f"{sensors[x]}2{sensors[y]}_duplex" for (x, y) in modalities] for model_name, (in_sensor, out_sensor) in tqdm(list(zip(model_names, modalities))[5:], total=9, desc = "Modalities"): for i in tqdm(range(5), desc = "Folds", leave = False): os.makedirs(f"data/interim/hips/best_fold{i}_{model_name}_features/") model = load_model(f"models/hips/best_fold{i}_{model_name}") feature_encoder = Model(model.input, model.get_layer("features").output) rmses = [] for fname in tqdm(os.listdir(base), desc = "files", leave = False): arr = np.load(base + "/" + fname) x = (arr - x_mean) / x_std x = (x - x_min) / (x_max - x_min) x = x * 2 - 1 features = feature_encoder.predict(x[:,:,:, in_sensor]) np.save(f"data/interim/hips/best_fold{i}_{model_name}_features/{fname}", features) rmse = np.mean(np.square(model.predict(x[:, :, :, in_sensor], verbose=0)[0] - x[:, :, :, out_sensor]), axis = 1) rmses.extend(rmse) logger.info(f"{model_name} fold {i} finished with rmse = {np.mean(rmses)}")
[ "loguru.logger.add", "numpy.mean", "os.listdir", "keras.models.load_model", "loguru.logger.info", "os.makedirs", "src.data.shl_data.std", "src.data.shl_data.shl_max", "src.data.shl_data.shl_min", "numpy.load", "numpy.save", "src.data.shl_data.mean" ]
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import numpy as np def evaluate(env, agent, num_runs, max_steps=np.inf): returns = np.zeros(num_runs) for run_ix in range(num_runs): env.reset() cumulative_reward = 0. step = 0 while env.steps_beyond_done is None and step < max_steps: next_state_actions = env.get_sa_pairs() choice_set = env.get_choice_set_array(next_state_actions) action = agent.choose_action(choice_set, stochastic=False) _, reward, _, _ = env.step(action) cumulative_reward += reward step += 1 returns[run_ix] = cumulative_reward return returns
[ "numpy.zeros" ]
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import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import formatter # m: number of machines # n: number of jobs # T: number of time indices # S: schedule # p: integer processing times # s: inclusive integer earlest starting times # f: exclusive itneger latest finishing times def new_schedule(m, n, T): return np.zeros((m, n, T), dtype=bool) def new_framework(m, n, T, relation): framework = np.zeros((m, n, T, m, n, T), dtype=bool) for i1 in range(m): for i2 in range(m): for j1 in range(n): for j2 in range(n): for t1 in range(T): for t2 in range(T): if relation(i1, j1, t1, i2, j2, t2): framework[i1, j1, t1, i2, j2, t2] = True return framework def alpha_relation(i1, j1, t1, i2, j2, t2): return i1 == i2 and j1 != j2 or t1 != t2 def new_set_relation(S): def relation(i1, j1, t1, i2, j2, t2): return i1 == i2 and j1 == j2 and t1 == t2 and S[i1, j1, t1] def draw_schedule(m, n, T, p, s, f, nfd, pfd, S): M = np.arange(m) N = np.arange(n) Tee = np.arange(T) MN = np.arange(m * n) fig = plt.figure() # hatch parameters mpl.rcParams['hatch.linewidth'] = 5 mpl.rcParams['hatch.color'] = 'grey' # Setup axis subplot = fig.add_subplot(111) right_axis = subplot.twinx() subplot.set_xlabel('time') subplot.set_ylabel('machine') right_axis.set_ylabel('job') subplot.xaxis.set_ticks(np.arange(T + 1)) subplot.set_xlim(0, T) subplot.set_ylim(-0.5, m*n - 0.5) subplot.yaxis.set_ticks(MN) subplot.yaxis.set_ticklabels(reversed([str(i + 1) for i in M] * n)) right_axis.yaxis.set_ticks(np.arange(n)) right_axis.yaxis.set_ticklabels(reversed([formatter.number_to_letters(j) for j in N])) right_axis.set_ylim(-0.5, n - 0.5) # Align right axis with left right_axis.barh(N, np.zeros(n)) X = nfd.copy() # Set ignores for i in M: for j in N: # gamma X[i, j, :s[i, j]] = True # delta X[i, j, f[i, j]:] = True # eta for t1 in Tee: if pfd[i, j, t1]: for t2 in Tee: if t2 <= t1 - p[j] or t2 >= t1 + p[j]: X[i, j, t2] = True # zeta for i1 in M: for j in N: if np.any(pfd[i1, j, :]): # clear all machines that are not i1 for i2 in M: if i1 != i2: X[i2, j, :] = True # Draw banded subplot.barh(MN, [((k // m) % 2) * T for k in MN], facecolor='whitesmoke', height=1) widths = np.repeat(p, m)[::-1] # Draw assigments on over jobs S2 = S.astype(int) # compute MxNxTee matrix where non-zeros are processing times for j in N: S2[:, j, :] *= p[j] assigned = np.sum(S2, axis=1) # aggregate over job assignments S3 = new_schedule(m, n, T).astype(int) # copy aggregate to each job to display for j in N: S3[:, j, :] = assigned for t in Tee: linewidths = S3[:, :, t].T.flatten().astype(int)[::-1] bars = subplot.barh(MN, linewidths, hatch='/', facecolor='None', left=t) # Draw ignores for t in Tee: # transpose: order is MN, not NM; convert from boolean to int; reverse y axis subplot.barh(MN, X[:, :, t].T.flatten().astype(int)[::-1], left=t, facecolor='grey') # Draw assigments for t in Tee: linewidths = S[:, :, t].T.flatten().astype(int)[::-1] bars = subplot.barh(MN, np.multiply(widths, linewidths), facecolor='black', left=t) plt.show() m = 2 n = 3 T = 6 p = np.array([2,3,2]) s = np.array([[0,1,0],[1,0,0]]) f = np.array([[4,6,6],[5,6,6]]) S = new_schedule(m, n, T) S[0, 0, 0] = True S[0, 2, 2] = True S[1, 1, 3] = True nfd = new_schedule(m, n, T) nfd[1, 1, 2] = True pfd = new_schedule(m, n, T) pfd[0, 2, 2] = True draw_schedule(m, n, T, p, s, f, nfd, pfd, S)
[ "numpy.multiply", "numpy.repeat", "numpy.any", "numpy.array", "numpy.zeros", "matplotlib.pyplot.figure", "numpy.sum", "formatter.number_to_letters", "numpy.arange", "matplotlib.pyplot.show" ]
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import tempfile, os, glob from scipy.stats import norm as ndist from traitlets import (HasTraits, Integer, Unicode, Float, Integer, Instance, Dict, Bool, default) import numpy as np import regreg.api as rr from selection.algorithms.lasso import lasso, lasso_full, lasso_full_modelQ from selection.algorithms.sqrt_lasso import choose_lambda from selection.truncated.gaussian import truncated_gaussian_old as TG from selection.randomized.lasso import lasso as random_lasso_method, form_targets from selection.randomized.modelQ import modelQ as randomized_modelQ from utils import BHfilter from selection.randomized.base import restricted_estimator # Rpy import rpy2.robjects as rpy from rpy2.robjects import numpy2ri methods = {} class generic_method(HasTraits): need_CV = False selectiveR_method = False wide_ok = True # ok for p>= n? # Traits q = Float(0.2) method_name = Unicode('Generic method') model_target = Unicode() @classmethod def setup(cls, feature_cov): cls.feature_cov = feature_cov def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): (self.X, self.Y, self.l_theory, self.l_min, self.l_1se, self.sigma_reid) = (X, Y, l_theory, l_min, l_1se, sigma_reid) def select(self): raise NotImplementedError('abstract method') @classmethod def register(cls): methods[cls.__name__] = cls def selected_target(self, active, beta): C = self.feature_cov[active] Q = C[:,active] return np.linalg.inv(Q).dot(C.dot(beta)) def full_target(self, active, beta): return beta[active] def get_target(self, active, beta): if self.model_target not in ['selected', 'full']: raise ValueError('Gaussian methods only have selected or full targets') if self.model_target == 'full': return self.full_target(active, beta) else: return self.selected_target(active, beta) # Knockoff selection class knockoffs_mf(generic_method): method_name = Unicode('Knockoffs') knockoff_method = Unicode('Second order') model_target = Unicode("full") def select(self): try: numpy2ri.activate() rpy.r.assign('X', self.X) rpy.r.assign('Y', self.Y) rpy.r.assign('q', self.q) rpy.r('V=knockoff.filter(X, Y, fdr=q)$selected') rpy.r('if (length(V) > 0) {V = V-1}') V = rpy.r('V') numpy2ri.deactivate() return np.asarray(V, np.int), np.asarray(V, np.int) except: return [], [] knockoffs_mf.register() class knockoffs_sigma(generic_method): factor_method = 'asdp' method_name = Unicode('Knockoffs') knockoff_method = Unicode("ModelX (asdp)") model_target = Unicode("full") @classmethod def setup(cls, feature_cov): cls.feature_cov = feature_cov numpy2ri.activate() # see if we've factored this before have_factorization = False if not os.path.exists('.knockoff_factorizations'): os.mkdir('.knockoff_factorizations') factors = glob.glob('.knockoff_factorizations/*npz') for factor_file in factors: factor = np.load(factor_file) feature_cov_f = factor['feature_cov'] if ((feature_cov_f.shape == feature_cov.shape) and (factor['method'] == cls.factor_method) and np.allclose(feature_cov_f, feature_cov)): have_factorization = True print('found factorization: %s' % factor_file) cls.knockoff_chol = factor['knockoff_chol'] if not have_factorization: print('doing factorization') cls.knockoff_chol = factor_knockoffs(feature_cov, cls.factor_method) numpy2ri.deactivate() def select(self): numpy2ri.activate() rpy.r.assign('chol_k', self.knockoff_chol) rpy.r(''' knockoffs = function(X) { mu = rep(0, ncol(X)) mu_k = X # sweep(X, 2, mu, "-") %*% SigmaInv_s X_k = mu_k + matrix(rnorm(ncol(X) * nrow(X)), nrow(X)) %*% chol_k return(X_k) } ''') numpy2ri.deactivate() try: numpy2ri.activate() rpy.r.assign('X', self.X) rpy.r.assign('Y', self.Y) rpy.r.assign('q', self.q) rpy.r('V=knockoff.filter(X, Y, fdr=q, knockoffs=knockoffs)$selected') rpy.r('if (length(V) > 0) {V = V-1}') V = rpy.r('V') numpy2ri.deactivate() return np.asarray(V, np.int), np.asarray(V, np.int) except: return [], [] knockoffs_sigma.register() def factor_knockoffs(feature_cov, method='asdp'): numpy2ri.activate() rpy.r.assign('Sigma', feature_cov) rpy.r.assign('method', method) rpy.r(''' # Compute the Cholesky -- from create.gaussian diag_s = diag(switch(method, equi = create.solve_equi(Sigma), sdp = create.solve_sdp(Sigma), asdp = create.solve_asdp(Sigma))) if (is.null(dim(diag_s))) { diag_s = diag(diag_s, length(diag_s)) } SigmaInv_s = solve(Sigma, diag_s) Sigma_k = 2 * diag_s - diag_s %*% SigmaInv_s chol_k = chol(Sigma_k) ''') knockoff_chol = np.asarray(rpy.r('chol_k')) SigmaInv_s = np.asarray(rpy.r('SigmaInv_s')) diag_s = np.asarray(rpy.r('diag_s')) np.savez('.knockoff_factorizations/%s.npz' % (os.path.split(tempfile.mkstemp()[1])[1],), method=method, feature_cov=feature_cov, knockoff_chol=knockoff_chol) return knockoff_chol class knockoffs_sigma_equi(knockoffs_sigma): knockoff_method = Unicode('ModelX (equi)') factor_method = 'equi' knockoffs_sigma_equi.register() class knockoffs_orig(generic_method): wide_OK = False # requires at least n>p method_name = Unicode("Knockoffs") knockoff_method = Unicode('Candes & Barber') model_target = Unicode('full') def select(self): try: numpy2ri.activate() rpy.r.assign('X', self.X) rpy.r.assign('Y', self.Y) rpy.r.assign('q', self.q) rpy.r('V=knockoff.filter(X, Y, statistic=stat.glmnet_lambdadiff, fdr=q, knockoffs=create.fixed)$selected') rpy.r('if (length(V) > 0) {V = V-1}') V = rpy.r('V') numpy2ri.deactivate() V = np.asarray(V, np.int) return V, V except: return [], [] knockoffs_orig.register() class knockoffs_fixed(generic_method): wide_OK = False # requires at least n>p method_name = Unicode("Knockoffs") knockoff_method = Unicode('Fixed') model_target = Unicode('full') def select(self): try: numpy2ri.activate() rpy.r.assign('X', self.X) rpy.r.assign('Y', self.Y) rpy.r.assign('q', self.q) rpy.r('V=knockoff.filter(X, Y, fdr=q, knockoffs=create.fixed)$selected') rpy.r('if (length(V) > 0) {V = V-1}') V = rpy.r('V') numpy2ri.deactivate() return np.asarray(V, np.int), np.asarray(V, np.int) except: return [], [] knockoffs_fixed.register() # Liu, Markovic, Tibs selection class parametric_method(generic_method): confidence = Float(0.95) def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): generic_method.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self._fit = False def select(self): if not self._fit: self.method_instance.fit() self._fit = True active_set, pvalues = self.generate_pvalues() if len(pvalues) > 0: selected = [active_set[i] for i in BHfilter(pvalues, q=self.q)] return selected, active_set else: return [], active_set class liu_theory(parametric_method): sigma_estimator = Unicode('relaxed') method_name = Unicode("Liu") lambda_choice = Unicode("theory") model_target = Unicode("full") dispersion = Float(0.) def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): parametric_method.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) n, p = X.shape if n < p: self.method_name = 'ROSI' self.lagrange = l_theory * np.ones(X.shape[1]) @property def method_instance(self): if not hasattr(self, "_method_instance"): n, p = self.X.shape self._method_instance = lasso_full.gaussian(self.X, self.Y, self.lagrange * np.sqrt(n)) return self._method_instance def generate_summary(self, compute_intervals=False): if not self._fit: self.method_instance.fit() self._fit = True X, Y, lagrange, L = self.X, self.Y, self.lagrange, self.method_instance n, p = X.shape if len(L.active) > 0: if self.sigma_estimator == 'reid' and n < p: dispersion = self.sigma_reid**2 elif self.dispersion != 0: dispersion = self.dispersion else: dispersion = None S = L.summary(compute_intervals=compute_intervals, dispersion=dispersion) return S def generate_pvalues(self): S = self.generate_summary(compute_intervals=False) if S is not None: active_set = np.array(S['variable']) pvalues = np.asarray(S['pval']) return active_set, pvalues else: return [], [] def generate_intervals(self): S = self.generate_summary(compute_intervals=True) if S is not None: active_set = np.array(S['variable']) lower, upper = np.asarray(S['lower_confidence']), np.asarray(S['upper_confidence']) return active_set, lower, upper else: return [], [], [] liu_theory.register() class liu_aggressive(liu_theory): lambda_choice = Unicode("aggressive") def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): liu_theory.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_theory * np.ones(X.shape[1]) * 0.8 liu_aggressive.register() class liu_modelQ_pop_aggressive(liu_aggressive): method_name = Unicode("Liu (ModelQ population)") @property def method_instance(self): if not hasattr(self, "_method_instance"): n, p = self.X.shape self._method_instance = lasso_full_modelQ(self.feature_cov * n, self.X, self.Y, self.lagrange * np.sqrt(n)) return self._method_instance liu_modelQ_pop_aggressive.register() class liu_modelQ_semi_aggressive(liu_aggressive): method_name = Unicode("Liu (ModelQ semi-supervised)") B = 10000 # how many samples to use to estimate E[XX^T] @classmethod def setup(cls, feature_cov): cls.feature_cov = feature_cov cls._chol = np.linalg.cholesky(feature_cov) @property def method_instance(self): if not hasattr(self, "_method_instance"): # draw sample of X for semi-supervised method _chol = self._chol p = _chol.shape[0] Q = 0 batch_size = int(self.B/10) for _ in range(10): X_semi = np.random.standard_normal((batch_size, p)).dot(_chol.T) Q += X_semi.T.dot(X_semi) Q += self.X.T.dot(self.X) Q /= (10 * batch_size + self.X.shape[0]) n, p = self.X.shape self._method_instance = lasso_full_modelQ(Q * self.X.shape[0], self.X, self.Y, self.lagrange * np.sqrt(n)) return self._method_instance liu_modelQ_semi_aggressive.register() class liu_sparseinv_aggressive(liu_aggressive): method_name = Unicode("ROSI") """ Force the use of the debiasing matrix. """ @property def method_instance(self): if not hasattr(self, "_method_instance"): n, p = self.X.shape self._method_instance = lasso_full.gaussian(self.X, self.Y, self.lagrange * np.sqrt(n)) self._method_instance.sparse_inverse = True return self._method_instance liu_sparseinv_aggressive.register() class liu_aggressive_reid(liu_aggressive): sigma_estimator = Unicode('Reid') pass liu_aggressive_reid.register() class liu_CV(liu_theory): need_CV = True lambda_choice = Unicode("CV") def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): liu_theory.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_min * np.ones(X.shape[1]) liu_CV.register() class liu_1se(liu_theory): need_CV = True lambda_choice = Unicode("1se") def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): liu_theory.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_1se * np.ones(X.shape[1]) liu_1se.register() class liu_sparseinv_1se(liu_1se): method_name = Unicode("ROSI") """ Force the use of the debiasing matrix. """ @property def method_instance(self): if not hasattr(self, "_method_instance"): n, p = self.X.shape self._method_instance = lasso_full.gaussian(self.X, self.Y, self.lagrange * np.sqrt(n)) self._method_instance.sparse_inverse = True return self._method_instance liu_sparseinv_1se.register() class liu_sparseinv_1se_known(liu_1se): method_name = Unicode("ROSI - known") dispersion = Float(1.) """ Force the use of the debiasing matrix. """ @property def method_instance(self): if not hasattr(self, "_method_instance"): n, p = self.X.shape self._method_instance = lasso_full.gaussian(self.X, self.Y, self.lagrange * np.sqrt(n)) self._method_instance.sparse_inverse = True return self._method_instance liu_sparseinv_1se_known.register() class liu_R_theory(liu_theory): selectiveR_method = True method_name = Unicode("Liu (R code)") def generate_pvalues(self): try: numpy2ri.activate() rpy.r.assign('X', self.X) rpy.r.assign('y', self.Y) rpy.r.assign('sigma_reid', self.sigma_reid) rpy.r('y = as.numeric(y)') rpy.r.assign('lam', self.lagrange[0]) rpy.r(''' p = ncol(X); n = nrow(X); sigma_est = 1. if (p >= n) { sigma_est = sigma_reid } else { sigma_est = sigma(lm(y ~ X - 1)) } penalty_factor = rep(1, p); lam = lam / sqrt(n); # lambdas are passed a sqrt(n) free from python code soln = selectiveInference:::solve_problem_glmnet(X, y, lam, penalty_factor=penalty_factor, loss="ls") PVS = selectiveInference:::inference_group_lasso(X, y, soln, groups=1:ncol(X), lambda=lam, penalty_factor=penalty_factor, sigma_est, loss="ls", algo="Q", construct_ci=FALSE) active_vars=PVS$active_vars - 1 # for 0-based pvalues = PVS$pvalues ''') pvalues = np.asarray(rpy.r('pvalues')) active_set = np.asarray(rpy.r('active_vars')) numpy2ri.deactivate() if len(active_set) > 0: return active_set, pvalues else: return [], [] except: return [np.nan], [np.nan] # some R failure occurred liu_R_theory.register() class liu_R_aggressive(liu_R_theory): lambda_choice = Unicode('aggressive') def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): liu_R_theory.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_theory * np.ones(X.shape[1]) * 0.8 liu_R_aggressive.register() class lee_full_R_theory(liu_theory): wide_OK = False # requires at least n>p method_name = Unicode("Lee (R code)") selectiveR_method = True def generate_pvalues(self): numpy2ri.activate() rpy.r.assign('x', self.X) rpy.r.assign('y', self.Y) rpy.r('y = as.numeric(y)') rpy.r.assign('sigma_reid', self.sigma_reid) rpy.r.assign('lam', self.lagrange[0]) rpy.r(''' sigma_est=sigma_reid n = nrow(x); gfit = glmnet(x, y, standardize=FALSE, intercept=FALSE) lam = lam / sqrt(n); # lambdas are passed a sqrt(n) free from python code if (lam < max(abs(t(x) %*% y) / n)) { beta = coef(gfit, x=x, y=y, s=lam, exact=TRUE)[-1] out = fixedLassoInf(x, y, beta, lam*n, sigma=sigma_est, type='full', intercept=FALSE) active_vars=out$vars - 1 # for 0-based pvalues = out$pv } else { pvalues = NULL active_vars = numeric(0) } ''') pvalues = np.asarray(rpy.r('pvalues')) active_set = np.asarray(rpy.r('active_vars')) numpy2ri.deactivate() if len(active_set) > 0: return active_set, pvalues else: return [], [] lee_full_R_theory.register() class lee_full_R_aggressive(lee_full_R_theory): lambda_choice = Unicode("aggressive") def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): lee_full_R_theory.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_theory * np.ones(X.shape[1]) * 0.8 lee_full_R_aggressive.register() # Unrandomized selected class lee_theory(parametric_method): model_target = Unicode("selected") method_name = Unicode("Lee") def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): parametric_method.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_theory * np.ones(X.shape[1]) @property def method_instance(self): if not hasattr(self, "_method_instance"): n, p = self.X.shape self._method_instance = lasso.gaussian(self.X, self.Y, self.lagrange * np.sqrt(n)) return self._method_instance def generate_summary(self, compute_intervals=False): if not self._fit: self.method_instance.fit() self._fit = True X, Y, lagrange, L = self.X, self.Y, self.lagrange, self.method_instance if len(L.active) > 0: S = L.summary(compute_intervals=compute_intervals, alternative='onesided') return S def generate_pvalues(self): S = self.generate_summary(compute_intervals=False) if S is not None: active_set = np.array(S['variable']) pvalues = np.asarray(S['pval']) return active_set, pvalues else: return [], [] def generate_intervals(self): S = self.generate_summary(compute_intervals=True) if S is not None: active_set = np.array(S['variable']) lower, upper = np.asarray(S['lower_confidence']), np.asarray(S['upper_confidence']) return active_set, lower, upper else: return [], [], [] def point_estimator(self): X, Y, lagrange, L = self.X, self.Y, self.lagrange, self.method_instance n, p = X.shape beta_full = np.zeros(p) if self.estimator == "LASSO": beta_full[L.active] = L.soln else: beta_full[L.active] = L.onestep_estimator return L.active, beta_full lee_theory.register() class lee_CV(lee_theory): need_CV = True lambda_choice = Unicode("CV") def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): lee_theory.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_min * np.ones(X.shape[1]) lee_CV.register() class lee_1se(lee_theory): need_CV = True lambda_choice = Unicode("1se") def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): lee_theory.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_1se * np.ones(X.shape[1]) lee_1se.register() class lee_aggressive(lee_theory): lambda_choice = Unicode("aggressive") def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): lee_theory.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = 0.8 * l_theory * np.ones(X.shape[1]) lee_aggressive.register() class lee_weak(lee_theory): lambda_choice = Unicode("weak") def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): lee_theory.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = 2 * l_theory * np.ones(X.shape[1]) lee_weak.register() class sqrt_lasso(parametric_method): method_name = Unicode('SqrtLASSO') kappa = Float(0.7) def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): parametric_method.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = self.kappa * choose_lambda(X) @property def method_instance(self): if not hasattr(self, "_method_instance"): self._method_instance = lasso.sqrt_lasso(self.X, self.Y, self.lagrange) return self._method_instance def generate_summary(self, compute_intervals=False): X, Y, lagrange, L = self.X, self.Y, self.lagrange, self.method_instance n, p = X.shape X = X / np.sqrt(n) if len(L.active) > 0: S = L.summary(compute_intervals=compute_intervals, alternative='onesided') return S def generate_pvalues(self): S = self.generate_summary(compute_intervals=False) if S is not None: active_set = np.array(S['variable']) pvalues = np.asarray(S['pval']) return active_set, pvalues else: return [], [] def generate_intervals(self): S = self.generate_summary(compute_intervals=True) if S is not None: active_set = np.array(S['variable']) lower, upper = np.asarray(S['lower_confidence']), np.asarray(S['upper_confidence']) return active_set, lower, upper else: return [], [], [] sqrt_lasso.register() # Randomized selected class randomized_lasso(parametric_method): method_name = Unicode("Randomized LASSO") model_target = Unicode("selected") lambda_choice = Unicode("theory") randomizer_scale = Float(1) ndraw = 10000 burnin = 1000 def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): parametric_method.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_theory * np.ones(X.shape[1]) @property def method_instance(self): if not hasattr(self, "_method_instance"): n, p = self.X.shape mean_diag = np.mean((self.X ** 2).sum(0)) self._method_instance = random_lasso_method.gaussian(self.X, self.Y, feature_weights = self.lagrange * np.sqrt(n), ridge_term=np.std(self.Y) * np.sqrt(mean_diag) / np.sqrt(n), randomizer_scale=self.randomizer_scale * np.std(self.Y) * np.sqrt(n)) return self._method_instance def generate_summary(self, compute_intervals=False): X, Y, lagrange, rand_lasso = self.X, self.Y, self.lagrange, self.method_instance n, p = X.shape if not self._fit: signs = self.method_instance.fit() self._fit = True signs = rand_lasso.fit() active_set = np.nonzero(signs)[0] active = signs != 0 # estimates sigma # JM: for transparency it's better not to have this digged down in the code X_active = X[:,active_set] rpy.r.assign('X_active', X_active) rpy.r.assign('Y', Y) rpy.r('X_active=as.matrix(X_active)') rpy.r('Y=as.numeric(Y)') rpy.r('sigma_est = sigma(lm(Y~ X_active - 1))') dispersion = rpy.r('sigma_est') print("dispersion (sigma est for Python)", dispersion) (observed_target, cov_target, cov_target_score, alternatives) = form_targets(self.model_target, rand_lasso.loglike, rand_lasso._W, active, **{'dispersion': dispersion}) if active.sum() > 0: _, pvalues, intervals = rand_lasso.summary(observed_target, cov_target, cov_target_score, alternatives, level=0.9, ndraw=self.ndraw, burnin=self.burnin, compute_intervals=compute_intervals) return active_set, pvalues, intervals else: return [], [], [] def generate_pvalues(self, compute_intervals=False): active_set, pvalues, _ = self.generate_summary(compute_intervals=compute_intervals) if len(active_set) > 0: return active_set, pvalues else: return [], [] def generate_intervals(self): active_set, _, intervals = self.generate_summary(compute_intervals=True) if len(active_set) > 0: return active_set, intervals[:,0], intervals[:,1] else: return [], [], [] class randomized_lasso_CV(randomized_lasso): need_CV = True lambda_choice = Unicode("CV") def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): randomized_lasso.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_min * np.ones(X.shape[1]) class randomized_lasso_1se(randomized_lasso): need_CV = True lambda_choice = Unicode("1se") def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): randomized_lasso.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_1se * np.ones(X.shape[1]) randomized_lasso.register(), randomized_lasso_CV.register(), randomized_lasso_1se.register() # More aggressive lambda choice class randomized_lasso_aggressive(randomized_lasso): lambda_choice = Unicode("aggressive") def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): randomized_lasso.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_theory * np.ones(X.shape[1]) * 0.8 class randomized_lasso_aggressive_half(randomized_lasso): lambda_choice = Unicode('aggressive') randomizer_scale = Float(0.5) def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): randomized_lasso.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_theory * np.ones(X.shape[1]) * 0.8 class randomized_lasso_weak_half(randomized_lasso): lambda_choice = Unicode('weak') randomizer_scale = Float(0.5) def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): randomized_lasso.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_theory * np.ones(X.shape[1]) * 2. randomized_lasso_weak_half.register() class randomized_lasso_aggressive_quarter(randomized_lasso): randomizer_scale = Float(0.25) def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): randomized_lasso.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_theory * np.ones(X.shape[1]) * 0.8 randomized_lasso_aggressive.register(), randomized_lasso_aggressive_half.register(), randomized_lasso_aggressive_quarter.register() # Randomized selected smaller randomization class randomized_lasso_half(randomized_lasso): randomizer_scale = Float(0.5) pass class randomized_lasso_half_CV(randomized_lasso_CV): need_CV = True randomizer_scale = Float(0.5) pass class randomized_lasso_half_1se(randomized_lasso_1se): need_CV = True randomizer_scale = Float(0.5) pass randomized_lasso_half.register(), randomized_lasso_half_CV.register(), randomized_lasso_half_1se.register() # selective mle class randomized_lasso_mle(randomized_lasso_aggressive_half): method_name = Unicode("Randomized MLE") randomizer_scale = Float(0.5) model_target = Unicode("selected") @property def method_instance(self): if not hasattr(self, "_method_instance"): n, p = self.X.shape self._method_instance = randomized_modelQ(self.feature_cov * n, self.X, self.Y, self.lagrange * np.sqrt(n), randomizer_scale=self.randomizer_scale * np.std(self.Y) * np.sqrt(n)) return self._method_instance def generate_pvalues(self): X, Y, lagrange, rand_lasso = self.X, self.Y, self.lagrange, self.method_instance n, p = X.shape if not self._fit: signs = self.method_instance.fit() self._fit = True signs = rand_lasso.fit() active_set = np.nonzero(signs)[0] Z, pvalues = rand_lasso.selective_MLE(target=self.model_target, solve_args={'min_iter':1000, 'tol':1.e-12})[-3:-1] print(pvalues, 'pvalues') print(Z, 'Zvalues') if len(pvalues) > 0: return active_set, pvalues else: return [], [] randomized_lasso_mle.register() # Using modelQ for randomized class randomized_lasso_half_pop_1se(randomized_lasso_half_1se): method_name = Unicode("Randomized ModelQ (pop)") randomizer_scale = Float(0.5) nsample = 15000 burnin = 2000 @property def method_instance(self): if not hasattr(self, "_method_instance"): n, p = self.X.shape self._method_instance = randomized_modelQ(self.feature_cov * n, self.X, self.Y, self.lagrange * np.sqrt(n), randomizer_scale=self.randomizer_scale * np.std(self.Y) * np.sqrt(n)) return self._method_instance class randomized_lasso_half_semi_1se(randomized_lasso_half_1se): method_name = Unicode("Randomized ModelQ (semi-supervised)") randomizer_scale = Float(0.5) B = 10000 nsample = 15000 burnin = 2000 @classmethod def setup(cls, feature_cov): cls.feature_cov = feature_cov cls._chol = np.linalg.cholesky(feature_cov) @property def method_instance(self): if not hasattr(self, "_method_instance"): # draw sample of X for semi-supervised method _chol = self._chol p = _chol.shape[0] Q = 0 batch_size = int(self.B/10) for _ in range(10): X_semi = np.random.standard_normal((batch_size, p)).dot(_chol.T) Q += X_semi.T.dot(X_semi) Q += self.X.T.dot(self.X) Q /= (10 * batch_size + self.X.shape[0]) n, p = self.X.shape self._method_instance = randomized_modelQ(Q * n, self.X, self.Y, self.lagrange * np.sqrt(n), randomizer_scale=self.randomizer_scale * np.std(self.Y) * np.sqrt(n)) return self._method_instance randomized_lasso_half_pop_1se.register(), randomized_lasso_half_semi_1se.register() # Using modelQ for randomized class randomized_lasso_half_pop_aggressive(randomized_lasso_aggressive_half): method_name = Unicode("Randomized ModelQ (pop)") randomizer_scale = Float(0.5) nsample = 10000 burnin = 2000 @property def method_instance(self): if not hasattr(self, "_method_instance"): n, p = self.X.shape self._method_instance = randomized_modelQ(self.feature_cov * n, self.X, self.Y, self.lagrange * np.sqrt(n), randomizer_scale=self.randomizer_scale * np.std(self.Y) * np.sqrt(n)) return self._method_instance class randomized_lasso_half_semi_aggressive(randomized_lasso_aggressive_half): method_name = Unicode("Randomized ModelQ (semi-supervised)") randomizer_scale = Float(0.25) B = 10000 nsample = 15000 burnin = 2000 @classmethod def setup(cls, feature_cov): cls.feature_cov = feature_cov cls._chol = np.linalg.cholesky(feature_cov) @property def method_instance(self): if not hasattr(self, "_method_instance"): # draw sample of X for semi-supervised method _chol = self._chol p = _chol.shape[0] Q = 0 batch_size = int(self.B/10) for _ in range(10): X_semi = np.random.standard_normal((batch_size, p)).dot(_chol.T) Q += X_semi.T.dot(X_semi) Q += self.X.T.dot(self.X) Q /= (10 * batch_size + self.X.shape[0]) n, p = self.X.shape self._method_instance = randomized_modelQ(Q * n, self.X, self.Y, self.lagrange * np.sqrt(n), randomizer_scale=self.randomizer_scale * np.std(self.Y) * np.sqrt(n)) return self._method_instance randomized_lasso_half_pop_aggressive.register(), randomized_lasso_half_semi_aggressive.register() # Randomized sqrt selected class randomized_sqrtlasso(randomized_lasso): method_name = Unicode("Randomized SqrtLASSO") model_target = Unicode("selected") randomizer_scale = Float(1) kappa = Float(0.7) @property def method_instance(self): if not hasattr(self, "_method_instance"): n, p = self.X.shape lagrange = np.ones(p) * choose_lambda(self.X) * self.kappa self._method_instance = random_lasso_method.gaussian(self.X, self.Y, lagrange, randomizer_scale=self.randomizer_scale * np.std(self.Y)) return self._method_instance def generate_summary(self, compute_intervals=False): X, Y, rand_lasso = self.X, self.Y, self.method_instance n, p = X.shape X = X / np.sqrt(n) if not self._fit: self.method_instance.fit() self._fit = True signs = self.method_instance.selection_variable['sign'] active_set = np.nonzero(signs)[0] active = signs != 0 (observed_target, cov_target, cov_target_score, alternatives) = form_targets(self.model_target, rand_lasso.loglike, rand_lasso._W, active) _, pvalues, intervals = rand_lasso.summary(observed_target, cov_target, cov_target_score, alternatives, ndraw=self.ndraw, burnin=self.burnin, compute_intervals=compute_intervals) if len(pvalues) > 0: return active_set, pvalues, intervals else: return [], [], [] class randomized_sqrtlasso_half(randomized_sqrtlasso): randomizer_scale = Float(0.5) pass randomized_sqrtlasso.register(), randomized_sqrtlasso_half.register() class randomized_sqrtlasso_bigger(randomized_sqrtlasso): kappa = Float(0.8) pass class randomized_sqrtlasso_bigger_half(randomized_sqrtlasso): kappa = Float(0.8) randomizer_scale = Float(0.5) pass randomized_sqrtlasso_bigger.register(), randomized_sqrtlasso_bigger_half.register() # Randomized full class randomized_lasso_full(randomized_lasso): model_target = Unicode('full') def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): randomized_lasso.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_theory * np.ones(X.shape[1]) class randomized_lasso_full_CV(randomized_lasso_full): need_CV = True lambda_choice = Unicode("CV") def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): randomized_lasso_full.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_min * np.ones(X.shape[1]) class randomized_lasso_full_1se(randomized_lasso_full): need_CV = True lambda_choice = Unicode("1se") def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): randomized_lasso_full.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_1se * np.ones(X.shape[1]) randomized_lasso_full.register(), randomized_lasso_full_CV.register(), randomized_lasso_full_1se.register() # Randomized full smaller randomization class randomized_lasso_full_half(randomized_lasso_full): randomizer_scale = Float(0.5) pass class randomized_lasso_full_half_CV(randomized_lasso_full_CV): randomizer_scale = Float(0.5) pass class randomized_lasso_full_half_1se(randomized_lasso_full_1se): need_CV = True randomizer_scale = Float(0.5) pass randomized_lasso_full_half.register(), randomized_lasso_full_half_CV.register(), randomized_lasso_full_half_1se.register() # Aggressive choice of lambda class randomized_lasso_full_aggressive(randomized_lasso_full): lambda_choice = Unicode("aggressive") def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): randomized_lasso_full.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_theory * np.ones(X.shape[1]) * 0.8 class randomized_lasso_full_aggressive_half(randomized_lasso_full_aggressive): randomizer_scale = Float(0.5) pass randomized_lasso_full_aggressive.register(), randomized_lasso_full_aggressive_half.register() class randomized_lasso_R_theory(randomized_lasso): method_name = Unicode("Randomized LASSO (R code)") selective_Rmethod = True def generate_summary(self, compute_intervals=False): numpy2ri.activate() rpy.r.assign('X', self.X) rpy.r.assign('y', self.Y) rpy.r('y = as.numeric(y)') rpy.r.assign('q', self.q) rpy.r.assign('lam', self.lagrange[0]) rpy.r.assign("randomizer_scale", self.randomizer_scale) rpy.r.assign("compute_intervals", compute_intervals) rpy.r(''' n = nrow(X) p = ncol(X) lam = lam * sqrt(n) mean_diag = mean(apply(X^2, 2, sum)) ridge_term = sqrt(mean_diag) * sd(y) / sqrt(n) result = randomizedLasso(X, y, lam, ridge_term=ridge_term, noise_scale = randomizer_scale * sd(y) * sqrt(n), family='gaussian') active_set = result$active_set if (length(active_set)==0){ active_set = -1 } else{ sigma_est = sigma(lm(y ~ X[,active_set] - 1)) cat("sigma est for R", sigma_est,"\n") targets = selectiveInference:::compute_target(result, 'partial', sigma_est = sigma_est, construct_pvalues=rep(TRUE, length(active_set)), construct_ci=rep(compute_intervals, length(active_set))) out = randomizedLassoInf(result, targets=targets, sampler = "norejection", level=0.9, burnin=1000, nsample=10000) active_set=active_set-1 pvalues = out$pvalues intervals = out$ci } ''') active_set = np.asarray(rpy.r('active_set'), np.int) print(active_set) if active_set[0]==-1: numpy2ri.deactivate() return [], [], [] pvalues = np.asarray(rpy.r('pvalues')) intervals = np.asarray(rpy.r('intervals')) numpy2ri.deactivate() return active_set, pvalues, intervals randomized_lasso_R_theory.register() class data_splitting_1se(parametric_method): method_name = Unicode('Data splitting') selection_frac = Float(0.5) def __init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid): parametric_method.__init__(self, X, Y, l_theory, l_min, l_1se, sigma_reid) self.lagrange = l_1se * np.ones(X.shape[1]) n, p = self.X.shape n1 = int(self.selection_frac * n) X1, X2 = self.X1, self.X2 = self.X[:n1], self.X[n1:] Y1, Y2 = self.Y1, self.Y2 = self.Y[:n1], self.Y[n1:] pen = rr.weighted_l1norm(np.sqrt(n1) * self.lagrange, lagrange=1.) loss = rr.squared_error(X1, Y1) problem = rr.simple_problem(loss, pen) soln = problem.solve() self.active_set = np.nonzero(soln)[0] self.signs = np.sign(soln)[self.active_set] self._fit = True def generate_pvalues(self): X2, Y2 = self.X2[:,self.active_set], self.Y2 if len(self.active_set) > 0: s = len(self.active_set) X2i = np.linalg.inv(X2.T.dot(X2)) beta2 = X2i.dot(X2.T.dot(Y2)) resid2 = Y2 - X2.dot(beta2) n2 = X2.shape[0] sigma2 = np.sqrt((resid2**2).sum() / (n2 - s)) Z2 = beta2 / np.sqrt(sigma2**2 * np.diag(X2i)) signed_Z2 = self.signs * Z2 pvalues = 1 - ndist.cdf(signed_Z2) return self.active_set, pvalues else: return [], [] data_splitting_1se.register()
[ "regreg.api.simple_problem", "numpy.random.standard_normal", "numpy.sqrt", "numpy.array", "scipy.stats.norm.cdf", "rpy2.robjects.numpy2ri.activate", "rpy2.robjects.r", "os.path.exists", "utils.BHfilter", "numpy.asarray", "os.mkdir", "traitlets.Unicode", "glob.glob", "selection.randomized.l...
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type=\'full\', intercept=FALSE)\n active_vars=out$vars - 1 # for 0-based\n pvalues = out$pv\n } else {\n pvalues = NULL\n active_vars = numeric(0)\n }\n """'], {}), '(\n """\n sigma_est=sigma_reid\n n = nrow(x);\n gfit = glmnet(x, y, standardize=FALSE, intercept=FALSE)\n lam = lam / sqrt(n); # lambdas are passed a sqrt(n) free from python code\n if (lam < max(abs(t(x) %*% y) / n)) {\n beta = coef(gfit, x=x, y=y, s=lam, exact=TRUE)[-1]\n out = fixedLassoInf(x, y, beta, lam*n, sigma=sigma_est, type=\'full\', intercept=FALSE)\n active_vars=out$vars - 1 # for 0-based\n pvalues = out$pv\n } else {\n pvalues = NULL\n active_vars = numeric(0)\n }\n """\n )\n', (16930, 17476), True, 'import rpy2.robjects as rpy\n'), ((17577, 17598), 'rpy2.robjects.numpy2ri.deactivate', 'numpy2ri.deactivate', ([], {}), '()\n', (17596, 17598), False, 'from rpy2.robjects import numpy2ri\n'), ((19854, 19865), 'numpy.zeros', 'np.zeros', (['p'], {}), '(p)\n', (19862, 19865), True, 'import numpy as np\n'), ((24538, 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'import rpy2.robjects as rpy\n'), ((40212, 40267), 'rpy2.robjects.r.assign', 'rpy.r.assign', (['"""randomizer_scale"""', 'self.randomizer_scale'], {}), "('randomizer_scale', self.randomizer_scale)\n", (40224, 40267), True, 'import rpy2.robjects as rpy\n'), ((40276, 40328), 'rpy2.robjects.r.assign', 'rpy.r.assign', (['"""compute_intervals"""', 'compute_intervals'], {}), "('compute_intervals', compute_intervals)\n", (40288, 40328), True, 'import rpy2.robjects as rpy\n'), ((40337, 41609), 'rpy2.robjects.r', 'rpy.r', (['"""\n n = nrow(X)\n p = ncol(X)\n lam = lam * sqrt(n)\n mean_diag = mean(apply(X^2, 2, sum))\n ridge_term = sqrt(mean_diag) * sd(y) / sqrt(n)\n result = randomizedLasso(X, y, lam, ridge_term=ridge_term,\n noise_scale = randomizer_scale * sd(y) * sqrt(n), family=\'gaussian\')\n active_set = result$active_set\n if (length(active_set)==0){\n active_set = -1\n } else{\n sigma_est = sigma(lm(y ~ X[,active_set] - 1))\n cat("sigma est for R", sigma_est,"\n")\n targets = selectiveInference:::compute_target(result, \'partial\', sigma_est = sigma_est,\n construct_pvalues=rep(TRUE, length(active_set)), \n construct_ci=rep(compute_intervals, length(active_set)))\n\n out = randomizedLassoInf(result,\n targets=targets,\n sampler = "norejection",\n level=0.9,\n burnin=1000,\n nsample=10000)\n active_set=active_set-1\n pvalues = out$pvalues\n intervals = out$ci\n }\n """'], {}), '(\n """\n n = nrow(X)\n p = ncol(X)\n lam = lam * sqrt(n)\n mean_diag = mean(apply(X^2, 2, sum))\n ridge_term = sqrt(mean_diag) * sd(y) / sqrt(n)\n result = randomizedLasso(X, y, lam, ridge_term=ridge_term,\n noise_scale = randomizer_scale * sd(y) * sqrt(n), family=\'gaussian\')\n active_set = result$active_set\n if (length(active_set)==0){\n active_set = -1\n } else{\n sigma_est = sigma(lm(y ~ X[,active_set] - 1))\n cat("sigma est for R", sigma_est,"\n")\n targets = selectiveInference:::compute_target(result, \'partial\', sigma_est = sigma_est,\n construct_pvalues=rep(TRUE, length(active_set)), \n construct_ci=rep(compute_intervals, length(active_set)))\n\n out = randomizedLassoInf(result,\n targets=targets,\n sampler = "norejection",\n level=0.9,\n burnin=1000,\n nsample=10000)\n active_set=active_set-1\n pvalues = out$pvalues\n intervals = out$ci\n }\n """\n )\n', (40342, 41609), True, 'import rpy2.robjects as rpy\n'), ((41891, 41912), 'rpy2.robjects.numpy2ri.deactivate', 'numpy2ri.deactivate', ([], {}), '()\n', (41910, 41912), False, 'from rpy2.robjects import numpy2ri\n'), ((42612, 42636), 'regreg.api.squared_error', 'rr.squared_error', (['X1', 'Y1'], {}), '(X1, Y1)\n', (42628, 42636), True, 'import regreg.api as rr\n'), ((42655, 42683), 'regreg.api.simple_problem', 'rr.simple_problem', (['loss', 'pen'], {}), '(loss, pen)\n', (42672, 42683), True, 'import regreg.api as rr\n'), ((2575, 2594), 'rpy2.robjects.numpy2ri.activate', 'numpy2ri.activate', ([], {}), '()\n', (2592, 2594), False, 'from rpy2.robjects import numpy2ri\n'), ((2607, 2632), 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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sat Aug 26 21:10:16 2017 @author: dhaval """ import argparse import sys from io import BytesIO import matplotlib import numpy as np import requests from PIL import Image matplotlib.use('agg') import matplotlib.pyplot as plt from keras.preprocessing import image from keras.models import load_model from keras.applications.inception_v3 import preprocess_input target_size = (299, 299) # fixed size for InceptionV3 architecture def predict(model, img, target_size): """Run model prediction on image Args: model: keras model img: PIL format image target_size: (w,h) tuple Returns: list of predicted labels and their probabilities """ if img.size != target_size: img = img.resize(target_size) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) preds = model.predict(x) return preds[0] def plot_preds(image, preds): """Displays image and the top-n predicted probabilities in a bar graph Args: image: PIL image preds: list of predicted labels and their probabilities """ """# For Spyder plt.imshow(image) plt.axis('off')""" plt.imshow(image) plt.axis('off') plt.figure() labels = ("cat") plt.barh([0, 1], preds, alpha=0.5) plt.yticks([0, 1], labels) plt.xlabel('Probability') plt.xlim(0, 1.01) plt.tight_layout() plt.savefig('out.png') if __name__ == "__main__": a = argparse.ArgumentParser() a.add_argument("--image", help="path to image") a.add_argument("--image_url", help="url to image") a.add_argument("--model") args = a.parse_args() if args.image is None and args.image_url is None: a.print_help() sys.exit(1) model = load_model(args.model) if args.image is not None: img = Image.open(args.image) preds = predict(model, img, target_size) plot_preds(img, preds) if args.image_url is not None: response = requests.get(args.image_url) img = Image.open(BytesIO(response.content)) preds = predict(model, img, target_size) plot_preds(img, preds)
[ "keras.preprocessing.image.img_to_array", "keras.applications.inception_v3.preprocess_input", "io.BytesIO", "sys.exit", "matplotlib.pyplot.imshow", "argparse.ArgumentParser", "matplotlib.pyplot.barh", "matplotlib.pyplot.xlabel", "matplotlib.pyplot.yticks", "matplotlib.pyplot.axis", "matplotlib.p...
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from collections import Counter import networkx as nx import numpy as np from features_infra.feature_calculators import NodeFeatureCalculator, FeatureMeta class BfsMomentsCalculator(NodeFeatureCalculator): def is_relevant(self): return True def weighted_avg_and_std(self, values, weights): """ Return the weighted average and standard deviation. values, weights -- Numpy ndarrays with the same shape. """ average = np.average(values, weights=weights) # Fast and numerically precise: variance = np.average((values - average) ** 2, weights=weights) return average, np.sqrt(variance) def _calculate(self, include: set): for node in self._gnx: # calculate BFS distances distances = nx.single_source_shortest_path_length(self._gnx, node) # distances.pop(node) # if not distances: # self._features[node] = [0., 0.] # continue node_dist = Counter(distances.values()) dists, weights = zip(*node_dist.items()) # This was in the previous version # instead of the above commented fix adjusted_dists = np.asarray([x + 1 for x in dists]) weights = np.asarray(weights) self._features[node] = [self.weighted_avg_and_std(adjusted_dists, weights)] def _get_feature(self,element): return list(self._features[element])[0] feature_entry = { "bfs_moments": FeatureMeta(BfsMomentsCalculator, {"bfs"}), } if __name__ == "__main__": from measure_tests.specific_feature_test import test_specific_feature test_specific_feature(BfsMomentsCalculator, is_max_connected=True)
[ "networkx.single_source_shortest_path_length", "numpy.sqrt", "numpy.average", "numpy.asarray", "features_infra.feature_calculators.FeatureMeta", "measure_tests.specific_feature_test.test_specific_feature" ]
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# -*- coding: utf-8 -*- """Oscillators are lifeforms that returns to its initial configuration after some time""" # Import modules import numpy as np from .base import Lifeform class Blinker(Lifeform): """A horizontal Blinker lifeform""" def __init__(self, length=3): """Initialize the class Parameters ---------- length : int Length of the blinker. Default is 3 """ super(Blinker, self).__init__() self.length = length @property def layout(self) -> np.ndarray: return np.ones(shape=(self.length, 1), dtype=int) class Toad(Lifeform): """A Toad lifeform oscillator""" def __init__(self): """Initialize the class""" super(Toad, self).__init__() @property def layout(self) -> np.ndarray: return np.array([[1, 1, 1, 0], [0, 1, 1, 1]]) class Pulsar(Lifeform): """A Pulsar lifeform oscillator""" def __init__(self): """Initialize the class""" super(Pulsar, self).__init__() @property def layout(self) -> np.ndarray: X = np.zeros((17, 17)) X[2, 4:7] = 1 X[4:7, 7] = 1 X += X.T X += X[:, ::-1] X += X[::-1, :] return X class FigureEight(Lifeform): """A Figure eight lifeform oscillator""" def __init__(self): """Initialize the class""" super(FigureEight, self).__init__() @property def layout(self) -> np.ndarray: X = np.zeros((6, 6)) X[0:3, 0:3] = 1 X[3:6, 3:6] = 1 return X class Beacon(Lifeform): """A Beacon lifeform oscillator""" def __init__(self): """Initialize the class""" super(Beacon, self).__init__() @property def layout(self) -> np.ndarray: X = np.zeros((4, 4)) X[0:2, 0:2] = 1 X[2:4, 2:4] = 1 return X
[ "numpy.array", "numpy.zeros", "numpy.ones" ]
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from flare.algorithm_zoo.distributional_rl_algorithms import C51 from flare.model_zoo.distributional_rl_models import C51Model from flare.algorithm_zoo.distributional_rl_algorithms import QRDQN from flare.model_zoo.distributional_rl_models import QRDQNModel from flare.algorithm_zoo.distributional_rl_algorithms import IQN from flare.model_zoo.distributional_rl_models import IQNModel import numpy as np import math import torch import torch.nn as nn import unittest class TestC51(unittest.TestCase): def initialize(self, bins=2): inner_size = 256 num_actions = 3 state_shape = [1] mlp = nn.Sequential(nn.Linear(inner_size, inner_size), nn.ReLU()) model = C51Model( dims=state_shape, num_actions=num_actions, perception_net=mlp, vmax=10, vmin=-10, bins=bins) alg = C51(model=model, exploration_end_steps=500000, update_ref_interval=100) return model, alg def test_select_q_distribution(self): model, alg = self.initialize() distribution = [[[0.1, 0.9], [0.2, 0.8], [0.3, 0.7]], [[0.4, 0.6], [0.5, 0.5], [0.6, 0.4]]] action = [0, 2] expected = np.array( [d[a] for d, a in zip(distribution, action)]).flatten() actual = alg.select_q_distribution( torch.tensor(distribution), torch.tensor(action)).numpy().flatten() self.assertEqual(len(expected), len(actual)) for x, y in zip(expected, actual): self.assertAlmostEqual(x, y) def test_check_alive(self): model, alg = self.initialize(3) values = [[[1, 2, 3]] * 2, [[3, 4, 5]] * 2, [[5, 6, 7]] * 2] alive = [1, 0, 1] next_values = torch.tensor(values).float() next_alive = torch.tensor(alive).float().view(-1, 1) expected = [ a if b == 1 else [[0, 1, 0]] * 2 for a, b in zip(values, alive) ] expected = np.array(expected) actual = alg.check_alive(next_values, next_alive).numpy() self.assertEqual(expected.shape, actual.shape) for x, y in zip(expected.flatten(), actual.flatten()): self.assertAlmostEqual(x, y) def one_backup(self, r, q, discount, model): N = len(q) m = [0.] * N for j in xrange(N): Tz = r + discount * model.atoms[j] Tz = min(Tz, 10) Tz = max(Tz, -10) b = (Tz + 10.) / model.delta_z l = int(math.floor(b)) u = int(math.ceil(b)) m[l] += q[j] * (u - b) m[u] += q[j] * (b - l) return m def test_backup(self): model, alg = self.initialize() discount = 0.9 reward = [[1.5], [-0.2], [0.]] next_q_distribution = [[0.1, 0.9], [0.2, 0.8], [0.3, 0.7]] expected = np.array([ self.one_backup(r[0], q, discount, model) for r, q in zip(reward, next_q_distribution) ]).flatten() actual = alg.backup( model.atoms, torch.FloatTensor([model.vmax]), torch.FloatTensor([model.vmin]), model.delta_z, torch.tensor(reward), discount, torch.tensor(next_q_distribution)).numpy().flatten() self.assertEqual(len(expected), len(actual)) for x, y in zip(expected, actual): self.assertAlmostEqual(x, y) def test_get_current_values(self): model, alg = self.initialize() A = "A" B = "B" alg.model.value = lambda x, y: (x, y) A_hat, B_hat = alg.get_current_values(A, B) self.assertEqual(A, A_hat) self.assertEqual(B, B_hat) def test_get_next_values(self): model, alg = self.initialize() A = "A" B = "B" C = {"q_value": A} alg.ref_model.value = lambda x, y: (x, y) C_hat, B_hat, A_hat = alg.get_next_values(C, B) self.assertEqual(A, A_hat) self.assertEqual(B, B_hat) self.assertEqual(C, C_hat) class TestQRDQN(unittest.TestCase): def initialize(self, bins=2): inner_size = 256 num_actions = 3 state_shape = [1] N = 32 mlp = nn.Sequential(nn.Linear(inner_size, inner_size), nn.ReLU()) alg = QRDQN( model=QRDQNModel( dims=state_shape, num_actions=num_actions, perception_net=mlp, N=N), exploration_end_steps=500000, update_ref_interval=100) return alg def test_check_alive(self): alg = self.initialize() values = [[[1], [2], [3]], [[3], [4], [5]], [[5], [6], [7]]] alive = [1, 0, 1] next_values = torch.tensor(values).float() next_alive = torch.tensor(alive).float().view(-1, 1) expected = [ a if b == 1 else [[0], [0], [0]] for a, b in zip(values, alive) ] expected = np.array(expected) actual = alg.check_alive(next_values, next_alive).numpy() self.assertEqual(expected.shape, actual.shape) for x, y in zip(expected.flatten(), actual.flatten()): self.assertAlmostEqual(x, y) def huber_loss(self, u, k=1): if abs(u) <= k: return 0.5 * u * u else: return k * (abs(u) - 0.5 * k) def quantile_huber_loss(self, u, tau, k=1): if u < 0: delta = 1 else: delta = 1 return abs(tau - delta) * self.huber_loss(u, k) def expection_quantile_huber_loss(self, theta, Ttheta, tau, k=1): r1 = 0 for theta_i, tau_i in zip(theta, tau): r2 = 0 for Ttheta_j in Ttheta: r2 += self.quantile_huber_loss(Ttheta_j - theta_i, tau_i, k) r1 += r2 / len(Ttheta) return r1 def batch_expection_quantile_huber_loss(self, q_distribution, critic_value, tau, k=1): expected = [] for theta, Ttheta, t in zip(q_distribution, critic_value, tau): expected.append( self.expection_quantile_huber_loss(theta, Ttheta, t, k)) return expected def test_get_quantile_huber_loss(self): alg = self.initialize() critic_value = [[-1., 2.], [3., 4.], [-5., -5.]] q_distribution = [[9., 8.5], [7., 6.], [-5., -5.]] tau = [[0.3, 0.6], [0.4, 0.8], [0.6, 0.1]] expected = self.batch_expection_quantile_huber_loss( q_distribution, critic_value, tau, k=1) expected = np.array(expected) critic_value = torch.tensor(critic_value) q_distribution = torch.tensor(q_distribution) tau = torch.tensor(tau) actual = alg.get_quantile_huber_loss(critic_value, q_distribution, tau).view(-1).numpy() self.assertEqual(expected.shape, actual.shape) for x, y in zip(expected.flatten(), actual.flatten()): self.assertAlmostEqual(x, y, places=6) class TestIQN(unittest.TestCase): def initialize(self): inner_size = 256 num_actions = 3 state_shape = [1] mlp = nn.Sequential(nn.Linear(inner_size, inner_size), nn.ReLU()) model = IQNModel( dims=state_shape, num_actions=num_actions, perception_net=mlp, inner_size=inner_size) alg = IQN(model=model, exploration_end_steps=500000, update_ref_interval=100) return alg def test_get_current_values(self): alg = self.initialize() A = "A" B = "B" N = 10 alg.model.value = lambda x, y, z: (x, y, z) alg.N = N A_hat, B_hat, N_hat = alg.get_current_values(A, B) self.assertEqual(A, A_hat) self.assertEqual(B, B_hat) self.assertEqual(N, N_hat) def tdest_get_next_values(self): alg = self.initialize() next_values = "A" next_value = "B" next_states_update = "C" a_list = [next_values, {"q_value": next_value}] alg.ref_model.value = lambda x, y, z: (x, y, z) A_hat, B_hat, C_hat = alg.get_next_values(a_list, next_states_update) self.assertEqual(next_values, A_hat) self.assertEqual(next_states_update, B_hat) self.assertEqual(next_value, C_hat) if __name__ == "__main__": unittest.main()
[ "torch.nn.ReLU", "math.ceil", "flare.model_zoo.distributional_rl_models.QRDQNModel", "math.floor", "flare.algorithm_zoo.distributional_rl_algorithms.C51", "numpy.array", "torch.tensor", "flare.algorithm_zoo.distributional_rl_algorithms.IQN", "torch.nn.Linear", "unittest.main", "flare.model_zoo.d...
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# http://github.com/timestocome # train a raspberry pi robot to wander the house while avoiding obstacles # and looking for cats # this robot uses wheels for steering # 4 wheel drive with separate controls each side # change from off policy learning in first try # adapted from https://morvanzhou.github.io/tutorials/ import numpy as np ############################################################################### # q learning happens here ############################################################################### actions = ['forward', 'reverse', 'turn_left', 'turn_right', 'hard_left', 'hard_right'] n_distance_states = 100 + 1 n_cat_states = 3 n_actions = 6 qTable = 'qTable.npy' # load saved table from file def load_q_table(): t_1d = np.load(qTable) table = t_1d.reshape(n_distance_states, n_cat_states, n_actions) return table q_table = load_q_table() print('--------------------------------') print('Final Q Table') for i in range(n_distance_states): for j in range(n_cat_states): print('distance %d, cat %d' %(i, j)) print('action values', q_table[i, j, :]) # print actions by distance no cat z = np.zeros(n_distance_states) for i in range(n_distance_states): for j in range(n_cat_states): if j == 2: # no cat z[i] = np.argmax(q_table[i, j, :]) print('--------- distance/ action -------------') for i in range(len(z)): a = int(z[i]) print(i, actions[a])
[ "numpy.zeros", "numpy.load", "numpy.argmax" ]
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import pandas as pd import numpy as np from sklearn.decomposition import PCA from sklearn import preprocessing import matplotlib.pyplot as plt #load and tranpose csv data #Data fetched on 2021-05-08 csvDataT = pd.read_csv('ticks.norm.csv') csvData = csvDataT.T #Symbol column like AEFES,AKBNK,AKSA... ticks = csvData.iloc[0] #Data columns. First column contains name of features so removed csvData = csvData.iloc[1:] #Append Headers csvData.columns = ticks #Data contains Index number which is equals to feature columns count # (PE ratio, Earning per share... vs.) csvData.index = [0] * csvData.shape[0] #Actual PCA math and data reduction centeredData = preprocessing.scale(csvData.T) pca = PCA() pca.fit(centeredData) reducedData = pca.transform(centeredData) #Reformat data and prepare PCA result component labels for scatter graph percentageVariance = np.round(pca.explained_variance_ratio_ * 100, decimals=1) labels = ['Comp' + str(n) for n in range(1, len(percentageVariance)+1)] #create pandas data frame matrix for 2d representation frame = pd.DataFrame(reducedData, index=[*ticks], columns=labels) #First 2 component of PCA analysis result contains most dominant data (out of 4 others in this case) # Comp1 and Comp2 will be used to create scatter plot plt.scatter(frame.Comp1, frame.Comp2) plt.title('Bist 100 PCA Analysis (as 2021-05-08)') plt.xlabel('Principle Component 1') plt.ylabel('Principle Component 2') for sample in frame.index: plt.annotate(sample, (frame.Comp1.loc[sample], frame.Comp2.loc[sample])) plt.show()
[ "pandas.read_csv", "matplotlib.pyplot.ylabel", "sklearn.decomposition.PCA", "numpy.round", "matplotlib.pyplot.xlabel", "matplotlib.pyplot.annotate", "matplotlib.pyplot.scatter", "pandas.DataFrame", "matplotlib.pyplot.title", "sklearn.preprocessing.scale", "matplotlib.pyplot.show" ]
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import random import os import time import neat import visualize import pickle from bareSnake import snakeGame import numpy as np import math from neat.graphs import feed_forward_layers from neat.graphs import required_for_output import tensorflow as tf tf.config.run_functions_eagerly(True) import pandas as pd df = pd.read_csv("testData3.csv") df.drop(columns=['Unnamed: 0']) hld = 2 dfMunch = df.query('intention == "munch"').drop(columns=['Unnamed: 0']).reset_index(drop=True) dfWall = df.query('intention == "wall move"').drop(columns=['Unnamed: 0']).reset_index(drop=True) dfFood = df.query('intention == "food move"').drop(columns=['Unnamed: 0']).reset_index(drop=True) MunchMoves = [[],[]] WallMoves = [[],[]] FoodMoves = [[],[]] for i in range(len(dfMunch)): finalIns = [] finalOuts = [] ins = np.array(dfMunch.values[i][0][1:-1].split(','),dtype=float) outs = np.array(dfMunch.values[i][1][1:-1].split(','),dtype=int) for j in range(len(ins)): finalIns.append(ins[j]) for j in range(len(outs)): finalOuts.append(outs[j]) MunchMoves[0].append(finalIns) MunchMoves[1].append(finalOuts) for i in range(len(dfFood)): ins = np.array(dfFood.values[i][0][1:-1].split(','),dtype=float) outs = np.array(dfFood.values[i][1][1:-1].split(','),dtype=int) FoodMoves[0].append(ins) FoodMoves[1].append(outs) for i in range(len(dfWall)): ins = np.array(dfWall.values[i][0][1:-1].split(','),dtype=float) outs = np.array(dfWall.values[i][1][1:-1].split(','),dtype=int) WallMoves[0].append(ins) WallMoves[1].append(outs) hold = 8 def find_index(array): s = np.array(array) sort_index = np.argsort(s) return sort_index def run(config_file): """ runs the NEAT algorithm to train a neural network to play flappy bird. :param config_file: location of config file :return: None """ config = neat.config.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet, neat.DefaultStagnation, config_file) p = neat.Population(config) genomes = p.population.items() genome1key = next(iter(p.population)) genome = p.population[genome1key] print(genome1key) print(genome) net = neat.nn.FeedForwardNetwork.create(genome, config) net = neat.nn.FeedForwardNetwork.create(genome, config) lr = .1 batchSize = 400 epochSize = 100 OgAvgMunch = net.batchAcc(MunchMoves[0][0:batchSize],MunchMoves[1][0:batchSize]) OgAvgFood = net.batchAcc(FoodMoves[0][0:batchSize], FoodMoves[1][0:batchSize]) OgAvgWall = net.batchAcc(WallMoves[0][0:batchSize], WallMoves[1][0:batchSize]) asdf = 9 for k in range(epochSize): net.backProp_GenomeFast(MunchMoves[0][0:batchSize],MunchMoves[1][0:batchSize],lr,genome) #print(net.batchAcc(MunchMoves[0][0:batchSize],MunchMoves[1][0:batchSize])) newAvgMunch = net.batchAcc(MunchMoves[0][0:batchSize],MunchMoves[1][0:batchSize]) asdfasdf = 95 print("at the end\n\n i hope") print("the first acc is %d", OgAvgMunch) print("the after training acc is $d", newAvgMunch) if __name__ == '__main__': #print('in start') # Determine path to configuration file. This path manipulation is # here so that the script will run successfully regardless of the # current working directory. local_dir = os.path.dirname(__file__) config_path = os.path.join(local_dir, 'configParam2.txt') run(config_path)
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import sys import os import glob import logging import operator import time import pickle import multiprocessing as mp import numpy as np from datetime import datetime from Bio.PDB import * import copy import gc # python 3 compatibility from functools import reduce from past.builtins import map sys.path.append('../../') from config import * sys.path.append(scripts_dir) from utils import * from pymol_helper import * from classes import * from validators import * def rmsd(V, W): D = len(V[0]) N = len(V) rmsd = 0.0 for v, w in zip(V, W): rmsd += sum([(v[i]-w[i])**2.0 for i in range(D)]) return np.sqrt(rmsd/N) def kabsch(P, Q): # Computation of the covariance matrix C = np.dot(np.transpose(P), Q) V, S, W = np.linalg.svd(C) d = (np.linalg.det(V) * np.linalg.det(W)) < 0.0 if d: S[-1] = -S[-1] V[:, -1] = -V[:, -1] # Create Rotation matrix U U = np.dot(V, W) return U def kabsch_rmsd(P, Q): P = rotate(P, Q) return rmsd(P, Q) def rotate(P, Q): U = kabsch(P, Q) # Rotate P P = np.dot(P, U) return P def convert_array(c1, c2): ret1 = [] ret2 = [] for i, j in zip(c1, c2): if i == 0 or j == 0: continue ret1.append(i) ret2.append(j) return np.array(ret1), np.array(ret2) def load_graph_data(fn): graph_data = {} fp = open(fn) cnt = 0 for line in fp.readlines(): pieces = line.split() node1 = strToNode(pieces[0]) node2 = strToNode(pieces[1]) if node1 not in graph_data: graph_data[node1] = {} if node2 not in graph_data: graph_data[node2] = {} # t1 t2 is for best alignment order #(t1, t2, z-score, local_aln_indx, local_aln_indx, seq1, seq2, aln_score) if node1 == strToNode(pieces[3]): graph_data[node1][node2] = (pieces[3], pieces[4], float(pieces[2]), '', '', '', '', 0.) graph_data[node2][node1] = (pieces[4], pieces[3], float(pieces[2]), '', '', '', '', 0.) else: graph_data[node1][node2] = (pieces[4], pieces[3], float(pieces[2]), '', '', '', '', 0.) graph_data[node2][node1] = (pieces[3], pieces[4], float(pieces[2]), '', '', '', '', 0.) cnt += 1 fp.close() # print('Maximum number of edges in cluster graph: ' + str(cnt)) return graph_data def load_cluster_alignment_data(alignment_data, clusters): cluster_alignment_data = {} for c_id in clusters: cluster_alignment_data[c_id] = {} for i in range(len(clusters[c_id])): node1 = strToNode(clusters[c_id][i]) if node1 not in cluster_alignment_data[c_id]: cluster_alignment_data[c_id][node1] = {} for j in range(len(clusters[c_id])): if i == j: continue node2 = strToNode(clusters[c_id][j]) if node2 not in cluster_alignment_data[c_id]: cluster_alignment_data[c_id][node2] = {} if node1 not in alignment_data or node2 not in alignment_data[node1]: logger.error('ERROR: (' + str(node1) + ', ' + str(node2) + ') pair not found in graph file!') sys.exit() cluster_alignment_data[c_id][node1][node2] = alignment_data[node1][node2] cluster_alignment_data[c_id][node2][node1] = alignment_data[node2][node1] return cluster_alignment_data, get_loops_in_cluster(clusters) def load_alignment_data(input_fname_base, alignment_dir, graphs_and_pickles_dir, alignment_fname, clusters, previous_graph_file_reused): alignment_data_fname = os.path.join(graphs_and_pickles_dir, 'alignment_data_' + input_fname_base + '.pickle2') if (sys.version_info >= (3, 0)): alignment_data_fname = os.path.join(graphs_and_pickles_dir, 'alignment_data_' + input_fname_base + '.pickle3') if previous_graph_file_reused == True: if is_valid_pickle(alignment_data_fname, clusters) == True: logger.info('Loading saved alignment data from previous run (In ' + alignment_data_fname[base_path_len:] + ') ...\n') f = open(alignment_data_fname, 'rb') cluster_alignment_data = pickle.load(f) f.close() return cluster_alignment_data logger.info('Loading alignment data from alignment files.') start_time = time.time() alignment_data = load_graph_data(alignment_fname) cluster_alignment_data, loops_node_list = load_cluster_alignment_data(alignment_data, clusters) # print('Number of loops in cluster file: ' + str(len(loops_in_cluster))) file_counter = 0 for node in loops_node_list: for r1 in get_all_loop_combination(str(node)): node1 = strToNode(r1) # for fn in glob.glob(os.path.join(alignment_dir, '*.aln')): fn = os.path.join(alignment_dir, r1 + '.aln') if not os.path.isfile(fn): return None # stime = datetime.now() file_counter += 1 print('Processsing ' + fn[base_path_len:] + ' ... (' + str(file_counter) + ')') # sys.stdout.flush() # r1 = os.path.basename(fn)[:-4] # node1 = strToNode(r1) # if node1 not in loops_in_cluster: # continue cid_nodelist_pair = find_nodes_in_cluster(node1, cluster_alignment_data) fp = open(fn) lines = fp.readlines() fp.close() # flag = 0 # print('No. of lines: ' + str(len(lines))) # print('Connected nodes: ' + str(len(node_dict))) line_index = 0 while line_index < len(lines): # print 'Reading line ' + str(line_index) # sys.exit() if lines[line_index].startswith('# Aligning'): test_r1 = lines[line_index].split('::')[1].split(' and ')[0].strip().strip(':') if r1 != test_r1: logger.error('ERROR: filename and loop mismatch. r1(filename): ' + r1 + ', r1(loop): ' + test_r1) sys.exit() r2 = lines[line_index].split('::')[1].split(' and ')[1].strip().strip(':') node2 = strToNode(r2) for c_id, node_dict in cid_nodelist_pair: if node2 in node_dict: t1 = cluster_alignment_data[c_id][node1][node2][0] t2 = cluster_alignment_data[c_id][node1][node2][1] # make sure the best alignment ordering is same as the ordering of loop in current file if not ((r1 == t1 and r2 == t2) or (r1 == t2 and r2 == t1)): # line_index += 12 continue #safety condition for reverse order (might be redundant) if (r1 == t2 and r2 == t1): t1 = r2 t2 = r1 score_text = lines[line_index+1].split(':')[1].strip() if score_text == '': score = -50. logger.error('ERROR: No alignment score found for: ' + r1 + ' and ' + r2) sys.exit() else: score = float(score_text) text = lines[line_index+3].split(':')[1].strip() cr1 = get_local_alignment_index(r1, text) text = lines[line_index+4].split(':')[1].strip() cr2 = get_local_alignment_index(r2, text) aln1 = lines[line_index+6].strip() aln2 = lines[line_index+7].strip() if len(aln1) == 0 or len(aln2) == 0: # set dummy alignment aln1 = 'A' aln2 = 'A' temp_cr1 = cr1.split(':') dummy_index = temp_cr1[1].split('_')[0].split('-')[0] cr1 = temp_cr1[0] + ':' + dummy_index + '-' + dummy_index temp_cr2 = cr2.split(':') dummy_index = temp_cr2[1].split('_')[0].split('-')[0] cr2 = temp_cr2[0] + ':' + dummy_index + '-' + dummy_index zscore = cluster_alignment_data[c_id][node1][node2][2] cluster_alignment_data[c_id][node1][node2] = (t1, t2, zscore, cr1, cr2, aln1, aln2, score) cluster_alignment_data[c_id][node2][node1] = (t2, t1, zscore, cr2, cr1, aln2, aln1, score) # break # we can skip at least 11 lines from current alignment data line_index += 11 line_index += 1 # while end # etime = datetime.now() # difftime = (etime - stime).total_seconds() # print('Time taken: ') # print(difftime) # break f = open(alignment_data_fname,"wb") pickle.dump(cluster_alignment_data, f) f.close() logger.info('Done') logger.info('Time taken: ' + str(round((time.time() - start_time), 3)) + ' seconds.\n') return cluster_alignment_data def extract_atom_coordinate(loop_coord, pdb_pm, pdb_id): loop_coord_backbone, loop_coord_sugar = loop_coord aligned_segment_coord = [] for chain_id, index, icode in pdb_pm: if chain_id == '': continue if (pdb_id, chain_id, index, icode) in loop_coord_backbone and loop_coord_backbone[(pdb_id, chain_id, index, icode)] != 0.: aligned_segment_coord.append(loop_coord_backbone[(pdb_id, chain_id, index, icode)]) if (pdb_id, chain_id, index, icode) in loop_coord_sugar and loop_coord_sugar[(pdb_id, chain_id, index, icode)] != 0.: aligned_segment_coord.append(loop_coord_sugar[(pdb_id, chain_id, index, icode)]) return aligned_segment_coord def generate_rmsd_data(input_fname_base, partial_pdbx_dir, graphs_and_pickles_dir, alignment_data, clusters, loop_list, previous_graph_file_reused): rmsd_data_fname = os.path.join(graphs_and_pickles_dir, 'rmsd_data_' + input_fname_base + '.pickle2') if (sys.version_info >= (3, 0)): rmsd_data_fname = os.path.join(graphs_and_pickles_dir, 'rmsd_data_' + input_fname_base + '.pickle3') if previous_graph_file_reused == True: if is_valid_pickle(rmsd_data_fname, clusters) == True: logger.info('Loading saved RMSD data from previous run (In ' + rmsd_data_fname[base_path_len:] + ') ...\n') f = open(rmsd_data_fname, 'rb') rmsd_data_dict = pickle.load(f) f.close() return rmsd_data_dict # print(alignment_data['GNGA'][strToNode('4V9F_0:2621-2626')][strToNode('4V9F_0:460-465')]) # print(alignment_data['GNAA'][strToNode('4V9F_0:2621-2626')][strToNode('4V9F_0:460-465')]) # sys.exit() logger.info('Generating RMSD data.') start_time = time.time() rmsd_data_dict = {} coord_dict = {} pdb_structure = None prev_pdb_chain = '' structure_counter = 0 # for lp in loop_list: # pdb_chain, regions = lp.split(':') # pdb = pdb_chain.split('_')[0] # pdb_pm = get_pdb_index_list(lp) # if prev_pdb_chain != pdb_chain: # pdb_structure = None # else: # structure_counter += 1 # if structure_counter % 500 == 0: # gc.collect() # coord_backbone, coord_sugar, pdb_structure = get_atom_coordinate(os.path.join(pdbx_dir, pdb+'.cif'), pdb_pm, pdb_structure) # coord_dict[lp] = (coord_backbone, coord_sugar) # prev_pdb_chain = pdb_chain for lp in loop_list: pdb_pm = get_pdb_index_list(lp) coord_backbone, coord_sugar, pdb_structure = get_atom_coordinate(os.path.join(partial_pdbx_dir, lp + '.cif'), pdb_pm) coord_dict[lp] = (coord_backbone, coord_sugar) # time_align_residue = 0 # time_get_coordinate = 0 # time_rmsd = 0 # time_start = time.time() for cluster_id in alignment_data: # if cluster_id not in clusters: # continue sum_of_avg_rmsd_for_c = 0. rmsd_data_list_dict = {} index_dict = {} i = 0 # print(loop_list) for l1 in alignment_data[cluster_id]: index_dict[l1] = i i += 1 # pdb_chain_dict = {} # pdb_res_mapping_dict = {} # fasta_seq_dict = {} # for l1 in alignment_data[cluster_id]: # pdb_chain, _ = str(l1).strip().split(':') # pdb_id, chain_id = pdb_chain.strip().split('_') # if pdb_id not in pdb_chain_dict: # pdb_chain_dict[pdb_id] = [] # pdb_chain_dict[pdb_id].append(chain_id) # if pdb_chain not in pdb_res_mapping_dict: # pdb_res_mapping_dict[pdb_chain] = load_pdb_res_map(pdb_chain) # for pdb_id in pdb_chain_dict: # fasta_seq_dict.update(load_fasta_seq(pdb_id, pdb_chain_dict[pdb_id])) pdb_res_mapping_dict, fasta_seq_dict = load_pdb_fasta_mapping_and_fasta_seq_dict(cluster_id, alignment_data) # i = 0 for l1 in alignment_data[cluster_id]: # if str(l1) not in loop_list: # continue fit_ret = [] sum_of_rmsd_for_l1 = 0. # rmsd_data_list_item = {} # j = 0 for l2 in alignment_data[cluster_id][l1]: # if str(l2) not in loop_list: # continue if l1 != l2: # print(cluster_id) r1, r2, zscore, cr1, cr2, aln_1, aln_2, score = alignment_data[cluster_id][l1][l2] # print('r1, r2: ') # print(r1, r2) # print(r1, r2, zscore, cr1, cr2, aln_1, aln_2, score) # if output_env == 'local': # time_s = time.time() pdb1_pm, pdb2_pm, i1_pm, i2_pm = aln_residue_temp(pdb_res_mapping_dict, fasta_seq_dict, r1, r2, cr1, cr2, aln_1, aln_2, 0, len(aln_1)-1, 0) # time_align_residue += time.time() - time_s # else: # pdb1_pm, pdb2_pm, i1_pm, i2_pm = aln_residue(r1, r2, cr1, cr2, aln_1, aln_2, 0, len(aln_1)-1, 0) pdb_chain1, _ = r1.split(':') pdb_chain2, _ = r2.split(':') pdb1 = pdb_chain1.split('_')[0] pdb2 = pdb_chain2.split('_')[0] # structures = {} #returns centroid of backbone atoms # time_s = time.time() coord1 = extract_atom_coordinate(coord_dict[str(l1)], pdb1_pm, pdb1) coord2 = extract_atom_coordinate(coord_dict[str(l2)], pdb2_pm, pdb2) # time_get_coordinate += time.time() - time_s # print(coord1, coord2) X, Y = convert_array(coord1, coord2) if len(X) != len(Y): logger.warning('WARNING: Corresponding co-ordinates for alignments not found! rmsd = 20 assigned.') rmsd = 20. elif len(X) == 0: logger.warning('WARNING: Co-ordinates for alignments not found! rmsd = 20 assigned.') rmsd = 20. else: XC = sum(X)/len(X) YC = sum(Y)/len(Y) # calculating relative co-ordinate using mean as reference X -= XC Y -= YC # time_s = time.time() rmsd = kabsch_rmsd(X, Y) # time_rmsd += time.time() - time_s sum_of_rmsd_for_l1 += rmsd # X.shape[0] represents the number of aligned nucleotides # fit_ret.append((index_dict[l2], str(l2), rmsd, X.shape[0])) fit_ret.append((index_dict[l2], str(l2), rmsd, len(pdb1_pm))) # end of if # j += 1 # end of l2 for loop # time_diff = time.time() - time_start # if time_diff > 30: # print(cluster_id, l1) # print('time_align_residue', time_align_residue) # print('time_get_coordinate', time_get_coordinate) # print('time_rmsd', time_rmsd) # time_start = time.time() # print('') avg_of_rmsd_for_l1 = 0.0 if (len(clusters[cluster_id]) - 1) > 0: avg_of_rmsd_for_l1 = sum_of_rmsd_for_l1 / (len(clusters[cluster_id]) - 1) sum_of_avg_rmsd_for_c += avg_of_rmsd_for_l1 # print str(i)+','+l1+'\t'+'|'.join(map(lambda x: str(x[0])+','+x[1]+','+str(x[2])+','+str(x[3]), sorted(fit_ret, key=lambda x: x[2]))) rmsd_data_list_dict[(index_dict[l1], str(l1))] = (avg_of_rmsd_for_l1, sorted(fit_ret, key=lambda x: x[2])) # i += 1 # end of l1 for loop avg_of_avg_rmsd_for_c = 0.0 if len(clusters[cluster_id]) > 0: avg_of_avg_rmsd_for_c = sum_of_avg_rmsd_for_c / len(clusters[cluster_id]) rmsd_data_dict[cluster_id] = (avg_of_avg_rmsd_for_c, rmsd_data_list_dict) # sorted(rmsd_data_list_dict, key=lambda x: x[0][1]) #order by loop average f = open(rmsd_data_fname,"wb") pickle.dump(rmsd_data_dict, f) f.close() logger.info('Done') logger.info('Time taken: ' + str(round((time.time() - start_time), 3)) + ' seconds.\n') return rmsd_data_dict def read_pdb_chain_organism_details(fname): fp = open(fname) first_line = True pdb_organism_details = {} for line in fp.readlines(): if first_line: first_line = False continue pieces = line.strip('\n').strip('\r').split('\t') # For each chain, store RNA Types, Organism, Class, Type (Manually Defined), Source if len(pieces) > 0: pdb_organism_details[pieces[0].strip()] = pieces[1:] fp.close() return pdb_organism_details def remove_based_on_zscore(zscore, rmsd, align_length, is_alignment_from_user): # if cluster_source == 'DeNovo': if is_alignment_from_user == False: # zscore < 0.0 if get_zscore_rank(zscore) == 100: return True # zscore 0 to 0.5, rmsd >= 1.0 if get_zscore_rank(zscore) >= 5 and get_rmsd_rank(rmsd, align_length, is_length_adjusted_score) >= 3: return True # zscore 0.5 to 1, rmsd >= 2.0 if get_zscore_rank(zscore) == 4 and get_rmsd_rank(rmsd, align_length, is_length_adjusted_score) >= 4: return True return False def remove_based_on_rmsd(rmsd, align_length): # rmsd >= 4.0 if get_rmsd_rank(rmsd, align_length, is_length_adjusted_score) >= 5: return True return False def extreme_filtering_based_on_rmsd(rmsd, align_length): if extreme_filtering == True and rmsd > rmsd_threshold_for_merging: return True return False def get_loop_string_for_filtering_log(r): loop_str = '' r_pdb_ind = convert_a_loop_from_FASTA_to_PDB(r) loop_str = r_pdb_ind if input_index_type == 'fasta': loop_str += ' (PDB) ' + r + ' (FASTA)' return loop_str def filter_loops_in_cluster(clusters, rmsd_data_dict, alignment_data, is_alignment_from_user): current_rmsd_data_dict = copy.deepcopy(rmsd_data_dict) removed_loops = 0 while True: max_cid_len = max([len(x) for x in clusters]) filtered_cluster = {} for cluster_id in sorted(clusters): loops = clusters[cluster_id] filtered_loops = [] for i in range(len(loops)): loops[i] = str(strToNode(loops[i])) _, cluster_pairwise_align_details = current_rmsd_data_dict[cluster_id] align_len_threshold = generate_align_length_threshold(cluster_pairwise_align_details) for (i, r1) in cluster_pairwise_align_details: if r1 in loops: _, pairwise_align_details = cluster_pairwise_align_details[(i, r1)] j, r2, rmsd, align_length = find_best_aligned_pair(pairwise_align_details, align_len_threshold) # (t1, t2, zscore, cr1, cr2, aln1, aln2, score) = alignment_data[cluster_id.strip().split('_')[0]][strToNode(r1)][strToNode(r2)] (t1, t2, zscore, cr1, cr2, aln1, aln2, score) = alignment_data[cluster_id][strToNode(r1)][strToNode(r2)] if extreme_filtering == True and not is_acceptable_align_len(align_length, align_len_threshold): logger.info('Filtering ' + get_loop_string_for_filtering_log(r1).ljust(75) + ' from ' + str(cluster_id).ljust(max_cid_len) + ' based on length \t(align_length: ' + str(align_length) + ') [Extreme filtering].') removed_loops += 1 elif extreme_filtering_based_on_rmsd(rmsd, align_length): logger.info('Filtering ' + get_loop_string_for_filtering_log(r1).ljust(75) + ' from ' + str(cluster_id).ljust(max_cid_len) + ' based on rmsd \t(zscore: ' + "{:.3f}".format(round(zscore, 3)) + ', rmsd: ' + "{:.3f}".format(round(rmsd, 3)) + ') [Extreme filtering].') removed_loops += 1 elif remove_based_on_zscore(zscore, rmsd, align_length, is_alignment_from_user): logger.info('Filtering ' + get_loop_string_for_filtering_log(r1).ljust(75) + ' from ' + str(cluster_id).ljust(max_cid_len) + ' based on zscore\t(zscore: ' + "{:.3f}".format(round(zscore, 3)) + ', rmsd: ' + "{:.3f}".format(round(rmsd, 3)) + ').') removed_loops += 1 elif remove_based_on_rmsd(rmsd, align_length): logger.info('Filtering ' + get_loop_string_for_filtering_log(r1).ljust(75) + ' from ' + str(cluster_id).ljust(max_cid_len) + ' based on rmsd \t(zscore: ' + "{:.3f}".format(round(zscore, 3)) + ', rmsd: ' + "{:.3f}".format(round(rmsd, 3)) + ').') removed_loops += 1 else: filtered_loops.append(r1) # filtered_loops = list(set(filtered_loops)) if len(filtered_loops) > 1: filtered_cluster[cluster_id] = filtered_loops else: removed_loops += len(filtered_loops) if len(get_loops_in_cluster(filtered_cluster)) == len(get_loops_in_cluster(clusters)): break clusters = copy.deepcopy(filtered_cluster) for cluster_id in clusters: loops = clusters[cluster_id] current_rmsd_data_dict[cluster_id] = extract_current_rmsd_data_dict(rmsd_data_dict, cluster_id, loops) if removed_loops > 0: logger.info('Removed ' + str(removed_loops) + ' loop' + ('s' if removed_loops > 1 else '') + ' from input data through filtering based on zvalue and rmsd.\n') return filtered_cluster # def filter_loops_in_cluster_denovo_result_analysis(clusters, rmsd_data_dict, alignment_data): # current_rmsd_data_dict = copy.deepcopy(rmsd_data_dict) # removed_loops = 0 # while True: # filtered_cluster = {} # for c_id in sorted(clusters, key=lambda x: x): # loops = clusters[c_id] # filtered_loops = [] # for i in range(len(loops)): # loops[i] = str(strToNode(loops[i])) # for cluster_id in sorted(current_rmsd_data_dict, key= lambda x: x): # _, cluster_pairwise_align_details = current_rmsd_data_dict[cluster_id] # align_len_threshold = generate_align_length_threshold(cluster_pairwise_align_details) # for (i, r1) in cluster_pairwise_align_details: # if r1 in loops: # _, pairwise_align_details = cluster_pairwise_align_details[(i, r1)] # j, r2, rmsd, align_length = find_best_aligned_pair(pairwise_align_details, align_len_threshold) # # (t1, t2, zscore, cr1, cr2, aln1, aln2, score) = alignment_data[cluster_id.strip().split('_')[0]][strToNode(r1)][strToNode(r2)] # (t1, t2, zscore, cr1, cr2, aln1, aln2, score) = alignment_data[cluster_id][strToNode(r1)][strToNode(r2)] # if remove_based_on_zscore(zscore, rmsd, align_length): # logger.info('removing ' + r1 + ' from ' + cluster_id + ' based on zscore (zscore: ' + str(zscore) + ', rmsd: ' + str(rmsd) + ').') # removed_loops += 1 # elif remove_based_on_rmsd(rmsd, align_length): # logger.info('removing ' + r1 + ' from ' + cluster_id + ' based on rmsd (zscore: ' + str(zscore) + ', rmsd: ' + str(rmsd) + ').') # removed_loops += 1 # else: # filtered_loops.append(r1) # filtered_loops = list(set(filtered_loops)) # if len(filtered_loops) > 1: # filtered_cluster[c_id] = filtered_loops # else: # removed_loops += len(filtered_loops) # if len(get_loops_in_cluster(filtered_cluster)) == len(get_loops_in_cluster(clusters)): # break # clusters = copy.deepcopy(filtered_cluster) # for cluster_id in clusters: # loops = clusters[cluster_id] # current_rmsd_data_dict[cluster_id] = extract_current_rmsd_data_dict(rmsd_data_dict, cluster_id, loops) # logger.info('Removed Loops from clusters through filtering zvalue and rmsd: ' + str(removed_loops)) # return filtered_cluster def generate_length_adjusted_rmsd_score(rmsd_data_dict): length_adjusted_rmsd_score_dict = {} for cluster_id in rmsd_data_dict: _, rmsd_data_list_dict = rmsd_data_dict[cluster_id] # total_rmsd = 0.0 new_rmsd_data_list_dict = {} rmsd_align_len_list = [] for (i, r1) in rmsd_data_list_dict: _, pairwise_align_details = rmsd_data_list_dict[(i, r1)] rmsd_align_len_list_for_r1 = [] # total_rmsd_for_r1 = 0.0 new_pairwise_align_details = [] for (j, r2, rmsd, align_length) in pairwise_align_details: adjusted_score = rmsd / math.sqrt(align_length) new_pairwise_align_details.append((j, r2, adjusted_score, align_length)) # total_rmsd_for_r1 += adjusted_score rmsd_align_len_list_for_r1.append((adjusted_score, align_length)) # avg_rmsd_for_r1 = total_rmsd_for_r1 / len(new_pairwise_align_details) avg_rmsd_for_r1, total_align_len_for_r1 = get_weighted_avg_rmsd(rmsd_align_len_list_for_r1) new_rmsd_data_list_dict[(i, r1)] = (avg_rmsd_for_r1, sorted(new_pairwise_align_details, key=lambda x: x[2])) # total_rmsd += avg_rmsd_for_r1 rmsd_align_len_list.append((avg_rmsd_for_r1, total_align_len_for_r1)) # avg_rmsd = total_rmsd / len(new_rmsd_data_list_dict) avg_rmsd, total_align_len = get_weighted_avg_rmsd(rmsd_align_len_list) length_adjusted_rmsd_score_dict[cluster_id] = (avg_rmsd, new_rmsd_data_list_dict) return length_adjusted_rmsd_score_dict # def extract_current_rmsd_data_dict_denovo_analysis(rmsd_data_dict, loops): # for i in range(len(loops)): # loops[i] = str(strToNode(loops[i])) # new_rmsd_data_list_dict = {} # # total_rmsd = 0.0 # rmsd_align_len_list = [] # for cluster_id in rmsd_data_dict: # _, rmsd_data_list_dict = rmsd_data_dict[cluster_id] # not_found_count = 0 # for (i, r1) in rmsd_data_list_dict: # if r1 in loops: # _, pairwise_align_details = rmsd_data_list_dict[(i, r1)] # # total_rmsd_for_r1 = 0.0 # rmsd_align_len_list_for_r1 = [] # new_pairwise_align_details = [] # for (j, r2, rmsd, align_length) in pairwise_align_details: # if r2 in loops: # new_pairwise_align_details.append((j, r2, rmsd, align_length)) # rmsd_align_len_list_for_r1.append((rmsd, align_length)) # # total_rmsd_for_r1 += rmsd # avg_rmsd_for_r1, total_align_len_for_r1 = get_weighted_avg_rmsd(rmsd_align_len_list_for_r1) # # avg_rmsd_for_r1 = 0.0 # # if len(new_pairwise_align_details) > 0: # # avg_rmsd_for_r1 = total_rmsd_for_r1 / len(new_pairwise_align_details) # new_rmsd_data_list_dict[(i, r1)] = (avg_rmsd_for_r1, sorted(new_pairwise_align_details, key=lambda x: x[2])) # rmsd_align_len_list.append((avg_rmsd_for_r1, total_align_len_for_r1)) # # total_rmsd += avg_rmsd_for_r1 # else: # # print r1 + ' not found' # not_found_count += 1 # #sys.exit() # # print str(not_fount_count) + ' loops not found.' # avg_rmsd, total_align_len = get_weighted_avg_rmsd(rmsd_align_len_list) # # avg_rmsd = 0.0 # # if len(new_rmsd_data_list_dict) > 0: # # avg_rmsd = total_rmsd / len(new_rmsd_data_list_dict) # return (avg_rmsd, new_rmsd_data_list_dict) def extract_current_rmsd_data_dict(rmsd_data_dict, cluster_id, loops): for i in range(len(loops)): loops[i] = str(strToNode(loops[i])) new_rmsd_data_list_dict = {} # total_rmsd = 0.0 rmsd_align_len_list = [] _, rmsd_data_list_dict = rmsd_data_dict[cluster_id] not_found_count = 0 for (i, r1) in rmsd_data_list_dict: if r1 in loops: _, pairwise_align_details = rmsd_data_list_dict[(i, r1)] # total_rmsd_for_r1 = 0.0 rmsd_align_len_list_for_r1 = [] new_pairwise_align_details = [] for (j, r2, rmsd, align_length) in pairwise_align_details: if r2 in loops: new_pairwise_align_details.append((j, r2, rmsd, align_length)) rmsd_align_len_list_for_r1.append((rmsd, align_length)) # total_rmsd_for_r1 += rmsd avg_rmsd_for_r1, total_align_len_for_r1 = get_weighted_avg_rmsd(rmsd_align_len_list_for_r1) # avg_rmsd_for_r1 = 0.0 # if len(new_pairwise_align_details) > 0: # avg_rmsd_for_r1 = total_rmsd_for_r1 / len(new_pairwise_align_details) new_rmsd_data_list_dict[(i, r1)] = (avg_rmsd_for_r1, sorted(new_pairwise_align_details, key=lambda x: x[2])) rmsd_align_len_list.append((avg_rmsd_for_r1, total_align_len_for_r1)) # total_rmsd += avg_rmsd_for_r1 else: # print r1 + ' not found' not_found_count += 1 #sys.exit() # print str(not_fount_count) + ' loops not found.' avg_rmsd, total_align_len = get_weighted_avg_rmsd(rmsd_align_len_list) # avg_rmsd = 0.0 # if len(new_rmsd_data_list_dict) > 0: # avg_rmsd = total_rmsd / len(new_rmsd_data_list_dict) return (avg_rmsd, new_rmsd_data_list_dict) def load_alignment_and_rmsd_data(clusters, loop_list, input_fname_base, partial_pdbx_dir, alignment_dir, graphs_and_pickles_dir, previous_graph_file_reused): graph_fname = os.path.join(graphs_and_pickles_dir, input_fname_base + '.z.graph') alignment_data = load_alignment_data(input_fname_base, alignment_dir, graphs_and_pickles_dir, graph_fname, clusters, previous_graph_file_reused) if alignment_data == None: return None, None rmsd_data_dict = generate_rmsd_data(input_fname_base, partial_pdbx_dir, graphs_and_pickles_dir, alignment_data, clusters, loop_list, previous_graph_file_reused) return alignment_data, rmsd_data_dict # def generate_superimposition_images(removable_text_file_list, partial_pdbx_dir, alignment_dir, superimposition_output_dir, subfamily_details_dir, loop_type, summary_dir, subfamilies_dir, superimposition_details_dir, representative_dir, progressive_dir, graphs_and_pickles_dir, pymol_session_dir, user_input_fname, clusters, loop_list, previous_graph_file_reused, is_alignment_from_user, draw_figures, filter_cluster, set_view_manually, show_extended_loop): def generate_superimposition_images(clusters, loop_list, alignment_data, rmsd_data_dict, draw_figures, filter_cluster, set_view_manually, show_extended_loop, is_alignment_from_user, user_input_fname, removable_text_file_list, directories, loop_type): (partial_pdbx_dir, summary_dir, subfamilies_dir, subfamily_details_dir, representative_dir, superimposition_output_dir, superimposition_details_dir, progressive_dir, pymol_session_dir) = directories if draw_figures == True: logger.info('Generating superimposition image and output files ...\n') else: logger.info('Generating superimposition files ...\n') start_time = time.time() length_adjusted_rmsd_score_dict = rmsd_data_dict if is_length_adjusted_score: length_adjusted_rmsd_score_dict = generate_length_adjusted_rmsd_score(rmsd_data_dict) pdb_organism_details = read_pdb_chain_organism_details(os.path.join(lib_dir, 'PDB_Chain_Organism_Details.tsv')) pdb_organism_details_scrapped = read_pdb_chain_organism_details(os.path.join(lib_dir, 'PDB_Chain_Organism_Details_scrapped.tsv')) for pdb_chain in pdb_organism_details_scrapped: if pdb_chain not in pdb_organism_details: pdb_organism_details[pdb_chain] = pdb_organism_details_scrapped[pdb_chain] if filter_cluster: clusters = filter_loops_in_cluster(clusters, length_adjusted_rmsd_score_dict, alignment_data, is_alignment_from_user) include_organism_info = True current_rmsd_data_dict = {} for cluster_id in clusters: loops = clusters[cluster_id] for lp in loops: pdb_chain = lp.strip().split(':')[0] if pdb_chain not in pdb_organism_details: include_organism_info = False break # print(cluster_id,len(loops)) logger.info('Extracting rmsd data dict for ' + cluster_id) current_rmsd_data_dict[cluster_id] = extract_current_rmsd_data_dict(length_adjusted_rmsd_score_dict, cluster_id, loops) _, cluster_pairwise_alignment_details = current_rmsd_data_dict[cluster_id] logger.info('Completed extracting rmsd data dict for ' + cluster_id) # print(len(cluster_pairwise_alignment_details)) # print(current_rmsd_data_dict[cluster_id]) # print('\n') # sys.exit() if include_organism_info == False and output_env == 'global': pdb_organism_details = {} # generate_pymol_images(current_rmsd_data_dict, loop_type, pymol_image_dir, alignment_data, mapping_dir, pdb_dir, aligned_dir, is_cif, is_normalized_score, merge_components, pdb_organism_details, log_file_list, is_length_adjusted_score, draw_pymol_figure) time_in_distance_calc = generate_pymol_images(0, removable_text_file_list, partial_pdbx_dir, summary_dir, superimposition_output_dir, subfamily_details_dir, superimposition_details_dir, representative_dir, pymol_session_dir, current_rmsd_data_dict, alignment_data, pdb_organism_details, loop_type, set_view_manually, draw_figures, show_extended_loop) if draw_figures == True: logger.info('Superimposition image and output file generation complete.') else: if set_view_manually == True: logger.info('View files for the first loop(s) set successfully.') return else: logger.info('Superimposition file generation complete.') logger.info('Time taken: ' + str(round((time.time() - start_time), 3)) + ' seconds.') print('\nProcessed input file: ' + os.path.join(data_dir, user_input_fname)[base_path_len:]) print('Basic configurations:') print('Input index type: ' + input_index_type) print('Annotation source: ' + annotation_source) print('Traversal algorithm: ' + traversal_algorithm) print('\nFor generated text outputs, please check the following directories') print('==================================================================') print('Superimposition details: '.ljust(60) + superimposition_details_dir[base_path_len:]) print('Annotation of representative motifs: '.ljust(60) + representative_dir[base_path_len:]) print('Subfamilywise annotations of all motifs: '.ljust(60) + os.path.join(superimposition_output_dir, 'subfamilywise_bp_ann')[base_path_len:]) print('Subfamily summary and familywise align length threshold: '.ljust(60) + summary_dir[base_path_len:]) if draw_figures == True: print('\nFor generated image outputs, please check the following directories') print('===================================================================') print('Superimposition outputs: '.ljust(60) + subfamilies_dir[base_path_len:]) print('Representative motifs: '.ljust(60) + representative_dir[base_path_len:]) print('Progressive superimposition images: '.ljust(60) + progressive_dir[base_path_len:]) if output_env == 'local': print('') print('Time in distance calculation: ' + str(time_in_distance_calc) + ' seconds.') # # Rotate the first loop of the cluster to define the orientation def rotate_first_loop_alignto(load_name, rotation_matrix): pdb_data = get_pdb_coordinates(load_name) pdb_translated = pdb_data# - centroid pdb_rotated = numpy.dot(pdb_translated, rotation_matrix) alter_structure(pdb_rotated, load_name) def generate_superimposition_images_using_alignto(superimposition_output_dir, partial_pdbx_dir, clusters, draw_figures): if draw_figures == False: return try: import pymol from pymol import stored except Exception as e: try: sys.path.append(pymol_py3_dir) import pymol from pymol import stored except Exception as e: logger.error('PyMOL not found.') sys.exit() logger.info('Generating superimposition image files using pymol default alignto.\n') start_time = time.time() pymol.finish_launching(['pymol', '-cq']) # pymol.finish_launching() # alignto_methods = ['align', 'super', 'cealign'] alignto_methods = ['align', 'super'] # alignto_methods = ['align'] # alignto_methods = ['super'] # alignto_methods = ['cealign'] # edit /usr/lib/python2.7/dist-packages/pymol/fitting.py line 30: window=8 => window=5 or 4 output_dir = os.path.join(superimposition_output_dir, 'alignto_output') create_directory(output_dir) for cluster_id in clusters: loops = clusters[cluster_id] # loops = list(set(loops)) converted_loops = [] reset_pymol() r1 = '' for i, loop in enumerate(loops): load_color = 'red' if i == 0 else 'green' converted_loops.append(convert_a_loop_from_FASTA_to_PDB(loop)) load_name = 'loop_'+str(i) if i == 0: r1 = loop # print('setting r1 to ' + r1) # sys.exit() pymol.cmd.load(os.path.join(partial_pdbx_dir, loop+'.cif'), load_name) pymol.cmd.hide('everything', load_name) pymol.cmd.show('cartoon', load_name) pymol.cmd.color(load_color, load_name) # pymol.commanding.sync() pymol.cmd.sync() for method in alignto_methods: rotation_matrices = get_multiple_orientation_rotation_matrices() for v, rotation_matrix in enumerate(rotation_matrices): rotation_version = 'v' + str(v + 1) image_fname = os.path.join(superimposition_output_dir, 'alignto_output', cluster_id + '_' + method + '_' + rotation_version + '.png') align_to_target = 'loop_0' view_fn = os.path.join(views_dir, str(r1) + '.view') if os.path.isfile(view_fn): logger.info('View file found for ' + r1 + '. Setting view of this loop for all loops in this cluster.') fv = open(view_fn) view_lines = fv.readlines() fv.close() pymol.cmd.set_view(view_lines[0].strip()) # sys.exit() # rotate_first_loop_alignto(align_to_target, rotation_matrix) # pymol.cmd.show('cartoon', align_to_target) pymol.cmd.alignto(align_to_target, method) # if os.path.isfile(view_fn): # logger.info('View file found for ' + r1 + '. Setting view of this loop for all loops in this cluster.') # fv = open(view_fn) # view_lines = fv.readlines() # fv.close() # pymol.cmd.set_view(view_lines[0].strip()) # print(v) # pymol.cmd._do('zoom') pymol.cmd.zoom() pymol.cmd.sync() # pymol.cmd._do('set ray_opaque_background, 0') # pymol.cmd.set(name='ray_opaque_background',value=0,quiet=1) pymol.cmd.png(image_fname, 1200, 1200, dpi=300, ray=1, quiet=1) pymol.cmd.sync() logger.info('Superimposition image generation (using PyMol \'alignto\') complete.') logger.info('Time taken: ' + str(round((time.time() - start_time), 3)) + ' seconds.')
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import csv import numpy as np def getDataSource(data_path): coffee_in_ml = [] sleep_in_hours = [] with open(data_path) as csv_file: csv_reader = csv.DictReader(csv_file) for row in csv_reader: coffee_in_ml.append(float(row["Coffee in ml"])) sleep_in_hours.append(float(row["sleep in hours"])) return {"x" : coffee_in_ml, "y": sleep_in_hours} def findCorrelation(datasource): correlation = np.corrcoef(datasource["x"], datasource["y"]) print("Correlation between Coffee in ml and sleep in hours :- \n--->",correlation[0,1]) def setup(): data_path = "./data/cups of coffee vs hours of sleep.csv" datasource = getDataSource(data_path) findCorrelation(datasource) setup()
[ "csv.DictReader", "numpy.corrcoef" ]
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from __future__ import print_function import torch import torch.nn as nn import torch.nn.parallel import torch.backends.cudnn as cudnn import torch.optim as optim import torch.utils.data import torchvision.datasets as dset import torchvision.transforms as transforms import torchvision.utils as vutils from torchvision.utils import make_grid import torch.autograd as autograd from torch.autograd import Variable import argparse import numpy as np import random import os import sys import shutil import time import math import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from utils import Logger from utils import inception_score from utils import CustomImgDataset # from is_utils import get_inception_score parser = argparse.ArgumentParser() parser.add_argument('--dataset', required=True, help='cifar10 | lsun | imagenet | folder | lfw ') parser.add_argument('--arch', required=True, help='cnn | resnet (TODO)') parser.add_argument('--loss', default='wgan', help='wgan | dcgan') parser.add_argument('--dataroot', default='~/dataset/', help='path to dataset') parser.add_argument('--workers', type=int, help='number of data loading workers', default=2) parser.add_argument('--batchSize', type=int, default=64, help='input batch size') parser.add_argument('--imageSize', type=int, default=64, help='the height / width of the input image to network') parser.add_argument('--nc', type=int, default=3, help='input image channels') parser.add_argument('--nz', type=int, default=100, help='size of the latent z vector') parser.add_argument('--ngf', type=int, default=64) parser.add_argument('--ndf', type=int, default=64) parser.add_argument('--niter', type=int, default=256, help='number of epochs to train for') parser.add_argument('--lrD', type=float, default= 0.00005, help='learning rate for Critic, default=0.00005') parser.add_argument('--lrG', type=float, default= 0.00005, help='learning rate for Generator, default=0.00005') parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5') parser.add_argument('--beta2', type=float, default=0.999, help='beta2 for adam. default=0.999') parser.add_argument('--cuda' , action='store_true', help='enables cuda') parser.add_argument('--ngpu' , type=int, default=1, help='number of GPUs to use') parser.add_argument('--netG', default='', help="path to netG (to continue training)") parser.add_argument('--netD', default='', help="path to netD (to continue training)") parser.add_argument('--norm_type', default='', help = ''' Can be 'OR': Orthonormal Regularization \lambda ||WtW-I||_2^2 Can be 'OR+Mani' Can be 'UVR': Decompose W into UDV, and add Orthonormal Regularization on U&V \lambda ||UtU-I||_2^2 + ||VtV-I||_2^2 Can be 'UVR+Mani': Can be 'WN': Weight Normalization Can be 'Sphere': Sphere Normalization Can be 'BN': Batch Normalization Can be 'LN': Layer Normalization Can be 'GP': Gradient Penalty Can be 'WC': Weight Clipping param norm to c (Wasserstein distance and Lipschitz continuity) Or you can use like 'OR+BN+WC' or 'OR+BN' ''') parser.add_argument('--clamp_lower', type=float, default=-0.01) parser.add_argument('--clamp_upper', type=float, default=0.01) parser.add_argument('--gpwei', type=float, default=10) parser.add_argument('--orthwei', type=float, default=1000) parser.add_argument('--orscale', type=float, default=1) #TODO parser.add_argument('--Diters', type=int, default=5, help='number of D iters per each G iter') parser.add_argument('--n_extra_layers', type=int, default=0, help='Number of extra layers on gen and disc') parser.add_argument('--experiment', default=None, help='Where to store samples and models') parser.add_argument('--addinfo', default="", help='additional information show up in path') parser.add_argument('--opt', default='adam', help='adam | rmsprop') parser.add_argument('--use_proj', action='store_true', help='apply projection after Optimization') parser.add_argument('--show_sv_info', action='store_true', help='apply projection after Optimization') opt = parser.parse_args() if opt.experiment is None: opt.experiment = os.path.dirname(os.path.abspath(__file__))+"/"+opt.dataset+'_'+opt.loss+'_'+opt.norm_type+('_PROJ' if opt.use_proj else '')+'_'+opt.arch os.system('mkdir {0}'.format(opt.experiment)) sys.stdout = Logger(opt.experiment+"/log.txt","w", sys.stdout) print(opt) opt.manualSeed = random.randint(1, 10000) # fix seed print("Random Seed: ", opt.manualSeed) random.seed(opt.manualSeed) torch.manual_seed(opt.manualSeed) cudnn.benchmark = True if torch.cuda.is_available() and not opt.cuda: print("WARNING: You have a CUDA device, so you should probably run with --cuda") if opt.cuda: torch.set_default_tensor_type("torch.cuda.FloatTensor") FloatTensor = torch.cuda.FloatTensor if opt.cuda else torch.FloatTensor LongTensor = torch.cuda.LongTensor if opt.cuda else torch.LongTensor ByteTensor = torch.cuda.ByteTensor if opt.cuda else torch.ByteTensor Tensor = FloatTensor if opt.dataset in ['imagenet', 'folder', 'lfw']: # folder dataset dataset = dset.ImageFolder(root=opt.dataroot, transform=transforms.Compose([ transforms.Scale(opt.imageSize), transforms.CenterCrop(opt.imageSize), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ])) elif opt.dataset == 'lsun': dataset = dset.LSUN(db_path=opt.dataroot, classes=['bedroom_train'], transform=transforms.Compose([ transforms.Scale(opt.imageSize), transforms.CenterCrop(opt.imageSize), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ])) elif opt.dataset == 'cifar10': dataset = dset.CIFAR10(root=opt.dataroot, download=True, transform=transforms.Compose([ transforms.Scale(opt.imageSize), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) ) assert dataset dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batchSize, shuffle=True, num_workers=int(opt.workers)) ngpu = int(opt.ngpu) nz = int(opt.nz) ngf = int(opt.ngf) ndf = int(opt.ndf) nc = int(opt.nc) n_extra_layers = int(opt.n_extra_layers) # custom weights initialization called on netG and netD def weights_init(m): classname = m.__class__.__name__ if classname.find('Conv') != -1: m.weight.data.normal_(0.0, 0.02) elif classname.find('BatchNorm') != -1: m.weight.data.normal_(1.0, 0.02) m.bias.data.fill_(0) ### set network if opt.dataset.startswith("cifar"): import models.cifar as models netD = models.__dict__[opt.arch+'_D'](opt.imageSize, nc, ngf, ngpu, n_extra_layers, opt.norm_type, opt.loss) netG = models.__dict__[opt.arch+'_G'](opt.imageSize, nz, nc, ndf, ngpu, n_extra_layers) if opt.netG != '': # load checkpoint if needed netG.load_state_dict(torch.load(opt.netG)) print(netG) if opt.netD != '': netD.load_state_dict(torch.load(opt.netD)) print(netD) input = torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) noise = torch.FloatTensor(opt.batchSize, nz, 1, 1) fixed_noise = torch.FloatTensor(opt.batchSize, nz, 1, 1).normal_(0, 1) one = torch.FloatTensor([1]) mone = one * -1 if opt.cuda: netD.cuda() netG.cuda() input = input.cuda() one, mone = one.cuda(), mone.cuda() noise, fixed_noise = noise.cuda(), fixed_noise.cuda() # setup optimizer if opt.opt == 'adam': optimizerD = optim.Adam(netD.parameters(), lr=opt.lrD, betas=(opt.beta1, opt.beta2)) optimizerG = optim.Adam(netG.parameters(), lr=opt.lrG, betas=(opt.beta1, opt.beta2)) elif opt.opt == 'rmsprop': optimizerD = optim.RMSprop(netD.parameters(), lr = opt.lrD) optimizerG = optim.RMSprop(netG.parameters(), lr = opt.lrG) def calc_gradient_penalty(netD, x, g): assert x.size() == g.size() a = torch.rand(x.size(0), 1) a = a.cuda() if opt.cuda else a a = a\ .expand(x.size(0), x.nelement()//x.size(0))\ .contiguous()\ .view( x.size(0), nc, opt.imageSize, opt.imageSize ) interpolated = Variable(a*x.data + (1-a)*g.data, requires_grad=True) c = netD(interpolated) gradients = autograd.grad( outputs=c, inputs=interpolated, grad_outputs=( torch.ones(c.size()).cuda() if opt.cuda else torch.ones(c.size()) ), create_graph=True, retain_graph=True, only_inputs=True )[0] gradients = gradients.view(gradients.size(0), -1) return opt.gpwei * ((gradients.norm(2, dim=1) - 1) ** 2).mean() # Start Training print("=================================================================") print("=======================Start Training===========================") print("=================================================================") gen_iterations = 0 IS_array = [] bestIS = 0 if opt.loss == "dcgan": criterion = nn.BCELoss() real_label = 1 fake_label = 0 for epoch in range(opt.niter): data_iter = iter(dataloader) i = 0 while i < len(dataloader): #################################################### # ----- train model_D ----- # WGAN: maximize D(x) - D(G(z)) # DCGAN: maximize log(D(x)) + log(1 - D(G(z))) #################################################### for p in netD.parameters(): # reset requires_grad p.requires_grad = True # they are set to False below in netG update # train the discriminator Diters times Diters = opt.Diters # if gen_iterations < 25 or gen_iterations % 500 == 0: # Diters = 100 j = 0 while j < Diters and i < len(dataloader): j += 1 # clamp parameters to a cube if 'WC' in opt.norm_type: for p in netD.parameters(): p.data.clamp_(opt.clamp_lower, opt.clamp_upper) if 'SN' in opt.norm_type: netD.update_sigma() data = data_iter.next() i += 1 # train with real real_cpu, _ = data netD.zero_grad() batch_size = real_cpu.size(0) if opt.cuda: real_cpu = real_cpu.cuda() input.resize_as_(real_cpu).copy_(real_cpu) real_inputv = Variable(input) output = netD(real_inputv) if opt.loss == "wgan": errD_real = -output.mean() elif opt.loss == "dcgan": label = FloatTensor(batch_size,) label.fill_(real_label) labelv = Variable(label) errD_real = criterion(output, labelv) errD_real.backward() # train with fake noise.resize_(batch_size, nz, 1, 1).normal_(0, 1) noisev = Variable(noise, volatile = True) # totally freeze netG fake = Variable(netG(noisev).data) fake_inputv = fake output = netD(fake_inputv) if opt.loss == "wgan": errD_fake = output.mean() elif opt.loss == "dcgan": label.fill_(fake_label) labelv = Variable(label) errD_fake = criterion(output, labelv) errD_fake.backward() errD = errD_real + errD_fake Distribution_D = errD if 'GP' in opt.norm_type: gradient_penalty = calc_gradient_penalty(netD, real_inputv, fake) gradient_penalty.backward() errD = errD + gradient_penalty if 'OR' in opt.norm_type or 'UVR' in opt.norm_type: orth_wei = opt.orthwei * (0.01**(epoch/opt.niter)) orth_penalty = netD.orth_penalty()*orth_wei orth_penalty.backward() errD = errD + orth_penalty optimizerD.step() if opt.use_proj: netD.project() #################################################### # ------ train model_G ------- # WGAN: maximize D(G(z)) # DCGAN; maximize log(D(G(z))) # train model_D more: because the better model_D is, # the better model_G will be #################################################### for p in netD.parameters(): p.requires_grad = False # to avoid computation netG.zero_grad() # in case our last batch was the tail batch of the dataloader, # make sure we feed a full batch of noise noise.resize_(opt.batchSize, nz, 1, 1).normal_(0, 1) noisev = Variable(noise) fake = netG(noisev) output = netD(fake) if opt.loss == "wgan": errG = -output.mean() elif opt.loss == "dcgan": label = FloatTensor(opt.batchSize) label.fill_(real_label) # fake labels are real for generator cost logd trick labelv = Variable(label) errG = criterion(output, labelv) errG.backward() optimizerG.step() gen_iterations += 1 print('[%d/%d][%d/%d][%d] Distribution_D: %f Loss_D: %f Loss_G: %f Loss_D_real: %f Loss_D_fake %f' % (epoch, opt.niter, i, len(dataloader), gen_iterations, Distribution_D, errD.data[0], errG.data[0], errD_real.data[0], errD_fake.data[0])) if gen_iterations % 500 == 0: real_cpu = real_cpu.mul(0.5).add(0.5) vutils.save_image(real_cpu, '{0}/real_samples.png'.format(opt.experiment)) fake = netG(Variable(fixed_noise, volatile=True)) fake.data = fake.data.mul(0.5).add(0.5) vutils.save_image(fake.data, '{0}/fake_samples_{1}.png'.format(opt.experiment, gen_iterations)) ### if(opt.show_sv_info): print ("Print Singular Value Information...") netD.showOrthInfo() ### Calculate inception_score # Generate 5000 data noisev = Variable(FloatTensor(5000,nz,1,1).normal_(0,1), volatile=True) netG.eval() print ("Calculating Inception Score...") fask_imgs = netG(noisev) IS = inception_score(CustomImgDataset(fask_imgs.data), batch_size=opt.batchSize, cuda=opt.cuda, splits=10) # fask_imgs = np.transpose(netG(noisev).data.cpu().numpy(),(0,2,3,1)) # fask_imgs = (fask_imgs*0.5+0.5)*255 # IS = get_inception_score(fask_imgs) IS_array.append(IS) print(IS) if bestIS < IS[0]: bestIS = IS[0] fask_imgs = np.transpose(netG(noisev).data.cpu().numpy(),(0,2,3,1)) fask_imgs = (fask_imgs*0.5+0.5)*255 np.save(opt.experiment+"/fakeimgs_best.npy",fask_imgs) if epoch%10 == 0: # do checkpointing torch.save(netG.state_dict(), '{0}/netG_epoch_{1}.pth'.format(opt.experiment, epoch)) torch.save(netD.state_dict(), '{0}/netD_epoch_{1}.pth'.format(opt.experiment, epoch)) noisev = Variable(FloatTensor(5000,nz,1,1).normal_(0,1), volatile=True) netG.eval() fask_imgs = np.transpose(netG(noisev).data.cpu().numpy(),(0,2,3,1)) fask_imgs = (fask_imgs*0.5+0.5)*255 np.save(opt.experiment+"/fakeimgs_final.npy",fask_imgs) shutil.copy2('./tfIS.py', opt.experiment) shutil.copy2('./is_utils.py', opt.experiment) IS_array = np.array(IS_array) plt.plot(range(len(IS_array[:,0])), IS_array[:,0], 'r-', label = 'Inception Score') plt.legend() plt.savefig(opt.experiment+'/inception_score.pdf', bbox_inches='tight',format="pdf", dpi = 300) plt.close() np.save(opt.experiment+"/inception_score.npy",{"IS_array":IS_array}) noise = Variable(FloatTensor(64,nz,1,1).normal_(0,1)) fake_u=netG(noise) imgs = make_grid(fake_u.data*0.5+0.5).cpu() # CHW plt.figure(figsize=(5,5)) plt.imshow(imgs.permute(1,2,0).numpy()) # HWC plt.savefig(opt.experiment+'/final_figures.pdf', bbox_inches='tight',format="pdf", dpi = 300) plt.close()
[ "numpy.array", "torch.cuda.is_available", "torchvision.utils.make_grid", "numpy.save", "argparse.ArgumentParser", "shutil.copy2", "torch.set_default_tensor_type", "matplotlib.pyplot.close", "torchvision.transforms.ToTensor", "torch.autograd.Variable", "random.randint", "matplotlib.pyplot.savef...
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import numpy as np import logging import time from stereovis.framed.algorithms import StereoMRF from spinn_utilities.progress_bar import ProgressBar import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt logger = logging.getLogger(__file__) class FramebasedStereoMatching(object): def __init__(self, resolution, max_disparity, algorithm='mrf', inputs=None): if algorithm == 'mrf': # reverse the resolution order since x-dimension corresponds to n_cols and y to n_rows # and the shape initialisation of numpy is (n_rows, n_cols) which is (y, x) x, y = resolution self.algorithm = StereoMRF(dim=(y, x), n_levels=max_disparity) if inputs is not None: # this means that the operational mode is offline and hence one can initialise the frame iterator self.frames_left = np.asarray(inputs['left']) self.frames_right = np.asarray(inputs['right']) self.frames_timestamps = np.asarray(inputs['ts']) # initialise the placeholder for the depth-resolved inputs self.depth_frames = [] else: raise NotImplementedError("Only MRF is supported.") def get_timestamps(self): return self.frames_timestamps def get_output(self): self.depth_frames = np.asarray(self.depth_frames) return self.depth_frames def run_one_frame(self, image_left, image_right, prior=None, **kwargs): """ Run one single frame of the frame-based stereo matching. Should be used when running online. Args: image_left: a numpy array representing the left image image_right: a numpy array representing the right image prior: optional, a numpy array with disparity values Keyword Args: prior_trust_factor: float, value between 0 and 1 for the prior influence prior_influence_mode: str, can be 'const' or `adaptive` for the prior incorporation strategy n_iter: int, number of iteration to run the algorithm Returns: A numpy array representing the depth map resolved by the algorithm. """ depth_map = self.algorithm.lbp(image_left, image_right, prior, **kwargs) self.depth_frames.append(depth_map) return depth_map def run(self, prior_info=None): """ Run the frame-based stereo matching on all frames and priors. Args: prior_info: optional, a list of priors a subset of which is used to initialise the algorithm. Returns: """ n_frames = len(self.frames_timestamps) if prior_info is not None: if len(prior_info['ts']) > n_frames: # pick the n closest ones (where n is the number of frames) prior_indices = [np.searchsorted(prior_info['ts'], t_frame, side="left") for t_frame in self.frames_timestamps] priors = prior_info['priors'][prior_indices] else: priors = prior_info['priors'] assert len(priors) == len(self.frames_left) == len(self.frames_right) pb = ProgressBar(n_frames, "Starting offline frame-based stereo matching with prior initialisation.") start_timer = time.time() for i, (left, right, prior) in enumerate(zip(self.frames_left, self.frames_right, priors)): self.run_one_frame(left, right, prior, prior_trust_factor=1.0, prior_influence_mode='adaptive', n_iter=10) pb.update() end_timer = time.time() pb.end() else: pb = ProgressBar(n_frames, "Starting offline frame-based stereo matching without prior initialisation.") start_timer = time.time() for i, (left, right) in enumerate(zip(self.frames_left, self.frames_right)): self.run_one_frame(left, right) plt.imsave('output/checkerboard_downsampled/left_{}.png'.format(i), left) plt.imsave('output/checkerboard_downsampled/right_{}.png'.format(i), right) plt.imsave('output/checkerboard_downsampled/result_{}.png'.format(i), self.depth_frames[i]) pb.update() end_timer = time.time() pb.end() logger.info("Frame-based stereo matching took {}s per image pair on average.".format((end_timer - start_timer) / n_frames))
[ "logging.getLogger", "matplotlib.use", "numpy.searchsorted", "numpy.asarray", "spinn_utilities.progress_bar.ProgressBar", "stereovis.framed.algorithms.StereoMRF", "time.time" ]
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from __future__ import print_function import os import sys import time import argparse import numpy as np import xml.dom.minidom as xdom from os.path import realpath, join, isdir, isfile, dirname, splitext from ..core.environ import environ U_ROOT = u"SimulationData" U_JOB = u"job" U_DATE = u"date" U_EVAL = u"Evaluation" U_EVAL_N = u"n" U_EVAL_D = u"d" U_EVAL_S = u"status" U_PARAMS = u"Parameters" U_RESP = u"Responses" IND = " " class TabularWriter(object): def __init__(self, filename, job): """Set up a logger object, which takes evaluation events and outputs an XML log file """ self.stack = [] self.filename = realpath(filename) if not self.filename.endswith('.edb'): self.filename += '.edb' self.evald = dirname(self.filename) if not isdir(self.evald): raise OSError('no such directory {0!r}'.format(self.evald)) self.start_document(job) pass def create_element(self, name, attrs): sp = IND * len(self.stack) a = " ".join('{0}="{1}"'.format(k, v) for (k, v) in attrs) with open(self.filename, "a") as stream: stream.write("{0}<{1} {2}/>\n".format(sp, name, a)) stream.flush() return def start_element(self, name, attrs, end=False): sp = IND * len(self.stack) a = " ".join('{0}="{1}"'.format(k, v) for (k, v) in attrs) with open(self.filename, "a") as stream: stream.write("{0}<{1} {2}>\n".format(sp, name, a)) stream.flush() self.stack.append(name) return def end_element(self, name): _name = self.stack.pop(-1) assert _name == name sp = IND * len(self.stack) with open(self.filename, "a") as stream: stream.write("{0}</{1}>\n".format(sp, name)) stream.flush() return def start_document(self, job): with open(self.filename, "w") as stream: stream.write("""<?xml version="1.0"?>\n""") stream.flush() now = time.asctime(time.localtime()) self.start_element(U_ROOT, ((U_JOB, job), (U_DATE, now))) return def end_document(self): _name = self.stack.pop(-1) assert _name == U_ROOT with open(self.filename, "a") as stream: stream.write("</{0}>\n".format(U_ROOT)) stream.flush() stream.close() return def write_eval_info(self, n, s, d, parameters, responses=None): """Write information for this evaluation Parameters ---------- n : int Evaluation number s : int Evaluation status d : int Evaluation directory parameters : list of tuple (name, value) pairs for each parameter respones : list of tuple (optional) (name, value) pairs for each response """ d = d.replace(self.evald, ".") self.start_element(U_EVAL, ((U_EVAL_N, n), (U_EVAL_S, s), (U_EVAL_D, d))) self.create_element(U_PARAMS, parameters) if responses: self.create_element(U_RESP, responses) self.end_element(U_EVAL) return def close(self): """ Clean up the logger object """ self.end_document() return def read_mml_evaldb(filepath): """Read the Material Model Laboratory tabular file Parameters ---------- filepath : str Path to index file to read Returns ------- sources : list of str Individual filepaths for each evaluation parameters : tuple of tuple (name, value) pairs for parameters for each evaluation """ D = realpath(dirname(filepath)) doc = xdom.parse(filepath) root = doc.getElementsByTagName(U_ROOT)[0] job = root.getAttribute(U_JOB) sources = [] parameters = {} responses = {} for evaluation in root.getElementsByTagName(U_EVAL): n = evaluation.getAttribute(U_EVAL_N) d = realpath(join(D, evaluation.getAttribute(U_EVAL_D))) f = join(d, "{0}.exo".format(job)) if isfile(f): sources.append(f) # get parameters nparams = evaluation.getElementsByTagName(U_PARAMS)[0] evars, enames = [], [] for (name, value) in nparams.attributes.items(): enames.append(name) evars.append(float(value)) parameters[f] = zip(enames, evars) # get responses nresponses = evaluation.getElementsByTagName(U_RESP) if nresponses: rvars, rnames = [], [] for (name, value) in nresponses[0].attributes.items(): rnames.append(name) rvars.append(float(value)) responses[f] = zip(rnames, rvars) return sources, parameters, responses def read_mml_evaldb_nd(filepath, nonan=1): sources, parameters, responses = read_mml_evaldb(filepath) head = [x[0] for x in parameters[sources[0]]] resp = responses.get(sources[0]) if resp: head.extend([x[0] for x in resp]) data = [] for source in sources: r = responses.get(source) if r is None: continue p = parameters[source] line = [x[1] for x in p] line.extend([x[1] for x in r]) data.append(line) data = np.array(data) if nonan: # remove nan's rows = np.where(np.isnan(data))[0] data = np.delete(data, rows, 0) return head, data, len(responses[sources[0]]) def correlations(filepath, nonan=1): title = "CORRELATIONS AMONG INPUT AND OUTPUT VARIABLES CREATED BY MATMODLAB" head, data, nresp = read_mml_evaldb_nd(filepath, nonan=nonan) H = " " * 13 + " ".join("{0:>12s}".format(x) for x in head) with open(splitext(filepath)[0] + ".corr", "w") as fobj: fobj.write("{0}\n".format(title)) # get correlation matrix corrcoef = np.corrcoef(data, rowvar=0) i = 1 fobj.write("{0}\n".format(H)) for row in corrcoef: fobj.write("{0:>12} {1}\n".format( head[i-1], " ".join("{0: 12.2f}".format(x) for x in row[:i]))) i += 1 return def plot_correlations(filepath, nonan=1, pdf=0): if environ.notebook == 2 and not pdf: return plot_bokeh_correlations(filepath, nonan) try: import matplotlib.pyplot as plt from matplotlib.ticker import FormatStrFormatter except ImportError: print("unable to import matplotlib") return head, data, nresp = read_mml_evaldb_nd(filepath, nonan=nonan) # create xy scatter plots y = data[:, -nresp] sort = np.argsort(y) y = y[sort] keys = head[:-nresp] colors = "bgrcmykw" pdf = "{0}.pdf".format(splitext(filepath)[0]) plt.clf() # set up subplots fig, axs = plt.subplots(1, len(keys), sharey=True) if len(keys) == 1: axs = [axs] ylabel = r"{0}".format(head[-1]) axs[0].set_ylabel(ylabel) for i, key in enumerate(keys): x = data[:, i][sort] m2, m, b = np.polyfit(x, y, 2) m2, (m, b) = 0, np.polyfit(x, y, 1) axs[i].plot(x, y, "{0}.".format(colors[i]), x, m2 * x * x + m * x + b, "-k") axs[i].set_xlabel(r"{0}".format(key)) plt.setp(axs[i].xaxis.get_majorticklabels(), rotation=45, fontsize="small") continue plt.savefig(pdf, transparent=True) return def plot_bokeh_correlations(filepath, nonan=1): from bokeh.plotting import figure, gridplot head, data, nresp = read_mml_evaldb_nd(filepath, nonan=nonan) # create xy scatter plots y = data[:, -nresp] sort = np.argsort(y) y = y[sort] keys = head[:-nresp] colors = ('blue', 'green', 'red', 'cyan', 'maroon', 'yellow', 'black', 'white') ylabel = r"{0}".format(head[-1]) plots = [] for i, key in enumerate(keys): x = data[:, i][sort] m2, m, b = np.polyfit(x, y, 2) m2, (m, b) = 0, np.polyfit(x, y, 1) TOOLS = "pan,wheel_zoom,box_zoom,reset,save,resize" y_axis_label = ylabel if not i else None p = figure(tools=TOOLS, x_axis_label=r'{0}'.format(key), y_axis_label=y_axis_label) p.scatter(x, y, color=colors[i]) p.line(x, m2 * x * x + m * x + b, color='black') plots.append(p) return gridplot([plots]) def is_evaldb(filename): if not isfile(filename) or not filename.endswith('.edb'): return False with open(filename, 'r') as fh: for i in range(4): if U_ROOT in fh.readline(): return True return False def main(argv): parser = argparse.ArgumentParser() parser.add_argument("action", choices=("plot", "table")) parser.add_argument("filepath") args = parser.parse_args(argv) if args.action == "plot": sys.exit(plot_correlations(args.filepath)) sys.exit(correlations(args.filepath)) if __name__ == "__main__": main(sys.argv[1:])
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''' This code will produce a porkchop plot of the J function over a range of launch times and flight times ''' import math import numpy import PyKEP as pk import matplotlib.pyplot as plt import Vector p1 = pk.planet.jpl_lp('earth') p2 = pk.planet.mpcorb('99942 19.2 0.15 K107N 202.49545 126.41859 204.43202 3.33173 0.1911104 1.11267324 0.9223398 1 MPO164109 1397 2 2004-2008 0.40 M-v 3Eh MPCAPO C802 (99942) Apophis 20080109') k2 = 0.6 n = 0 isp_chem = 350 isp_lt = 4000 t0_range = [0, 5000] tof_range = [100, 900] @numpy.vectorize def J(t0, tof): ep1 = pk.epoch(t0) ep2 = pk.epoch(t0 + tof) r1, v1 = p1.eph(ep1) r2, v2 = p2.eph(ep2) resJ = [] for lw in [False, True]: prob = pk.lambert_exposin(r1, r2, tof * pk.DAY2SEC, pk.MU_SUN, lw, n, k2) for i in range(prob.num_solutions()): exps = prob.get_exposins()[i] dv1 = Vector.mag(Vector.sub(prob.get_v1()[i], v1)) dv2 = Vector.mag(Vector.sub(prob.get_v2()[i], v2)) dvlt = exps.get_delta_v(pk.MU_SUN) resJ.append(1.0 - math.exp(-(dv1 + dv2) / 9.81 / isp_chem - dvlt / 9.81 / isp_lt)) if len(resJ) == 0: return numpy.nan else: return numpy.nanmin(resJ) t0 = numpy.linspace(t0_range[0], t0_range[1], 200) tof = numpy.linspace(tof_range[0], tof_range[1], 200) X, Y = numpy.meshgrid(t0, tof) Z = J(X, Y) mZ = numpy.ma.array(Z, mask=numpy.isnan(Z)) plt.figure() plt.pcolor(X, Y, mZ, vmin=numpy.nanmin(Z), vmax=numpy.nanmax(Z)) plt.colorbar() plt.show()
[ "PyKEP.planet.mpcorb", "matplotlib.pyplot.colorbar", "PyKEP.planet.jpl_lp", "matplotlib.pyplot.figure", "numpy.linspace", "numpy.isnan", "PyKEP.epoch", "numpy.nanmax", "numpy.nanmin", "numpy.meshgrid", "math.exp", "PyKEP.lambert_exposin", "matplotlib.pyplot.show" ]
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import numpy as np import scipy.constants as c import math, cmath from sympy import * from math import e import numba from numba import jit import sympy as sp r, x, a, theta = symbols('r x a theta') init_printing(use_unicode=True) @jit(nopython=True, cache=True, parallel=True) def spherical_to_cartesian(r, theta, phi): x = r * np.sin(theta) * np.cos(phi) y = r * np.sin(theta) * np.sin(phi) z = r * np.cos(theta) return x, y, z @jit(nopython=True, cache=True, parallel=True) def cartesian_to_spherical(x, y, z): r = math.sqrt(x ** 2 + y ** 2 + z ** 2) theta = np.arccos(z / math.sqrt(x ** 2 + y ** 2 + z ** 2)) if r != 0 else 0 if x == 0: phi = 0 if (y == 0) else 1.5708 else: phi = np.arctan(y / x) return r, theta, phi @jit(nopython=True, cache=True, parallel=True) def absolute(number): return (number.real**2 + number.imag**2)**0.5 def P_l(l): ans = 1 / (2 ** l * factorial(l)) * sp.diff((x ** 2 - 1) ** l, x, int(l)) return ans def Pm_l(m, l): ans = ((1 - x ** 2) ** (abs(m) / 2)) * sp.diff(P_l(l), x, abs(int(m))) return ans def angular_wave_func(m, l, theta_value, phi): if m > 0: E = (-1)**m else: E = 1 A_factor_1 = (2*l+1)/(4*math.pi) A_factor_2 = math.factorial(l-abs(m)) / math.factorial(l+abs(m)) A = math.sqrt(A_factor_1*A_factor_2) B = cmath.exp(m*phi*1j) C = (Pm_l(m,l).subs(x,cos(theta)).subs(theta, theta_value)).evalf() long_ans = complex(E * A * B * C) ans = round(long_ans.real, 5) + round(long_ans.imag, 5)*1j return ans def Lq(q): ans = e ** x * sp.diff(e ** -x * x ** q, x, int(q)) return ans def assoc_Lq(p, q): ans = (-1) ** p * sp.diff(Lq(q), x, int(p)) return ans bohr=c.physical_constants['Bohr radius'][0] def radial_wave_func(n, l, radius): if radius == 0: return np.nan else: A_factor_1 = (2/(n*a))**3 A_factor_2 = (math.factorial(n-l-1)) / (2*n*(math.factorial(n+l))**3) A = (A_factor_1 * A_factor_2)**0.5 B = e**(-radius/(n*a)) C = ((2*radius)/(n*a))**l p = 2*l + 1 q = n-l-1+p D = assoc_Lq(p,q) expression = A * B * C * D / (a**(-3/2)) subbed = expression.subs(x, 2*r/(n*a)).subs(r, radius).subs(a, bohr) return round(subbed.evalf(), 5) #@jit def linspace(start, stop, num=50): increment = (stop-start)/(num-1) current = start output = [] for i in range(0,int((stop-start)/increment)+1): output.append(round(float(current),5)) current+=increment return output def meshgrid(x,y,z): output = [[],[],[]] output_0, output_1, output_2 = [],[],[] x_list, y_list, z_list = [],[],[] z_inner = [] for k,z_i in enumerate(z): z_inner.append(z_i) for i,x_i in enumerate(x): x_list.append([x_i,x_i]) z_list.append(z_inner) for j,y_i in enumerate(y): y_inner = [[y_i,y_i] for i in range(len(x))] y_list.append(y_inner) output_0.append(x_list) output_2.append(z_list) return output_0, y_list, output_2 cartesian_to_spherical_vector = (np.vectorize(cartesian_to_spherical)) angular_wave_vector = (np.vectorize(angular_wave_func)) radial_wave_vector = (np.vectorize(radial_wave_func)) absolute_vector = (np.vectorize(absolute)) def hydrogen_wave_func(n, l, m, roa, Nx, Ny, Nz): x_space = np.linspace(-roa, roa, Nx) y_space = np.linspace(-roa, roa, Ny) z_space = np.linspace(-roa, roa, Nz) xx, yy, zz = np.meshgrid(y_space, x_space, z_space) r, theta, phi = (cartesian_to_spherical_vector(yy, xx, zz)) if m == 0: angular = angular_wave_vector(m, l, theta, phi) elif m < 0: angular = (1j / math.sqrt(2)) * (angular_wave_vector(m, l, theta, phi) - (-1) ** m * angular_wave_vector(-m, l, theta, phi)) elif m > 0: angular = (1 / math.sqrt(2)) * (angular_wave_vector(-m, l, theta, phi) + (-1) ** m * angular_wave_vector(m, l, theta, phi)) radial = radial_wave_vector(n, l, r * a) mag = absolute_vector(radial * angular) ** 2 return np.array(yy), np.array(xx), np.array(zz), np.round(mag, 5), radial * angular #don't use? def hydrogen_wave_func_cross(n, l, m, roa, Nx, Ny, Nz): x_space = np.linspace(-roa, roa, int(Nx/2)) y_space = np.linspace(-roa, roa, Ny) z_space = np.linspace(-roa, roa, Nz) xx, yy, zz = np.meshgrid(y_space, x_space, z_space) r, theta, phi = (cartesian_to_spherical_vector(yy, xx, zz)) if m == 0: angular = angular_wave_vector(m, l, theta, phi) elif m < 0: angular = (1j / math.sqrt(2)) * (angular_wave_vector(m, l, theta, phi) - (-1) ** m * angular_wave_vector(-m, l, theta, phi)) elif m > 0: angular = (1 / math.sqrt(2)) * (angular_wave_vector(-m, l, theta, phi) + (-1) ** m * angular_wave_vector(m, l, theta, phi)) radial = radial_wave_vector(n, l, r * a) mag_true = radial * angular mag = absolute_vector(mag_true) ** 2 return np.array(yy), np.array(xx), np.array(zz), np.round(mag, 5), mag_true
[ "math.factorial", "math.sqrt", "numpy.array", "cmath.exp", "numba.jit", "numpy.linspace", "numpy.cos", "numpy.sin", "numpy.meshgrid", "numpy.vectorize", "numpy.round", "numpy.arctan" ]
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## Bootstrapped from https://github.com/cfld/locusts import os import random import backoff import rasterio import ee ee.Initialize() from polygon_geohasher.polygon_geohasher import geohash_to_polygon, polygon_to_geohashes from shapely import geometry import urllib from urllib.request import urlretrieve import numpy as np from scipy.spatial import ConvexHull sentinel_channels = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B11', 'B12', 'QA60'] def geohash2cell(geohash): polygon = geohash_to_polygon(geohash) cell = ee.Geometry(geometry.mapping(polygon)) return cell def maskS2clouds(image): qa = image.select('QA60') cloudBitMask = 1 << 10 cirrusBitMask = 1 << 11 mask = qa.bitwiseAnd(cloudBitMask).eq(0) mask = mask.bitwiseAnd(cirrusBitMask).eq(0) return image.updateMask(mask) @backoff.on_exception(backoff.constant, urllib.error.HTTPError, max_tries=4, interval=2) def safe_urlretrieve(url, outpath): _ = urlretrieve(url, outpath) def get_one_sentinel(date_start, date_end, geohash, outpath, transform): cell = geohash2cell(geohash) collection = ( ee.ImageCollection('COPERNICUS/S2') .select(sentinel_channels) .filterDate(date_start, date_end) .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)).map(maskS2clouds) # Apply cloud mask ) image = collection.sort('system:index', opt_ascending=False).median() try: url = image.clip(cell).getDownloadURL( params={ "name": geohash, "crs": "EPSG:4326", "crs_transform": transform } ) _ = safe_urlretrieve(url, outpath) except: pass def geotiff_to_geohashes(geotiff, max_pts = 260000000): img = rasterio.open(geotiff) transform = list(img.transform)[:6] nz = np.nonzero(img.read(1)) coords = np.empty((len(nz[0]), 2)) for k, (i,j) in enumerate(zip(nz[0], nz[1])): coords[k] = np.array(img.transform*(i,j)) random_pts = coords[np.random.choice(coords.shape[0], max_pts, replace=False), :] hull = ConvexHull(random_pts) polygon = geometry.Polygon(random_pts[hull.vertices, :]) # hull_vertices = np.load('hull-vertices.npy') # polygon = geometry.Polygon(hull_vertices) geohashes = polygon_to_geohashes(polygon, precision=5, inner=True) return geohashes, transform def main( geotiff, out_dir='data/sentinel_2_download', date_start="2016-01-01", date_end="2016-12-31", ): os.makedirs(out_dir, exist_ok=True) geohashes, transform = geotiff_to_geohashes(geotiff) for geohash in geohashes: outpath = os.path.join(out_dir, f'{geohash}.zip') get_one_sentinel(date_start, date_end, geohash, outpath, transform) if __name__ == '__main__': main('data/land-cover-10m.tiff')
[ "os.makedirs", "urllib.request.urlretrieve", "numpy.random.choice", "rasterio.open", "os.path.join", "ee.ImageCollection", "shapely.geometry.mapping", "backoff.on_exception", "scipy.spatial.ConvexHull", "numpy.array", "shapely.geometry.Polygon", "polygon_geohasher.polygon_geohasher.geohash_to_...
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import pandas as pd import numpy as np from misc import data_io DATA_DIR = 'data/ut-interaction/' """ Folder structure <'set1' or 'set2'>/keypoints <video_name>/ <video_name>_<frame_num>_keypoints.json ... Ex: DATA_DIR + 'set1/keypoints/0_1_4/0_1_4_000000000042_keypoints.json' """ VIDEOS = [ ['0_1_4','1_1_2','2_1_1','3_1_3','4_1_0','5_1_5','6_2_4','7_2_5','8_2_0', '9_2_2','10_2_1','11_2_3','12_3_4','13_3_2','14_3_1','15_3_3','16_3_5', '17_3_0','18_4_4','19_4_1','20_4_2','21_4_0','22_4_3','23_4_5','24_5_0', '25_5_4','26_5_2','27_5_1','28_5_3','29_5_5','30_6_2','31_6_5','32_6_1', '33_6_3','34_6_0','35_7_0','36_7_5','37_7_4','38_7_2','39_7_3','40_7_1', '41_8_0','42_8_2','43_8_4','44_8_4','45_8_5','46_8_3','47_8_1','48_9_3', '49_9_5','50_9_2','51_9_4','52_9_0','53_9_1','54_10_0','55_10_4','56_10_5', '57_10_3','58_10_1','59_10_2'], #set1 ['0_11_4','1_11_2','2_11_5','3_11_0','4_11_3','5_11_1','6_12_0','7_12_3', '8_12_5','9_12_1','10_12_4','11_12_2','12_13_4','13_13_2','14_13_1', '15_13_3','16_13_5','17_13_0','18_14_0','19_14_1','20_14_5','21_14_3', '22_14_4','23_14_2','24_15_1','25_15_0','26_15_4','27_15_2','28_15_3', '29_15_5','30_16_3','31_16_0','32_16_1','33_16_4','34_16_2','35_16_5', '36_17_1','37_17_0','38_17_3','39_17_5','40_17_4','41_17_2','42_18_2', '43_18_4','44_18_1','45_18_3','46_18_5','47_18_0','48_19_0','49_19_1', '50_19_4','51_19_3','52_19_5','53_19_2','54_20_1','55_20_0','56_20_5', '57_20_3','58_20_4','59_20_2'] #set2 ] ACTIONS = ['Hand Shaking','Hugging','Kicking','Pointing','Punching','Pushing'] def get_ground_truth(data_dir=DATA_DIR): video_lst, setid_lst, seq_lst, path_lst, action_lst = [], [], [], [], [] for set_id, set_videos in enumerate(VIDEOS): video_lst = video_lst + set_videos setid_lst = setid_lst + len(set_videos)*[set_id+1] for video in set_videos: num, seq, action = video.split('_') seq_lst.append(int(seq)) action_lst.append(int(action)) path = '{}set{}/keypoints/{}/'.format(data_dir, set_id+1, video) path_lst.append(path) dataframe_dict = {'video_id': video_lst, 'setid': setid_lst, 'seq': seq_lst, 'path': path_lst, 'action': action_lst} ground_truth = pd.DataFrame(dataframe_dict).set_index('video_id') return ground_truth def get_folds(setid): if setid == 1: folds = np.arange(10) elif setid == 2: folds = np.arange(10, 20) else: raise ValueError("setid must be 1 or 2, value entered: "+str(setid)) return folds def get_train_gt(fold_num): if fold_num < 0 or fold_num > 19: raise ValueError("fold_num must be within 0 and 19, value entered: "+str(fold_num)) if fold_num < 10: setid = 1 sequences = np.arange(10) fold_sequences = sequences[sequences != fold_num] + 1 else: setid = 2 sequences = np.arange(10, 20) fold_sequences = sequences[sequences != fold_num] + 1 ground_truth = get_ground_truth() gt_split = ground_truth[ground_truth.setid == setid] gt_split = gt_split[gt_split.seq.isin(fold_sequences)] return gt_split def get_val_gt(fold_num): if fold_num < 0 or fold_num > 19: raise ValueError("fold_num must be within 0 and 19, value entered: "+str(fold_num)) if fold_num < 10: setid = 1 sequences = np.arange(10) fold_sequences = sequences[sequences == fold_num] + 1 else: setid = 2 sequences = np.arange(10, 20) fold_sequences = sequences[sequences == fold_num] + 1 ground_truth = get_ground_truth() gt_split = ground_truth[ground_truth.setid == setid] gt_split = gt_split[gt_split.seq.isin(fold_sequences)] return gt_split def get_train(fold_num, **kwargs): if fold_num < 0 or fold_num > 19: raise ValueError("fold_num must be within 0 and 19, value entered: "+str(fold_num)) if fold_num < 10: setid = 1 sequences = np.arange(10) fold_sequences = sequences[sequences != fold_num] + 1 else: setid = 2 sequences = np.arange(10, 20) fold_sequences = sequences[sequences != fold_num] + 1 return get_seqs(setid, fold_sequences, **kwargs) def get_val(fold_num, **kwargs): if fold_num < 0 or fold_num > 19: raise ValueError("fold_num must be within 0 and 19, value entered: "+str(fold_num)) if fold_num < 10: setid = 1 sequences = np.arange(10) fold_sequences = sequences[sequences == fold_num] + 1 else: setid = 2 sequences = np.arange(10, 20) fold_sequences = sequences[sequences == fold_num] + 1 return get_seqs(setid, fold_sequences, **kwargs) def get_seqs(setid, selected_sequences, **kwargs): if setid < 1 or setid > 2: raise ValueError("setid must be 1 or 2, value entered: "+str(setid)) ground_truth = get_ground_truth() gt_split = ground_truth[ground_truth.setid == setid] gt_split = gt_split[gt_split.seq.isin(selected_sequences)] X, Y = data_io.get_data(gt_split, pose_style='OpenPose', **kwargs) return X, Y
[ "pandas.DataFrame", "misc.data_io.get_data", "numpy.arange" ]
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#!/usr/bin/env python # -*- coding: utf-8 -*- """ Benchmark different GUI image draw times in tkinter Environment setup instructions: conda create -n gui-test tk matplotlib pillow vispy pip install pyopengltk """ import time import tkinter as tk import numpy as np from PIL import Image, ImageTk from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg from matplotlib.figure import Figure import vispy from vispy import scene from vispy.app import use_app def pil_gui_test(arr): # https://stackoverflow.com/questions/52459277/convert-a-c-or-numpy-array-to-a-tkinter-photoimage-with-a-minimum-number-of-copi root = tk.Tk() start = time.time() img = ImageTk.PhotoImage(Image.fromarray(arr)) stop = time.time() print(f"Pillow run took {stop-start} s") lbl = tk.Label(root, image=img) lbl.pack() root.mainloop() def matplotlib_gui_test(arr): # https://matplotlib.org/3.1.0/gallery/user_interfaces/embedding_in_tk_sgskip.html root = tk.Tk() f = Figure() canvas = FigureCanvasTkAgg(f,root) canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=1) start = time.time() f.add_subplot(111).imshow(arr) canvas.draw() stop = time.time() print(f"Matplotlib run took {stop-start} s") lbl = tk.Label(root) lbl.pack() root.mainloop() def vispy_gui_test(arr): # https://github.com/vispy/vispy/issues/2168 # https://vispy.org/gallery/scene/image.html root = tk.Tk() app = use_app("tkinter") canvas = vispy.scene.SceneCanvas(keys='interactive', show=True, parent=root, app=app) # Set up a viewbox to display the image with interactive pan/zoom view = canvas.central_widget.add_view() # Set 2D camera (the camera will scale to the contents in the scene) view.camera = scene.PanZoomCamera(aspect=1) view.camera.flip = (0, 1, 0) view.camera.zoom(1.0) # TODO: This isn't setting the window size correctly. # Need to manually expand the window to see the image canvas.native.pack(side=tk.TOP, fill=tk.BOTH, expand=1) # Create the image start = time.time() image = scene.visuals.Image(arr, interpolation='nearest', parent=view.scene, method='subdivide') view.camera.set_range() stop = time.time() print(f"Vispy run took {stop-start} s") app.run() if __name__ == "__main__": # generate image array to plot arr = np.random.randint(low=255, size=(100, 100, 3), dtype=np.uint8) pil_gui_test(arr) matplotlib_gui_test(arr) vispy_gui_test(arr)
[ "vispy.app.use_app", "PIL.Image.fromarray", "vispy.scene.visuals.Image", "matplotlib.figure.Figure", "vispy.scene.SceneCanvas", "numpy.random.randint", "tkinter.Tk", "tkinter.Label", "vispy.scene.PanZoomCamera", "time.time", "matplotlib.backends.backend_tkagg.FigureCanvasTkAgg" ]
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#!/usr/bin/python import kaldi_io import sys import os from os.path import join, isdir from numpy.random import permutation import itertools import keras import numpy as np from keras.preprocessing.sequence import pad_sequences import queue from threading import Thread import random import glob import sys sys.path.insert(0, '/mnt/matylda3/vydana/HOW2_EXP/MT_Transformer') from MT_TransV1.CMVN import CMVN from MT_TransV1.Load_sp_model import Load_sp_models #=============================================== #----------------------------------------------- class DataLoader(object): def __init__(self, input_dict): #def __init__(self,files, max_batch_label_len, max_batch_len, max_feat_len, max_label_len, Src_model, Tgt_model, queue_size=100,apply_cmvn=1,min_words=3,min_len_ratio=1.5): self.files = input_dict['files'] #### print(self.files) if self.files==[]: print('input to data generator in empty') exit(0) self.Src_model = input_dict['Src_model'] self.Tgt_model = input_dict['Tgt_model'] self.max_batch_len = input_dict['max_batch_len'] self.max_batch_label_len = input_dict['max_batch_label_len'] self.max_feat_len = input_dict['max_feat_len'] self.max_label_len = input_dict['max_label_len'] self.min_len_ratio = input_dict['min_len_ratio'] self.min_words = input_dict['min_words'] self.max_words = input_dict['max_words'] self.apply_cmvn = input_dict['apply_cmvn'] self.queue = queue.Queue(input_dict['queue_size']) self.Src_padding_id = self.Src_model.__len__() self.Tgt_padding_id = self.Tgt_model.__len__() self.word_space_token = self.Src_model.EncodeAsIds('_____')[0] self._thread = Thread(target=self.__load_data) self._thread.daemon = True self._thread.start() def __reset_the_data_holders(self): self.batch_names=[] self.batch_Src_data=[] self.batch_Src_length=[] self.batch_Src_labels=[] self.batch_Src_label_length=[] self.batch_Src_text=[] self.batch_Src_text_length=[] self.batch_Tgt_labels=[] self.batch_Tgt_label_length=[] self.batch_Tgt_text=[] self.batch_Tgt_text_length=[] #--------------------------------------------------------------------- def make_batching_dict(self): #---------------------------------------- smp_Src_data= pad_sequences(self.batch_Src_data,maxlen=max(self.batch_Src_length),dtype='float32',padding='post',value=0.0) smp_Src_labels = pad_sequences(self.batch_Src_labels,maxlen=max(self.batch_Src_label_length),dtype='int32',padding='post',value=self.Src_padding_id) smp_Tgt_labels = pad_sequences(self.batch_Tgt_labels,maxlen=max(self.batch_Tgt_label_length),dtype='int32',padding='post',value=self.Tgt_padding_id) smp_Src_Text = pad_sequences(self.batch_Src_text, maxlen=max(self.batch_Src_text_length),dtype=object,padding='post',value='') smp_Tgt_Text = pad_sequences(self.batch_Tgt_text, maxlen=max(self.batch_Tgt_text_length),dtype=object,padding='post',value='') batch_data_dict={ 'smp_names':self.batch_names, 'smp_Src_data':smp_Src_data, 'smp_Src_labels':smp_Src_labels, 'smp_Tgt_labels':smp_Tgt_labels, 'smp_Src_Text':smp_Src_Text, 'smp_Tgt_Text':smp_Tgt_Text, 'smp_Src_data_length':self.batch_Src_length, 'smp_Src_label_length':self.batch_Src_label_length, 'smp_Src_text_length':self.batch_Src_text_length, 'smp_Tgt_label_length':self.batch_Tgt_label_length, 'smp_Tgt_text_length':self.batch_Tgt_text_length} return batch_data_dict #------------------------------------------ #------------------------------------------ def __load_data(self): ###initilize the lists while True: self.__reset_the_data_holders() max_batch_label_len = self.max_batch_label_len random.shuffle(self.files) for inp_file in self.files: with open(inp_file) as f: print(inp_file) for line in f: #============================ split_lines=line.split(' @@@@ ') #============================ ####this is mostly for the joint model ###usuvally MT setup will not have scp so just fill the space with the default vallues #breakpoint() ##assigining key = split_lines[0] scp_path = split_lines[1] #will be 'None' fo MT setup scp_path = 'None' if scp_path == '' else scp_path ####some times None is missed by empty strings #============================ ### Char labels #============================ src_text = split_lines[3] src_tok = split_lines[4] # print(src_text,src_tok,split_lines,len(src_tok)) if len(src_tok)>0: src_tok = [int(i) for i in src_tok.split(' ')] else: print(split_lines) continue; #============================ ##Word models #============================ tgt_text = split_lines[5] tgt_tok = split_lines[6] # print("tgt_text,tgt_tok",tgt_text,tgt_tok,len(tgt_tok)) if len(tgt_tok)>0: tgt_tok = [int(i) for i in tgt_tok.split(' ')] else: print(split_lines) continue; #============================ ### text #============================ Src_tokens = src_tok Tgt_tokens = tgt_tok Src_Words_Text = src_text.split(' ') Tgt_Words_Text = tgt_text.split(' ') #print(Src_Words_Text, Tgt_Words_Text) ########################################################### if (Src_Words_Text[0] == 'None') or (Tgt_Words_Text[0] == 'None'): #breakpoint() continue; ########################################################### ### ### text_filtering if (len(Src_Words_Text) < self.min_words) or (len(Tgt_Words_Text) < self.min_words): #print("skippeddue to min_words", self.min_words,len(Src_Words_Text)) continue; ## if ((len(Src_Words_Text)/len(Tgt_Words_Text)) > self.min_len_ratio) or ((len(Tgt_Words_Text)/len(Src_Words_Text)) > self.min_len_ratio): #print("skippped due to min_len_ratio", self.min_len_ratio,'.......................',len(Src_Words_Text)/len(Tgt_Words_Text)) continue; ## if (len(Src_Words_Text) > self.max_words) or (len(Tgt_Words_Text) > self.max_words): #print("skippeddue to min_words", self.max_words,len(Src_Words_Text)) continue; #-------------------------- if ((scp_path != 'None')): mat = kaldi_io.read_mat(scp_path) if self.apply_cmvn: mat = CMVN(mat) ####pruning the Acoustic features based on length ###for joint model if (mat.shape[0]>self.max_feat_len) or (len(Src_tokens) > self.max_label_len): #print("key,mat.shape,Src_Words_Text,Src_tokens,self.max_label_len",key,mat.shape,len(Src_Words_Text),len(Src_tokens),self.max_label_len) continue; else: mat=np.zeros((100,83),dtype=np.float32) ####For MT model ###Src_tokens more than self.max_feat_len or Tgt_tokens more than self.max_label_len ### should be removed ### if (len(Src_tokens) > self.max_feat_len) or (len(Tgt_tokens) > self.max_label_len): #print("key,Src_tokens, self.max_feat_len, Tgt_tokens, self.max_label_len",key,len(Src_tokens), self.max_feat_len, len(Tgt_tokens), self.max_label_len) continue; #-------------------------- #============================================================== ###Add to the list #### self.batch_Src_data.append(mat) self.batch_names.append(key) self.batch_Src_length.append(mat.shape[0]) self.batch_Src_labels.append(Src_tokens) self.batch_Src_label_length.append(len(Src_tokens)) self.batch_Tgt_labels.append(Tgt_tokens) self.batch_Tgt_label_length.append(len(Tgt_tokens)) self.batch_Src_text.append(Src_Words_Text) self.batch_Src_text_length.append(len(Src_Words_Text)) self.batch_Tgt_text.append(Tgt_Words_Text) self.batch_Tgt_text_length.append(len(Tgt_Words_Text)) #============================================================== #============================================================== # total_labels_in_batch is used to keep track of the length of sequences in a batch, just make sure it does not overflow the gpu ##in general lstm training we are not using this because self.max_batch_len will be around 10-20 and self.max_batch_label_len is usuvally set very high #------------------------------------------------------------------------------- if not (scp_path == 'None'): expect_len_of_features=max(max(self.batch_Src_length,default=0),mat.shape[0]) expect_len_of_labels=max(max(self.batch_Tgt_label_length,default=0),len(Tgt_tokens)) total_labels_in_batch= (expect_len_of_features + expect_len_of_labels)*(len(self.batch_names)+4) else: expect_len_of_features=max(max(self.batch_Src_label_length,default=0),len(Src_tokens)) expect_len_of_labels=max(max(self.batch_Tgt_label_length,default=0),len(Tgt_tokens)) total_labels_in_batch= (expect_len_of_features + expect_len_of_labels)*(len(self.batch_names)+4) #------------------------------------------------------------------------------- ###check if ypu have enough labels output and if you have then push to the queue ###else keep adding them to the lists #print(len(self.batch_Src_data), self.max_batch_len) if total_labels_in_batch > self.max_batch_label_len or len(self.batch_Src_data)==self.max_batch_len: # #============================================================== # ####to clumsy -------> for secound level of randomization # CCCC=list(zip(batch_data,batch_names,batch_labels,batch_Tgt_Words_Text,batch_word_text,batch_label_length,batch_length,batch_Tgt_label_length,batch_word_text_length)) # random.shuffle(CCCC) # batch_data,batch_names,batch_labels,batch_Tgt_Words_Text,batch_word_text,batch_label_length,batch_length,batch_Tgt_label_length,batch_word_text_length=zip(*CCCC) # #============================================================== batch_data_dict = self.make_batching_dict() self.queue.put(batch_data_dict) ###after pushing data to lists reset them self.__reset_the_data_holders() if len(self.batch_names)>0: ### Collect the left over stuff as the last batch #----------------------------------------------- batch_data_dict = self.make_batching_dict() self.queue.put(batch_data_dict) #exit(0) def next(self, timeout=30000): return self.queue.get(block=True, timeout=timeout) #=================================================================== # sys.path.insert(0,'/mnt/matylda3/vydana/HOW2_EXP/KAT_Attention') # import Attention_arg # from Attention_arg import parser # args = parser.parse_args() # print(args) # ###debugger # args.Src_model_path='/mnt/matylda3/vydana/benchmarking_datasets/Timit/models/Timit_PHSEQ_100/Timit_PHSEQ__100__word.model' # args.Tgt_model_path='/mnt/matylda3/vydana/benchmarking_datasets/Timit/models/Timit_PHSEQ_100/Timit_PHSEQ__100__word.model' # args.text_file = '/mnt/matylda3/vydana/benchmarking_datasets/Timit/All_text' # args.train_path='/mnt/matylda3/vydana/benchmarking_datasets/Timit/scp_files/train/' # args.dev_path='/mnt/matylda3/vydana/benchmarking_datasets/Timit/scp_files/dev/' # Src_model=Load_sp_models(args.Src_model_path) # Tgt_model=Load_sp_models(args.Tgt_model_path) # train_gen = DataLoader(files=glob.glob(args.train_path + "*"),max_batch_label_len=20000, max_batch_len=4,max_feat_len=2000,max_label_len=200,Src_model=Src_model,Tgt_model=Tgt_model,text_file=args.text_file) # for i in range(10): # B1 = train_gen.next() # print(B1.keys()) # #breakpoint()
[ "sys.path.insert", "kaldi_io.read_mat", "random.shuffle", "numpy.zeros", "threading.Thread", "queue.Queue", "MT_TransV1.CMVN.CMVN" ]
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"""Standard library imports""" import numpy as np # 1.19.4 import pandas as pd # 1.2.0 import scipy as sp # import matplotlib.pyplot as plt from scipy.interpolate import interp1d from scipy.optimize import fsolve """Local modules""" from pyloads.blade_data import BladeFeatures from pyloads.aerodynamic_profiles import AeroProfiles from pyloads.operation_dtu10mw import Operation class Rotor(Operation): """Create a Rotor object (by Default is the DTU 10 MW) by defining the blade geometry (cord, twist and thickness), as well as the aerodynamic profiles (i.e as output from XFOIL interactive program for design). - Calculate the Normal and Tangential loads for given operational conditions. - Calculate the Power and Thrust coefficient. - Calculate and plot the Rotor Power Curve. Parameters ---------- radio : array , default=None twist : array , default=None cord : array , default=None t_c : array , default=None profiles : array , default='Default'""" # class attributes number_of_blades = 3 radio = 89.17 # [m] rho = 1.225 # [km/m3] operation = pd.DataFrame( {'u': [4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., ], 'pitch': [2.751, 1.966, 0.896, 0., 0., 0., 0., 0., 4.502, 7.266, 9.292, 10.958, 12.499, 13.896, 15.2, 16.432, 17.618, 18.758, 19.86, 20.927, 21.963, 22.975], 'RPM': [6., 6., 6., 6., 6.426, 7.229, 8.032, 8.836, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, ] }) # blade_data = pd.read_csv('bladedat.txt', sep='\t', names=['r', 'twist', 'c', 't/c']) # class constructor def __init__(self, radio='DTU_10MW', twist='DTU_10MW', cord='DTU_10MW', t_c='DTU_10MW', profiles='DTU_10MW'): super().__init__() bld = BladeFeatures() # Take the parameters from BladeFeatures corresponding to DTU 10MW if radio == 'DTU_10MW': self.radio = bld.radio if twist == 'DTU_10MW': self.twist = bld.twist if cord == 'DTU_10MW': self.cord = bld.cord if t_c == 'DTU_10MW': self.t_c = bld.t_c """Load the aerodynamics profiles.""" # TODO: # This should be in a child class from Rotor called DTU-10MW if profiles == 'DTU_10MW': aero_prof = AeroProfiles() ffa_241 = aero_prof.ffa_241 ffa_301 = aero_prof.ffa_301 ffa_360 = aero_prof.ffa_360 ffa_480 = aero_prof.ffa_480 ffa_600 = aero_prof.ffa_600 cylinder = aero_prof.cylinder self.ffa_dict = dict(zip(np.arange(6), [ffa_241, ffa_301, ffa_360, ffa_480, ffa_600, cylinder])) # TODO: # check that the len of the features is the same # check that the data has valid ranges.. @staticmethod def integrate(y, r): """Useful function for numerical integration. parameters ---------- y : array function to be integrated r : array integrate over r radial positions. """ M = 0 # dummy assignment before loop for k in range(len(y) - 1): A_k = (y[k + 1] - y[k]) / (r[k + 1] - r[k]) B_k = (y[k] * r[k + 1] - y[k + 1] * r[k]) / (r[k + 1] - r[k]) M += 1 / 3 * A_k * ((r[k + 1]) ** 3 - (r[k]) ** 3) + 0.5 * B_k * ((r[k + 1]) ** 2 - (r[k]) ** 2) return M @staticmethod def thruster(pN, r): """Compute the total Thrust (T * num_blades) for the Rotor. parameter --------- pN : array normal loads vector (i.e as returned by norma_tangential_loads method. r : array vector with radial position [m]""" # [r] m T = 0 B = Rotor.number_of_blades for i in range(len(pN) - 1): T += (pN[i + 1] + pN[i]) * 0.5 * (r[i + 1] - r[i]) # print(f'thrust {T} for item num {i}') return T * B def lift_drag_coeff(self, alpha, t_c): # TODO: # raise exceptions for thick and alpha # read how to write docstring in Python PEP-8 """Interpolation for drag and lift coefficients. Returns: (Cl, Cd) --------------parameters: t_c: float (ie. 24.1) alpha: int or float (rad) """ t = t_c cdthick, clthick = np.zeros(6), np.zeros(6) for k in range(6): f1cl = interp1d(self.ffa_dict[k].iloc[:, 0], self.ffa_dict[k].iloc[:, 1]) f1cd = interp1d(self.ffa_dict[k].iloc[:, 0], self.ffa_dict[k].iloc[:, 2]) clthick[k] = f1cl(alpha * 180 / np.pi) # must convert into degrees cdthick[k] = f1cd(alpha * 180 / np.pi) thick_prof = np.array([24.1, 30.1, 36., 48., 60., 100.]) f2cl = interp1d(thick_prof, clthick) f2cd = interp1d(thick_prof, cdthick) Cl = f2cl(t) Cd = f2cd(t) # print(f'alpha:{alpha}, t_c={t_c}') # print(f'Cd= {Cd} \t Cl= {Cl}') return Cl, Cd def normal_tangential_loads(self, tsr, v_0, r, theta, c, t_c, a=0.2, aa=0.2, imax=100, verbose=False): # TODO: # Add velocity triangle vector plot """Calculate the Tangential and Normal loads (in N/m) given the wind speed and tip speed ratio (tsr) for a given radial position (twist, cord length and thickness/cord ratio at the radial position must be given). Parameters ---------- tsr : int or float Tip speed ratio. v_0 : int or float Wind speed [m/s]. r : int or float Radial position. theta : int or float Local twist at r. c : int or float Cord length. t_c : int or float Thickness/cord ratio. a : int or float, default=0.2 tangential induction factor aa : int or float, default=0.2 normal induction factor imax : int, default=100 max number of iter before stoping loop. verbose : bool, default=False if True, print and plot the variables for each iteration. Returns ------- pT : float tangential load [N/m] at radial position. pN : float normal load [N/m] at radial position. """ def glauert_equation(x, sigma, F, phi, Cn): return [x[0] - ((1 - x[1]) ** 2 * sigma * Cn) / (np.sin(phi) ** 2), x[0] - 4 * x[1] * (1 - 0.25 * (5 - 3 * x[1]) * x[1]) * F] i = 0 tol_a, tol_aa = 10, 10 B = Rotor.number_of_blades sigma = (c * B) / (2 * np.pi * r) if verbose: a_list, aa_list, phi_list, alpha_list, i_list = [], [], [], [], [] while tol_a > 10 ** (-3) and tol_aa > 10 ** (-3) and i < imax: a0, aa0 = a, aa phi = np.arctan(((1 - a) * Rotor.radio) / ((1 + aa) * tsr * r)) alpha = np.rad2deg(phi) - theta alpha = np.deg2rad(alpha) Cl, Cd = self.lift_drag_coeff(alpha, t_c) Cn = Cl * np.cos(phi) + Cd * np.sin(phi) Ct = Cl * np.sin(phi) - Cd * np.cos(phi) # if i == 0: #get info of first values to check # print(Rotor.radio) # print(r) # print(tsr) # print('phi:',phi) # print('theta:',theta) # print('alpha:',alpha) # print('Cl:',Cl) # print('Cl:', Cd) F = (2 / np.pi) * np.arccos(np.exp(-(B / 2) * (Rotor.radio - r) / (r * np.sin(abs(phi))))) if a <= 1 / 3: a = 1 / (((4 * F * np.sin(phi) ** 2) / (sigma * Cn)) + 1) else: CT, a = fsolve(glauert_equation, [1, a], args=(sigma, F, phi, Cn)) # [1, a] is necessary. why? aa = 1 / (((4 * F * np.sin(phi) * np.cos(phi)) / (sigma * Ct)) - 1) tol_a, tol_aa = abs(a - a0), abs(aa - aa0) if verbose: print('iter #:',i) print('\t a: ',a) print('\t a_prime: ',aa) print('\t phi: ',phi) print('\t alpha: ',alpha) a_list.append(a) aa_list.append(aa) phi_list.append(phi) alpha_list.append(alpha) i_list.append(i) i += 1 if verbose: print('final iteration (i):',i) if i>1: # TODO # review if figsize is correct. fig, axes = plt.subplots(2,2, figsize=(10,4)) axes[0, 0].plot(i_list, a_list, marker='o') axes[0, 0].set_ylabel('a', fontsize=14) axes[0, 0].set_xlabel('iteration num') axes[0, 1].plot(i_list, aa_list,marker='o') axes[0, 1].set_ylabel('a\'', fontsize=14) axes[0, 1].set_xlabel('iteration num') axes[1, 0].plot(i_list, phi_list,marker='o') axes[1, 0].set_ylabel('phi', fontsize=14) axes[1, 0].set_xlabel('iteration num') axes[1, 1].plot(i_list, alpha_list,marker='o') axes[1, 1].set_ylabel('alpha', fontsize=14) axes[1, 1].set_xlabel('iteration num') fig.tight_layout(pad=3.0) plt.show() v_rel = (v_0 / np.sin(phi)) * (1 - a) pT = 0.5 * Ct * Rotor.rho * (v_rel ** 2) * c pN = 0.5 * Cn * Rotor.rho * (v_rel ** 2) * c if i == imax: print(f'warning: Not converged for {imax} iter at radial position = {r} m') return pT, pN def power_thrust_coefficient(self, tsr, u, r, theta, c, t_c, plot_Loads=False): """Calculate the power and thrust for given operational parameters. This method uses the norma_tangential_loads in order to calculate the loads. :parameter ---------- tsr : int or float Tip speed ratio. u : int or float Wind speed [m/s]. r : int or float Radial position. c : int or float Cord length. t_c : int or float Thickness/cord ratio. plot_Loads : bool, default=False Plot the normal and tangential loads against radial position. :return ------- power : float Total power considering Rotor.number_of_blades. thrust : float Total thrust considering Rotor.number_of_blades. pT : float tangential load [N/m] at radial position. pN : float normal load [N/m] at radial position. """ pT = np.zeros(len(r)) pN = np.zeros(len(r)) for i in range(len(r)): try: pT[i], pN[i] = self.normal_tangential_loads(tsr, u, r[i], theta[i], c[i], t_c[i]) except TypeError: pT[i], pN[i] = np.nan, np.nan # append and assign values at r=R r = np.append(r, Rotor.radio) pT = np.append(pT[:-1], 0) # The -1 is a rusty way to solve the problem pN = np.append(pN[:-1], 0) w = tsr * u / Rotor.radio power = Rotor.integrate(pT, r) * Rotor.number_of_blades * w # print(f'power integral{Rotor.integrate(pT,r)}') # print(f'thrust integral{Rotor.thruster(pN, r)}') thrust = Rotor.thruster(pN, r) if plot_Loads: # ( == True) plt.figure() plt.plot(self.radio, pN) plt.plot(self.radio, pT) plt.grid() plt.ylabel('normal loads [N/m]', fontsize=14) plt.xlabel('rotor radius [m]', fontsize=14) plt.show() return power, thrust, pT, pN def power_curve(self, u_vector, w_vector, pitch_vector, plot_curve=True): """Calculate and plot the Power Curve given a vector of wind speed, rotational speed (in RPM) and corresponding pitch angle. :parameter ---------- u_vector : array range of speed for the power curve w_vector : array vector with rotational speed (in RPM) pitch_vector : array pitch angle vector :return ------- """ P, T = np.zeros(len(u_vector)), np.zeros(len(u_vector)) # df = Rotor.blade_data.iloc[0:] pN = np.zeros([len(u_vector), len(self.radio)]) pT = np.zeros([len(u_vector), len(self.radio)]) # print(u_vector) for j in range(len(u_vector)): u = u_vector.values[j] w = w_vector.values[j] * np.pi / 30 # convert from RPM to rad/s pitch = pitch_vector.values[j] TSR = w * Rotor.radio / u # print(TSR, w, pitch) P[j], T[j], pT[j,], pN[j,] = self.power_thrust_coefficient(TSR, u, self.radio, self.twist + pitch, self.cord, self.t_c) if plot_curve: plt.plot(u_vector, P / 1e6, linestyle='--', marker='o') plt.xlabel('Wind speed') plt.ylabel('power [MW]') plt.grid() return P, T if __name__ == "__main__": print(f'numpy version {np.__version__} , \t pandas vers {pd.__version__} , \t scipy vers {sp.__version__}') print('stop') # instance a rotor object. # WT_data = pd.read_csv('operation.txt', sep='\s+') # WT_data.index = WT_data.u # print(WT_data.loc[6]) # u, pitch, rpm = WT_data.loc[6] dtu_10mw = Rotor() print(type(dtu_10mw)) # <class '__main__.Rotor'> # test power method oper_df = dtu_10mw.show_operation() # returns a DataFrame u, pitch, rpm = dtu_10mw.show_operation(u=6) tsr = (rpm * np.pi / 30) * Rotor.radio / u # P, T = dtu_10mw.power_curve(oper_df.u, oper_df.RPM, oper_df.pitch) power, thrust, pT, pN = dtu_10mw.power_thrust_coefficient(tsr, u, dtu_10mw.radio, dtu_10mw.twist + pitch, dtu_10mw.cord, dtu_10mw.t_c, plot_Loads=True) # tan_i, norm_i = dtu_10mw.normal_tangential_loads(tsr, u, dtu_10mw.radio[0], dtu_10mw.twist[0] + pitch, # dtu_10mw.cord[0], dtu_10mw.t_c[0], verbose=True) # TODO# # * Power and Thrust are NEGATIVE... # * Review... different results as IPYNB : # [56.73502664, 128.66511904, 196.82870201, 955.83255009, # 1372.0058535, 1685.01982088, 2137.75442846, 2601.22578824, <<<<< 2601 is the first different value. # 2986.14621076, 3263.07927127, 3431.68926186, 3461.78866371, # 3474.49291476, 3421.50774457, 3224.94102283, 2815.05298268, # 2063.40641495, 0.]) # * Improve plots # * let DTU_10MW be a subclass and define Rotor with user params. # * add power and thrust calculation DONE ! # * add DEFLECTION ! print('Finish ran static loads.')
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import numpy as np from sklearn.metrics import average_precision_score def load_data(data_path): """load array data from data_path""" data = np.load(data_path) return data['X_train'], data['y_train'], data['X_test'], data['y_test'] def calculate_average_precision(label, index, similarity, num_search_sample): """calculate average precision of similar search result. The average precison is calculated over num_search_sample """ label_idx = np.array([label[idx] for idx in index]) label_idx_true = np.array([np.where(row == row[0], 1, 0) for row in label_idx]) label_idx_true = label_idx_true[:, 1:] ap = [] for i in range(num_search_sample): ap.append(average_precision_score(label_idx_true[i], similarity[i])) return ap
[ "numpy.where", "numpy.array", "numpy.load", "sklearn.metrics.average_precision_score" ]
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"""Module that handles shared information for all network objects.""" import xml.etree.ElementTree as et import numbers import numpy as np import pandas as pd import scipy.sparse as sps from paminco.utils.readin import parse_number, xml_find_root from paminco.utils.misc import Cache from paminco.utils.typing import sparse_format, is_int, is_iterable, IntEnum2 import paminco._doc as _doc ID_UNMAPPED = -9999 LBL_UNMAPPED = "Invalid" class FlowDirection(IntEnum2): """Enum defining the type flow for the graph.""" DIRECTED = 0 """All edges can only take flow >= 0.""" UNDIRECTED = 1 """All edges can take any flow.""" MIXED = 2 """Some edges may only take flow >= 0.""" class Edges: """ Class that contains the edges/links of a network. An edges object can be instantiated in several ways: Edges(e) where ``e`` is an Edges object. Data in ``e`` will be copied if specfied by parameter ``copy``. Edges(st) where ``st`` is array_like. Parameter st is converted to ndarray and is expected to me of shape (m, 2). Can be node indices or node labels specifying an edge. If labels are given, indices are mapped by ``map_labels_to_indices``, given indices are mapped by ``map_indices_to_labels``. Edge bounds are determined by the parameter ``directed``. Edges((st, bounds)) where ``st`` is array_like and ``bounds`` is tuple (lower, upper) specifying bounds used for all edges or array_like of shape (m, 2) marking individual bounds for all edges. Edges((labels, indices, bounds)) where ``labels``, ``indices`` are array_like of shape (m, 2) and ``bounds`` is tuple (lower, upper) specifying bounds used for all edges or array_like of shape (m, 2) containing individual bounds for all edges. Parameters ---------- data : ndarray, or tuple of ndarray Edge data. directed_flow : bool, default=True Controls default values for ``None`` in bounds. If ``True``, lower bounds are set to 0 and ``False`` to -inf. Missing upper bounds are set to inf. map_labels_to_indices : None, bool, dict, or callable, default=True Determines mapping of labels to indices if no indices are given. If ``None`` or ``False``, indices of edges will be set to -9999, denoting invalid edge indices. If ``dict``, labels will be mapped by this dict. If ``True``, node indices are set to 0, 1, ..., n-1. If ``callable``, use callable with signature ``indices = callable(labels)``. map_indices_to_labels : None, bool, dict, or callable, default=True Determines mapping of indices to indices if no labels are given. If ``None`` or ``False``, indices of edges will be set to 'invalid', denoting invalid edge labels. If ``dict``, indices will be mapped by this dict. If ``True``, node labels are set to node indices as str. If ``callable``, use callable with signature ``labels = callable(indices)``. dtype_float : dtype, default=numpy.float_ Datatype for edge bounds. dtype_int : dtype, default=int Datatype for edge bounds. copy : bool, default=False Whether to create a copy of the inputs in data. Attributes ---------- flow_directions : ndarray Ndarray of shape (m, ). A ``-1`` denotes an edge with lb < 0 and ub <= 0. A ``0`` denotes an edge with lb < 0 and ub > 0. A ``1`` denotes an edge with lb >=0 and ub > 0. """ def __init__( self, data, directed_flow: bool = True, map_labels_to_indices=True, # optional map_indices_to_labels=True, # optional dtype_float=None, dtype_int=None, copy: bool = False, ) -> None: # Collect kwargs kw = { "directed_flow": directed_flow, "map_labels_to_indices": map_labels_to_indices, "map_indices_to_labels": map_indices_to_labels, "dtype_float": dtype_float, "dtype_int": dtype_int, "copy": copy, } if isinstance(data, Edges): d = (data.labels, data.indices, data.bounds) return self.__init__(d, dtype_float=data.dtype_float, dtype_int=data.dtype_int) elif isinstance(data, tuple): if len(data) == 3: pass elif len(data) == 2: # (labels or indices, bounds) st, bounds = data st = np.array(st) if st.dtype.kind in {'U', 'S'}: # (labels, bounds) if isinstance(map_labels_to_indices, dict): st_ids = np.vectorize(map_labels_to_indices.__getitem__)(st) elif map_labels_to_indices is True: # Automap labels # Get unique labels and sort them if quasi-ints unique_lbl = np.unique(st) try: unique_lbl = sorted(unique_lbl, key=int) except ValueError: pass d = dict(zip(unique_lbl, np.arange(len(unique_lbl)))) st_ids = np.vectorize(d.__getitem__)(st) elif map_labels_to_indices is None or map_labels_to_indices is False: # Set to invalid indices st_ids = np.full(st.shape, ID_UNMAPPED, dtype=int) else: # Map labels by callable st_ids = map_labels_to_indices(st) return self.__init__((st, st_ids, bounds), **kw) elif issubclass(st.dtype.type, numbers.Integral): # (indices, bounds) unique_st = np.unique(st) if np.array_equal(np.sort(unique_st), np.arange(len(unique_st))) is False: raise ValueError(f"Indices must be all integers from 0 to {len(unique_st) - 1}.") if isinstance(map_indices_to_labels, dict): st_lbl = np.vectorize(map_indices_to_labels.__getitem__)(st) elif map_indices_to_labels is True: st_lbl = st.astype(str) elif map_indices_to_labels is None or map_indices_to_labels is False: # Set to invalid indices st_lbl = np.full(st.shape, LBL_UNMAPPED) else: st_lbl = map_indices_to_labels(st) return self.__init__((st_lbl, st, bounds), **kw) else: raise ValueError(f"Invalid edge data: {data}.") else: raise ValueError(f"Invalid edge data: {data}.") else: # Only labels or indices given -> build lower and upper bounds by directed if directed_flow is True: return self.__init__((data, (0, np.inf)), **kw) return self.__init__((data, (-np.inf, np.inf)), **kw) # Handle datatypes if dtype_float is None: dtype_float = np.float64 if dtype_int is None: dtype_int = int self._dtype_float = dtype_float self._dtype_int = dtype_int # Unpack data labels, indices, bounds = data self.labels = np.array(labels, dtype=str, copy=copy) self.indices = np.array(indices, dtype=dtype_int, copy=copy) # Broadcast bounds if lower, upper for all edges given if not isinstance(bounds, np.ndarray): bounds = np.array(bounds) if len(bounds.shape) == 1: bounds = bounds.reshape(1, -1) bounds = np.repeat(bounds, len(labels), axis=0) # Handle 'None' bounds bkw = {"posinf": np.inf, "neginf": -np.inf} self.bounds = np.array(bounds, dtype=dtype_float, copy=copy) self.bounds[:, 1] = np.nan_to_num(self.bounds[:, 1], copy=False, nan=np.inf, **bkw) if directed_flow is True: self.bounds[:, 0] = np.nan_to_num(self.bounds[:, 0], copy=False, nan=0., **bkw) else: self.bounds[:, 0] = np.nan_to_num(self.bounds[:, 0], copy=False, nan=-np.inf, **bkw) # Check consistency of labels, indices and bounds if self.labels.shape[1] != 2: raise ValueError( f"Invalid edge data, labels are of shape {self.labels.shape}." ) if (self.labels.shape == self.indices.shape == self.bounds.shape) is False: raise ValueError( "Inconsistent shapes. " f"Labels: {self.labels.shape}, " f"indices: {self.indices.shape}, " f"bounds: {self.bounds.shape}." ) # Set edge directions and get type of graph self.flow_directions = np.zeros(len(self)) self.flow_directions[self.lb < 0] -= 1 self.flow_directions[self.ub > 0] += 1 if len(self.flow_undirected) == len(self): self.flow_dir = FlowDirection.UNDIRECTED elif len(self.flow_undirected) == 0: self.flow_dir = FlowDirection.DIRECTED else: self.flow_dir = FlowDirection.MIXED self.cache = Cache() def __eq__(self, other) -> bool: for att in ["labels", "indices", "bounds"]: if np.array_equal(getattr(self, att), getattr(other, att)) is False: return False return True def __len__(self) -> int: return len(self.indices) def __getitem__(self, idx): if is_iterable(idx): return [self[i] for i in idx] return {att: getattr(self, att)[idx] for att in ["source_lbl", "target_lbl", "s", "t", "lb", "ub"]} def to_df(self, **kwargs) -> pd.DataFrame: """Get object as DataFrame. Parameters ---------- **kwargs : keyword arguments, optional Passed to DataFrame constructor. Returns ------- df : pandas.DataFrame Edges with source/target labels, source/target ids, lower and upper bounds. """ data = np.hstack([self.labels, self.indices, self.bounds]) df = pd.DataFrame(data, **kwargs) df.columns = ["source_lbl", "target_lbl", "s", "t", "lb", "ub"] df[["source_lbl", "target_lbl"]] = df[["source_lbl", "target_lbl"]].astype(str) df[["s", "t"]] = df[["s", "t"]].astype(self._dtype_int) df[["lb", "ub"]] = df[["lb", "ub"]].astype(self._dtype_float) return df def get_flow_df( self, x, labels: bool = True, colname_flow: str = "flow" ) -> pd.DataFrame: if isinstance(x, (int, float)): x = np.full(len(self), x) if labels is True: s, t = self.source_lbl, self.target_lbl dtype = str else: s, t = self.s, self.t dtype = int df = pd.DataFrame({"source": s, "target": t, colname_flow: x}) df[["source", "target"]] = df[["source", "target"]].astype(dtype) return df def get_directed( self, w=None, backward_positive: bool = True ) -> tuple: if self.cache.is_valid("directed_elements") is False: forward = self.ub > 0 backward = self.lb < 0 s_fw, t_fw = self.indices[forward, :].T t_bw, s_bw = self.indices[backward, :].T s = np.hstack((s_fw, s_bw)) t = np.hstack((t_fw, t_bw)) self.cache["directed_elements"] = (forward, backward, s, t) else: (forward, backward, s, t) = self.cache["directed_elements"] if w is not None: w_fw = w[forward] w_bw = w[backward] if backward_positive is False: w_bw = - w_bw # Stack weight similar to source, target w = np.hstack((w_fw, w_bw)) return s, t, w return s, t def get_duplicate_edges(self) -> np.ndarray: # Dubplicates -> s/t both are the same st = pd.Series([str(a) + "-" + str(b) for (a, b) in self.indices]) return np.where(st.duplicated())[0] def map_labels(self, d: dict) -> None: """Map edge labels by d.""" self.indices = np.vectorize(d.__getitem__)(self.labels).astype(self.dtype_int) def _delete_edges( self, del_idx, return_indices: bool = False ): del_idx = np.array(del_idx) # Delete edges in all numpy arrays self.labels = np.delete(self.labels, del_idx, axis=0) self.indices = np.delete(self.indices, del_idx, axis=0) self.bounds = np.delete(self.bounds, del_idx, axis=0) self.flow_directions = np.delete(self.flow_directions, del_idx, axis=0) # Recompute bounded edges for cs graph self.cache.set_invalid("directed_elements") if return_indices is True: if del_idx.dtype == "bool": return np.where(del_idx)[0].reshape(-1,) return del_idx.reshape(-1,) def _delete_nodes( self, nodes, return_indices: bool = False ): nodes = np.array(nodes).reshape(1, -1) # get indices of edges to delete idx_s = ((self.s.reshape(-1, 1) - nodes) == 0).any(axis=1) idx_t = ((self.t.reshape(-1, 1) - nodes) == 0).any(axis=1) del_idx = idx_s | idx_t return self._delete_edges(del_idx, return_indices=return_indices) def add_to_etree( self, root: et.Element, overwrite: bool = True, cost_writer=None, ) -> None: """Add edge data to XML Element. Parameters ---------- root : Element Element to which 'edges' will be appended to. overwrite : bool, default=True If True, existing 'edges' Element in `root` will be deleted. If False, edge data will be appended to the existing data. cost_writer: callable, optional Function that adds cost information for every edge. Will be called with ``cost_writer(edge_node, index, overwrite)`` where ``edge_node`` is xml Element, ``index`` is int and ``overwrite`` = overwrite. """ edges = root.find("edges") if overwrite is True and edges is not None: root.remove(edges) edges = root.find("edges") if edges is None: root.append(et.Element("edges")) for (i, edge) in enumerate(self.to_df().T.to_dict().values()): edge_node = et.SubElement(root.find("edges"), 'edge') edge_node.attrib['from'] = edge['source_lbl'] edge_node.attrib['to'] = edge['target_lbl'] edge_node.attrib['lb'] = str(edge['lb']) edge_node.attrib['ub'] = str(edge['ub']) if cost_writer is not None: cost_writer(edge_node, i, overwrite=overwrite) return root def _read_edge(edge_node): source_target = (edge_node.attrib["from"], edge_node.attrib["to"]) lb = parse_number(edge_node.attrib.get("lb", None)) ub = parse_number(edge_node.attrib.get("ub", None)) return source_target, (lb, ub) @classmethod def from_xml( cls, data, return_data: bool = False, **kwargs ): data = xml_find_root(data) edges = data.find("edges") if edges is None: return None source_target = [] bounds = [] for e in edges: st, b = Edges._read_edge(e) source_target.append(st) bounds.append(b) if return_data is True: return source_target, bounds return cls((source_target, bounds), **kwargs) from_xml.__func__.__doc__ = _doc.from_xml.__doc__ def make_save_dict( self, prefix: str = "", save_dict=None ) -> dict: if save_dict is None: save_dict = {} for k in ["labels", "indices", "bounds"]: save_dict[prefix + k] = getattr(self, k) return save_dict make_save_dict.__doc__ = _doc.make_save_dict.__doc__ def save_to_numpy( self, file: str, **kwargs ) -> None: save_dict = self.make_save_dict() save_dict.update(**kwargs) np.savez(file, **save_dict) save_to_numpy.__doc__ = _doc.save_to_numpy.__doc__ @classmethod def from_npz( cls, data, prefix: str = "", **kwargs ): if isinstance(data, str): data = np.load(data) edge_data = (data[prefix + "labels"], data[prefix + "indices"], data[prefix + "bounds"]) return cls(edge_data, **kwargs) from_npz.__func__.__doc__ = _doc.from_npz.__doc__ @property def flow_forward(self) -> np.ndarray: return np.where(self.flow_directions == 1)[0] @property def flow_backward(self) -> np.ndarray: return np.where(self.flow_directions == -1)[0] @property def flow_undirected(self) -> np.ndarray: return np.where(self.flow_directions == 0)[0] @property def lb(self) -> np.ndarray: """ndarray (m, ) of floats: lower bound.""" return self.bounds[:, 0] @property def ub(self) -> np.ndarray: """ndarray (m, ) of floats: upper bound.""" return self.bounds[:, 1] @property def s(self) -> np.ndarray: """ndarray (m, ) of int: source ids.""" return self.indices[:, 0] @property def t(self) -> np.ndarray: """ndarray (m, ) of int: target ids.""" return self.indices[:, 1] @property def source_lbl(self) -> np.ndarray: """ndarray (m, ) of str: sources labels.""" return self.labels[:, 0] @property def target_lbl(self) -> np.ndarray: """ndarray (m, ) of str: target labels.""" return self.labels[:, 1] @property def dtype_int(self): """dtype of int data.""" return self.indices.dtype @property def dtype_float(self): """dtype of float data.""" return self.bounds.dtype class Nodes: """Class that contains the nodes/vertices of a network. A Nodes object can be instantiated in several ways: Nodes(n) where ``n`` is a Nodes object. Nodes(nodes) where ``nodes`` is array_like and contains either node labels or node indices. If no indices are given, they are set automatically. Nodes(nodes, zone) where ``nodes`` and ``zone`` are array_like of shape (n, ). ``zone`` must be boolean array denoting if a node is a zone, mostly used for traffic networks. Nodes(nodes, xy, zone) where ``nodes`` and ``zone`` are array_like of shape (n, ) and ``xy`` is array_like of shape (n, 2) and contains the coordinates of the nodes. Nodes(node_labels, node_indices, xy, zone) where ``node_labels`` and ``node_indices`` and ``zone`` are are array_like of shape (n, ) and ``xy`` is array_like of shape (n, 2). Parameters ---------- data : node_data Input data. dtype_float : dtype, default=numpy.float_ Datatype for X and Y coordinates. dtype_int : dtype, default=int Datatype for node indices. map_labels: None, bool, dict, or callable, default=True Determines mapping of labels to indices if no indices are given or vice versa. If ``None`` or ``False``, indices / labels are set to -9999 / 'invalid'. If ``dict``, mapping by this dict. If ``True``, indices are set to 0, 1, ..., n-1, labels to indices as str. If ``callable``, use callable with signature ``indices = callable(labels)`` or ``labels = callable(indices)``. copy : bool, default=False Whether to create a copy of the inputs in data. Attributes ---------- index node zone has_zones x y """ def __init__( self, data, dtype_float=None, dtype_int=None, map_labels=True, # optional copy: bool = False, ) -> None: # Collect kwargs kw = { "map_labels": map_labels, "dtype_float": dtype_float, "dtype_int": dtype_int, "copy": copy, } if isinstance(data, Nodes): d = (data.labels, data.indices, data.xy, data.zone) return self.__init__(d, dtype_float=data.dtype_float, dtype_int=data.dtype_int) elif isinstance(data, tuple): if len(data) == 4: pass elif len(data) == 3: # (labels or indices, xy, zone) node, xy, zone = data node = np.array(node) if node.dtype.kind in {'U', 'S'}: # Case Labels if map_labels is True: # Automap labels indices = np.arange(len(node)) elif map_labels is None or map_labels is False: indices = np.full(len(node), ID_UNMAPPED, dtype=int) else: indices = map_labels(node) return self.__init__((node, indices, xy, zone), **kw) elif issubclass(node.dtype.type, numbers.Integral): if np.array_equal(np.sort(node), np.arange(len(node))) is False: raise ValueError(f"Indices must of integers from 0 to {len(node) - 1} in any order.") if map_labels is True: labels = node.astype(str) elif map_labels is None or map_labels is False: # Set to invalid indices labels = np.full(len(node), "None") else: labels = map_labels(node) kw["dtype_int"] = node.dtype return self.__init__((labels, node, xy, zone), **kw) else: raise ValueError(f"Nodes must be ints or strings, are: {node.dtype}.") elif len(data) == 2: # (labels or indices, xy) zone = np.full(len(data[0]), False, dtype=bool) return self.__init__((*data, zone), **kw) else: raise ValueError("TODO") else: # Only labels or Id's specified data = np.array(data, copy=False) return self.__init__((data, None), **kw) # Handle datatypes if dtype_float is None: dtype_float = np.float64 if dtype_int is None: dtype_int = int self.dtype_int = dtype_int self.dtype_float = dtype_float labels, indices, xy, zone = data indices = np.argsort(indices) self.labels = np.array(labels, dtype=str, copy=copy)[indices] if isinstance(zone, bool): zone = [zone] * len(self.labels) self.zone = np.array(zone, dtype=bool, copy=copy)[indices] if xy is not None: xy = np.array(xy) if xy.shape != (len(self.labels), 2): raise ValueError(f"Coordinates have wrong shape: {xy.shape}, should be {len(labels), 2}.") self.xy = np.array(xy, dtype=dtype_float, copy=copy)[indices] else: self.xy = None if (len(self.labels) == len(indices) == len(self.zone)) is False: raise ValueError( "Invalid shape of node data, " f"labels: {self.labels.shape}, " f"indices: {indices.shape}, " f"zone: {self.zone.shape}." ) self.set_mappings() def __len__(self) -> int: return len(self.labels) def __eq__(self, other) -> bool: for att in ["labels", "indices", "zone"]: if np.array_equal(getattr(self, att), getattr(other, att)) is False: return False return True def set_mappings(self) -> None: """(Re)-set labels <-> indices mappings.""" self.lbl2id = dict(zip(self.labels, self.indices)) self.id2lbl = dict(zip(self.indices, self.labels)) def get_pos(self) -> dict: if self.xy is None: raise ValueError("No node coordinates set.") return dict(zip(self.labels, self.xy)) def delete_nodes( self, nodes, return_indices: bool = False ): """Delete nodes from Nodes object. Parameters ---------- nodes : int, ndarray of int Indices of nodes to delete. return_indices : bool, default=False If True, node indices of deleted nodes are returned. Returns ------- ndarray, optional If return_indices is True, node indices of deleted nodes are returned. See Also -------- numpy.delete """ self.labels = np.delete(self.labels, nodes) self.zone = np.delete(self.zone, nodes) if self.xy is not None: self.xy = np.delete(self.xy, nodes, axis=0) if return_indices is True: return np.array(nodes) def to_df( self, **kwargs ) -> pd.DataFrame: """Get object as pandas DataFrame. Parameters ---------- **kwargs : keyword arguments, optional Keyword arguments passed to pd.DataFrame constructor. Returns ------- pandas.DataFrame Nodes data with node label, coordinates (x, y) and zone (bool). """ df = pd.DataFrame({"label": self.labels, "zone": self.zone}, **kwargs) df["label"] = df["label"].astype(str) df["zone"] = df["zone"].astype(bool) if self.xy is not None: df[["x", "y"]] = self.xy.astype(self.dtype_float) return df def add_to_etree( self, root: et.Element, overwrite: bool = True ): """Add node data to xml.etree.ElementTree.Element. Parameters ---------- root : xml.etree.ElementTree.Element Element to which 'nodes' will be appended to. overwrite : bool, default=True If True, existing 'nodes' Element in root will be deleted. If False, node data will be appended to the existing data. """ nodes = root.find("nodes") if overwrite is True and nodes is not None: root.remove(nodes) nodes = root.find("nodes") if nodes is None: root.append(et.Element("nodes")) for node in self.to_df().T.to_dict().values(): n_node = et.SubElement(root.find("nodes"), 'node') n_node.attrib['node'] = node['label'] if 'x' in node: n_node.attrib['x'] = str(node['x']) n_node.attrib['y'] = str(node['y']) if node['zone'] is True: n_node.attrib['zone'] = "true" return root @classmethod def from_edges( cls, edges: Edges, **kw): d = dict(zip(edges.labels.ravel(), edges.indices.ravel())) labels = np.array(list(d.keys())) indices = np.array(list(d.values())) if np.array_equal(np.sort(indices), np.arange(len(indices))) is False: raise ValueError("Invalid edge indices.") return cls(labels[indices.argsort()], **kw) @classmethod def from_xml( cls, data, return_data: bool = False, **kwargs ): data = xml_find_root(data) nodes = data.find("nodes") if nodes is None: return None lbl = [] xy = [] zone = [] for n in nodes: lbl.append(n.get('node')) x = parse_number(n.get('x', 0.0)) y = parse_number(n.get('y', 0.0)) xy.append([x, y]) zone.append(n.get('zone', 'false').strip().lower() == 'true') if return_data is True: return (lbl, xy, zone) return cls((lbl, xy, zone), **kwargs) from_xml.__func__.__doc__ = _doc.from_xml.__doc__ def make_save_dict(self, prefix: str = "", save_dict=None) -> dict: if save_dict is None: save_dict = {} for k in ["labels", "zone", "xy"]: save_dict[prefix + k] = getattr(self, k) return save_dict make_save_dict.__doc__ = _doc.make_save_dict.__doc__ def save_to_numpy( self, file: str, **kwargs ) -> None: save_dict = self.make_save_dict() save_dict.update(kwargs) np.savez(file, **save_dict) save_to_numpy.__doc__ = _doc.save_to_numpy.__doc__ @classmethod def from_npz( cls, data, prefix: str = "", **kwargs, ): if isinstance(data, str): data = np.load(data) # make empty edge object and fill with data node_data = ( data[prefix + "labels"], data[prefix + "xy"], data[prefix + "zone"], ) return cls(node_data, **kwargs) from_npz.__func__.__doc__ = _doc.from_npz.__doc__ def _get_node(self, idx): return {att: getattr(self, att)[idx] for att in ["index", "node", "x", "z", "zone"]} @property def indices(self) -> np.ndarray: """ndarray (m, ) of int: node indices.""" return np.arange(len(self.labels), dtype=self.dtype_int) @property def has_zones(self) -> bool: """bool: Whether Nodes object has any zone.""" return self.zone.any() @property def x(self) -> np.ndarray: """ndarray (m, ) of floats: X coordinates.""" if self.xy is None: raise AttributeError("Nodes has no coordinates.") return self.xy[:, 0] @property def y(self) -> np.ndarray: """ndarray (m, ) of floats: Y coordinates.""" if self.xy is None: raise AttributeError("Nodes has no coordinates.") return self.xy[:, 1] class Shared: """Class that acts as a shared object for a Network. Consists mainly of nodes and edges object, handles label and index mappings. Parameters ---------- edge_data : tuple of ndarray Data to construct Edges object. node_data : array_like or tuple of ndarray, optional Data to construct Nodes object. dtype_float : dtype, default=numpy.float_ Datatype for all float ndarray. dtype_int : dtype, default=int Datatype for all int ndarray. See Also -------- Edges Nodes """ def __init__( self, edge_data, node_data=None, dtype_float=np.float_, dtype_int=int, **kwargs ) -> None: if node_data is None: self.edges = Edges(edge_data, dtype_float=dtype_float, dtype_int=dtype_int, **kwargs) self.nodes = Nodes.from_edges(self.edges, dtype_float=dtype_float, dtype_int=dtype_int) else: self.nodes = Nodes(node_data, dtype_float=dtype_float, dtype_int=dtype_int) self.edges = Edges(edge_data, map_labels_to_indices=self.nodes.lbl2id, map_indices_to_labels=self.nodes.id2lbl, dtype_float=dtype_float, dtype_int=dtype_int, **kwargs) self._update_edges() self.cache = Cache() def __eq__(self, other) -> bool: for att in ["edges", "nodes"]: if getattr(self, att) != getattr(other, att): return False return True def update(self): """Update internal node and edge mappings.""" self._update_nodes() self._update_edges() def reset_cache(self, hard: bool = False) -> None: self.cache.reset() def _update_nodes(self): self.nodes.set_mappings() def _update_edges(self): self._set_edge_indices() def _set_edge_indices(self) -> None: self.edges.map_labels(self.node2id) self._set_edge_id_mapping() def _set_edge_id_mapping(self) -> None: # create mapping (nodeid, nodeid) -> edgeid k_id = [tuple(row) for row in self.edges.indices] self._nodes2edge = dict(zip(k_id, range(len(k_id)))) def _get_dtypes( self, dtype_int=None, dtype_float=None ) -> tuple: if dtype_int is None: dtype_int = self.dtype_int if dtype_float is None: dtype_float = self.dtype_float return (dtype_int, dtype_float) def delete_edges( self, edges, update: bool = True, **kwargs ): """Delete edge(s) from Edges object. Parameters ---------- edges : int or ndarray of ints Indices to delete. update : bool, default=True Whether to reset mapping of node labels to node ids. return_indices, default=False If True, edge indices of deleted edges are returned. Returns ------- ndarray, optional If return_indices is True, edge indices of deleted nodes are returned. See Also -------- numpy.delete """ self.cache.set_invalid("gamma", "gamma_T") ret = self.edges._delete_edges(edges, **kwargs) if update is True: self._update_edges() return ret def delete_nodes( self, nodes, update: bool = True, is_label: bool = True, **kwargs ): """Delete node(s) from Nodes object. Parameters ---------- nodes : int, array of ints, str, or array of str Indices or labels of nodes to delete. update : bool, default=True Whether to reset mapping of node labels to node ids. is_label : bool, default=True Whether to delete by label or internal node index. return_indices : bool, default=False If True, node indices of deleted nodes are returned. Returns ------- ndarray, optional If return_indices is True, node indices of deleted nodes are returned. See Also -------- numpy.delete """ if is_label is True: nodes = self.get_node_id(nodes, vectorize=True) ret = self.nodes.delete_nodes(nodes, **kwargs) if update is True: self._update_nodes() return ret def delete_nodes_in_edges( self, nodes, update: bool = True, is_label: bool = False, **kwargs ): """Delete node(s) from Edges object. An edge is deleted if either source or target id equals that of a node in nodes. Parameters ---------- nodes : int, array of ints, str, or array of str Nodes to delete. return_indices : bool, default=False If True, edge indices of deleted edges are returned. Returns ------- ndarray, optional If return_indices is True, edge indices of deleted edges are returned. """ self.cache.set_invalid("gamma", "gamma_T") if is_label is True: nodes = self.get_node_id(nodes, vectorize=True) ret = self.edges._delete_nodes(nodes, **kwargs) if update is True: self._update_edges() return ret def incidence_matrix(self, *args, **kwargs) -> sps.spmatrix: """Alias for :func:`Shared.Gamma`.""" return self.Gamma(*args, **kwargs) def Gamma( self, return_as: str = 'csr', transpose: bool = False, ) -> sps.spmatrix: """Return the incidence matrix Gamma of the network. Gamma is of shape (m, n) and is defined as:: Gamma[v, e] = 1 if edge e enters vertex v, Gamma[v, e] = -1 if edge e leaves vertex v, Gamma[v, e] = 0 otherwise. Parameters ---------- return_as : str, default='csr' Sparse matrix type to be returned. transpose : bool, default=True Whether to transpose Gamma matrix. Returns ------- Gamma : spmatrix Incidence matrix of the network. See Also -------- scipy.sparse References ---------- https://en.wikipedia.org/wiki/Incidence_matrix Examples -------- Sioux-Falls: >>> import paminco >>> net = paminco.net.load_sioux() >>> net.Gamma().toarray()[:5, :5] array([[-1, -1, 1, 0, 1], [ 1, 0, -1, -1, 0], [ 0, 1, 0, 0, -1], [ 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0]]) """ # Rebuild gamma if neccessary if self.cache.is_valid("gamma") is False: i = self.edges.indices.T.ravel() j = np.hstack([np.array(range(self.m))] * 2) vals = np.hstack(([-1] * self.m, [1] * self.m)) coo = sps.coo_matrix((vals, (i, j)), shape=(self.n, self.m)) # Cache gamma and transpose gamma = sparse_format(coo, return_as) self.cache["gamma"] = gamma self.cache["gamma_T"] = gamma.T.tocsr() if transpose: return sparse_format(self.cache["gamma_T"], return_as) return sparse_format(self.cache["gamma"], return_as) def adjacency_matrix(self, *args, **kw) -> sps.csr_matrix: """Alias for :func:`~Shared.csgraph`.""" return self.csgraph(*args, **kw) def csgraph( self, weight=None, respect_bounds: bool = True, backward_positive: bool = False, dtype=None, ) -> sps.csr_matrix: """Get the compressed sparse graph, shape (n, n). A network/graph with n nodes can be represented by an node to node adjacency matrix H. If there is a connection from node i to node j, then H[i, j] = w, where w is the weight of the connection. Parameters ---------- weight : ndarray Weight on network edges, shape (m, ). respect_bounds : bool, default=True If True, an undirected edge from s to t with ``lb<0 and ub>0`` will lead to separate entries in H. I.e., H[s, t] = w and H[t, s] = w. backward_positive : bool, default=False Whether to negate weight for undirected edges if ``respect_bounds`` is True. I.e., H[s, t] = w and H[t, s] = -w. dtype : dtype, optional Datatype of csgraph. Returns ------- csr_matrix Compressed sparse network graph. Examples -------- SiouxFalls: >>> import paminco >>> net = paminco.net.load_sioux() >>> H = net.shared.csgraph(np.arange(net.m) + 1) >>> H[:5, :5].toarray() array([[ 0., 1., 2., 0., 0.], [ 3., 0., 0., 0., 0.], [ 5., 0., 0., 6., 0.], [ 0., 0., 8., 0., 9.], [ 0., 0., 0., 11., 0.]]) """ if dtype is None: dtype = self.dtype_float if weight is None: weight = np.ones(self.m) if respect_bounds is True: s, t, w = self.edges.get_directed(weight, backward_positive=backward_positive) else: if self.cache.is_valid("csgraph") is False: s, t = self.edges.indices.T w = weight return sps.csr_matrix((w, (s, t)), shape=(self.n, self.n), dtype=dtype) def get_edge_id( self, nodes ): """Map node indices to edge indices. Parameters ---------- nodes : (sequence of) tuple (int, int) Node indices (source, target) to be mapped to edge indices. Returns ------- int, or list of int Edge indices for nodes. """ if isinstance(nodes, tuple): return self.nodes2edge[nodes] return [self.get_edge_id(n) for n in nodes] def get_node_id( self, nodes, vectorize: bool = False ): """Map labels in nodes to node indices. Parameters ---------- nodes : str or array_like Node labels to map to node indices. vectorize : bool, default=False Whether to vectorize over ``nodes`` (must be ndarray). Better performance for larger arrays. Returns ------- int, list or ndarray Node indices. ``int`` : If nodes is str. ``ndarray`` : If nodes is ndarray and vectorize is True. ``list of int`` : else. """ # Single entry if isinstance(nodes, str): return self.node2id[nodes] # Vectorize for arrays, better performance for larger arrays if (isinstance(nodes, np.ndarray) and (vectorize is True or nodes.ndim > 1)): return np.vectorize(self.node2id.__getitem__)(nodes) if is_iterable(nodes): return [self.node2id[n] for n in nodes] return ValueError("'nodes' must be either str, iterable or array.") def get_node_label( self, nodes, vectorize: bool = False ): """Map indices in nodes to node labels. Parameters ---------- nodes : int or array_like Node indices to map to node labels. vectorize : bool, default=False Whether to vectorize over ``nodes`` (must be ndarray). Better performance for larger arrays. Returns ------- str, list or ndarray Node label(s). ``str`` : If nodes is int. ``ndarray`` : If nodes is ndarray and vectorize is True. ``list of str`` : else. Raises ------ ValueError: Nodes is neither int, ndarray or iterable. """ # Reverse dict d = self.nodes.id2lbl # Case single entry if is_int(nodes): return d[nodes] # Vectorize for numpy arrays, better performance for larger arrays if (isinstance(nodes, np.ndarray) and (vectorize is True or nodes.ndim > 1)): return np.vectorize(d.__getitem__)(nodes) if is_iterable(nodes): return [d[n] for n in nodes] return ValueError("'nodes' must be either int, iterable or array.") @classmethod def from_xml(cls, data, **kwargs): edges_data = Edges.from_xml(data, return_data=True) nodes_data = Nodes.from_xml(data, return_data=True) return cls(edges_data, nodes_data, **kwargs) from_xml.__func__.__doc__ = _doc.from_xml.__doc__ def make_save_dict( self, prefix: str = "", save_dict=None ) -> dict: sd = self.edges.make_save_dict(prefix=prefix + "edge_", save_dict=save_dict) sd = self.nodes.make_save_dict(prefix=prefix + "node_", save_dict=sd) return sd make_save_dict.__doc__ = _doc.make_save_dict.__doc__ def save_to_numpy( self, file: str, **kwargs ) -> None: save_dict = self.make_save_dict() save_dict.update(kwargs) np.savez(file, **save_dict) save_to_numpy.__doc__ = _doc.save_to_numpy.__doc__ @classmethod def from_npz( cls, data, prefix: str = "", kw_edges=None, kw_nodes=None, ): """Load Shared from .npz file. Parameters ---------- data : str or NpzFile Filename as str or :class:`~numpy.lib.npyio.NpzFile`. prefix : str, default="" Object data is stored with ``key = (prefix + internal_name)``. kw_edges : keyword arguments, optional Further arguments passed to edge constructor. kw_nodes : keyword arguments, optional Further arguments passed to nodes constructor. Returns ------- s : Shared Object to be shared among network objects. """ if kw_edges is None: kw_edges = {} if kw_nodes is None: kw_nodes = {} if isinstance(data, str): data = np.load(data) nodes = Nodes.from_npz(data, prefix + "node_", **kw_nodes) edges = Edges.from_npz(data, prefix + "edge_", **kw_edges) return cls(edges, nodes) @property def flow_direction(self) -> FlowDirection: """The direction of flow on the edges. See Also -------- paminco.net.shared.FlowDirection """ return self.edges.flow_dir @property def n(self) -> int: """Get number of nodes in network.""" return len(self.nodes) @property def m(self) -> int: """Get number of edges in network.""" return len(self.edges) @property def nodes2edge(self) -> dict: """Get dict that maps (node_id, node_id) -> edge_id.""" return self._nodes2edge @property def node2id(self) -> dict: """Get dict that maps node label (str) -> node id (int).""" return self.nodes.lbl2id @property def dtype_int(self): """Get int data type, used for node ids in network.""" return self.edges.dtype_int @property def dtype_float(self): """Get float dtype for network.""" return self.edges.dtype_float
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import functools import gc import itertools import logging import pathlib import shutil import hetnetpy.hetnet import hetnetpy.matrix import hetnetpy.permute import hetnetpy.readwrite import numpy import pandas import scipy.sparse import hetmatpy.degree_weight import hetmatpy.matrix def hetmat_from_graph( graph, path, save_metagraph=True, save_nodes=True, save_edges=True ): """ Create a hetmat.HetMat from a hetnetpy.hetnet.Graph. """ assert isinstance(graph, hetnetpy.hetnet.Graph) hetmat = HetMat(path, initialize=True) hetmat.metagraph = graph.metagraph # Save metanodes metanodes = list(graph.metagraph.get_nodes()) for metanode in metanodes: path = hetmat.get_nodes_path(metanode) rows = list() node_to_position = hetnetpy.matrix.get_node_to_position(graph, metanode) for node, position in node_to_position.items(): rows.append((position, node.identifier, node.name)) node_df = pandas.DataFrame(rows, columns=["position", "identifier", "name"]) path = hetmat.get_nodes_path(metanode) node_df.to_csv(path, index=False, sep="\t") # Save metaedges metaedges = list(graph.metagraph.get_edges(exclude_inverts=True)) for metaedge in metaedges: rows, cols, matrix = hetnetpy.matrix.metaedge_to_adjacency_matrix( graph, metaedge, dense_threshold=1 ) path = hetmat.get_edges_path(metaedge, file_format=None) save_matrix(matrix, path) return hetmat def hetmat_from_permuted_graph(hetmat, permutation_id, permuted_graph): """ Assumes subdirectory structure and that permutations inherit nodes but not edges. """ permuted_hetmat = initialize_permutation_directory(hetmat, permutation_id) permuted_hetmat = hetmat_from_graph( permuted_graph, permuted_hetmat.directory, save_metagraph=False, save_nodes=False, ) return permuted_hetmat def initialize_permutation_directory(hetmat, permutation_id): """ Initializes the directory structure of a HetMat permutation. Parameters ---------- hetmat : HetMat permutation_id : str Returns ------- HetMat """ if not hetmat.permutations_directory.is_dir(): hetmat.permutations_directory.mkdir() directory = hetmat.permutations_directory.joinpath(f"{permutation_id}.hetmat") if directory.is_dir(): # If directory exists, back it up using a .bak extension backup_directory = directory.with_name(directory.name + ".bak") if backup_directory.is_dir(): shutil.rmtree(backup_directory) shutil.move(directory, backup_directory) permuted_hetmat = HetMat(directory, initialize=True) permuted_hetmat.is_permutation = True permuted_hetmat.metagraph_path.symlink_to("../../metagraph.json") permuted_hetmat.nodes_directory.rmdir() permuted_hetmat.nodes_directory.symlink_to("../../nodes", target_is_directory=True) return permuted_hetmat def read_matrix(path, file_format="infer"): path = str(path) if file_format == "infer": if path.endswith(".sparse.npz"): file_format = "sparse.npz" if path.endswith(".npy"): file_format = "npy" if file_format == "infer": raise ValueError("Could not infer file_format for {path}") if file_format == "sparse.npz": # https://docs.scipy.org/doc/scipy-1.0.0/reference/generated/scipy.sparse.load_npz.html return scipy.sparse.load_npz(path) if file_format == "npy": # https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.load.html return numpy.load(path) raise ValueError(f"file_format={file_format} is not supported.") def save_matrix(matrix, path): """ Save a matrix to a the file specified by path. Path should not include it's extension which is inferred. """ path = pathlib.Path(path) if not path.parent.exists(): path.parent.mkdir() path = str(path) if isinstance(matrix, numpy.ndarray): if not path.endswith(".npy"): path += ".npy" numpy.save(path, matrix) elif scipy.sparse.issparse(matrix): if not path.endswith(".sparse.npz"): path += ".sparse.npz" scipy.sparse.save_npz(path, matrix, compressed=True) def read_first_matrix(specs, delete_failures=False): """ Attempt to read each path provided by specs, until one exists. If none of the specs point to an existing path, raise a FileNotFoundError. specs should be a list where each element is a dictionary specifying a potential path from which to read a matrix. Currently, the spec dictionary supports the following keys: - path: path to the file - transpose: whether to transpose the file after reading it. If omitted, then False. - file_format: format of the matrix. If omitted, then infer. """ paths = list() for spec in specs: path = pathlib.Path(spec["path"]) paths.append(str(path)) if not path.is_file(): continue transpose = spec.get("transpose", False) file_format = spec.get("file_format", "infer") try: matrix = read_matrix(path, file_format=file_format) except Exception as error: logging.warning(f"Error reading matrix at {path}:\n{error}") if delete_failures: path.unlink() logging.warning(f"Deleting file at {path}") continue if transpose: matrix = matrix.transpose() return matrix raise FileNotFoundError( "No matrix files found at the specified paths:\n" + "\n".join(paths) ) compression_extension = { "gzip": ".gz", "bz2": ".bz2", "zip": ".zip", "xz": ".xz", None: "", } class HetMat: # Supported formats for nodes files nodes_formats = { "tsv", # 'feather', # 'pickle', # 'json', } # Supported formats for edges files edges_formats = { "npy", "sparse.npz", # 'tsv', } def __init__(self, directory, initialize=False): """ Initialize a HetMat with its MetaGraph. """ self.directory = pathlib.Path(directory) self.metagraph_path = self.directory.joinpath("metagraph.json") self.nodes_directory = self.directory.joinpath("nodes") self.edges_directory = self.directory.joinpath("edges") self.path_counts_directory = self.directory.joinpath("path-counts") self.path_counts_cache = None # Permutations should set is_permutation=True self.is_permutation = False self.permutations_directory = self.directory.joinpath("permutations") if initialize: self.initialize() def initialize(self): """ Initialize the directory structure. This function is intended to be called when creating new HetMat instance on disk. """ # Create directories directories = [ self.directory, self.nodes_directory, self.edges_directory, ] for directory in directories: if not directory.is_dir(): directory.mkdir() @property @functools.lru_cache() def permutations(self): """ Return a dictionary of permutation name to permutation directory. Assumes permutation name is the directory name minus its .hetmat extension. """ permutations = {} for directory in sorted(self.permutations_directory.glob("*.hetmat")): if not directory.is_dir(): continue permutation = HetMat(directory) permutation.is_permutation = True name, _ = directory.name.rsplit(".", 1) permutations[name] = permutation return permutations def permute_graph( self, num_new_permutations=None, namer=None, start_from=None, multiplier=10, seed=0, ): """ Generate and save permutations of the HetMat adjacency matrices. Parameters ---------- num_new_permutations : int The number of new, permuted HetMats to generate namer : generator Yields the names of new permutations. Cannot pass names of existing permutations start_from : str Name of permutation to use as starting point. For multiple permutations, the first permutation starts from start_from, and future permutations continue from the previous one. multiplier : int How many attempts to make when cross-swapping edges. seed : int Random seed for generating new permutations """ if namer is None: # If no namer given, continue increasing names by one for new permutations namer = (f"{x:03}" for x in itertools.count(start=1)) stat_dfs = list() for _ in range(num_new_permutations): permutation_name = next(namer) new_hetmat = initialize_permutation_directory(self, permutation_name) if start_from is None: start_from = self elif isinstance(start_from, str): start_from = self.permutations[start_from] assert isinstance(start_from, HetMat) metaedges = list(self.metagraph.get_edges(exclude_inverts=True)) for metaedge in metaedges: rows, cols, original_matrix = start_from.metaedge_to_adjacency_matrix( metaedge, dense_threshold=1 ) is_directed = metaedge.direction != "both" permuted_matrix, stats = hetmatpy.matrix.permute_matrix( original_matrix, directed=is_directed, multiplier=multiplier, seed=seed, ) path = new_hetmat.get_edges_path(metaedge, file_format=None) save_matrix(permuted_matrix, path) stat_df = pandas.DataFrame(stats) stat_df["metaedge"] = metaedge stat_df["abbrev"] = metaedge.get_abbrev() stat_df["permutation"] = permutation_name stat_dfs.append(stat_df) start_from = permutation_name seed += 1 self.permutations[permutation_name] = new_hetmat return pandas.concat(stat_dfs) @property @functools.lru_cache() def metagraph(self): """ HetMat.metagraph is a cached property. Hence reading the metagraph from disk should only occur once, the first time the metagraph property is accessed. See https://stackoverflow.com/a/19979379/4651668. If this method has issues, consider using cached_property from https://github.com/pydanny/cached-property. """ return hetnetpy.readwrite.read_metagraph(self.metagraph_path) @metagraph.setter def metagraph(self, metagraph): """ Set the metagraph property by writing the metagraph to disk. """ hetnetpy.readwrite.write_metagraph(metagraph, self.metagraph_path) def get_nodes_path(self, metanode, file_format="tsv"): """ Get the path for the nodes file for the specified metanode. Setting file_format=None returns the path without any extension suffix. """ metanode = self.metagraph.get_metanode(metanode) path = self.nodes_directory.joinpath(f"{metanode}") if file_format is not None: path = path.with_name(f"{path.name}.{file_format}") return path def get_edges_path(self, metaedge, file_format="npy"): """ Get the path for the edges file for the specified metaedge. Setting file_format=None returns the path without any extension suffix. """ metaedge_abbrev = self.metagraph.get_metaedge(metaedge).get_abbrev() path = self.edges_directory.joinpath(f"{metaedge_abbrev}") if file_format is not None: path = path.with_name(f"{path.name}.{file_format}") return path def get_path_counts_path(self, metapath, metric, damping, file_format): """ Setting file_format=None returns the path without any extension suffix. Supported metrics are 'dwpc' and 'dwwc'. """ damping = float(damping) path = self.path_counts_directory.joinpath(f"{metric}-{damping}/{metapath}") if file_format is not None: path = path.with_name(f"{path.name}.{file_format}") return path def get_running_degree_group_path( self, metapath, metric, damping, extension=".tsv.gz" ): """ Get path for degree-grouped permutatation running metrics. Must specify extension. """ damping = float(damping) path = self.directory.joinpath( "adjusted-path-counts", f"{metric}-{damping}", "degree-grouped-permutations", f"{metapath}{extension}", ) return path def get_metapath_summary_path(self, metapath, metric, damping, compression=None): damping = float(damping) compr = compression_extension[compression] path = self.directory.joinpath( "adjusted-path-counts", f"{metric}-{damping}", "adjusted-dwpcs", f"{metapath}.tsv{compr}", ) return path @functools.lru_cache() def get_node_identifiers(self, metanode): """ Returns a list of node identifiers for a metapath """ path = self.get_nodes_path(metanode, file_format="tsv") node_df = pandas.read_csv(path, sep="\t") return list(node_df["identifier"]) @functools.lru_cache() def count_nodes(self, metanode): nodes = self.get_node_identifiers(metanode) return len(nodes) def metaedge_to_adjacency_matrix( self, metaedge, dtype=None, dense_threshold=None, file_formats=["sparse.npz", "npy"], ): """ file_formats sets the precedence of which file to read in """ metaedge = self.metagraph.get_metaedge(metaedge) specs = list() configurations = itertools.product(file_formats, (True, False)) for file_format, invert in configurations: path = self.get_edges_path( metaedge=metaedge.inverse if invert else metaedge, file_format=file_format, ) spec = {"path": path, "transpose": invert, "file_format": file_format} specs.append(spec) matrix = read_first_matrix(specs) if dense_threshold is not None: matrix = hetnetpy.matrix.sparsify_or_densify( matrix, dense_threshold=dense_threshold ) if dtype is not None: matrix = matrix.astype(dtype) row_ids = self.get_node_identifiers(metaedge.source) col_ids = self.get_node_identifiers(metaedge.target) return row_ids, col_ids, matrix def read_path_counts( self, metapath, metric, damping, file_formats=["sparse.npz", "npy"] ): """ Read matrix with values of a path-count-based metric. Attempts to locate any files with the matrix (or with trivial transformations). """ category = hetmatpy.degree_weight.categorize(metapath) metrics = [metric] if metric == "dwpc" and category == "no_repeats": metrics.append("dwwc") if metric == "dwwc" and category == "no_repeats": metrics.append("dwpc") specs = list() configurations = itertools.product( file_formats, metrics, (True, False), ) for file_format, metric, invert in configurations: path = self.get_path_counts_path( metapath=metapath.inverse if invert else metapath, metric=metric, damping=damping, file_format=file_format, ) spec = {"path": path, "transpose": invert, "file_format": file_format} specs.append(spec) row_ids = self.get_node_identifiers(metapath.source()) col_ids = self.get_node_identifiers(metapath.target()) matrix = read_first_matrix(specs) return row_ids, col_ids, matrix def clear_caches(self): """ Clear cached assets of this HetMat and force garbage collection. """ # See workaround for methods with @property and @lru_cache decoration # https://stackoverflow.com/a/45283290/4651668 for lru_cached_function in [ type(self).permutations.fget, type(self).metagraph.fget, self.get_node_identifiers, self.count_nodes, ]: lru_cached_function.cache_clear() self.path_counts_cache = None gc.collect()
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r""" ========================================================= Utilities Abaqus (:mod:`desicos.abaqus.abaqus_functions`) ========================================================= .. currentmodule:: desicos.abaqus.abaqus_functions Includes all utilities functions that must be executed from Abaqus. """ from __future__ import absolute_import import math import numpy as np from .constants import (TOL, FLOAT, COLORS, COLOR_WHINE, COLOR_DARK_BLUE, COLOR_BLACK) from . import utils def configure_session(): """Improve layout and colors of the current figures in visualization """ from abaqus import session from abaqusConstants import (ON, OFF, SMALL, DASHED, OUTSIDE, HOLLOW_CIRCLE, DECIMAL, INCREMENT) plot_names=session.xyDataObjects.keys() if not 'XYPlot-1' in session.xyPlots.keys(): xyp=session.XYPlot('XYPlot-1') else: xyp=session.xyPlots['XYPlot-1'] chartName=xyp.charts.keys()[0] chart=xyp.charts[chartName] tmp=session.xyDataObjects.keys() if len(tmp)==0: return xy1=session.xyDataObjects[tmp[0]] c1=session.Curve(xyData=xy1) chart.setValues(curvesToPlot=(c1,),) session.viewports['Viewport: 1'].setValues(displayedObject=xyp) chart=session.charts['Chart-1'] chart.minorAxis1GridStyle.setValues(show=True) chart.majorAxis1GridStyle.setValues(show=True) chart.majorAxis1GridStyle.setValues(style=DASHED) chart.minorAxis2GridStyle.setValues(show=True) chart.majorAxis2GridStyle.setValues(show=True) chart.majorAxis2GridStyle.setValues(style=DASHED) chart.gridArea.style.setValues(fill=False) chart.legend.setValues(show=False) # necessary to update legend values chart.legend.setValues(show=True) chart.legend.area.setValues(inset=True) chart.legend.area.setValues(originOffset=(0.,0.)) chart.legend.area.style.setValues(fill=True) chart.legend.textStyle.setValues( font='-*-arial narrow-medium-r-normal-*-*-480-*-*-p-*-*-*') for name in plot_names: c=session.Curve(xyData=session.xyDataObjects[name]) chart=session.xyPlots['XYPlot-1'].charts['Chart-1'] chart.setValues(curvesToPlot=(c,)) chart.fitCurves(fitAxes1=True, fitAxes2=True) curve=session.charts['Chart-1'].curves[name] curve.curveOptions.setValues(showSymbol=ON) curve.curveOptions.setValues(symbolSize=SMALL) curve.lineStyle.setValues(thickness=1.6) curve.symbolStyle.setValues(size=5, marker=HOLLOW_CIRCLE) ax=chart.axes1[0] ay=chart.axes2[0] ax.labelStyle.setValues( font='-*-arial narrow-bold-r-normal-*-*-480-*-*-p-*-*-*', color=COLOR_BLACK) ax.titleStyle.setValues( font='-*-arial narrow-bold-r-normal-*-*-480-*-*-p-*-*-*', color=COLOR_BLACK) ay.labelStyle.setValues( font='-*-arial narrow-bold-r-normal-*-*-480-*-*-p-*-*-*', color=COLOR_BLACK) ay.titleStyle.setValues( font='-*-arial narrow-bold-r-normal-*-*-480-*-*-p-*-*-*', color=COLOR_BLACK) ax.setValues(tickPlacement=OUTSIDE) ax.axisData.setValues(labelFormat=DECIMAL, labelNumDigits=0, minorTickCount=4,) ay.setValues(tickPlacement=OUTSIDE) ay.axisData.setValues(labelFormat=DECIMAL, labelNumDigits=0,) if ax.axisData.title.find('ispl')>-1: ax.axisData.setValues(labelNumDigits=1) if name.find('circumference') > -1: ax.axisData.setValues(tickMode=INCREMENT, tickIncrement=20, minorTickCount=0, minAutoCompute=False, minValue=-180, maxAutoCompute=False, maxValue=185) # if (name.find('FI_HSNFCCRT') > -1 or name.find('FI_HSNFTCRT') > -1 or name.find('FI_HSNMCCRT') > -1 or name.find('FI_HSNMTCRT') > -1 or name.find('FI_TSAIW') > -1): ay.axisData.setValues(labelNumDigits=1, minAutoCompute=False, minValue=0, maxAutoCompute=False, maxValue=2) curve.lineStyle.setValues(thickness=1.6, color=COLOR_WHINE) curve.curveOptions.setValues(showSymbol=OFF) ay.titleStyle.setValues(color=COLOR_WHINE) ay.labelStyle.setValues(color=COLOR_WHINE) # if (name.find('MS_HSNFCCRT') > -1 or name.find('MS_HSNFTCRT') > -1 or name.find('MS_HSNMCCRT') > -1 or name.find('MS_HSNMTCRT') > -1 or name.find('MS_TSAIW') > -1 or name.find('MS_MAX') > -1 or name.find('MS_MIN') > -1): ay.axisData.setValues(labelNumDigits=1, minAutoCompute=False, minValue=-0.5, maxAutoCompute=False, maxValue=1.0) curve.lineStyle.setValues(thickness=1.6, color=COLOR_DARK_BLUE) curve.curveOptions.setValues(showSymbol=OFF) ay.titleStyle.setValues(color=COLOR_DARK_BLUE) ay.labelStyle.setValues(color=COLOR_DARK_BLUE) def print_png(filename): """Print a png file from the current viewport Parameters ---------- filename : str The name of the output png file. """ from abaqus import session from abaqusConstants import PNG viewport=session.viewports[session.currentViewportName] session.printToFile(fileName=filename, format=PNG, canvasObjects=(viewport,)) def set_default_view(cc): """Set a default view in order to compare figures from different models Parameters ---------- cc : :class:`.ConeCyl` object """ from abaqusConstants import (USER_SPECIFIED, NODAL, COMPONENT, EXTRA_FINE, FREE, UNIFORM, CONTINUOUS, ON, OFF) odb=cc.attach_results() if not odb: print('No .odb file found for %s!' % cc.jobname) return dtm=odb.rootAssembly.datumCsyses[ 'ASSEMBLY__T-INSTANCECYLINDER-CSYSCYLINDER'] viewport=session.viewports[session.currentViewportName] viewport.odbDisplay.basicOptions.setValues( averageElementOutput=False, transformationType=USER_SPECIFIED, datumCsys=dtm) viewport.odbDisplay.setPrimaryVariable( variableLabel='U', outputPosition=NODAL, refinement=(COMPONENT, 'U1'),) viewport.obasicOptions.setValues(averageElementOutput=True, curveRefinementLevel=EXTRA_FINE) viewport.odbDisplay.commonOptions.setValues(visibleEdges=FREE, deformationScaling=UNIFORM, uniformScaleFactor=5) viewport.odbDisplay.contourOptions.setValues(contourStyle=CONTINUOUS) viewport.restore() viewport.viewportAnnotationOptions.setValues(compass=OFF) viewport.viewportAnnotationOptions.setValues(triad=ON) viewport.viewportAnnotationOptions.setValues(title=OFF) viewport.viewportAnnotationOptions.setValues(state=OFF) viewport.viewportAnnotationOptions.setValues(legend=ON) viewport.viewportAnnotationOptions.setValues(legendTitle=OFF) viewport.viewportAnnotationOptions.setValues(legendBox=OFF) viewport.viewportAnnotationOptions.setValues( legendFont='-*-arial narrow-bold-r-normal-*-*-140-*-*-p-*-*-*') viewport.viewportAnnotationOptions.setValues( legendFont='-*-arial narrow-bold-r-normal-*-*-180-*-*-p-*-*-*') viewport.viewportAnnotationOptions.setValues(legendPosition=(1, 99)) viewport.viewportAnnotationOptions.setValues(legendDecimalPlaces=1) viewport.setValues(origin=(0.0, -1.05833435058594), height=188.030563354492, width=203.452590942383) viewport.view.setValues(viewOffsetX=-2.724, viewOffsetY=-52.6898, cameraUpVector=(-0.453666, -0.433365, 0.778705), nearPlane=1192.17, farPlane=2323.39, width=750.942, height=665.183, cameraPosition=(1236.44, 1079.87, 889.94), cameraTarget=(27.3027, -54.758, 306.503)) def edit_keywords(mod, text, before_pattern=None, insert=False): """Edit the keywords to add commands not available in Abaqus CAE Parameters ---------- mod : Abaqus Model object The model for which the keywords will be edited. text : str The text to be included. before_pattern : str, optional One pattern used to find where to put the given text. insert : bool, optional Insert the text instead of replacing it. """ mod.keywordBlock.synchVersions(storeNodesAndElements=False) sieBlocks=mod.keywordBlock.sieBlocks if before_pattern is None: index=len(sieBlocks) - 2 else: index=None for i in range(len(sieBlocks)): sieBlock=sieBlocks[i] if sieBlock.find(before_pattern) > -1: index=i-1 break if index is None: print('WARNING - *edit_keywords failed !') print(' %s pattern not found !' % before_pattern) #TODO better error handling here... if insert: mod.keywordBlock.insert(index, text) else: mod.keywordBlock.replace(index, text) def create_composite_layup(name, stack, plyts, mat_names, region, part, part_csys, symmetric=False, scaling_factor=1., axis_normal=2): r"""Creates a composite layup Parameters ---------- name : str Name of the new composite layup. stack : list Stacking sequence represented by a list of orientations in degress. The stacking sequence starts inwards a ends outwards. The 0 degree angle is along the axial direction and the angles are measured using the right-hand rule with the normal direction being normal to the shell surface pointing outwards. plyts : list List containing the ply thicknesses. mat_names : list List containing the material name for each ply. region : an Abaqus Region object The region consisting of geometric faces, where this laminate will be assigned to. part : an Abaqus part Object A part object where the layup will be created. part_csys : a valid Datum object The cylindrical coordinate system of the part object. symmetric : bool, optional A boolean telling whether the laminate is symmetric. scaling_factor : float, optional A scaling factor to be applied to each ply thickness. Used to apply thickness imperfection in some cases. axis_normal : int, optional Reference """ from abaqusConstants import (MIDDLE_SURFACE, FROM_SECTION, SHELL, ON, OFF, DEFAULT, UNIFORM, SIMPSON, GRADIENT, SYSTEM, ROTATION_NONE, AXIS_1, AXIS_2, AXIS_3, SPECIFY_THICKNESS, SPECIFY_ORIENT, SINGLE_VALUE) myLayup=part.CompositeLayup(name=name, description='stack from inside to outside', offsetType=MIDDLE_SURFACE, symmetric=False, thicknessAssignment=FROM_SECTION, elementType=SHELL) myLayup.Section(preIntegrate=OFF, integrationRule=SIMPSON, thicknessType=UNIFORM, poissonDefinition=DEFAULT, temperature=GRADIENT, useDensity=OFF) if axis_normal == 1: axis = AXIS_1 elif axis_normal == 2: axis = AXIS_2 elif axis_normal == 3: axis = AXIS_3 else: raise ValueError('Invalid value for `axis_normal`') myLayup.ReferenceOrientation(orientationType=SYSTEM, localCsys=part_csys, fieldName='', additionalRotationType=ROTATION_NONE, angle=0., additionalRotationField='', axis=axis) #CREATING ALL PLIES numIntPoints=3 if len(stack)==1: numIntPoints=5 for i, angle in enumerate(stack): plyt=plyts[i] mat_name=mat_names[i] myLayup.CompositePly(suppressed=False, plyName='ply_%02d' % (i+1), region=region, material=mat_name, thicknessType=SPECIFY_THICKNESS, thickness=plyt*scaling_factor, orientationValue=angle, orientationType=SPECIFY_ORIENT, numIntPoints=numIntPoints) def create_isotropic_section(name, mat_names, region, part, model,T,Sect_name,OFFTS): """Creates an isotropic section """ from abaqusConstants import (MIDDLE_SURFACE, FROM_SECTION, SHELL, ON, OFF, DEFAULT, UNIFORM, SIMPSON, GRADIENT, SYSTEM, ROTATION_NONE, AXIS_1, AXIS_2, AXIS_3, SPECIFY_THICKNESS, SPECIFY_ORIENT,NO_IDEALIZATION, SINGLE_VALUE) model.HomogeneousShellSection(name=name, preIntegrate=OFF, material=mat_names[0], thicknessType=UNIFORM, thickness=T, thicknessField='', idealization=NO_IDEALIZATION, poissonDefinition=DEFAULT, thicknessModulus=None, temperature=GRADIENT, useDensity=OFF, integrationRule=SIMPSON, numIntPts=5) region = region if OFFTS==0.0: part.SectionAssignment(region=region, sectionName=Sect_name, offset=OFFTS,offsetType=MIDDLE_SURFACE, offsetField='', thicknessAssignment=FROM_SECTION) else: part.SectionAssignment(region=region, sectionName=Sect_name, offset=OFFTS,offsetType=SINGLE_VALUE, offsetField='', thicknessAssignment=FROM_SECTION) def modify_composite_layup(part, layup_name, modify_func): """Modify plies within a composite layup Directly modififying plies within a CompositeLayup is not possible, as the plies are read-only after creation. This function emulates modifying, by deleting and then re-creating plies, with modifications. Parameters ---------- part : an Abaqus part object The part that the to-be-modified layup is attached to. layup_name : str Name of the layup that is to be modified. modify_func : function Function that will be called for each ply. It should take as arguments the ply index and a dictionary of keyword arguments. This dictionary contains all keyword arguments that would re-create the original ply, if passed to the ``CompositePly``-constructor. This function should should make the necessary changes this dictionary and then return it. The returned dictionary will then be used to create the new ply. """ from abaqusConstants import SPECIFY_ORIENT, CSYS layup = part.compositeLayups[layup_name] ply_data = [] STORE_PLY_ATTRS = ['additionalRotationField', 'additionalRotationType', 'angle', 'axis', 'material', 'numIntPoints', 'orientation', 'orientationType', 'orientationValue', 'plyName', 'region', 'suppressed', 'thickness', 'thicknessType'] for ply in layup.plies.values(): ply_data.append(dict((attr, getattr(ply, attr)) for attr in STORE_PLY_ATTRS)) layup.deletePlies() for i, kwargs in enumerate(ply_data): kwargs['region'] = part.sets[kwargs['region'][0]] if kwargs['orientationType'] != SPECIFY_ORIENT: kwargs.pop('orientationValue') if kwargs['orientationType'] != CSYS: kwargs.pop('orientation') kwargs = modify_func(i, kwargs) layup.CompositePly(**kwargs) def createDiscreteField(mod, odb, step_name, frame_num): from abaqusConstants import (NODES, PRESCRIBEDCONDITION_DOF) u=odb.steps[step_name].frames[frame_num].fieldOutputs['U'] ur=odb.steps[step_name].frames[frame_num].fieldOutputs['UR'] datas=[] for u_value, ur_value in zip(u.values, ur.values): id=u_value.nodeLabel data=np.concatenate((u_value.data, ur_value.data)) datas.append([id, data]) datas.sort(key=lambda x: x[0]) list_ids=[] list_dof_values=[] for data in datas: list_ids += [data[0] for i in range(6)] for dof in range(1,7): list_dof_values += [float(dof), data[1][dof-1]] tuple_ids=tuple(list_ids) tuple_dof_values=tuple(list_dof_values) mod.DiscreteField(name='discreteField', description='', location=NODES, fieldType=PRESCRIBEDCONDITION_DOF, dataWidth=2, defaultValues=(0.0, 0.0, 0.0, 0.0, 0.0, 0.0), data=(('', 2, tuple_ids, tuple_dof_values),)) def create_sketch_plane(cc, entity): """Creates a sketch plane tangent to the shell surface Parameters ---------- cc : :class:`.ConeCyl` object entity : object Any object with the attribute: ``thetadeg``, usually a :class:`.Imperfection`. Returns ------- plane : :class:`.Plane` object """ from abaqus import mdb from .utils import geom part = mdb.models[cc.model_name].parts[cc.part_name_shell] for plane in cc.sketch_planes: if abs(plane.thetadeg - entity.thetadeg) < TOL: return plane x1, y1, z1 = utils.cyl2rec(1.05*cc.r, entity.thetadeg, 0.) v1 = np.array([x1, y1, z1], dtype=FLOAT) x2, y2, z2 = utils.cyl2rec(1.05*cc.r2, entity.thetadeg, cc.h) v2 = np.array([x2, y2, z2], dtype=FLOAT) v3 = np.cross(v2, v1) if abs(v3.max()) > abs(v3.min()): v3 = v3/v3.max() * cc.h/2. else: v3 = v3/abs(v3.min()) * cc.h/2. x3, y3, z3 = v2 + v3 pt = part.DatumPointByCoordinate(coords=(x1, y1, z1)) p1 = part.datums[pt.id] pt = part.DatumPointByCoordinate(coords=(x2, y2, z2)) p2 = part.datums[pt.id] pt = part.DatumPointByCoordinate(coords=(x3, y3, z3)) p3 = part.datums[pt.id] plane = geom.Plane() plane.p1 = p1 plane.p2 = p2 plane.p3 = p3 plane.part = part plane.create() plane.thetadeg = entity.thetadeg cc.sketch_planes.append(plane) return plane def set_colors_ti(cc): from abaqus import mdb, session from abaqusConstants import ON part = mdb.models[cc.model_name].parts[cc.part_name_shell] viewport = session.viewports[session.currentViewportName] if viewport.displayedObject is None: viewport.setValues(displayedObject=part) cmap = viewport.colorMappings['Set'] viewport.setColor(colorMapping=cmap) viewport.enableMultipleColors() viewport.setColor(initialColor='#BDBDBD') keys = part.sets.keys() names = [k for k in keys if 'Set_measured_imp_t' in k] # If there are not enough colors for all thicknesses, # repeat the same color for multiple subsequent thickness sets repeat = int(math.ceil(max(len(names), 1.0) / float(len(COLORS)))) overrides = dict((name, (True, COLORS[i//repeat], 'Default', COLORS[i//repeat])) for i, name in enumerate(names)) dummylen = len(keys)-len(overrides) new_COLORS = tuple([COLORS[-1]]*dummylen + list(COLORS)) session.autoColors.setValues(colors=new_COLORS) cmap.updateOverrides(overrides=overrides) keys_to_hide = set(keys) - set(names) overrides = dict([[k, (False, )] for k in keys_to_hide]) cmap.updateOverrides(overrides=overrides) viewport.partDisplay.setValues(mesh=ON) viewport.partDisplay.geometryOptions.setValues(referenceRepresentation=ON) viewport.disableMultipleColors() def printLBmodes(): from abaqus import session from abaqusConstants import DPI_1200, EXTRA_FINE, OFF, PNG vp = session.viewports[session.currentViewportName] session.psOptions.setValues(logo=OFF, resolution=DPI_1200, shadingQuality=EXTRA_FINE) session.printOptions.setValues(reduceColors=False) for i in range(1,51): vp.odbDisplay.setFrame(step=0, frame=i) session.printToFile(fileName='mode %02d.png'%i, format=PNG, canvasObjects=(vp,)) def get_current_odbdisplay(): from abaqus import session viewport = session.viewports[session.currentViewportName] try: name = viewport.odbDisplay.name except: return None return viewport.odbDisplay def get_current_odb(): from abaqus import session viewport = session.viewports[session.currentViewportName] odbdisplay = get_current_odbdisplay() if odbdisplay: return session.odbs[odbdisplay.name] else: return None def get_current_step_name(): odbdisplay = get_current_odbdisplay() if odbdisplay: index, frame_num = odbdisplay.fieldFrame return odbdisplay.fieldSteps[index][0] else: return None def get_current_frame(): odbdisplay = get_current_odbdisplay() if not odbdisplay: return None step_name = get_current_step_name() step_num, frame_num = odbdisplay.fieldFrame odb = get_current_odb() step = odb.steps[step_name] return step.frames[frame_num]
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__author__ = 'Dante' import random from sklearn.svm import SVC from sklearn.metrics import accuracy_score as acc import numpy as np import density_weight as dw import matplotlib as plt from matplotlib import pyplot import math from sklearn import cross_validation as cv from collections import Counter import scipy.cluster.hierarchy as sch from structures import fingerprinter as fptr from structures import kernel_fns as kf from chem import chem def dw_performance(matrix, classes, filename, initial=2, iterations=100, beta=1.0, C=1.0, gamma=0.005, degree=2, kernel='rbf', batch=1, decf=False, seed=None, simfp=fptr.integer_sim): """This function takes as input the positive and negative samples from a known isozyme and compares how the learning curve changes between an SVC trained on an increasing number of randomly selected samples and an SVC trained with an increasing number of samples selected via an uncertainty sampling/density weighting method.""" rand_res = [] dw_res = [] k_dict = {'rbf': 'rbf', 'poly': 'poly', 'tanimoto': kf.tanimoto} #Iterates n times. for iteration in range(iterations): #If you set the random_state kwarg, it will make the test set uniform across all iterations. x_train, x_test, y_train, y_test = cv.train_test_split(matrix, classes, test_size=0.4, random_state=seed) i = range(x_train.shape[0]) #Change the second argument to start with a different number of samples. The while loop ensures that the initial #training smaple is instantiated with at least one example from each class. rand_train = random.sample(i, initial) while set(y_train[rand_train]) != {-1, 1}: del rand_train[-1] for index in random.sample(i, 1): rand_train.append(index) #Initialize the training set for the DW curve with the same points as the random curve. dw_train = [] dw_train += rand_train #Results storage. n = [] rand_scores = [] dw_scores = [] #Each data point in each iteration si created here. while len(rand_train) < x_train.shape[0]: clf_rand = SVC(C=C, kernel=k_dict[kernel], gamma=gamma, degree=degree, probability=True) clf_dw = SVC(C=C, kernel=k_dict[kernel], gamma=gamma, degree=degree, probability=True) n.append(len(rand_train)) #Fit, predict and generate accuracy scores. clf_rand.fit(x_train[rand_train], y_train[rand_train]) clf_dw.fit(x_train[dw_train], y_train[dw_train]) r = clf_rand.predict(x_test) d = clf_dw.predict(x_test) rand_scores.append(acc(y_test, r)) dw_scores.append(acc(y_test, d)) #Update the available points that can be chosen for random. available_rand_ = list(set(i) - set(rand_train)) if len(available_rand_) != 0 and len(available_rand_) % batch < len(available_rand_): for index in random.sample(available_rand_, batch): rand_train.append(index) elif len(available_rand_) != 0 and len(available_rand_) % batch == len(available_rand_): rand_train += available_rand_ else: pass #Update the available points that can be chosen for DW, and create index table to maintain identity of each #example as they are depleted. available_dw_ = list(set(i) - set(dw_train)) index_table_ = {orig: update for update, orig in enumerate(available_dw_)} pairwise_tc_avg = dw.avg_proximity(x_train[available_dw_], x_train[available_dw_], f=simfp) if len(available_dw_) != 0 and len(available_dw_) % batch < len(available_dw_): if decf: xi = [dw.weight(dw.hyper_distance(clf_dw.decision_function(x_train[a])), pairwise_tc_avg[index_table_[a]], beta=beta) for a in available_dw_] else: xi = [dw.weight(dw.entropy(clf_dw.predict_proba(x_train[a])[:, 0]), pairwise_tc_avg[index_table_[a]], beta=beta) for a in available_dw_] foo = sorted(zip(available_dw_, xi), key=lambda x: x[1], reverse=True) dw_train += [ele[0] for ele in foo[:batch]] elif len(available_dw_) != 0 and len(available_dw_) % batch == len(available_dw_): dw_train += available_dw_ else: pass clf_rand_last = SVC(C=C, kernel=k_dict[kernel], gamma=gamma, degree=degree).fit(x_train[rand_train], y_train[rand_train]) clf_dw_last = SVC(C=C, kernel=k_dict[kernel], gamma=gamma, degree=degree).fit(x_train[dw_train], y_train[dw_train]) n.append(len(rand_train)) r_last = clf_rand_last.predict(x_test) d_last = clf_dw_last.predict(x_test) rand_scores.append(acc(y_test, r_last)) dw_scores.append(acc(y_test, d_last)) rand_res.append(np.array(rand_scores)) dw_res.append(np.array(dw_scores)) rand_avg = np.sum(np.vstack(tuple(rand_res)), 0) / iterations dw_avg = np.sum(np.vstack(tuple(dw_res)), 0) / iterations rand_err = 1.96 * np.std(np.vstack(tuple(rand_res)), 0) / math.sqrt(iterations) dw_err = 1.96 * np.std(np.vstack(tuple(dw_res)), 0) / math.sqrt(iterations) xdesc = 'Number of Training Samples' ydesc = 'Accuracy Score' plt.rcParams['font.sans-serif'] = ['Arial'] pyplot.errorbar(n, rand_avg, fmt='s-', yerr=rand_err, color='darkred', markersize=9, lw=2, label='Random Selection') pyplot.errorbar(n, dw_avg, fmt='v-', yerr=dw_err, color='darkblue', markersize=9, lw=2, label='Density Weighted') pyplot.tick_params(labelsize=14) leg_title = "Final Accuracy = %s\nC = %s" % (str(round(rand_avg[-1], 3) * 100) + '%', str(C)) pyplot.legend(loc=4, title=leg_title) pyplot.xlabel(xdesc, size=18, labelpad=14) pyplot.ylabel(ydesc, size=18, labelpad=14) pyplot.savefig(filename + "C_%s_decisionf_%s.svg" % (str(C), str(decf))) pyplot.show() def dw_ins(matrix, classes, filename, smiles_acc, initial=2, iterations=100, beta=1.0, C=1.0, gamma=0.005, degree=2, kernel='rbf', decf=False, batch=1, seed=None): """This function trains SVCs--initialized with a number of compounds from randomly partitioned train/test sets--with progressively more compounds from the test set selected via active learning and outputs a figure showing the distribution of selections of compounds in the unlabelled set.""" k_dict = {'rbf': 'rbf', 'poly': 'poly', 'tanimoto': kf.tanimoto} rankings = {k: [] for k in smiles_acc} #Iterates n times. for iteration in range(iterations): #Set the random_state kwarg, it will make the test set uniform across al iterations. x_train, x_test, y_train, y_test = cv.train_test_split(matrix, classes, test_size=0.4, random_state=seed) i = range(x_train.shape[0]) #Change the second argument to start with a different number of samples. rand_train = random.sample(i, initial) while set(y_train['label'][rand_train]) != {-1, 1}: del rand_train[-1] for index in random.sample(i, 1): rand_train.append(index) #Initialize the training set for the DW curve with the same points as the random curve. dw_train = [] dw_train += rand_train #Results storage. n = [] #Each data point in each iteration is created here. while len(dw_train) < x_train.shape[0]: clf_dw = SVC(C=C, kernel=k_dict[kernel], gamma=gamma, degree=degree, probability=True) n.append(len(dw_train)) #Fit, predict and generate accuracy scores. clf_dw.fit(x_train[dw_train], y_train['label'][dw_train]) #Update the available points that can be chosen for DW, and create index table to maintain identity of each #example as they are depleted. available_dw_ = list(set(i) - set(dw_train)) index_table_ = {orig: update for update, orig in enumerate(available_dw_)} pairwise_tc_avg = dw.avg_proximity(x_train[available_dw_], x_train[available_dw_]) if len(available_dw_) != 0 and len(available_dw_) % batch < len(available_dw_): #Density weight scores calculated in two lists to find the difference between the score of the "best" #compound from the background and the "best" hidden labelled compound. if decf: xi = [dw.weight(dw.hyper_distance(clf_dw.decision_function(x_train[a])), pairwise_tc_avg[index_table_[a]], beta=beta) for a in available_dw_] else: xi = [dw.weight(dw.entropy(clf_dw.predict_proba(x_train[a])[:, 0]), pairwise_tc_avg[index_table_[a]], beta=beta) for a in available_dw_] foo = sorted(zip(available_dw_, xi), key=lambda x_: x_[1], reverse=True) adds = [ele[0] for ele in foo[:batch]] dw_train += adds smiles_added = [y_train['smiles'][idx] for idx in adds] for s in smiles_added: rankings[s].append((len(dw_train) - 2) / batch) elif len(available_dw_) != 0 and len(available_dw_) % batch == len(available_dw_): smiles_added = [y_train['smiles'][idx] for idx in available_dw_] for s in smiles_added: rankings[s].append(((len(dw_train) - 2) / batch) + 1) dw_train += available_dw_ else: pass n.append(len(dw_train)) final = {k: Counter(v) for k, v in rankings.iteritems()} positions = [j + 1 for j in range(max([x for val in rankings.values() for x in val]))] data_ = [] for cpd, ctr in final.iteritems(): data_.append(np.array([ctr[pos] for pos in positions])) data_in_ = np.vstack(tuple(data_)).T row = positions plt.rcParams['font.sans-serif'] = ['Arial'] fig = pyplot.figure() #Plots a dendrogram above the heatmap. axdendro = fig.add_axes([0.06, 0.68, 0.8, 0.27]) Y = sch.linkage(np.vstack(tuple([fptr.reconstruct_fp(s, fptype='FP2') for s in smiles_acc])), method='single', metric='jaccard') Z = sch.dendrogram(Y, orientation='top') axdendro.set_xticks([]) axdendro.set_yticks([]) #Plotting the heat map. 'pcolor' outputs a mappable object that is used as a mandatory argument to 'colorbar().' #add axes arguments are: distance from left, distance from bottom, width, height. ax = fig.add_axes([0.06, 0.05, 0.8, 0.6]) #Grab the order of the leaves of the dendrogram so the heatmap can be reordered to match. index = Z['leaves'] D = data_in_.T[index] hmap = ax.pcolor(D.T, cmap='gist_heat') horiz = np.arange(data_in_.shape[1]) + 0.5 vert = np.arange(data_in_.shape[0]) + 0.5 pyplot.ylim([0, vert[-1] + 0.5]) pyplot.xlim([0, horiz[-1] + 0.5]) pyplot.ylabel('Position Selected', size=16) ax.set_xticks(horiz, minor=False) ax.set_yticks(vert, minor=False) names = [] for s in classes['smiles']: name_entry = chem.calc_name(s) names.append(unicode(name_entry, "utf-8")) col = [names[m] + ' (%s)' % str(classes['label'][m]) for m in index] ax.set_xticklabels(col, minor=False, rotation=90, ha='center', size=11) ax.set_yticklabels(row, minor=False, size=11) #Plots the colorbar on separate axes so that the dendrogram can be aligned to the heatmap alone. axcolor = fig.add_axes([0.89, 0.05, 0.02, 0.6]) cbar = pyplot.colorbar(hmap, cax=axcolor) axcolor.set_ylabel('Selection Frequency', size=16, rotation=270) #Eliminates white lines in Inkscape due to viewer bug; makes colorbar render with overlapping segments. cbar.solids.set_edgecolor("face") pyplot.savefig(filename + "C_%s_decisionf_%s.svg" % (str(C), str(decf))) pyplot.show() def dw_exp_val(matrix, classes, filename, excl, initial=2, iterations=100, beta=1.0, C=1.0, gamma=0.005, degree=2, kernel='rbf', batch=1, decf=False, seed=None, simfp=fptr.integer_sim): """This function takes as input the positive and negative samples from a known isozyme plus additional data collected experimentally (added in an outside script) using active learning. A 'random' curve is constructed by splitting the whole set, then excluding the newest batch of data from training, and finally calculating the accuracy at incrementally increasing training set sizes. To assess 'improvement' due to AL, the 'dw' curve is constructed in the same way using training data with an equal number of compounds (to the ones excluded in making the random curve) excluded from training.""" rand_res = [] dw_res = [] k_dict = {'rbf': 'rbf', 'poly': 'poly', 'tanimoto': kf.tanimoto} # Iterates n times. for iteration in range(iterations): #If you set the random_state kwarg, it will make the test set uniform across al iterations. x_train, x_test, y_train, y_test = cv.train_test_split(matrix, classes, test_size=0.4, random_state=seed) #Exclude experimental compounds from the random curve, and random compounds for the dw curve. rand_excl = [i for i, smi in enumerate(y_train['smiles']) if smi in excl] dw_excl = random.sample(range(x_train.shape[0]), len(rand_excl)) i = list(set(range(x_train.shape[0])) - set(rand_excl)) j = list(set(range(x_train.shape[0])) - set(dw_excl)) #Change the second argument to start with a different number of samples. The while loop ensures that the initial #training sample is instantiated with at least one example from each class. rand_train = random.sample(i, initial) while set(y_train['label'][rand_train]) != {-1, 1}: del rand_train[-1] for index in random.sample(i, 1): rand_train.append(index) #Initialize the training set for the DW curve points randomly chosen from the allowed dw indices. dw_train = random.sample(j, initial) while set(y_train['label'][dw_train]) != {-1, 1}: del dw_train[-1] for index in random.sample(j, 1): dw_train.append(index) #Results storage. n = [] rand_scores = [] dw_scores = [] #Each data point in each iteration is created here. while len(rand_train) < x_train.shape[0] - len(excl): clf_rand = SVC(C=C, kernel=k_dict[kernel], gamma=gamma, degree=degree, probability=True) clf_dw = SVC(C=C, kernel=k_dict[kernel], gamma=gamma, degree=degree, probability=True) n.append(len(rand_train)) #Fit, predict and generate accuracy scores. clf_rand.fit(x_train[rand_train], y_train['label'][rand_train]) clf_dw.fit(x_train[dw_train], y_train['label'][dw_train]) r = clf_rand.predict(x_test) d = clf_dw.predict(x_test) rand_scores.append(acc(y_test['label'], r)) dw_scores.append(acc(y_test['label'], d)) #Update the available points that can be chosen for random. available_rand_ = list(set(i) - set(rand_train)) if len(available_rand_) != 0 and len(available_rand_) % batch < len(available_rand_): for index in random.sample(available_rand_, batch): rand_train.append(index) elif len(available_rand_) != 0 and len(available_rand_) % batch == len(available_rand_): rand_train += available_rand_ else: pass #Update the available points that can be chosen for DW, and create index table to maintain identity of each #example as they are depleted. available_dw_ = list(set(j) - set(dw_train)) index_table_ = {orig: update for update, orig in enumerate(available_dw_)} pairwise_tc_avg = dw.avg_proximity(x_train[available_dw_], x_train[available_dw_], f=simfp) if len(available_dw_) != 0 and len(available_dw_) % batch < len(available_dw_): if decf: xi = [dw.weight(dw.hyper_distance(clf_dw.decision_function(x_train[a])), pairwise_tc_avg[index_table_[a]], beta=beta) for a in available_dw_] else: xi = [dw.weight(dw.entropy(clf_dw.predict_proba(x_train[a])[:, 0]), pairwise_tc_avg[index_table_[a]], beta=beta) for a in available_dw_] foo = sorted(zip(available_dw_, xi), key=lambda x: x[1], reverse=True) dw_train += [ele[0] for ele in foo[:batch]] elif len(available_dw_) != 0 and len(available_dw_) % batch == len(available_dw_): dw_train += available_dw_ else: pass clf_rand_last = SVC(C=C, kernel=k_dict[kernel], gamma=gamma, degree=degree).fit(x_train[rand_train], y_train['label'][rand_train]) clf_dw_last = SVC(C=C, kernel=k_dict[kernel], gamma=gamma, degree=degree).fit(x_train[dw_train], y_train['label'][dw_train]) n.append(len(rand_train)) r_last = clf_rand_last.predict(x_test) d_last = clf_dw_last.predict(x_test) rand_scores.append(acc(y_test['label'], r_last)) dw_scores.append(acc(y_test['label'], d_last)) rand_res.append(np.array(rand_scores)) dw_res.append(np.array(dw_scores)) rand_avg = np.sum(np.vstack(tuple(rand_res)), 0) / iterations dw_avg = np.sum(np.vstack(tuple(dw_res)), 0) / iterations rand_err = 1.96 * np.std(np.vstack(tuple(rand_res)), 0) / math.sqrt(iterations) dw_err = 1.96 * np.std(np.vstack(tuple(dw_res)), 0) / math.sqrt(iterations) xdesc = 'Number of Training Samples' ydesc = 'Accuracy Score' plt.rcParams['font.sans-serif'] = ['Arial'] pyplot.errorbar(n, rand_avg, fmt='s-', yerr=rand_err, color='darkred', markersize=9, lw=2, label='Random Selection') pyplot.errorbar(n, dw_avg, fmt='v-', yerr=dw_err, color='darkblue', markersize=9, lw=2, label='Density Weighted') pyplot.tick_params(labelsize=14) leg_title = "Final Random Accuracy = %s\nFinal DW Accuracy = %s\nC = %s" % (str(round(rand_avg[-1], 3) * 100) + '%', str(round(dw_avg[-1], 3) * 100) + '%', str(C)) pyplot.legend(loc=4, title=leg_title) pyplot.xlabel(xdesc, size=18, labelpad=14) pyplot.ylabel(ydesc, size=18, labelpad=14) pyplot.savefig(filename + "C_%s_decisionf_%s.svg" % (str(C), str(decf))) pyplot.show() def dw_exp_ins(matrix, classes, filename, smiles_acc, excl, initial=2, iterations=100, beta=1.0, C=1.0, gamma=0.005, degree=2, kernel='rbf', batch=1, decf=False, seed=None, simfp=fptr.integer_sim): """This function takes as input the positive and negative samples from a known isozyme plus additional data collected experimentally (added in an outside script) using active learning. A 'random' curve is constructed by splitting the whole set, then excluding the newest batch of data from training, and finally calculating the accuracy at incrementally increasing training set sizes. To assess 'improvement' due to AL, the 'dw' curve is constructed in the same way using training data with an equal number of compounds (to the ones excluded in making the random curve) excluded from training.""" rand_res = [] dw_res = [] rankings = {k: [] for k in smiles_acc} k_dict = {'rbf': 'rbf', 'poly': 'poly', 'tanimoto': kf.tanimoto} # Iterates n times. for iteration in range(iterations): #If you set the random_state kwarg, it will make the test set uniform across al iterations. x_train, x_test, y_train, y_test = cv.train_test_split(matrix, classes, test_size=0.4, random_state=seed) #Exclude experimental compounds from the random curve, and random compounds for the dw curve. rand_excl = [i for i, smi in enumerate(y_train['smiles']) if smi in excl] dw_excl = random.sample(range(x_train.shape[0]), len(rand_excl)) j = list(set(range(x_train.shape[0])) - set(dw_excl)) #Initialize the training set for the DW curve points randomly chosen from the allowed dw indices. dw_train = random.sample(j, initial) while set(y_train['label'][dw_train]) != {-1, 1}: del dw_train[-1] for index in random.sample(j, 1): dw_train.append(index) #Results storage. n = [] #Each data point in each iteration is created here. while len(dw_train) < x_train.shape[0] - len(excl): clf_dw = SVC(C=C, kernel=k_dict[kernel], gamma=gamma, degree=degree, probability=True) n.append(len(dw_train)) #Fit, predict and generate accuracy scores. clf_dw.fit(x_train[dw_train], y_train['label'][dw_train]) #Update the available points that can be chosen for DW, and create index table to maintain identity of each #example as they are depleted. available_dw_ = list(set(j) - set(dw_train)) index_table_ = {orig: update for update, orig in enumerate(available_dw_)} pairwise_tc_avg = dw.avg_proximity(x_train[available_dw_], x_train[available_dw_], f=simfp) if len(available_dw_) != 0 and len(available_dw_) % batch < len(available_dw_): if decf: xi = [dw.weight(dw.hyper_distance(clf_dw.decision_function(x_train[a])), pairwise_tc_avg[index_table_[a]], beta=beta) for a in available_dw_] else: xi = [dw.weight(dw.entropy(clf_dw.predict_proba(x_train[a])[:, 0]), pairwise_tc_avg[index_table_[a]], beta=beta) for a in available_dw_] foo = sorted(zip(available_dw_, xi), key=lambda x: x[1], reverse=True) adds = [ele[0] for ele in foo[:batch]] dw_train += adds smiles_added = [y_train['smiles'][idx] for idx in adds] for s in smiles_added: rankings[s].append((len(dw_train) - 2) / batch) elif len(available_dw_) != 0 and len(available_dw_) % batch == len(available_dw_): smiles_added = [y_train['smiles'][idx] for idx in available_dw_] for s in smiles_added: rankings[s].append(((len(dw_train) - 2) / batch) + 1) dw_train += available_dw_ else: pass n.append(len(dw_train)) smiles_added = [y_train['smiles'][idx] for idx in available_dw_] for s in smiles_added: rankings[s].append(((len(dw_train) - 2) / batch) + 1) final = {k: Counter(v) for k, v in rankings.iteritems()} positions = [j + 1 for j in range(max([x for val in rankings.values() for x in val]))] data_ = [] for cpd, ctr in final.iteritems(): data_.append(np.array([ctr[pos] for pos in positions])) data_in_ = np.vstack(tuple(data_)).T row = positions plt.rcParams['font.sans-serif'] = ['Arial'] fig = pyplot.figure() # Plots a dendrogram above the heatmap. axdendro = fig.add_axes([0.06, 0.68, 0.8, 0.27]) Y = sch.linkage(np.vstack(tuple([fptr.reconstruct_fp(s, fptype='FP2') for s in smiles_acc])), method='single', metric='jaccard') Z = sch.dendrogram(Y, orientation='top') axdendro.set_xticks([]) axdendro.set_yticks([]) #Plotting the heat map. 'pcolor' outputs a mappable object that is used as a mandatory argument to 'colorbar().' #add axes arguments are: distance from left, distance from bottom, width, height. ax = fig.add_axes([0.06, 0.05, 0.8, 0.6]) #Grab the order of the leaves of the dendrogram so the heatmap can be reordered to match. index = Z['leaves'] D = data_in_.T[index] hmap = ax.pcolor(D.T, cmap='gist_heat') horiz = np.arange(data_in_.shape[1]) + 0.5 vert = np.arange(data_in_.shape[0]) + 0.5 pyplot.ylim([0, vert[-1] + 0.5]) pyplot.xlim([0, horiz[-1] + 0.5]) pyplot.ylabel('Position Selected', size=16) ax.set_xticks(horiz, minor=False) ax.set_yticks(vert, minor=False) names = [] for s in classes['smiles']: name_entry = chem.calc_name(s) names.append(unicode(name_entry, "utf-8")) col = [names[m] + ' (%s)' % str(classes['label'][m]) for m in index] ax.set_xticklabels(col, minor=False, rotation=90, ha='center', size=11) ax.set_yticklabels(row, minor=False, size=11) #Plots the colorbar on separate axes so that the dendrogram can be aligned to the heatmap alone. axcolor = fig.add_axes([0.89, 0.05, 0.02, 0.6]) cbar = pyplot.colorbar(hmap, cax=axcolor) axcolor.set_ylabel('Selection Frequency', size=16, rotation=270) #Eliminates white lines in Inkscape due to viewer bug; makes colorbar render with overlapping segments. cbar.solids.set_edgecolor("face") pyplot.savefig(filename + "C_%s_decisionf_%s.svg" % (str(C), str(decf))) pyplot.show()
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""" filename: generic_model.py author: <NAME> version: 15.04.2021 description: helper functions (plotting, report generation, vgg base model) """ import numpy as np import matplotlib.pyplot as plt import seaborn as sns import tensorflow as tf import cv2 from glob import glob from sklearn.metrics import confusion_matrix, classification_report, roc_auc_score from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelBinarizer from tensorflow.keras.utils import to_categorical from tensorflow.keras.layers import Input, Dense, Flatten, Dropout from tensorflow.keras.models import Model, load_model from tensorflow.keras.applications import VGG19 from tensorflow.keras.preprocessing import image from tensorflow.keras.preprocessing.image import ImageDataGenerator def split_images_and_process(covid_files, normal_files): print('Total number of covid images: {}'.format(len(covid_files))) print('Total number of non-covid images: {}'.format(len(normal_files))) # Preparing Labels covid_labels = [] normal_labels = [] covid_images=[] normal_images=[] for i in range(len(covid_files)): image = cv2.imread(covid_files[i]) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) image = cv2.resize(image,(224,224)) covid_images.append(image) covid_labels.append('Covid') for i in range(len(normal_files)): image = cv2.imread(normal_files[i]) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) image = cv2.resize(image,(224,224)) normal_images.append(image) normal_labels.append('Normal') # Convert to array and Normalize to interval of [0,1] covid_images = np.array(covid_images) / 255 normal_images = np.array(normal_images) / 255 # split into training and testing covid_x_train, covid_x_test, covid_y_train, covid_y_test = train_test_split(covid_images, covid_labels, test_size=0.2) normal_x_train, normal_x_test, normal_y_train, normal_y_test = train_test_split(normal_images, normal_labels, test_size=0.2) X_train = np.concatenate((normal_x_train, covid_x_train), axis=0) X_test = np.concatenate((normal_x_test, covid_x_test), axis=0) y_train = np.concatenate((normal_y_train, covid_y_train), axis=0) y_test = np.concatenate((normal_y_test, covid_y_test), axis=0) # make labels into categories - either 0 or 1 lb = LabelBinarizer() #print(y_train[0]) y_train = lb.fit_transform(y_train) #print(y_train[0]) y_train = to_categorical(y_train) #print(y_train[0]) y_test = lb.transform(y_test) y_test = to_categorical(y_test) return [X_train, X_test, y_train, y_test] def vgg_model(lr=1e-3, dropout_val=0.2, fc_neurons=64): vggModel = VGG19(weights="imagenet", include_top=False,input_tensor=Input(shape=(224, 224, 3))) outputs = vggModel.output outputs = Flatten()(outputs) outputs = Dense(fc_neurons, activation='relu')(outputs) outputs = Dropout(dropout_val)(outputs) outputs = Dense(2, activation='softmax')(outputs) model = Model(inputs=vggModel.input, outputs=outputs) for layer in vggModel.layers: layer.trainable = False opt = tf.keras.optimizers.Adam(learning_rate = lr) model.compile(loss='binary_crossentropy',optimizer=opt, metrics=['accuracy']) return model def plot_model_acc_loss(history, title): plt.plot(history.history['accuracy']) plt.plot(history.history['val_accuracy']) plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title(title) plt.ylabel('Accuracy/Loss') plt.xlabel('Epoch') plt.legend(['train_acc','val_acc','train_loss','val_loss']) plt.show() def plot_confusion_matrix(classes, y_true, y_pred): tick_marks = [0.5,1.5] cn = confusion_matrix(y_true, y_pred) sns.heatmap(cn,cmap='plasma',annot=True) plt.xticks(tick_marks, classes) plt.yticks(tick_marks, classes) plt.title('Confusion Matrix') plt.ylabel('True label') plt.xlabel('Predicted label') plt.show() def report(y_true, y_pred): cm = confusion_matrix(y_true, y_pred) tn, fp, fn, tp = cm.ravel() acc = (tp + tn)/np.sum(cm) sens = tp/(tp + fn) spec = tn/(fp + tn) prec = tp/(tp + fp) rec = tp/(tp + fn) f1 = (2*prec*rec)/(prec + rec) auc = roc_auc_score(y_true, y_pred) print("\nAccuracy: ", acc) print("Sensitivity: ", sens) print("Specificity: ", spec) print("Precision: ", prec) print("Recall: ", rec) print("F1 Score: ", f1) print("AUC Score: ", auc) print("\nClassification report:") print(classification_report(y_true, y_pred))
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# -*- coding: utf-8 -*- """ femagtools.dxfsl.machine ~~~~~~~~~~~~~~~~~~~~~~~~ a machine consists of 2 parts and has a geometry Authors: <NAME>, <NAME> """ from __future__ import print_function import numpy as np import logging from .shape import Element, Circle, Arc, Line, Shape from .corner import Corner from .functions import point, points_are_close, distance from .functions import alpha_angle, normalise_angle, middle_angle, third_angle from .functions import line_m, line_n, mirror_point from .functions import within_interval, part_of_circle from .functions import less, less_equal, greater, greater_equal logger = logging.getLogger('femagtools.geom') ############################# # Machine # ############################# class Machine(object): def __init__(self, geom, center, radius, startangle=0, endangle=0): self.geom = geom self.center = center self.radius = radius self.startangle = startangle self.endangle = endangle self.mirror_orig_geom = None self.mirror_geom = None self.mirror_startangle = 0.0 self.mirror_endangle = 0.0 self.part = self.part_of_circle() self.airgaps = [] self.airgap_radius = 0.0 self.airgap2_radius = 0.0 self.geom.center = center self.previous_machine = None def __str__(self): return "Machine\n" + \ "Center: ({})\n".format(self.center) + \ "Radius: {}\n".format(self.radius) + \ "Angles: Start={}, End={}\n".format(self.startangle, self.endangle) + \ "Mirror: {}\n".format(self.mirror_geom is not None) def is_a_machine(self): return self.radius > 0.0 def is_in_middle(self): return self.radius > 0.0 and \ points_are_close(self.center, [0.0, 0.0], 1e-8) def is_full(self): return self.radius > 0.0 and \ self.startangle == 0.0 and self.endangle == 0.0 def is_half_up(self): return self.radius > 0.0 and \ np.isclose(self.startangle, 0.0, 1e-8) and \ (np.isclose(self.endangle, np.pi, 1e-8) or np.isclose(self.endangle, -np.pi, 1e-8)) def is_half_down(self): return self.radius > 0.0 and \ (np.isclose(self.endangle, np.pi, 1e-8) or np.isclose(self.endangle, -np.pi, 1e-8)) and \ np.isclose(self.startangle, 0.0, 1e-8) def is_half_left(self): return self.radius > 0.0 and \ np.isclose(self.startangle, np.pi/2, 1e-8) and \ np.isclose(self.endangle, -np.pi/2, 1e-8) def is_half_right(self): return self.radius > 0.0 and \ np.isclose(self.startangle, -np.pi/2, 1e-8) and \ np.isclose(self.endangle, np.pi/2, 1e-8) def is_half(self): return self.is_half_up() or self.is_half_down() or \ self.is_half_left() or self.is_half_right() def is_quarter(self): return self.radius > 0.0 and \ np.isclose(alpha_angle(self.startangle, self.endangle), np.pi/2) def is_startangle_zero(self): return np.isclose(self.startangle, 0.0) def move_to_middle(self): if not self.is_in_middle(): if self.radius > 0.0: offset = [-(self.center[0]), -(self.center[1])] self.geom.move(offset) self.set_center(0.0, 0.0) self.geom.clear_cut_lines() return True return False def set_center(self, x, y): self.center[0] = x self.center[1] = y self.geom.center = [x, y] def set_radius(self, radius): self.radius = radius def set_kind(self, kind): self.geom.kind = kind def set_inner(self): self.geom.is_inner = True def set_outer(self): self.geom.is_outer = True def clear_cut_lines(self): self.geom.clear_cut_lines() if self.mirror_geom is not None: self.mirror_geom.clear_cut_lines() def cut_is_possible(self, r_in, r_out): if r_in > 0.0: if less(self.geom.min_radius, r_in, rtol=0.0001): if less_equal(self.geom.max_radius, r_in, rtol=0.0001): return False return True if r_out > 0.0: if greater(self.geom.max_radius, r_out, rtol=0.0001): if greater_equal(self.geom.min_radius, r_out, rtol=0.0001): return False return True return False def cut(self, r_in, r_out): radius_in = r_in radius_out = r_out if r_out == 0.0: radius_out = self.radius + 10 clone = self.geom.copy_shape(self.center, self.radius, 0.0, 2*np.pi, radius_in, radius_out, False, append_inner=(r_in > 0.0), append_outer=(r_out > 0.0)) if r_out == 0.0: r_out = self.radius m = Machine(clone, self.center, r_out, self.startangle, self.endangle) m.mirror_geom = self.mirror_geom m.part = self.part m.set_minmax_radius() m.create_auxiliary_lines() m.set_alfa_and_corners() m.set_kind(self.geom.kind) return m def copy(self, startangle, endangle, airgap=False, inside=True, split=False): if airgap and self.airgap_radius > 0.0: if inside: if self.airgap2_radius > 0.0: new_radius = min(self.airgap_radius, self.airgap2_radius) else: new_radius = self.airgap_radius clone = self.geom.copy_shape(self.center, self.radius, startangle, endangle, 0.0, new_radius, split) else: new_radius = self.radius gap_radius = max(self.airgap_radius, self.airgap2_radius) clone = self.geom.copy_shape(self.center, self.radius, startangle, endangle, gap_radius, self.radius+9999, split) circ = Circle(Element(center=self.center, radius=self.airgap_radius)) clone.add_cut_line(circ) else: new_radius = self.radius clone = self.geom.copy_shape(self.center, self.radius, startangle, endangle, 0.0, self.radius+9999, split) if not np.isclose(normalise_angle(startangle), normalise_angle(endangle), 0.0): start_p = point(self.center, self.radius+5, startangle) start_line = Line(Element(start=self.center, end=start_p)) clone.add_cut_line(start_line) end_p = point(self.center, self.radius+5, endangle) end_line = Line(Element(start=self.center, end=end_p)) clone.add_cut_line(end_line) if not np.isclose(alpha_angle(startangle, endangle), 2*np.pi): return Machine(clone, self.center, new_radius, startangle, endangle) else: # Der Originalwinkel bleibt bestehen return Machine(clone, self.center, new_radius, self.startangle, self.endangle) def full_copy(self): clone = self.geom.copy_shape(self.center, self.radius, 0.0, 2*np.pi, 0.0, self.radius+9999) return clone.get_machine() def copy_mirror(self, startangle, midangle, endangle): geom1 = self.geom.copy_shape(self.center, self.radius, startangle, midangle, 0.0, self.radius+9999, rtol=1e-08, atol=1e-08) geom2 = self.geom.copy_shape(self.center, self.radius, midangle, endangle, 0.0, self.radius+9999, rtol=1e-08, atol=1e-08) machine = Machine(geom1, self.center, self.radius, startangle, midangle) machine.mirror_orig_geom = self.geom machine.mirror_geom = geom2 machine.mirror_geom.center = self.center machine.mirror_startangle = midangle machine.mirror_endangle = endangle return machine def has_mirrored_windings(self): if not self.is_mirrored(): return False return self.geom.area_close_to_endangle(2) > 0 def undo_mirror(self): assert(self.is_mirrored()) assert(self.previous_machine) self.previous_machine.set_minmax_radius() # self.previous_machine.complete_hull() self.set_alfa_and_corners() self.previous_machine.create_auxiliary_lines() self.previous_machine.set_kind(self.geom.kind) return self.previous_machine def rotate_to(self, new_startangle): if np.isclose(new_startangle, self.startangle): return if points_are_close(self.center, [0.0, 0.0]): angle = new_startangle - self.startangle self.geom.rotate(angle) self.startangle = new_startangle self.endangle += angle def airgap(self, correct_airgap=0.0, correct_airgap2=0.0, atol=0.1): logger.debug('locking for airgap') self.airgap_radius = 0.0 self.airgap2_radius = 0.0 if np.isclose(self.radius, 0.0): logger.debug('no radius') return False if correct_airgap < 0: logger.debug('no airgap') return False # no airgap self.airgaps = [] airgaps = self.geom.detect_airgaps(self.center, self.startangle, self.endangle, atol) alpha = alpha_angle(self.startangle, self.endangle) if len(airgaps) == 1: self.airgaps = airgaps elif len(airgaps) > 0: lower_radius = -1.0 upper_radius = -1.0 for g in airgaps: if np.isclose(g[0], upper_radius): if not self.geom.delete_airgap_circle(self.center, lower_radius, upper_radius, g[1], alpha): lower_radius = g[0] else: if lower_radius > 0.0: self.airgaps.append((lower_radius, upper_radius)) lower_radius = g[0] upper_radius = g[1] self.airgaps.append((lower_radius, upper_radius)) if len(self.airgaps) > 0: airgap_candidates = [] for g in self.airgaps: gap_radius = round((g[0]+g[1])/2.0, 6) gap_dist = g[1] - g[0] circle = Circle(Element(center=self.center, radius=gap_radius)) ok, borders = self.geom.is_airgap(self.center, self.radius, self.startangle, self.endangle, circle, atol) if not ok: logger.error("FATAL: No Airgap with radius {}". format(gap_radius)) print("FATAL: No Airgap with radius {}". format(gap_radius)) self.geom.airgaps.append(circle) return True # bad exit airgap_candidates.append((borders, circle, gap_dist)) self.geom.airgaps.append(circle) if correct_airgap > 0.0: if within_interval(correct_airgap, g[0], g[1], 0.0, 0.0): self.airgap_radius = gap_radius # ok if correct_airgap2 > 0.0: if within_interval(correct_airgap2, g[0], g[1], 0.0, 0.0): self.airgap2_radius = gap_radius # ok if correct_airgap > 0.0 and self.airgap_radius == 0.0: logger.error("No airgap with radius {} found" .format(correct_airgap)) self.show_airgap_candidates(airgap_candidates, False) return True # bad exit if correct_airgap2 > 0.0 and self.airgap2_radius == 0.0: logger.error("No airgap2 with radius {} found" .format(correct_airgap2)) self.show_airgap_candidates(airgap_candidates, False) return True # bad exit if len(self.airgaps) == 0: logger.debug('No airgap found') return False # no airgaps found if self.airgap_radius > 0.0: return False # correct airgap set gaps = [c for b, c, d in airgap_candidates if b == 0] if len(gaps) == 1: # one candidate without border intersection self.airgap_radius = gaps[0].radius return False # ok if len(airgap_candidates) == 1: # one candidate found self.airgap_radius = airgap_candidates[0][1].radius return False # ok self.airgap_radius = self.show_airgap_candidates(airgap_candidates, True) return False # ok def show_airgap_candidates(self, airgap_candidates, get_one): if get_one: logger.info("{} airgap candidate(s) found:" .format(len(airgap_candidates))) else: print("{} airgap candidate(s) found:" .format(len(airgap_candidates))) dist = 999 circle = None pos_list = [] for b, c, d in airgap_candidates: if get_one: logger.info(" --- {} (width={})".format(c.radius, d)) else: print(" --- {} (width={})".format(c.radius, d)) if d < dist: dist = d circle = c inner_pc = (c.radius - self.geom.min_radius) / \ (self.geom.max_radius - self.geom.min_radius) pos = np.abs(inner_pc * 100 - 50) logger.debug("Abstand Mitte = {} %".format(pos)) if pos < 20: pos_list.append([pos, d, c]) if get_one: if pos_list: dist_list = [[d, c] for pos, d, c in pos_list] dist_list.sort() circle = dist_list[0][1] logger.info("airgap {} prefered".format(circle.radius)) return circle.radius print("Use options --airgap/--airgap2 <float> to specify") return None def has_airgap(self): return self.airgap_radius > 0.0 def airgap_x(self): return self.airgap_radius def airgap_y(self): return 0.1 def part_of_circle(self, pos=3): return part_of_circle(self.startangle, self.endangle, pos) def delete_center_circle(self): gaps = self.geom.get_gaplist(self.center) if len(gaps) < 2: return first_gap = gaps[0][1] second_gap = gaps[1][0] if first_gap != second_gap: return first_dist = gaps[0][1] second_dist = gaps[1][1] - gaps[1][0] if first_dist < 1.0: if second_dist / first_dist > 10.0: self.geom.delete_circle((0.0, 0.0), first_dist) def repair_hull(self): logger.debug('repair_hull') if self.is_full() and not self.has_airgap(): self.delete_center_circle() if self.startangle == self.endangle: logger.info('end of repair_hull: circle') return self.repair_hull_geom(self.geom, self.startangle, self.endangle) if self.mirror_geom: self.repair_hull_geom(self.mirror_geom, self.mirror_startangle, self.mirror_endangle) logger.debug('end of repair_hull') def repair_hull_geom(self, geom, startangle, endangle): logger.debug('repair_hull_geom') c_corner = Corner(self.center, self.center) start_corners = geom.get_corner_list(self.center, startangle) end_corners = geom.get_corner_list(self.center, endangle) geom.repair_hull_line(self.center, startangle, start_corners, c_corner in end_corners) geom.repair_hull_line(self.center, endangle, end_corners, c_corner in start_corners) logger.debug('end of repair_hull_geom') def set_minmax_radius(self): self.geom.set_minmax_radius(self.center) def complete_hull(self, is_inner, is_outer): logger.info('complete_hull') start_corners = self.geom.complete_hull_line(self.center, self.startangle) end_corners = self.geom.complete_hull_line(self.center, self.endangle) if start_corners[0].is_new_point or end_corners[0].is_new_point: self.geom.complete_hull_arc(self.center, self.startangle, start_corners[0], self.endangle, end_corners[0], self.geom.min_radius) if start_corners[1].is_new_point or end_corners[1].is_new_point: self.geom.complete_hull_arc(self.center, self.startangle, start_corners[1], self.endangle, end_corners[1], self.geom.max_radius) self.set_alfa_and_corners() def create_auxiliary_lines(self): self.geom.create_auxiliary_lines(self.startangle, self.endangle) def set_alfa_and_corners(self): self.geom.start_corners = self.geom.get_corner_nodes(self.center, self.startangle) self.geom.end_corners = self.geom.get_corner_nodes(self.center, self.endangle) self.geom.alfa = alpha_angle(self.startangle, self.endangle) if self.mirror_geom is not None: self.geom.mirror_corners = self.geom.end_corners def is_mirrored(self): return self.mirror_geom is not None def num_of_layers(self): w = self.geom.num_of_windings() if w > 0 and self.is_mirrored(): return w*2 return w def find_symmetry(self, sym_tolerance): if self.radius <= 0.0: return False return self.geom.find_symmetry(self.center, self.radius, self.startangle, self.endangle, sym_tolerance) def get_symmetry_slice(self): logger.debug("begin get_symmetry_slice") if not self.geom.has_symmetry_area(): logger.debug("end get_symmetry_slice: no symmetry area") return None machine_slice = self.copy(self.geom.symmetry_startangle(), self.geom.symmetry_endangle()) machine_slice.clear_cut_lines() machine_slice.repair_hull() machine_slice.rotate_to(0.0) machine_slice.set_alfa_and_corners() logger.debug("end get_symmetry_slice: angle start: {}, end: {}" .format(self.geom.symmetry_startangle(), self.geom.symmetry_endangle())) return machine_slice def get_third_symmetry_mirror(self): logger.debug("begin get_third_symmetry_mirror") first_thirdangle = third_angle(self.startangle, self.endangle) second_thirdangle = middle_angle(first_thirdangle, self.endangle) machine_mirror_1 = self.copy_mirror(self.startangle, first_thirdangle, second_thirdangle) machine_mirror_1.clear_cut_lines() machine_mirror_1.repair_hull() machine_mirror_1.set_alfa_and_corners() if not machine_mirror_1.check_symmetry_graph(0.001, 0.05): logger.debug("end get_third_symmetry_mirror: no mirror first third") return None machine_mirror_2 = self.copy_mirror(first_thirdangle, second_thirdangle, self.endangle) machine_mirror_2.clear_cut_lines() machine_mirror_2.repair_hull() machine_mirror_2.set_alfa_and_corners() if not machine_mirror_2.check_symmetry_graph(0.001, 0.05): logger.debug("end get_third_symmetry_mirror: no mirror second third") return None machine_mirror_1.previous_machine = self logger.debug("end get_third_symmetry_mirror: ok") return machine_mirror_1 def get_symmetry_mirror(self): logger.debug("begin get_symmetry_mirror") if self.part == 1: # a complete machine startangle = 0.0 endangle = 0.0 midangle = np.pi else: startangle = self.startangle endangle = self.endangle midangle = middle_angle(self.startangle, self.endangle) machine_mirror = self.get_third_symmetry_mirror() if machine_mirror: logger.debug("end get_symmetry_mirror: third found") return machine_mirror logger.debug(" - angles: start: {}, mid: {}, end: {}" .format(startangle, midangle, endangle)) machine_mirror = self.copy_mirror(startangle, midangle, endangle) machine_mirror.clear_cut_lines() machine_mirror.repair_hull() machine_mirror.set_alfa_and_corners() if machine_mirror.check_symmetry_graph(0.001, 0.05): machine_mirror.previous_machine = self machine_mirror.rotate_to(0.0) machine_mirror.set_alfa_and_corners() logger.debug("end get_symmetry_mirror: found") return machine_mirror logger.debug("end get_symmetry_mirror: no mirror") return None def get_symmetry_part(self): if self.is_mirrored(): return self.part/2 else: return self.part def check_symmetry_graph(self, rtol, atol): logger.debug("begin check_symmetry_graph") axis_p = point(self.center, self.radius, self.mirror_startangle) axis_m = line_m(self.center, axis_p) axis_n = line_n(self.center, axis_m) def is_node_available(n, nodes): mirror_p = mirror_point(n, self.center, axis_m, axis_n) for p in nodes: if points_are_close(p, mirror_p, rtol, atol): return True return False def get_hit_factor(nodes1, nodes2): hit = 0 if not nodes1: return 0.0 for n in nodes1: if is_node_available(n, nodes2): hit += 1 else: d = distance(self.center, n) logger.debug(" -- r={}, d={}".format(self.radius, d)) if np.isclose(d, self.radius, rtol=0.075, atol=atol): # very tolerant logger.debug("NO HIT FOR {} ON OUTER HULL".format(n)) hit += 1 return float(hit) / len(nodes1) hit_factor1 = get_hit_factor(self.geom.g.nodes(), self.mirror_geom.g.nodes()) logger.debug("=> hit_factor1 = {}".format(hit_factor1)) if hit_factor1 < 0.9: return False # not ok hit_factor2 = get_hit_factor(self.mirror_geom.g.nodes(), self.geom.g.nodes()) logger.debug("=> hit_factor2 = {}".format(hit_factor2)) if hit_factor2 < 0.9: return False # not ok if hit_factor1 < 0.93 and hit_factor2 < 0.93: return False # not ok logger.debug("end check_symmetry_graph: ok") return True def sync_with_counterpart(self, cp_machine): self.geom.sym_counterpart = cp_machine.get_symmetry_part() self.geom.sym_part = self.get_symmetry_part() cp_machine.geom.sym_counterpart = self.get_symmetry_part() cp_machine.geom.sym_part = cp_machine.get_symmetry_part() def search_subregions(self): self.geom.search_subregions() def delete_tiny_elements(self, mindist): self.geom.delete_tiny_elements(mindist) def create_mirror_lines_outside_windings(self): if not self.geom.has_areas_touching_both_sides(): return midangle = middle_angle(self.startangle, self.endangle) pts = self.geom.split_and_get_intersect_points(self.center, self.radius+10, midangle) pts.sort() if self.geom.create_lines_outside_windings(pts): self.geom.area_list = [] logger.debug("create subregions again") self.geom.create_list_of_areas(crunch=True) self.geom.search_subregions()
[ "logging.getLogger", "numpy.abs", "numpy.isclose" ]
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import torch.nn as nn import torch import numpy as np class VNet(nn.Module): def __init__(self, nb_classes, in_channels=1, depth=5, start_filters=16, batchnorm=True, mode="AE", input_size=None): assert mode in ['AE', 'classifier'], "Unknown mode selected, currently supported are: 'AE' and 'classifier'" if mode == 'classifier' and (input_size is None or len(input_size) != 3): raise ValueError('The input size must be set as HxWxD') super(VNet, self).__init__() self.mode = mode self.input_size = input_size self.nb_classes = nb_classes self.in_channels = in_channels self.start_filters = start_filters if self.mode == "AE": self.up = [] self.down = [] nconvs = [min(cnt+1, 3) for cnt in range(depth)] # Nb of convs in each Down module # Create the encoder pathway for cnt in range(depth): in_channels = self.in_channels if cnt == 0 else out_channels out_channels = self.start_filters * (2 ** cnt) dconv = False if cnt == 0 else True # apply a down conv ? self.down.append( Down(in_channels, out_channels, nconv=nconvs[cnt], dconv=dconv, batchnorm=batchnorm)) if self.mode == "AE": # Create the decoder pathway # - careful! decoding only requires depth-1 blocks for cnt in range(depth - 1): in_channels = out_channels out_channels = in_channels // 2 self.up.append( Up(in_channels, out_channels, nconv=nconvs[-1-cnt], batchnorm=batchnorm)) # Add the list of modules to current module self.down = nn.ModuleList(self.down) if self.mode == "AE": self.up = nn.ModuleList(self.up) # Get ouptut segmentation if self.mode == "AE": self.final_layer = nn.Conv3d(out_channels, self.nb_classes, kernel_size=1, groups=1, stride=1) else: # Classification (h, w, d) = np.array(self.input_size) // 2**(depth -1) self.final_layer = nn.Sequential( nn.Linear(out_channels*h*w*d, 128), nn.ReLU(True), nn.Dropout(), nn.Linear(128, 128), nn.ReLU(True), nn.Dropout(), nn.Linear(128, self.nb_classes)) # Weight initialization self.weight_initializer() def weight_initializer(self): for module in self.modules(): if isinstance(module, nn.ConvTranspose3d) or isinstance(module, nn.Conv3d): nn.init.xavier_normal_(module.weight) nn.init.constant_(module.bias, 0) elif isinstance(module, nn.BatchNorm2d): nn.init.constant_(module.weight, 1) nn.init.constant_(module.bias, 0) elif isinstance(module, nn.Linear): nn.init.normal_(module.weight, 0, 0.01) nn.init.constant_(module.bias, 0) def forward(self, x): encoder_outs = [] for module in self.down: x = module(x) encoder_outs.append(x) encoder_outs = encoder_outs[:-1][::-1] for cnt, module in enumerate(self.up): x_up = encoder_outs[cnt] x = module(x, x_up) x = self.final_layer(x) return x class LUConv(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=5, stride=1, padding=0, batchnorm=True, bias=True, mode="conv"): super(LUConv, self).__init__() if mode == "conv": # Usual Conv self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, bias=bias) elif mode == "transpose": # UpConv self.conv = nn.ConvTranspose3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, bias=bias) if batchnorm: self.bn = nn.BatchNorm3d(out_channels) self.relu = nn.ReLU(True) self.ops = nn.Sequential(self.conv, self.bn, self.relu) def forward(self, x): x = self.ops(x) return x class NConvs(nn.Module): def __init__(self, in_channels, out_channels, nconv=3, kernel_size=5, stride=1, padding=0, batchnorm=True, bias=True, mode="conv"): super(NConvs, self).__init__() self.ops = nn.Sequential(LUConv(in_channels, out_channels, kernel_size, stride, padding, batchnorm, bias, mode), *[LUConv(out_channels, out_channels, kernel_size, stride, padding, batchnorm, bias, mode) for _ in range(nconv-1)]) def forward(self, x): x = self.ops(x) return x class Down(nn.Module): def __init__(self, in_channels, out_channels, nconv=3, dconv=True, batchnorm=True): super(Down, self).__init__() self.dconv = dconv self.in_channels = in_channels if dconv: self.down_conv = NConvs(in_channels, out_channels, 1, kernel_size=2, stride=2, batchnorm=batchnorm) self.nconvs = NConvs(out_channels, out_channels, nconv, kernel_size=5, stride=1, padding=2, batchnorm=batchnorm) else: self.nconvs = NConvs(in_channels, out_channels, nconv, kernel_size=5, stride=1, padding=2, batchnorm=batchnorm) def forward(self, x): if self.dconv: x_down = self.down_conv(x) else: x_down = x x_out = self.nconvs(x_down) # Add the input in order to learn only the residual if self.in_channels == 1 or self.dconv: x = x_out + x_down else: x = x_out return x class Up(nn.Module): def __init__(self, in_channels, out_channels, nconv=3, batchnorm=True): super(Up, self).__init__() self.up_conv = NConvs(in_channels, out_channels, 1, kernel_size=2, stride=2, batchnorm=batchnorm, mode="transpose") self.nconvs = NConvs(in_channels, out_channels, nconv, kernel_size=5, stride=1, padding=2, batchnorm=batchnorm) def forward(self, x_down, x_up): x_down = self.up_conv(x_down) xcat = torch.cat((x_up, x_down), dim=1) x = self.nconvs(xcat) x = x + x_down return x
[ "torch.nn.ReLU", "torch.nn.Dropout", "torch.nn.ConvTranspose3d", "torch.nn.init.constant_", "torch.nn.ModuleList", "torch.nn.Sequential", "torch.nn.init.xavier_normal_", "numpy.array", "torch.nn.init.normal_", "torch.nn.Linear", "torch.nn.BatchNorm3d", "torch.cat", "torch.nn.Conv3d" ]
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""" Filename: visualization.py Purpose: Set of go-to plotting functions Author: <NAME> Date created: 28.11.2018 Possible problems: 1. """ import os import numpy as np from tractor.galaxy import ExpGalaxy from tractor import EllipseE from tractor.galaxy import ExpGalaxy import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from matplotlib.colors import LogNorm, SymLogNorm from matplotlib.patches import Ellipse from matplotlib.patches import Rectangle from skimage.segmentation import find_boundaries from astropy.visualization import hist from scipy import stats import config as conf import matplotlib.cm as cm import random from time import time from astropy.io import fits import logging logger = logging.getLogger('farmer.visualization') # Random discrete color generator colors = cm.rainbow(np.linspace(0, 1, 1000)) cidx = np.arange(0, 1000) random.shuffle(cidx) colors = colors[cidx] def plot_background(brick, idx, band=''): fig, ax = plt.subplots(figsize=(20,20)) vmin, vmax = brick.background_images[idx].min(), brick.background_images[idx].max() vmin = -vmax img = ax.imshow(brick.background_images[idx], cmap='RdGy', norm=SymLogNorm(linthresh=0.03)) # plt.colorbar(img, ax=ax) out_path = os.path.join(conf.PLOT_DIR, f'B{brick.brick_id}_{band}_background.pdf') ax.axis('off') ax.margins(0,0) fig.savefig(out_path, dpi = 300, overwrite=True, pad_inches=0.0) plt.close() logger.info(f'Saving figure: {out_path}') def plot_mask(brick, idx, band=''): fig, ax = plt.subplots(figsize=(20,20)) img = ax.imshow(brick.masks[idx]) out_path = os.path.join(conf.PLOT_DIR, f'B{brick.brick_id}_{band}_mask.pdf') ax.axis('off') ax.margins(0,0) fig.savefig(out_path, dpi = 300, overwrite=True, pad_inches=0.0) plt.close() logger.info(f'Saving figure: {out_path}') def plot_brick(brick, idx, band=''): fig, ax = plt.subplots(figsize=(20,20)) backlevel, noisesigma = brick.backgrounds[idx] vmin, vmax = np.max([backlevel + noisesigma, 1E-5]), brick.images[idx].max() # vmin, vmax = brick.images[idx].min(), brick.images[idx].max() if vmin > vmax: logger.warning(f'{band} brick not plotted!') return vmin = -vmax norm = SymLogNorm(linthresh=0.03) img = ax.imshow(brick.images[idx], cmap='RdGy', origin='lower', norm=norm) # plt.colorbar(img, ax=ax) out_path = os.path.join(conf.PLOT_DIR, f'B{brick.brick_id}_{band}_brick.pdf') ax.axis('off') ax.margins(0,0) fig.savefig(out_path, dpi = 300, overwrite=True, pad_inches=0.0) plt.close() logger.info(f'Saving figure: {out_path}') def plot_blob(myblob, myfblob): fig, ax = plt.subplots(ncols=4, nrows=1+myfblob.n_bands, figsize=(5 + 5*myfblob.n_bands, 10), sharex=True, sharey=True) back = myblob.backgrounds[0] mean, rms = back[0], back[1] noise = np.random.normal(mean, rms, size=myfblob.dims) tr = myblob.solution_tractor norm = LogNorm(np.max([mean + rms, 1E-5]), myblob.images.max(), clip='True') img_opt = dict(cmap='Greys', norm=norm) # img_opt = dict(cmap='RdGy', vmin=-5*rms, vmax=5*rms) mmask = myblob.masks[0].copy() mmask[mmask==1] = np.nan ax[0, 0].imshow(myblob.images[0], **img_opt) ax[0, 0].imshow(mmask, alpha=0.5, cmap='Greys') ax[0, 1].imshow(myblob.solution_model_images[0] + noise, **img_opt) ax[0, 2].imshow(myblob.images[0] - myblob.solution_model_images[0], cmap='RdGy', vmin=-5*rms, vmax=5*rms) ax[0, 3].imshow(myblob.solution_chi_images[0], cmap='RdGy', vmin = -7, vmax = 7) ax[0, 0].set_ylabel(f'Detection ({myblob.bands[0]})') ax[0, 0].set_title('Data') ax[0, 1].set_title('Model') ax[0, 2].set_title('Data - Model') ax[0, 3].set_title('$\chi$-map') band = myblob.bands[0] for j, src in enumerate(myblob.solution_catalog): try: mtype = src.name except: mtype = 'PointSource' flux = src.getBrightness().getFlux(band) chisq = myblob.solved_chisq[j] topt = dict(color=colors[j], transform = ax[0, 3].transAxes) ystart = 0.99 - j * 0.4 ax[0, 3].text(1.05, ystart - 0.1, f'{j}) {mtype}', **topt) ax[0, 3].text(1.05, ystart - 0.2, f' F({band}) = {flux:4.4f}', **topt) ax[0, 3].text(1.05, ystart - 0.3, f' $\chi^{2}$ = {chisq:4.4f}', **topt) objects = myblob.bcatalog[j] e = Ellipse(xy=(objects['x'], objects['y']), width=6*objects['a'], height=6*objects['b'], angle=objects['theta'] * 180. / np.pi) e.set_facecolor('none') e.set_edgecolor('red') ax[0, 0].add_artist(e) try: for i in np.arange(myfblob.n_bands): back = myfblob.backgrounds[i] mean, rms = back[0], back[1] noise = np.random.normal(mean, rms, size=myfblob.dims) tr = myfblob.solution_tractor # norm = LogNorm(np.max([mean + rms, 1E-5]), myblob.images.max(), clip='True') # img_opt = dict(cmap='Greys', norm=norm) img_opt = dict(cmap='RdGy', vmin=-5*rms, vmax=5*rms) ax[i+1, 0].imshow(myfblob.images[i], **img_opt) ax[i+1, 1].imshow(myfblob.solution_model_images[i] + noise, **img_opt) ax[i+1, 2].imshow(myfblob.images[i] - myfblob.solution_model_images[i], cmap='RdGy', vmin=-5*rms, vmax=5*rms) ax[i+1, 3].imshow(myfblob.solution_chi_images[i], cmap='RdGy', vmin = -7, vmax = 7) ax[i+1, 0].set_ylabel(myfblob.bands[i]) band = myfblob.bands[i] for j, src in enumerate(myfblob.solution_catalog): try: mtype = src.name except: mtype = 'PointSource' flux = src.getBrightness().getFlux(band) chisq = myfblob.solution_chisq[j, i] Nres = myfblob.n_residual_sources[i] topt = dict(color=colors[j], transform = ax[i+1, 3].transAxes) ystart = 0.99 - j * 0.4 ax[i+1, 3].text(1.05, ystart - 0.1, f'{j}) {mtype}', **topt) ax[i+1, 3].text(1.05, ystart - 0.2, f' F({band}) = {flux:4.4f}', **topt) ax[i+1, 3].text(1.05, ystart - 0.3, f' $\chi^{2}$ = {chisq:4.4f}', **topt) if Nres > 0: ax[i+1, 3].text(1.05, ystart - 0.4, f'{Nres} residual sources found!', **topt) res_x = myfblob.residual_catalog[i]['x'] res_y = myfblob.residual_catalog[i]['y'] for x, y in zip(res_x, res_y): ax[i+1, 3].scatter(x, y, marker='+', color='r') for s, src in enumerate(myfblob.solution_catalog): x, y = src.pos color = colors[s] for i in np.arange(1 + myfblob.n_bands): for j in np.arange(4): ax[i,j].plot([x, x], [y - 10, y - 5], c=color) ax[i,j].plot([x - 10, x - 5], [y, y], c=color) except: logger.warning('Could not plot multiwavelength diagnostic figures') [[ax[i,j].set(xlim=(0,myfblob.dims[1]), ylim=(0,myfblob.dims[0])) for i in np.arange(myfblob.n_bands+1)] for j in np.arange(4)] #fig.suptitle(f'Solution for {blob_id}') fig.subplots_adjust(wspace=0.01, hspace=0, right=0.8) if myblob._is_itemblob: sid = myblob.bcatalog['source_id'][0] fig.savefig(os.path.join(conf.PLOT_DIR, f'{myblob.brick_id}_B{myblob.blob_id}_S{sid}.pdf')) else: fig.savefig(os.path.join(conf.PLOT_DIR, f'{myblob.brick_id}_B{myblob.blob_id}.pdf')) plt.close() def plot_srcprofile(blob, src, sid, bands=None): if bands is None: band = conf.MODELING_NICKNAME nickname = conf.MODELING_NICKNAME bidx = [0,] bands = [band,] outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_S{sid}_{conf.MODELING_NICKNAME}_srcprofile.pdf') elif (len(bands) == 1) & (bands[0] == conf.MODELING_NICKNAME): band = conf.MODELING_NICKNAME nickname = conf.MODELING_NICKNAME bidx = [0,] bands = [band,] outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_S{sid}_{conf.MODELING_NICKNAME}_srcprofile.pdf') else: bidx = [blob._band2idx(b, bands=blob.bands) for b in bands] if bands[0].startswith(conf.MODELING_NICKNAME): nickname = conf.MODELING_NICKNAME else: nickname = conf.MULTIBAND_NICKNAME if len(bands) > 1: outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_S{sid}_{nickname}_srcprofile.pdf') else: outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_S{sid}_{bands[0]}_srcprofile.pdf') import matplotlib.backends.backend_pdf pdf = matplotlib.backends.backend_pdf.PdfPages(outpath) for idx, band in zip(bidx, bands): if band == conf.MODELING_NICKNAME: zpt = conf.MODELING_ZPT rband = conf.MODELING_NICKNAME elif band.startswith(conf.MODELING_NICKNAME): band_name = band[len(conf.MODELING_NICKNAME)+1:] # zpt = conf.MULTIBAND_ZPT[blob._band2idx(band_name)] if band_name == conf.MODELING_NICKNAME: zpt = conf.MODELING_ZPT rband = band else: zpt = conf.MULTIBAND_ZPT[blob._band2idx(band_name)] rband = conf.MODELING_NICKNAME + '_' + band_name else: zpt = conf.MULTIBAND_ZPT[blob._band2idx(band)] rband = conf.MODELING_NICKNAME + '_' + band # information bid = blob.blob_id bsrc = blob.bcatalog[blob.bcatalog['source_id'] == sid] ra, dec = bsrc['RA'][0], bsrc['DEC'][0] if nickname == conf.MODELING_NICKNAME: xp0, yp0 = bsrc['x_orig'][0] - blob.subvector[1], bsrc['y_orig'][0] - blob.subvector[0] else: xp0, yp0 = bsrc['x_orig'][0] - blob.subvector[1] - blob.mosaic_origin[1] + conf.BRICK_BUFFER, bsrc['y_orig'][0] - blob.subvector[0] - blob.mosaic_origin[0] + conf.BRICK_BUFFER xp, yp = src.pos[0], src.pos[1] xps, yps = xp, yp flux, flux_err = bsrc[f'FLUX_{band}'][0], bsrc[f'FLUXERR_{band}'][0] mag, mag_err = bsrc[f'MAG_{band}'][0], bsrc[f'MAGERR_{band}'][0] n_blob = bsrc['N_BLOB'][0] chi2 = bsrc[f'CHISQ_{band}'][0] snr = bsrc[f'SNR_{band}'][0] is_resolved = False if src.name not in ('PointSource', 'SimpleGalaxy'): is_resolved = True col = np.array(bsrc.colnames)[np.array([tcoln.startswith('REFF') for tcoln in bsrc.colnames])][0] rband = col[len('REFF_'):] reff, reff_err = np.exp(bsrc[f'REFF_{rband}'][0])*conf.PIXEL_SCALE, np.exp(bsrc[f'REFF_{rband}'][0])*bsrc[f'REFF_ERR_{rband}'][0]*2.303*conf.PIXEL_SCALE ab, ab_err = bsrc[f'AB_{rband}'][0], bsrc[f'AB_ERR_{rband}'][0] if ab == -99.0: ab = -99 ab_err = -99 theta, theta_err = bsrc[f'THETA_{rband}'][0], bsrc[f'THETA_ERR_{rband}'][0] if 'Sersic' in src.name: nre, nre_err = bsrc[f'N_{rband}'][0], bsrc[f'N_ERR_{rband}'][0] # images img = blob.images[idx] wgt = blob.weights[idx] err = 1. / np.sqrt(wgt) mask = blob.masks[idx] seg = blob.segmap.copy() seg[blob.segmap != sid] = 0 mod = blob.solution_model_images[idx] chi = blob.solution_tractor.getChiImage(idx) chi[blob.segmap != sid] = 0 res = img - mod rms = np.median(blob.background_rms_images[idx]) xpix, ypix = np.nonzero(seg) dx, dy = (np.max(xpix) - np.min(xpix)) / 2., (np.max(ypix) - np.min(ypix)) / 2. buff = np.min([conf.BLOB_BUFFER, 10.]) xlim, ylim = np.array([-(dx + buff), (dx + buff)]) * conf.PIXEL_SCALE, np.array([-(dy + buff), (dy + buff)]) * conf.PIXEL_SCALE h, w = np.shape(img) dw, dh = w - xp - 1, h - yp - 1 extent = np.array([-xp, dw, -yp, dh]) * conf.PIXEL_SCALE xp0, yp0 = (xp0 - xp) * conf.PIXEL_SCALE, (yp0 - yp) * conf.PIXEL_SCALE xp, yp = 0., 0. if is_resolved: aeff = reff #* conf.PIXEL_SCALE beff = reff / ab #* conf.PIXEL_SCALE xa = xp + np.cos(np.deg2rad(90-theta)) * np.array([-1, 1]) * aeff ya = yp + np.sin(np.deg2rad(90-theta)) * np.array([-1, 1]) * aeff xb = xp + np.cos(np.deg2rad(theta)) * np.array([-1, 1]) * beff yb = yp + np.sin(np.deg2rad(theta)) * np.array([1, -1]) * beff # tests res_seg = res[blob.segmap==sid].flatten() try: k2, p_norm = stats.normaltest(res_seg) except: k2, p_norm = -99, -99 chi_seg = chi[blob.segmap==sid].flatten() chi_sig = np.std(chi_seg) chi_mu = np.mean(chi_seg) # plotting fig, ax = plt.subplots(ncols=4, nrows=4, figsize=(15, 15)) # row 1 -- image, info if rms > 0.95*np.nanmax(img): normmin = 1.05*np.nanmin(abs(img)) else: normmin = rms normmax = 0.95*np.nanmax(img) if normmin != normmax: norm = LogNorm(normmin, normmax, clip='True') else: norm=None ax[0,0].imshow(img, norm=norm, cmap='Greys', extent=extent) ax[0,0].text(0.05, 1.03, band, transform=ax[0,0].transAxes) ax[0,0].scatter(xp0, yp0, c='purple', marker='+', alpha=0.5) ax[0,0].scatter(xp, yp, c='royalblue', marker='x', alpha=0.9) ax[0,0].set(xlim=xlim, ylim=ylim) ax[0,1].axis('off') ax[0,2].axis('off') ax[0,3].axis('off') ax[0,1].text(0, 0.90, s = f'Source: {sid} | Blob: {bid} | Brick: {blob.brick_id} | RA: {ra:6.6f}, Dec: {dec:6.6f}', transform=ax[0,1].transAxes) if is_resolved: if 'Sersic' in src.name: ax[0,1].text(0, 0.70, s = f'{src.name} with Reff: {reff:3.3f}+/-{reff_err:3.3f}, n: {nre:3.3f}+/-{nre_err:3.3f}, A/B: {ab:3.3f}+/-{ab_err:3.3f}, Theta: {theta:3.3f}+/-{theta_err:3.3f}', transform=ax[0,1].transAxes) else: ax[0,1].text(0, 0.70, s = f'{src.name} with Reff: {reff:3.3f}+/-{reff_err:3.3f}, A/B: {ab:3.3f}+/-{ab_err:3.3f}, and Theta: {theta:3.3f}+/-{theta_err:3.3f}', transform=ax[0,1].transAxes) else: ax[0,1].text(0, 0.70, s = f'{src.name}', transform=ax[0,1].transAxes) ax[0,1].text(0, 0.50, s = f'{band} | {flux:3.3f}+/-{flux_err:3.3f} uJy | {mag:3.3f}+/-{mag_err:3.3f} AB | S/N = {snr:3.3f}', transform=ax[0,1].transAxes) ax[0,1].text(0, 0.30, s = f'Chi2/N: {chi2:3.3f} | N_blob: {n_blob} | '+r'$\mu(\chi)$'+f'={chi_mu:3.3f}, '+r'$\sigma(\chi)$'+f'={chi_sig:3.3f} | K2-test: {k2:3.3f}', transform=ax[0,1].transAxes) # row 2 -- image, weights, mask, segment ax[1,0].imshow(img, vmin=-3*rms, vmax=3*rms, cmap='RdGy', extent=extent) ax[1,0].text(0.05, 1.03, 'Image', transform=ax[1,0].transAxes) ax[1,0].set(xlim=xlim, ylim=ylim) ax[1,0].contour(img, levels=np.arange(2*rms, np.min([5*rms, np.max(img)]), rms), colors='royalblue', extent=extent, alpha=0.5) ax[1,1].imshow(err, cmap='Greys', extent=extent) ax[1,1].text(0.05, 1.03, r'med($\sigma$)'+f'={rms*10**(-0.4 * (zpt - 23.9)):5.5f} uJy', transform=ax[1,1].transAxes) ax[1,1].set(xlim=xlim, ylim=ylim) ax[1,1].contour(img, levels=np.arange(2*rms, np.min([5*rms, np.max(img)]), rms), colors='royalblue', extent=extent, alpha=0.5) ax[1,2].imshow(mask, cmap='Greys', extent=extent) ax[1,2].text(0.05, 1.03, 'Blob', transform=ax[1,2].transAxes) ax[1,2].set(xlim=xlim, ylim=ylim) ax[1,2].contour(img, levels=np.arange(2*rms, np.min([5*rms, np.max(img)]), rms), colors='royalblue', extent=extent, alpha=0.5) ax[1,3].imshow(~seg, cmap='Greys', extent=extent) ax[1,3].text(0.05, 1.03, 'Segment', transform=ax[1,3].transAxes) ax[1,3].set(xlim=xlim, ylim=ylim) ax[1,3].contour(img, levels=np.arange(2*rms, np.min([5*rms, np.max(img)]), rms), colors='royalblue', extent=extent, alpha=0.5) ax[1,0].scatter(xp, yp, c='royalblue', marker='x') ax[1,2].scatter(xp0, yp0, c='purple', marker='+', alpha=0.5) ax[1,2].scatter(xp, yp, c='royalblue', marker='x', alpha=0.9) ax[1,3].scatter(xp0, yp0, c='purple', marker='+', alpha=0.5) ax[1,3].scatter(xp, yp, c='royalblue', marker='x', alpha=0.9) ax[1,2].plot() # row 3 -- image, model, residual, chi ax[2,0].imshow(img/err, vmin=-3, vmax=3, cmap='RdGy', extent=extent) ax[2,0].text(0.05, 1.03, 'S/N', transform=ax[2,0].transAxes) ax[2,0].set(xlim=xlim, ylim=ylim) ax[2,0].contour(img, levels=np.arange(2*rms, np.min([5*rms, np.max(img)]), rms), colors='royalblue', extent=extent, alpha=0.5) # ax[2,1].imshow(mod, vmin=-3*rms, vmax=3*rms, cmap='RdGy', extent=extent) ax[2,1].imshow(mod, norm=norm, cmap='Greys', extent=extent) ax[2,1].text(0.05, 1.03, 'Model', transform=ax[2,1].transAxes) ax[2,1].set(xlim=xlim, ylim=ylim) ax[2,1].contour(img, levels=np.arange(2*rms, np.min([5*rms, np.max(img)]), rms), colors='royalblue', extent=extent, alpha=0.5) ax[2,2].imshow(res, vmin=-3*rms, vmax=3*rms, cmap='RdGy', extent=extent) ax[2,2].text(0.05, 1.03, 'Residual', transform=ax[2,2].transAxes) ax[2,2].set(xlim=xlim, ylim=ylim) ax[2,2].contour(img, levels=np.arange(2*rms, np.min([5*rms, np.max(img)]), rms), colors='royalblue', extent=extent, alpha=0.5) ax[2,3].imshow(chi, vmin=-3, vmax=3, cmap='RdGy', extent=extent) ax[2,3].text(0.05, 1.03, r'$\chi$', transform=ax[2,3].transAxes) ax[2,3].set(xlim=xlim, ylim=ylim) ax[2,3].contour(img, levels=np.arange(2*rms, np.min([5*rms, np.max(img)]), rms), colors='royalblue', extent=extent, alpha=0.5) if is_resolved: ax[2,0].plot(xa, ya, c='royalblue', alpha=0.7) ax[2,0].plot(xb, yb, c='royalblue', alpha=0.7) ax[2,1].plot(xa, ya, c='royalblue', alpha=0.7) ax[2,1].plot(xb, yb, c='royalblue', alpha=0.7) ax[2,2].plot(xa, ya, c='royalblue', alpha=0.7) ax[2,2].plot(xb, yb, c='royalblue', alpha=0.7) ax[2,3].plot(xa, ya, c='royalblue', alpha=0.7) ax[2,3].plot(xb, yb, c='royalblue', alpha=0.7) else: ax[2,0].scatter(xp, yp, c='royalblue', marker='x', alpha=0.7) ax[2,1].scatter(xp, yp, c='royalblue', marker='x', alpha=0.7) ax[2,2].scatter(xp, yp, c='royalblue', marker='x', alpha=0.7) ax[2,3].scatter(xp, yp, c='royalblue', marker='x', alpha=0.7) # row 4 -- psf, x-slice, y-slice, hist psfmodel = blob.psfimg[band] xax = np.arange(-np.shape(psfmodel)[0]/2 + 0.5, np.shape(psfmodel)[0]/2 + 0.5) [ax[3,0].plot(xax * 0.15, psfmodel[x], c='royalblue', alpha=0.5) for x in np.arange(0, np.shape(psfmodel)[0])] ax[3,0].axvline(0, ls='dotted', c='k') ax[3,0].set(xlim=(-5, 5), yscale='log', ylim=(1E-6, 1E-1), xlabel='arcsec') ax[3,0].text(0.05, 1.03, 'PSF', transform=ax[3,0].transAxes) # x slice imgx = blob.images[idx][:, int(xps)] errx = 1./np.sqrt(blob.weights[idx][:, int(xps)]) modx = blob.solution_model_images[idx][:, int(xps)] sign = 1 if bsrc[f'RAWFLUX_{band}'][0] < 0: sign = -1 modxlo = blob.solution_model_images[idx][:, int(xps)] / bsrc[f'RAWFLUX_{band}'][0] * (bsrc[f'RAWFLUX_{band}'][0] - sign * bsrc[f'RAWFLUXERR_{band}'][0]) modxhi = blob.solution_model_images[idx][:, int(xps)] / bsrc[f'RAWFLUX_{band}'][0] * (bsrc[f'RAWFLUX_{band}'][0] + sign * bsrc[f'RAWFLUXERR_{band}'][0]) resx = imgx - modx # y slice imgy = blob.images[idx][int(yps), :] erry = 1./np.sqrt(blob.weights[idx][int(yps), :]) mody = blob.solution_model_images[idx][int(yps), :] if bsrc[f'RAWFLUX_{band}'][0] < 0: sign = -1 modylo = blob.solution_model_images[idx][int(yps), :] / bsrc[f'RAWFLUX_{band}'][0] * (bsrc[f'RAWFLUX_{band}'][0] - sign * bsrc[f'RAWFLUXERR_{band}'][0]) modyhi = blob.solution_model_images[idx][int(yps), :] / bsrc[f'RAWFLUX_{band}'][0] * (bsrc[f'RAWFLUX_{band}'][0] + sign * bsrc[f'RAWFLUXERR_{band}'][0]) resy = imgy - mody ylim = (0.9*np.min([np.min(imgx), np.min(imgy)]), 1.1*np.max([np.max(imgx), np.max(imgy)])) xax = np.linspace(extent[2], extent[3]+conf.PIXEL_SCALE, len(imgx)) ax[3,2].errorbar(xax, imgx, yerr=errx, c='k') ax[3,2].plot(xax, modx, c='r') ax[3,2].fill_between(xax, modxlo, modxhi, color='r', alpha=0.3) ax[3,2].plot(xax, resx, c='g') ax[3,2].axvline(0, ls='dotted', c='k') ax[3,2].set(ylim =ylim, xlabel='arcsec', xlim=xlim) ax[3,2].text(0.05, 1.03, 'Y', transform=ax[3,2].transAxes) yax = np.linspace(extent[0], extent[1]+conf.PIXEL_SCALE, len(imgy)) ax[3,1].errorbar(yax, imgy, yerr=erry, c='k') ax[3,1].plot(yax, mody, c='r') ax[3,1].fill_between(yax, modylo, modyhi, color='r', alpha=0.3) ax[3,1].plot(yax, resy, c='g') ax[3,1].axvline(0, ls='dotted', c='k') ax[3,1].set(ylim=ylim, xlabel='arcsec', xlim=xlim) ax[3,1].text(0.05, 1.03, 'X', transform=ax[3,1].transAxes) hist(chi_seg, ax=ax[3,3], bins='freedman', histtype='step', density=True) ax[3,3].axvline(0, ls='dotted', color='grey') ax[3,3].text(0.05, 1.03, 'Residual '+r'$\sigma(\chi)$'+f'={chi_sig:3.3f}', transform=ax[3,3].transAxes) ax[3,3].set(xlim=(-10, 10), xlabel=r'$\chi$') ax[3,3].axvline(chi_mu, c='royalblue', ls='dashed') ax[3,3].axvline(0, c='grey', ls='dashed', alpha=0.3) ax[3,3].axvline(chi_mu-chi_sig, c='royalblue', ls='dotted') ax[3,3].axvline(chi_mu+chi_sig, c='royalblue', ls='dotted') ax[3,3].axvline(-1, c='grey', ls='dotted', alpha=0.3) ax[3,3].axvline(1, c='grey', ls='dotted', alpha=0.3) pdf.savefig(fig) plt.close() logger.info(f'Saving figure: {outpath}') pdf.close() def plot_apertures(blob, band=None): pass def plot_iterblob(blob, tr, iteration, bands=None): if blob._is_itemblob: sid = blob.bcatalog['source_id'][0] if bands is None: band = conf.MODELING_NICKNAME nickname = conf.MODELING_NICKNAME bidx = [0,] bands = [band,] outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_S{sid}_{conf.MODELING_NICKNAME}_{blob._level}_{blob._sublevel}_{iteration}_iterblob.pdf') else: bidx = [blob._band2idx(b, bands=blob.bands) for b in bands] if bands[0].startswith(conf.MODELING_NICKNAME): nickname = conf.MODELING_NICKNAME else: nickname = conf.MULTIBAND_NICKNAME if len(bands) > 1: outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_S{sid}_{nickname}_{blob._level}_{blob._sublevel}_{iteration}_iterblob.pdf') else: outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_S{sid}_{bands[0]}_{blob._level}_{blob._sublevel}_{iteration}_iterblob.pdf') else: if bands is None: band = conf.MODELING_NICKNAME nickname = conf.MODELING_NICKNAME bidx = [0,] bands = [band,] outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_{conf.MODELING_NICKNAME}_{blob._level}_{blob._sublevel}_{iteration}_iterblob.pdf') else: bidx = [blob._band2idx(b, bands=blob.bands) for b in bands] if bands[0].startswith(conf.MODELING_NICKNAME): nickname = conf.MODELING_NICKNAME else: nickname = conf.MULTIBAND_NICKNAME if len(bands) > 1: outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_{nickname}_{blob._level}_{blob._sublevel}_{iteration}_iterblob.pdf') else: outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_{bands[0]}_{blob._level}_{blob._sublevel}_{iteration}_iterblob.pdf') import matplotlib.backends.backend_pdf pdf = matplotlib.backends.backend_pdf.PdfPages(outpath) for idx, band in zip(bidx, bands): if band == conf.MODELING_NICKNAME: zpt = conf.MODELING_ZPT elif band.startswith(conf.MODELING_NICKNAME): band_name = band[len(conf.MODELING_NICKNAME)+1:] zpt = conf.MULTIBAND_ZPT[blob._band2idx(band_name)] else: zpt = conf.MULTIBAND_ZPT[blob._band2idx(band)] cat = tr.getCatalog() xp, yp = [src.pos[0] for src in cat], [src.pos[1] for src in cat] back = blob.background_images[idx] back_rms = blob.background_rms_images[idx] mean, rms = np.nanmean(back), np.nanmean(back_rms) img_opt = dict(cmap='RdGy', vmin=-5*rms, vmax=5*rms) # image img = blob.images[idx] # model mod = tr.getModelImage(idx) # residual res = img - mod # chi2 chi2 = tr.getChiImage(idx) fig, ax = plt.subplots(ncols=4) ax[0].imshow(img, **img_opt) ax[1].imshow(mod, **img_opt) ax[2].imshow(res, **img_opt) ax[3].imshow(chi2, cmap='RdGy', vmin=-5, vmax=5) fig.suptitle(f'Blob {blob.blob_id} | {band} | iter: {iteration}') [ax[i].scatter(xp, yp, marker='x', c='royalblue') for i in np.arange(4)] [ax[i].set_title(title, fontsize=20) for i, title in enumerate(('Image', 'Model', 'Image-Model', '$\chi^{2}$'))] pdf.savefig(fig) plt.close() logger.info(f'Saving figure: {outpath}') pdf.close() def plot_modprofile(blob, band=None): if band is None: band = conf.MODELING_NICKNAME idx = 0 else: idx = blob._band2idx(band, bands=blob.bands) psfmodel = blob.psfimg[band] back = blob.background_images[idx] back_rms = blob.background_rms_images[idx] mean, rms = np.nanmean(back), np.nanmean(back_rms) noise = np.random.normal(mean, rms, size=blob.dims) tr = blob.solution_tractor norm = LogNorm(mean + 3*rms, blob.images[idx].max(), clip='True') img_opt = dict(cmap='Greys', norm=norm) img_opt = dict(cmap='RdGy', vmin=-5*rms, vmax=5*rms) xlim = (-np.shape(blob.images[idx])[1]/2, np.shape(blob.images[idx])[1]/2) fig, ax = plt.subplots(ncols = 5, nrows = 2, figsize=(20,10)) ax[1,0].imshow(blob.images[idx], **img_opt) ax[1,1].imshow(blob.solution_model_images[idx], **img_opt) residual = blob.images[idx] - blob.solution_model_images[idx] ax[1,2].imshow(residual, **img_opt) xax = np.arange(-np.shape(blob.images[idx])[1]/2, np.shape(blob.images[idx])[1]/2) [ax[0,0].plot(xax * 0.15, blob.images[idx][x], c='royalblue', alpha=0.5) for x in np.arange(0, np.shape(blob.images[idx])[0])] ax[0,0].axvline(0, ls='dotted', c='k') ax[0,0].set(yscale='log', xlabel='arcsec') xax = np.arange(-np.shape(blob.solution_model_images[idx])[1]/2, np.shape(blob.solution_model_images[idx])[1]/2) [ax[0,1].plot(xax * 0.15, blob.solution_model_images[idx][x], c='royalblue', alpha=0.5) for x in np.arange(0, np.shape(blob.solution_model_images[idx])[0])] ax[0,1].axvline(0, ls='dotted', c='k') ax[0,1].set(yscale='log', xlabel='arcsec') xax = np.arange(-np.shape(residual)[1]/2, np.shape(residual)[1]/2) [ax[0,2].plot(xax * 0.15, residual[x], c='royalblue', alpha=0.5) for x in np.arange(0, np.shape(residual)[0])] ax[0,2].axvline(0, ls='dotted', c='k') ax[0,2].set(yscale='log', xlabel='arcsec') norm = LogNorm(1e-5, 0.1*np.nanmax(psfmodel), clip='True') img_opt = dict(cmap='Blues', norm=norm) ax[1,3].imshow(psfmodel, norm=norm, extent=0.15 *np.array([-np.shape(psfmodel)[0]/2, np.shape(psfmodel)[0]/2, -np.shape(psfmodel)[0]/2, np.shape(psfmodel)[0]/2,])) ax[1,3].set(xlim=xlim, ylim=xlim) xax = np.arange(-np.shape(psfmodel)[0]/2 + 0.5, np.shape(psfmodel)[0]/2 + 0.5) [ax[0,3].plot(xax * 0.15, psfmodel[x], c='royalblue', alpha=0.5) for x in np.arange(0, np.shape(psfmodel)[0])] ax[0,3].axvline(0, ls='dotted', c='k') ax[0,3].set(xlim=xlim, yscale='log', ylim=(1E-6, 1E-1), xlabel='arcsec') for j, src in enumerate(blob.solution_catalog): try: mtype = src.name except: mtype = 'PointSource' flux = src.getBrightness().getFlux(band) chisq = blob.solution_chisq[j, idx] band = band.replace(' ', '_') if band == conf.MODELING_NICKNAME: zpt = conf.MODELING_ZPT elif band.startswith(conf.MODELING_NICKNAME): band_name = band[len(conf.MODELING_NICKNAME)+1:] zpt = conf.MULTIBAND_ZPT[blob._band2idx(band_name)] else: zpt = conf.MULTIBAND_ZPT[blob._band2idx(band)] mag = zpt - 2.5 * np.log10(flux) topt = dict(color=colors[j], transform = ax[0, 3].transAxes) ystart = 0.99 - j * 0.5 ax[0, 4].text(1.05, ystart - 0.1, f'{j}) {mtype}', **topt) ax[0, 4].text(1.05, ystart - 0.2, f' F({band}) = {flux:4.4f}', **topt) ax[0, 4].text(1.05, ystart - 0.3, f' M({band}) = {mag:4.4f}', **topt) ax[0, 4].text(1.05, ystart - 0.4, f' zpt({band}) = {zpt:4.4f}', **topt) ax[0, 4].text(1.05, ystart - 0.5, f' $\chi^{2}$ = {chisq:4.4f}', **topt) ax[0, 4].axis('off') ax[1, 4].axis('off') for i in np.arange(3): ax[0, i].set(xlim=(0.15*xlim[0], 0.15*xlim[1]), ylim=(np.nanmedian(blob.images[idx]), blob.images[idx].max())) # ax[1, i].set(xlim=(-15, 15), ylim=(-15, 15)) ax[0, 3].set(xlim=(0.15*xlim[0], 0.15*xlim[1])) if blob._is_itemblob: sid = blob.bcatalog['source_id'][0] outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_S{sid}_{band}_debugprofile.pdf') else: outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_{band}_debugprofile.pdf') logger.info(f'Saving figure: {outpath}') fig.savefig(outpath) plt.close() def plot_xsection(blob, band, src, sid): if band is None: band = conf.MODELING_NICKNAME idx = 0 else: idx = blob._band2idx(band, bands=blob.bands) back = blob.background_images[idx] back_rms = blob.background_rms_images[idx] mean, rms = np.nanmean(back), np.nanmean(back_rms) fig, ax = plt.subplots(ncols=2) posx, posy = src.pos[0], src.pos[1] try: # x slice imgx = blob.images[idx][:, int(posx)] errx = 1/np.sqrt(blob.weights[idx][:, int(posx)]) modx = blob.solution_model_images[idx][:, int(posx)] resx = imgx - modx # y slice imgy = blob.images[idx][int(posy), :] erry = 1/np.sqrt(blob.weights[idx][int(posy), :]) mody = blob.solution_model_images[idx][int(posy), :] resy = imgy - mody except: plt.close() logger.warning('Could not make plot -- object may have escaped?') return # idea: show areas outside segment in grey ylim = (0.9*np.min([np.min(imgx), np.min(imgy)]), 1.1*np.max([np.max(imgx), np.max(imgy)])) xax = np.arange(-np.shape(blob.images[idx])[0]/2, np.shape(blob.images[idx])[0]/2) * conf.PIXEL_SCALE ax[0].errorbar(xax, imgx, yerr=errx, c='k') ax[0].plot(xax, modx, c='r') ax[0].plot(xax, resx, c='g') ax[0].axvline(0, ls='dotted', c='k') ax[0].set(ylim =ylim, xlabel='arcsec') yax = np.arange(-np.shape(blob.images[idx])[1]/2, np.shape(blob.images[idx])[1]/2) * conf.PIXEL_SCALE ax[1].errorbar(yax, imgy, yerr=erry, c='k') ax[1].plot(yax, mody, c='r') ax[1].plot(yax, resy, c='g') ax[1].axvline(0, ls='dotted', c='k') ax[1].set(ylim=ylim, xlabel='arcsec') # for j, src in enumerate(blob.solution_catalog): # try: # mtype = src.name # except: # mtype = 'PointSource' # flux = src.getBrightness().getFlux(band) # chisq = blob.solution_chisq[j, idx] # band = band.replace(' ', '_') # if band == conf.MODELING_NICKNAME: # zpt = conf.MODELING_ZPT # else: # zpt = conf.MULTIBAND_ZPT[idx] # mag = zpt - 2.5 * np.log10(flux) # topt = dict(color=colors[j], transform = ax[0, 3].transAxes) # ystart = 0.99 - j * 0.4 # ax[0, 4].text(1.05, ystart - 0.1, f'{j}) {mtype}', **topt) # ax[0, 4].text(1.05, ystart - 0.2, f' F({band}) = {flux:4.4f}', **topt) # ax[0, 4].text(1.05, ystart - 0.3, f' M({band}) = {mag:4.4f}', **topt) # ax[0, 4].text(1.05, ystart - 0.4, f' $\chi^{2}$ = {chisq:4.4f}', **topt) outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_S{sid}_{band}_xsection.pdf') logger.info(f'Saving figure: {outpath}') fig.savefig(outpath) plt.close() def plot_detblob(blob, fig=None, ax=None, band=None, level=0, sublevel=0, final_opt=False, init=False): if band is None: idx = 0 band = '' else: # print(blob.bands) # print(band) idx = np.argwhere(np.array(blob.bands) == band)[0][0] back = blob.background_images[idx] rms = blob.background_rms_images[idx] mean, rms = np.nanmean(back), np.nanmean(rms) noise = np.random.normal(mean, rms, size=blob.dims) tr = blob.solution_tractor norm = LogNorm(np.max([mean + rms, 1E-5]), blob.images.max(), clip='True') img_opt = dict(cmap='Greys', norm=norm) img_opt = dict(cmap='RdGy', vmin=-5*rms, vmax=5*rms) # Init if fig is None: plt.ioff() fig, ax = plt.subplots(figsize=(24,48), ncols=6, nrows=13) # Detection image ax[0,0].imshow(blob.images[idx], **img_opt) [ax[0,i].axis('off') for i in np.arange(1, 6)] if blob.n_sources == 1: objects = blob.bcatalog e = Ellipse(xy=(objects['x'], objects['y']), width=6*objects['a'], height=6*objects['b'], angle=objects['theta'] * 180. / np.pi) e.set_facecolor('none') e.set_edgecolor(colors[0]) ax[0, 0].add_artist(e) else: for j, src in enumerate(blob.bcatalog): objects = blob.bcatalog[j] e = Ellipse(xy=(objects['x'], objects['y']), width=6*objects['a'], height=6*objects['b'], angle=objects['theta'] * 180. / np.pi) e.set_facecolor('none') e.set_edgecolor(colors[j]) ax[0, 0].add_artist(e) ax[0,1].text(0.1, 0.9, f'Blob #{blob.blob_id}', transform=ax[0,1].transAxes) ax[0,1].text(0.1, 0.8, f'{blob.n_sources} source(s)', transform=ax[0,1].transAxes) [ax[1,i+1].set_title(title, fontsize=20) for i, title in enumerate(('Model', 'Model+Noise', 'Image-Model', '$\chi^{2}$', 'Residuals'))] if blob._is_itemblob: sid = blob.bcatalog['source_id'][0] outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_S{sid}_{conf.MODELING_NICKNAME}_{band}.pdf') else: outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_{conf.MODELING_NICKNAME}_{band}.pdf') fig.savefig(outpath) logger.info(f'Saving figure: {outpath}') elif final_opt: nrow = 4 * level + 2* sublevel + 2 [[ax[i,j].axis('off') for i in np.arange(nrow+1, 11)] for j in np.arange(0, 6)] ax[11,0].axis('off') residual = blob.images[idx] - blob.pre_solution_model_images[idx] ax[11,1].imshow(blob.pre_solution_model_images[idx], **img_opt) ax[11,2].imshow(blob.pre_solution_model_images[idx] + noise, **img_opt) ax[11,3].imshow(residual, cmap='RdGy', vmin=-5*rms, vmax=5*rms) ax[11,4].imshow(blob.tr.getChiImage(idx), cmap='RdGy', vmin = -5, vmax = 5) bins = np.linspace(np.nanmin(residual), np.nanmax(residual), 30) minx, maxx = 0, 0 for i, src in enumerate(blob.bcatalog): res_seg = residual[blob.segmap==src['source_id']].flatten() ax[11,5].hist(res_seg, bins=20, histtype='step', color=colors[i], density=True) resmin, resmax = np.nanmin(res_seg), np.nanmax(res_seg) if resmin < minx: minx = resmin if resmax > maxx: maxx = resmax ax[11,5].set_xlim(minx, maxx) ax[11,5].axvline(0, c='grey', ls='dotted') ax[11,5].set_ylim(bottom=0) ax[12,0].axis('off') residual = blob.images[idx] - blob.solution_model_images[idx] ax[12,1].imshow(blob.solution_model_images[idx], **img_opt) ax[12,2].imshow(blob.solution_model_images[idx] + noise, **img_opt) ax[12,3].imshow(residual, cmap='RdGy', vmin=-5*rms, vmax=5*rms) ax[12,4].imshow(blob.tr.getChiImage(idx), cmap='RdGy', vmin = -5, vmax = 5) ax[12,1].set_ylabel('Solution') bins = np.linspace(np.nanmin(residual), np.nanmax(residual), 30) minx, maxx = 0, 0 for i, src in enumerate(blob.bcatalog): res_seg = residual[blob.segmap==src['source_id']].flatten() ax[12,5].hist(res_seg, bins=20, histtype='step', color=colors[i], density=True) resmin, resmax = np.nanmin(res_seg), np.nanmax(res_seg) if resmin < minx: minx = resmin if resmax > maxx: maxx = resmax ax[12,5].set_xlim(minx, maxx) ax[12,5].axvline(0, c='grey', ls='dotted') ax[12,5].set_ylim(bottom=0) # Solution params for i, src in enumerate(blob.solution_catalog): ax[1,0].text(0.1, 0.8 - 0.4*i, f'#{blob.bcatalog[i]["source_id"]} Model:{src.name}', color=colors[i], transform=ax[1,0].transAxes) ax[1,0].text(0.1, 0.8 - 0.4*i - 0.1, f' Flux: {src.brightness[0]:3.3f}', color=colors[i], transform=ax[1,0].transAxes) ax[1,0].text(0.1, 0.8 - 0.4*i - 0.2, ' '+r'$\chi^{2}$/N:'+f'{blob.solution_chisq[i,0]:3.3f}', color=colors[i], transform=ax[1,0].transAxes) #fig.subplots_adjust(wspace=0, hspace=0) if blob._is_itemblob: sid = blob.bcatalog['source_id'][0] outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_S{sid}_{conf.MODELING_NICKNAME}_{band}.pdf') else: outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_{conf.MODELING_NICKNAME}_{band}.pdf') fig.savefig(outpath) plt.close() logger.info(f'Saving figure: {outpath}') else: if conf.DECISION_TREE == 1: if init: nrow = 4 * level + 2 * sublevel + 1 else: nrow = 4 * level + 2 * sublevel + 2 elif conf.DECISION_TREE == 2: if level == 0: if sublevel == 0: nrow = 1 if sublevel == 1: nrow = 3 if level == 1: if sublevel == 0: nrow = 5 if level == 2: if sublevel == 0: nrow = 7 if level == 3: if sublevel == 0: nrow = 9 if not init: nrow += 1 residual = blob.images[idx] - blob.tr.getModelImage(idx) ax[nrow,0].axis('off') ax[nrow,1].imshow(blob.tr.getModelImage(idx), **img_opt) ax[nrow,2].imshow(blob.tr.getModelImage(idx) + noise, **img_opt) ax[nrow,3].imshow(residual, cmap='RdGy', vmin=-5*rms, vmax=5*rms) ax[nrow,4].imshow(blob.tr.getChiImage(idx), cmap='RdGy', vmin = -5, vmax = 5) if conf.DECISION_TREE == 1: models = {1:'PointSource', 3:'SimpleGalaxy', 5:'ExpGalaxy', 7:'DevGalaxy', 9:'CompositeGalaxy'} elif conf.DECISION_TREE == 2: models = {1:'PointSource', 3:'SimpleGalaxy', 5:'SersicGalaxy', 7:'SersicCoreGalaxy', 9:'CompositeGalaxy'} if init: ax[nrow,1].set_ylabel(models[nrow]) bins = np.linspace(np.nanmin(residual), np.nanmax(residual), 30) minx, maxx = 0, 0 for i, src in enumerate(blob.bcatalog): if np.shape(residual) != np.shape(blob.segmap): plt.figure() plt.imshow(blob.segmap, cmap='Greys', norm=LogNorm()) plt.savefig(os.path.join(conf.PLOT_DIR,'debug_segmap.pdf')) plt.figure() plt.imshow(residual, cmap='Greys', norm=LogNorm()) plt.savefig(os.path.join(conf.PLOT_DIR,'debug_residual.pdf')) res_seg = residual[blob.segmap==src['source_id']].flatten() ax[nrow,5].hist(res_seg, histtype='step', color=colors[i], density=True) resmin, resmax = np.nanmin(res_seg), np.nanmax(res_seg) if resmin < minx: minx = resmin if resmax > maxx: maxx = resmax if not init: ax[nrow,4].text(0.02, 0.9 - 0.1*i, r'$\chi^{2}$/N'+f'={blob.rchisq[i, level, sublevel]:2.2f} | BIC={blob.bic[i, level, sublevel]:2.2f}', color=colors[i], transform=ax[nrow,4].transAxes) ax[nrow,5].set_xlim(minx, maxx) ax[nrow,5].axvline(0, c='grey', ls='dotted') ax[nrow,5].set_ylim(bottom=0) for i, src in enumerate(blob.bcatalog): x, y = src['x'], src['y'] color = colors[i] if not blob._solved[i]: ax[nrow,1].plot([x, x], [y - 10, y - 5], ls='dotted', c=color) ax[nrow,1].plot([x - 10, x - 5], [y, y], ls='dotted', c=color) else: ax[nrow,1].plot([x, x], [y - 10, y - 5], c=color) ax[nrow,1].plot([x - 10, x - 5], [y, y], c=color) if blob._is_itemblob: sid = blob.bcatalog['source_id'][0] outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_S{sid}_{conf.MODELING_NICKNAME}_{band}.pdf') else: outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_{conf.MODELING_NICKNAME}_{band}.pdf') fig.savefig(outpath) logger.info(f'Saving figure: {outpath}') return fig, ax def plot_fblob(blob, band, fig=None, ax=None, final_opt=False, debug=False): idx = np.argwhere(blob.bands == band)[0][0] back = blob.backgrounds[idx] mean, rms = back[0], back[1] noise = np.random.normal(mean, rms, size=blob.dims) tr = blob.solution_tractor norm = LogNorm(np.max([mean + rms, 1E-5]), 0.98*blob.images.max(), clip='True') img_opt = dict(cmap='Greys', norm=norm) img_opt = dict(cmap='RdGy', vmin=-5*rms, vmax=5*rms) if final_opt: ax[2,0].axis('off') residual = blob.images[idx] - blob.solution_model_images[idx] ax[2,1].imshow(blob.solution_model_images[idx], **img_opt) ax[2,2].imshow(blob.solution_model_images[idx] + noise, **img_opt) ax[2,3].imshow(residual, cmap='RdGy', vmin=-5*rms, vmax=5*rms) ax[2,4].imshow(blob.tr.getChiImage(idx), cmap='RdGy', vmin = -5, vmax = 5) ax[2,1].set_ylabel('Solution') bins = np.arange(np.nanpercentile(residua, 5), np.nanpercentile(residual, 95), 0.1) minx, maxx = 0, 0 for i, src in enumerate(blob.bcatalog): img_seg = blob.images[idx][blob.segmap==src['source_id']].flatten() ax[2,5].hist(img_seg, bins=bins, linestyle='dotted', histtype='step', color=colors[i], density=True) res_seg = residual[blob.segmap==src['source_id']].flatten() ax[2,5].hist(res_seg, bins=bins, histtype='step', color=colors[i], density=True) resmin, resmax = np.nanpercentile(res_seg, 5), np.nnanpercentile(res_seg, 95) if resmin < minx: minx = resmin if resmax > maxx: maxx = resmax ax[2,5].set_xlim(-5, 5) #minx, maxx) ax[2,5].axvline(0, c='grey', ls='dotted') ax[2,5].set_ylim(bottom=0) dof = '/N' # Solution params for i, src in enumerate(blob.solution_catalog): original_zpt = np.array(conf.MULTIBAND_ZPT)[idx] target_zpt = 23.9 flux_ujy = src.getBrightness().getFlux(band) * 10 ** (0.4 * (target_zpt - original_zpt)) flux_var = blob.forced_variance fluxerr_ujy = np.sqrt(flux_var[i].brightness.getParams()[idx]) * 10**(0.4 * (target_zpt - original_zpt)) ax[1,0].text(0.1, 0.8 - 0.4*i, f'#{blob.bcatalog[i]["source_id"]} Model:{src.name}', color=colors[i], transform=ax[1,0].transAxes) ax[1,0].text(0.1, 0.8 - 0.4*i - 0.1, f' Flux: {flux_ujy:3.3f}+\-{fluxerr_ujy:3.3f} uJy', color=colors[i], transform=ax[1,0].transAxes) ax[1,0].text(0.1, 0.8 - 0.4*i - 0.2, f' Chi2{dof}: {blob.solution_chisq[i,idx]:3.3f}', color=colors[i], transform=ax[1,0].transAxes) #fig.subplots_adjust(wspace=0, hspace=0) outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_{blob.bands[idx]}.pdf') fig.savefig(outpath) plt.close() logger.info(f'Saving figure: {outpath}') else: # Init if fig is None: plt.ioff() fig, ax = plt.subplots(figsize=(24,8), ncols=6, nrows=3) # Detection image ax[0,0].imshow(blob.images[idx], **img_opt) [ax[0,i].axis('off') for i in np.arange(1, 6)] for j, src in enumerate(blob.bcatalog): objects = blob.bcatalog[j] position = [src[f'x'], src[f'y']] position[0] -= (blob.subvector[1] + blob.mosaic_origin[1] - conf.BRICK_BUFFER) position[1] -= (blob.subvector[0] + blob.mosaic_origin[0] - conf.BRICK_BUFFER) e = Ellipse(xy=(position[0], position[1]), width=6*objects['a'], height=6*objects['b'], angle=objects['theta'] * 180. / np.pi) e.set_facecolor('none') e.set_edgecolor(colors[j]) ax[0, 0].add_artist(e) ax[0,1].text(0.1, 0.9, f'{band} | Blob #{blob.blob_id}', transform=ax[0,1].transAxes) ax[0,1].text(0.1, 0.8, f'{blob.n_sources} source(s)', transform=ax[0,1].transAxes) [ax[0,j].axis('off') for j in np.arange(1, 6)] ax[1,0].axis('off') residual = blob.images[idx] - blob.tr.getModelImage(idx) ax[1,0].axis('off') ax[1,1].imshow(blob.tr.getModelImage(idx), **img_opt) ax[1,2].imshow(blob.tr.getModelImage(idx) + noise, **img_opt) ax[1,3].imshow(residual, cmap='RdGy', vmin=-5*rms, vmax=5*rms) ax[1,4].imshow(blob.tr.getChiImage(idx), cmap='RdGy', vmin = -5, vmax = 5) bins = np.linspace(np.nanmin(residual), np.nanmax(residual), 30) minx, maxx = 0, 0 for i, src in enumerate(blob.bcatalog): res_seg = residual[blob.segmap==src['source_id']].flatten() try: k2, p_norm = stats.normaltest(res_seg) except: k2, p_norm = -99, -99 ax[1,5].hist(res_seg, bins=20, histtype='step', color=colors[i], density=True) resmin, resmax = np.nanmin(res_seg), np.nanmax(res_seg) if resmin < minx: minx = resmin if resmax > maxx: maxx = resmax ax[1,5].text(0.05, 0.9 - 0.1*i, s=f"k={k2:3.3f} (p={p_norm:2.2f})", trasnform=ax[1,5].transAxes) ax[1,5].set_xlim(minx, maxx) ax[1,5].axvline(0, c='grey', ls='dotted') ax[1,5].set_ylim(bottom=0) [ax[1,i+1].set_title(title, fontsize=20) for i, title in enumerate(('Model', 'Model+Noise', 'Image-Model', '$\chi^{2}$', 'Residuals'))] if debug: outpath = os.path.join(conf.PLOT_DIR, f'T{blob.brick_id}_B{blob.blob_id}_{blob.bands[idx]}_DEBUG.pdf') fig.savefig(outpath) plt.close() logger.info(f'DEBUG | Saving figure: {outpath}') return fig, ax def plot_blobmap(brick, image=None, band=None, catalog=None, mode='rms'): if image is None: image = brick.images[0] if band is None: band = brick.bands[0] if catalog is None: catalog = brick.catalog fig, ax = plt.subplots(figsize=(20,20)) # imgs_marked = mark_boundaries(brick.images[0], brick.blobmap, color='red')[:,:,0] imgs_marked = find_boundaries(brick.blobmap, mode='thick').astype(int) imgs_marked[imgs_marked==0] = -99 backlevel, noisesigma = brick.backgrounds[0] if mode == 'log': vmin, vmax = np.max([backlevel + noisesigma, 1E-5]), brick.images[0].max() norm = LogNorm(np.max([backlevel + noisesigma, 1E-5]), 0.9*np.max(image), clip='True') ax.imshow(image, cmap='Greys', origin='lower', norm=norm) elif mode == 'rms': ax.imshow(image - backlevel, cmap='RdGy', origin='lower', vmin=-3*noisesigma, vmax=3*noisesigma) mycmap = plt.cm.Greens mycmap.set_under('k', alpha=0) ax.imshow(imgs_marked, alpha=0.9, cmap=mycmap, vmin=0, zorder=2, origin='lower') ax.scatter(catalog['x'], catalog['y'], marker='+', color='limegreen', s=0.1) ax.add_patch(Rectangle((conf.BRICK_BUFFER, conf.BRICK_BUFFER), conf.BRICK_HEIGHT, conf.BRICK_WIDTH, fill=False, alpha=0.3, edgecolor='purple', linewidth=1)) for i in np.arange(brick.n_blobs): idx, idy = np.nonzero(brick.blobmap == i+1) xlo, xhi = np.min(idx) - conf.BLOB_BUFFER, np.max(idx) + 1 + conf.BLOB_BUFFER ylo, yhi = np.min(idy) - conf.BLOB_BUFFER, np.max(idy) + 1 + conf.BLOB_BUFFER w = xhi - xlo #+ 2 * conf.BLOB_BUFFER h = yhi - ylo #+ 2 * conf.BLOB_BUFFER rect = Rectangle((ylo, xlo), h, w, fill=False, alpha=0.3, edgecolor='red', zorder=3, linewidth=1) ax.add_patch(rect) ax.annotate(str(i+1), (ylo, xlo), color='r', fontsize=2) #ax.scatter(x + width/2., y + height/2., marker='+', c='r') # Add collection to axes #ax.axis('off') out_path = os.path.join(conf.PLOT_DIR, f'B{brick.brick_id}_{band}_{mode}_blobmaster.pdf') ax.axis('off') ax.margins(0,0) fig.suptitle(brick.bands[0]) fig.savefig(out_path, dpi = 300, overwrite=True, pad_inches=0.0) plt.close() logger.info(f'Saving figure: {out_path}') def plot_ldac(tab_ldac, band, xlims=None, ylims=None, box=False, sel=None): fig, ax = plt.subplots() xbin = np.arange(0, 15, 0.1) ybin = np.arange(12, 26, 0.1) ax.hist2d(tab_ldac['FLUX_RADIUS'], tab_ldac['MAG_AUTO'], bins=(xbin, ybin), cmap='Greys', norm=LogNorm()) if box: rect = Rectangle((xlims[0], ylims[0]), xlims[1] - xlims[0], ylims[1] - ylims[0], fill=False, alpha=0.3, edgecolor='r', facecolor=None, zorder=3, linewidth=1) ax.add_patch(rect) if (sel is not None) & box: ax.scatter(tab_ldac['FLUX_RADIUS'][sel], tab_ldac['MAG_AUTO'][sel], s=0.1, c='r') fig.subplots_adjust(bottom = 0.15) ax.set(xlabel='Flux Radius (px)', xlim=(0, 15), ylabel='Mag Auto (AB)', ylim=(26, 12)) ax.grid() if sel is not None: nsel = np.sum(sel) ax.text(x=0.05, y=0.95, s=f'N = {nsel}', transform=ax.transAxes) fig.savefig(os.path.join(conf.PLOT_DIR, f'{band}_box_{box}_ldac.pdf'), overwrite=True) plt.close() def plot_psf(psfmodel, band, show_gaussian=False): fig, ax = plt.subplots(ncols=3, figsize=(30,10)) norm = LogNorm(1e-8, 0.1*np.nanmax(psfmodel), clip='True') img_opt = dict(cmap='Blues', norm=norm) ax[0].imshow(psfmodel, **img_opt, extent=conf.PIXEL_SCALE *np.array([-np.shape(psfmodel)[0]/2, np.shape(psfmodel)[0]/2, -np.shape(psfmodel)[0]/2, np.shape(psfmodel)[0]/2,])) ax[0].set(xlim=(-15,15), ylim=(-15, 15)) ax[0].axvline(0, color='w', ls='dotted') ax[0].axhline(0, color='w', ls='dotted') xax = np.arange(-np.shape(psfmodel)[0]/2 + 0.5, np.shape(psfmodel)[0]/2+0.5) [ax[1].plot(xax * conf.PIXEL_SCALE, psfmodel[x], c='royalblue', alpha=0.5) for x in np.arange(0, np.shape(psfmodel)[1])] ax[1].axvline(0, ls='dotted', c='k') ax[1].set(xlim=(-15, 15), yscale='log', ylim=(1E-6, 1E-1), xlabel='arcsec') if show_gaussian: from scipy.optimize import curve_fit def gaus(x,a,x0,sigma): return a*np.exp(-(x-x0)**2/(2*sigma**2)) mean = 0 sigma = 1 ax[1].plot(xax * conf.PIXEL_SCALE,psfmodel[int(np.shape(psfmodel)[1]/2)], 'r') popt,pcov = curve_fit(gaus,xax * conf.PIXEL_SCALE,psfmodel[int(np.shape(psfmodel)[1]/2)],p0=[1,mean,sigma]) ax[1].plot(xax*conf.PIXEL_SCALE,gaus(xax*conf.PIXEL_SCALE,*popt),'green') x = xax y = x.copy() xv, yv = np.meshgrid(x, y) radius = np.sqrt(xv**2 + xv**2) cumcurve = [np.sum(psfmodel[radius<i]) for i in np.arange(0, np.shape(psfmodel)[0]/2)] ax[2].plot(np.arange(0, np.shape(psfmodel)[0]/2) * conf.PIXEL_SCALE, cumcurve) fig.suptitle(band) figname = os.path.join(conf.PLOT_DIR, f'{band}_psf.pdf') logger.info(f'Saving figure: {figname}') fig.savefig(figname) plt.close(fig)
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import os from unittest import mock import idelib import numpy as np import pandas as pd import pytest import hypothesis as hyp import hypothesis.strategies as hyp_st import hypothesis.extra.numpy as hyp_np import endaq.batch.analyzer from endaq.calc.stats import rms, L2_norm np.random.seed(0) @pytest.fixture() def ide_SSX70065(): with idelib.importFile(os.path.join("tests", "batch", "SSX70065.IDE")) as doc: yield doc @pytest.fixture() def analyzer_raw(): analyzer_mock = mock.create_autospec( endaq.batch.analyzer.CalcCache, spec_set=False, instance=True ) analyzer_mock.MPS2_TO_G = endaq.batch.analyzer.MPS2_TO_G analyzer_mock.MPS_TO_MMPS = endaq.batch.analyzer.MPS_TO_MMPS analyzer_mock.M_TO_MM = endaq.batch.analyzer.M_TO_MM analyzer_mock.PV_NATURAL_FREQS = endaq.batch.analyzer.CalcCache.PV_NATURAL_FREQS return analyzer_mock @pytest.fixture() def analyzer_bulk(analyzer_raw): analyzer_mock = analyzer_raw analyzer_mock._channels = { "acc": mock.Mock(axis_names=list("XYZ")), "gps": mock.Mock(axis_names=["Latitude", "Longitude"]), "spd": mock.Mock(axis_names=["Ground"]), "gyr": mock.Mock(axis_names=list("XYZ")), "mic": mock.Mock(axis_names=["Mic"]), "tmp": mock.Mock(axis_names=["Control"]), "pre": mock.Mock(axis_names=["Control"]), } analyzer_mock._accelerationFs = 3000 analyzer_mock._accelerationData = pd.DataFrame( np.random.random((21, 3)), index=pd.Series(np.arange(21) / 3000, name="time"), columns=pd.Series(["X", "Y", "Z"], name="axis"), ) analyzer_mock._accelerationResultantData = analyzer_mock._accelerationData.apply( L2_norm, axis="columns" ).to_frame() analyzer_mock._microphoneData = pd.DataFrame( np.random.random(21), index=pd.Series(np.arange(21) / 3000, name="time"), columns=pd.Series(["Mic"], name="axis"), ) analyzer_mock._velocityData = pd.DataFrame( np.random.random((21, 3)), index=pd.Series(np.arange(21) / 3000, name="time"), columns=pd.Series(["X", "Y", "Z"], name="axis"), ) analyzer_mock._displacementData = pd.DataFrame( np.random.random((21, 3)), index=pd.Series(np.arange(21) / 3000, name="time"), columns=pd.Series(["X", "Y", "Z"], name="axis"), ) analyzer_mock._pressureData = pd.DataFrame( np.random.random(5), index=pd.Series(np.arange(5) / 5, name="time"), columns=pd.Series(["Control"], name="axis"), ) analyzer_mock._temperatureData = pd.DataFrame( np.random.random(5), index=pd.Series(np.arange(5) / 5, name="time"), columns=pd.Series(["Control"], name="axis"), ) analyzer_mock._gyroscopeData = pd.DataFrame( np.random.random(11), index=pd.Series(np.arange(11) / 5, name="time"), columns=pd.Series(["Gyro"], name="axis"), ) return analyzer_mock # ============================================================================== # Analyzer class tests # ============================================================================== class TestAnalyzer: def test_from_ide_vs_from_literal(self, ide_SSX70065): dataset = ide_SSX70065 calc_params = endaq.batch.analyzer.CalcParams( accel_start_time=None, accel_end_time=None, accel_start_margin=None, accel_end_margin=None, accel_highpass_cutoff=1, accel_integral_tukey_percent=0, accel_integral_zero="mean", psd_freq_bin_width=1, psd_window="hann", pvss_init_freq=1, pvss_bins_per_octave=12, vc_init_freq=1, vc_bins_per_octave=3, ) dataset_cache = endaq.batch.analyzer.CalcCache.from_ide(dataset, calc_params) raw_cache = endaq.batch.analyzer.CalcCache.from_raw_data( [ ( endaq.ide.to_pandas(dataset.channels[32], time_mode="timedelta"), ("Acceleration", "g"), ), ( endaq.ide.to_pandas( dataset.channels[36].subchannels[0], time_mode="timedelta" ), ("Pressure", "Pa"), ), ( endaq.ide.to_pandas( dataset.channels[36].subchannels[1], time_mode="timedelta" ), ("Temperature", "°C"), ), ], calc_params, ) assert set(dataset_cache._channels) == set(raw_cache._channels) for (ds_struct, raw_struct) in ( (dataset_cache._channels[measure_key], raw_cache._channels[measure_key]) for measure_key in dataset_cache._channels ): assert ds_struct.units == raw_struct.units pd.testing.assert_frame_equal( ds_struct.to_pandas(time_mode="timedelta"), raw_struct.to_pandas(time_mode="timedelta"), ) @hyp.given( df=hyp_np.arrays( elements=hyp_st.floats(-1e7, 1e7), shape=(20, 2), dtype=np.float64, ).map( lambda array: pd.DataFrame( array, index=np.timedelta64(200, "ms") * np.arange(20) ) ), ) def test_accelerationData(self, df): calc_params = endaq.batch.analyzer.CalcParams( accel_start_time=None, accel_end_time=None, accel_start_margin=None, accel_end_margin=None, accel_highpass_cutoff=1, accel_integral_tukey_percent=0, accel_integral_zero="mean", psd_freq_bin_width=1, psd_window="hann", pvss_init_freq=1, pvss_bins_per_octave=12, vc_init_freq=1, vc_bins_per_octave=3, ) data_cache = endaq.batch.analyzer.CalcCache.from_raw_data( [(df, ("Acceleration", "m/s\u00b2"))], calc_params ) df_accel = endaq.calc.filters.butterworth( df, low_cutoff=calc_params.accel_highpass_cutoff ) pd.testing.assert_frame_equal(data_cache._accelerationData, df_accel) (_df_accel, df_vel, df_displ) = endaq.calc.integrate.integrals( df_accel, n=2, zero=calc_params.accel_integral_zero, highpass_cutoff=calc_params.accel_highpass_cutoff, tukey_percent=calc_params.accel_integral_tukey_percent, ) pd.testing.assert_frame_equal(data_cache._velocityData, df_vel) pd.testing.assert_frame_equal(data_cache._displacementData, df_displ) def test_accRMSFull(self, analyzer_bulk): calc_result = endaq.batch.analyzer.CalcCache.accRMSFull.func(analyzer_bulk)[ "Resultant" ] expt_result = endaq.batch.analyzer.MPS2_TO_G * rms( analyzer_bulk._accelerationData.apply(L2_norm, axis="columns") ) assert calc_result == pytest.approx(expt_result) def test_velRMSFull(self, analyzer_bulk): calc_result = endaq.batch.analyzer.CalcCache.velRMSFull.func(analyzer_bulk)[ "Resultant" ] expt_result = endaq.batch.analyzer.MPS_TO_MMPS * rms( analyzer_bulk._velocityData.apply(L2_norm, axis="columns") ) assert calc_result == pytest.approx(expt_result) def test_disRMSFull(self, analyzer_bulk): calc_result = endaq.batch.analyzer.CalcCache.disRMSFull.func(analyzer_bulk)[ "Resultant" ] expt_result = endaq.batch.analyzer.M_TO_MM * rms( analyzer_bulk._displacementData.apply(L2_norm, axis="columns") ) assert calc_result == pytest.approx(expt_result) def test_accPeakFull(self, analyzer_bulk): calc_result = endaq.batch.analyzer.CalcCache.accPeakFull.func(analyzer_bulk)[ "Resultant" ] expt_result = endaq.batch.analyzer.MPS2_TO_G * rms( analyzer_bulk._accelerationData.apply(L2_norm, axis="columns").max() ) assert calc_result == pytest.approx(expt_result) def test_pseudoVelPeakFull(self, analyzer_bulk): pass def test_gpsLocFull(self, analyzer_bulk): pass def test_gpsSpeedFull(self, analyzer_bulk): pass def test_gyroRMSFull(self, analyzer_bulk): pass def test_micRMSFull(self, analyzer_bulk): calc_result = endaq.batch.analyzer.CalcCache.micRMSFull.func(analyzer_bulk)[ "Mic" ] expt_result = rms(analyzer_bulk._microphoneData) assert calc_result == pytest.approx(expt_result) def test_pressFull(self, analyzer_bulk): calc_result = endaq.batch.analyzer.CalcCache.pressFull.func(analyzer_bulk)[ "Control" ] expt_result = analyzer_bulk._pressureData.mean() assert calc_result == pytest.approx(expt_result) def test_tempFull(self, analyzer_bulk): calc_result = endaq.batch.analyzer.CalcCache.tempFull.func(analyzer_bulk)[ "Control" ] expt_result = analyzer_bulk._temperatureData.mean() assert calc_result == pytest.approx(expt_result) ########################################################################### # Live File Tests def testLiveFile(self, ide_SSX70065): analyzer = endaq.batch.analyzer.CalcCache.from_ide( ide_SSX70065, endaq.batch.analyzer.CalcParams( accel_start_time=None, accel_end_time=None, accel_start_margin=None, accel_end_margin=None, accel_highpass_cutoff=1, accel_integral_tukey_percent=0, accel_integral_zero="mean", psd_freq_bin_width=1, psd_window="hann", pvss_init_freq=1, pvss_bins_per_octave=12, vc_init_freq=1, vc_bins_per_octave=3, ), ) raw_accel = ide_SSX70065.channels[32].getSession().arrayValues() np.testing.assert_allclose( analyzer.accRMSFull["Resultant"], rms(L2_norm(raw_accel - raw_accel.mean(axis=-1, keepdims=True), axis=0)), rtol=1e-3, ) np.testing.assert_allclose( analyzer.pressFull, 1e-3 * ide_SSX70065.channels[36] .subchannels[0] .getSession() .arrayValues() .mean(), rtol=1e-5, ) np.testing.assert_allclose( analyzer.tempFull, ide_SSX70065.channels[36].subchannels[1].getSession().arrayValues().mean(), rtol=1e-4, ) @pytest.mark.parametrize( "filename", [ os.path.join("tests", "batch", "test1.IDE"), os.path.join("tests", "batch", "test2.IDE"), ], ) def testLiveFiles1(self, filename): ds = idelib.importFile(filename) analyzer = endaq.batch.analyzer.CalcCache.from_ide( ds, endaq.batch.analyzer.CalcParams( accel_start_time=None, accel_end_time=None, accel_start_margin=None, accel_end_margin=None, accel_highpass_cutoff=1, accel_integral_tukey_percent=0, accel_integral_zero="mean", psd_freq_bin_width=1, psd_window="hann", pvss_init_freq=1, pvss_bins_per_octave=12, vc_init_freq=1, vc_bins_per_octave=3, ), ) raw_accel = ds.channels[8].getSession().arrayValues(subchannels=[0, 1, 2]) np.testing.assert_allclose( analyzer.accRMSFull["Resultant"], rms(L2_norm(raw_accel - raw_accel.mean(axis=-1, keepdims=True), axis=0)), rtol=0.55, # this is... probably a little too high... ) audio_scale = 5.307530522779073 np.testing.assert_allclose( analyzer.micRMSFull, audio_scale * rms(ds.channels[8].subchannels[3].getSession().arrayValues()), rtol=1e-3, ) np.testing.assert_allclose( analyzer.gyroRMSFull["Resultant"], rms(L2_norm(ds.channels[84].getSession().arrayValues(), axis=0)), rtol=1e-2, ) np.testing.assert_allclose( analyzer.pressFull, 1e-3 * ds.channels[36].subchannels[0].getSession().arrayValues().mean(), rtol=1e-3, ) np.testing.assert_allclose( analyzer.tempFull, ds.channels[36].subchannels[1].getSession().arrayValues().mean(), rtol=0.05, # ...GEFGW? ) np.testing.assert_allclose( analyzer.humidFull, ds.channels[59].subchannels[2].getSession().arrayValues().mean(), ) @pytest.mark.parametrize( "filename", [ os.path.join("tests", "batch", "DAQ12006_000005.IDE"), ], ) def testLiveFiles2(self, filename): """ Checks that audio in units of Pascals are properly handled. """ ds = idelib.importFile(filename) analyzer = endaq.batch.analyzer.CalcCache.from_ide( ds, endaq.batch.analyzer.CalcParams( accel_start_time=None, accel_end_time=None, accel_start_margin=None, accel_end_margin=None, accel_highpass_cutoff=1, accel_integral_tukey_percent=0, accel_integral_zero="mean", psd_freq_bin_width=1, psd_window="hann", pvss_init_freq=1, pvss_bins_per_octave=12, vc_init_freq=1, vc_bins_per_octave=3, ), ) np.testing.assert_allclose( analyzer.micRMSFull, rms(ds.channels[8].subchannels[3].getSession().arrayValues()), rtol=1e-3, ) @pytest.mark.parametrize( "filename", [ os.path.join("tests", "batch", "test3.IDE"), ], ) def testLiveFile3(self, filename): ds = idelib.importFile(filename) analyzer = endaq.batch.analyzer.CalcCache.from_ide( ds, endaq.batch.analyzer.CalcParams( accel_start_time=None, accel_end_time=None, accel_start_margin=None, accel_end_margin=None, accel_highpass_cutoff=1, accel_integral_tukey_percent=0, accel_integral_zero="mean", psd_freq_bin_width=1, psd_window="hann", pvss_init_freq=1, pvss_bins_per_octave=12, vc_init_freq=1, vc_bins_per_octave=3, ), ) ch_rot = ds.channels[84] np.testing.assert_allclose( analyzer.gyroRMSFull["Resultant"], rms(L2_norm(ch_rot.getSession().arrayValues(), axis=0)), rtol=0.005, ) @pytest.mark.parametrize( "filename, sample_index", [ (os.path.join("tests", "batch", "test_GPS_2.IDE"), -2), (os.path.join("tests", "batch", "test_GPS_3.IDE"), -4), ], ) def testLiveFileGPS(self, filename, sample_index): ds = idelib.importFile(filename) analyzer = endaq.batch.analyzer.CalcCache.from_ide( ds, endaq.batch.analyzer.CalcParams( accel_start_time=None, accel_end_time=None, accel_start_margin=None, accel_end_margin=None, accel_highpass_cutoff=1, accel_integral_tukey_percent=0, accel_integral_zero="mean", psd_freq_bin_width=1, psd_window="hann", pvss_init_freq=1, pvss_bins_per_octave=12, vc_init_freq=1, vc_bins_per_octave=3, ), ) ch_gps = ds.channels[88] # confirming channel format for gps file assert ch_gps.subchannels[0].name == "Latitude" assert ch_gps.subchannels[1].name == "Longitude" assert ch_gps.subchannels[3].name == "Ground Speed" assert tuple(analyzer.gpsLocFull) == tuple( ch_gps.getSession().arrayValues()[[0, 1], sample_index], ) # Resampling throws off mean calculation # -> use resampled data for comparsion # gps_speed = analyzer.MPS_TO_KMPH * ch_gps.getSession().arrayValues(subchannels=[3]) gps_speed = analyzer._gpsSpeedData np.testing.assert_allclose( analyzer.gpsSpeedFull, np.mean(gps_speed[gps_speed != 0]), )
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from numpy import array def scigrid_2011_01_04_13(): ppc = {"version": '2'} ppc["baseMVA"] = 100.0 ppc["bus"] = array([ [586, 3, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [589, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [590, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [593, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [594, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [595, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [598, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [599, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [601, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [602, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [603, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [607, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [608, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [609, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [612, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [613, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [614, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [616, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [617, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [618, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [619, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [621, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [623, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [624, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [628, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [629, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [632, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [637, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [638, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [640, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [641, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [642, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [643, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [646, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [647, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [650, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [652, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [655, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [661, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [663, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [666, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [668, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [670, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [672, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [676, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [681, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [683, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [687, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [689, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [691, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [693, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [694, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [695, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [696, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [697, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [698, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [702, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [704, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [705, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [707, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [708, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [711, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [713, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [714, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [716, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [717, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [719, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [722, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [724, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [727, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [728, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [730, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [731, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [732, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [735, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [737, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [738, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [741, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [742, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [743, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [746, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [747, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [748, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [749, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [750, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [753, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [758, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [760, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [762, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [763, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [765, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [767, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [769, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [771, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [772, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [774, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [776, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [777, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [778, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [781, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [784, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [785, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [787, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [788, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [789, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [791, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [792, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [795, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [801, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [802, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [805, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [806, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [808, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [809, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [811, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [814, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [816, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [817, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [821, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [822, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [826, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [829, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [830, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9 ], [835, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [836, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [837, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [839, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9 ], [841, 2, 0, 0, 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38.07556, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1932, 10.876649, 0, 9999, -9999, 1.0, 100, 1, 46.722379, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1933, 10.473207, 0, 9999, -9999, 1.0, 100, 1, 44.239188, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1934, 117.537767, 0, 9999, -9999, 1.0, 100, 1, 383.418198, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1935, 34.374027, 0, 9999, -9999, 1.0, 100, 1, 62.335643, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1936, 2.950282, 0, 9999, -9999, 1.0, 100, 1, 6.00797, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1937, 70.310402, 0, 9999, -9999, 1.0, 100, 1, 134.605733, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1938, 10.589034, 0, 9999, -9999, 1.0, 100, 1, 89.425619, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1939, 34.352206, 0, 9999, -9999, 1.0, 100, 1, 103.003683, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1940, 8.662273, 0, 9999, -9999, 1.0, 100, 1, 18.980829, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1941, 28.419023, 0, 9999, -9999, 1.0, 100, 1, 104.495097, 0.0, 0, 0, 0, 0, 0, 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9999, -9999, 1.0, 100, 1, 67.723951, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1953, 5.075202, 0, 9999, -9999, 1.0, 100, 1, 8.928556, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1954, 7.198599, 0, 9999, -9999, 1.0, 100, 1, 12.726892, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1955, 3.726843, 0, 9999, -9999, 1.0, 100, 1, 6.625255, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1956, 21.634918, 0, 9999, -9999, 1.0, 100, 1, 38.724888, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1957, 38.55709, 0, 9999, -9999, 1.0, 100, 1, 131.682322, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1958, 26.153938, 0, 9999, -9999, 1.0, 100, 1, 59.791759, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1959, 5.715564, 0, 9999, -9999, 1.0, 100, 1, 35.986928, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1960, 7.46671, 0, 9999, -9999, 1.0, 100, 1, 13.579895, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1961, 10.128456, 0, 9999, -9999, 1.0, 100, 1, 17.841481, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1962, 1.777909, 0, 9999, -9999, 1.0, 100, 1, 3.150179, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1963, 0.405646, 0, 9999, -9999, 1.0, 100, 1, 0.73138, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1964, 3.914947, 0, 9999, -9999, 1.0, 100, 1, 66.594121, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1965, 1.169719, 0, 9999, -9999, 1.0, 100, 1, 18.785491, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1966, 1.10539, 0, 9999, -9999, 1.0, 100, 1, 2.674199, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1967, 58.064298, 0, 9999, -9999, 1.0, 100, 1, 99.074235, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1968, 115.522641, 0, 9999, -9999, 1.0, 100, 1, 201.733891, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1969, 8.791022, 0, 9999, -9999, 1.0, 100, 1, 15.048118, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1970, 146.011633, 0, 9999, -9999, 1.0, 100, 1, 236.871781, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1971, 8.074517, 0, 9999, -9999, 1.0, 100, 1, 14.404409, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1972, 0.015521, 0, 9999, -9999, 1.0, 100, 1, 0.028378, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1973, 0.292438, 0, 9999, -9999, 1.0, 100, 1, 0.534696, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1974, 1.504534, 0, 9999, -9999, 1.0, 100, 1, 2.750907, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1975, 36.887137, 0, 9999, -9999, 1.0, 100, 1, 81.92918, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1976, 1.406463, 0, 9999, -9999, 1.0, 100, 1, 2.17499, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1977, 122.592738, 0, 9999, -9999, 1.0, 100, 1, 226.383637, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1978, 0.706432, 0, 9999, -9999, 1.0, 100, 1, 1.331592, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1979, 7.337064, 0, 9999, -9999, 1.0, 100, 1, 189.722792, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1980, 52.239056, 0, 9999, -9999, 1.0, 100, 1, 100.61941, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1981, 79.320419, 0, 9999, -9999, 1.0, 100, 1, 144.682717, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1982, 69.614382, 0, 9999, -9999, 1.0, 100, 1, 134.93778, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1983, 67.761201, 0, 9999, -9999, 1.0, 100, 1, 155.990147, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1984, 43.229883, 0, 9999, -9999, 1.0, 100, 1, 94.470611, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1985, 18.449298, 0, 9999, -9999, 1.0, 100, 1, 41.975835, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1986, 108.814425, 0, 9999, -9999, 1.0, 100, 1, 298.346979, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1987, 189.251646, 0, 9999, -9999, 1.0, 100, 1, 393.914067, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1988, 144.661852, 0, 9999, -9999, 1.0, 100, 1, 251.944939, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1989, 6.390172, 0, 9999, -9999, 1.0, 100, 1, 10.378288, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1990, 30.763561, 0, 9999, -9999, 1.0, 100, 1, 50.351426, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1991, 399.179592, 0, 9999, -9999, 1.0, 100, 1, 849.576944, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1992, 125.280995, 0, 9999, -9999, 1.0, 100, 1, 233.477991, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1993, 121.464694, 0, 9999, -9999, 1.0, 100, 1, 242.698643, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1994, 45.014021, 0, 9999, -9999, 1.0, 100, 1, 255.834576, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1995, 80.531008, 0, 9999, -9999, 1.0, 100, 1, 262.446698, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1996, 20.04469, 0, 9999, -9999, 1.0, 100, 1, 91.306832, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1997, 9.226652, 0, 9999, -9999, 1.0, 100, 1, 26.592561, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1998, 5.256357, 0, 9999, -9999, 1.0, 100, 1, 12.126511, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1999, 67.284228, 0, 9999, -9999, 1.0, 100, 1, 199.184531, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2000, 253.175263, 0, 9999, -9999, 1.0, 100, 1, 579.835051, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2001, 28.564477, 0, 9999, -9999, 1.0, 100, 1, 122.315703, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2002, 2.736658, 0, 9999, -9999, 1.0, 100, 1, 30.606436, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2003, 12.738107, 0, 9999, -9999, 1.0, 100, 1, 23.645071, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2004, 8.692812, 0, 9999, -9999, 1.0, 100, 1, 17.73338, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2005, 28.121382, 0, 9999, -9999, 1.0, 100, 1, 72.071456, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2006, 25.924839, 0, 9999, -9999, 1.0, 100, 1, 59.660888, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2007, 0.938003, 0, 9999, -9999, 1.0, 100, 1, 1.681507, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2008, 0.065103, 0, 9999, -9999, 1.0, 100, 1, 0.116706, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ]) ppc["branch"] = array([ [586, 1, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ], [589, 108, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ], [590, 108, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ], [593, 112, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ], [594, 114, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ], [595, 115, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ], [598, 118, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360 ], [599, 119, 0, 1e-05, 0, 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[ "numpy.array" ]
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2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1182, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1183, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1184, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1185, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1186, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1187, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1188, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1189, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1190, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1191, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1192, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1196, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1197, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1198, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1199, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1200, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1204, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1206, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1208, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1211, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1212, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1213, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1214, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1215, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1216, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1217, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1218, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1219, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1220, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1221, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1222, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1224, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1225, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1226, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1227, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1229, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1230, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1231, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1232, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1233, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1235, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1236, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1237, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1238, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1239, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1240, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1241, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1242, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1243, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1244, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1245, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1246, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1247, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1248, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1249, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1250, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1251, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1252, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1253, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1254, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1255, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1256, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1257, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1258, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1259, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1260, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1261, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1264, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1266, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1267, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1268, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1269, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1270, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1274, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1275, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1276, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1277, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1278, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1280, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1281, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1282, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1283, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1285, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1286, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1287, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1288, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1289, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1290, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1291, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1292, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1293, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1294, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1295, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1296, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1297, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1298, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1299, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1300, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1301, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1302, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1303, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1306, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1307, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1308, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1312, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1316, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1317, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1319, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1323, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1326, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1327, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1328, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1329, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1331, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1333, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1336, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1337, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1339, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1340, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1345, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1346, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1348, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1349, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1356, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1357, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1359, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1360, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1361, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1362, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1366, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1367, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1372, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1373, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1374, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1375, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1376, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1377, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1378, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1379, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1380, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1381, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1382, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1383, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1384, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1385, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1386, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1387, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1388, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1389, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1390, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1391, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1392, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1393, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1394, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1395, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1396, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1397, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1398, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1399, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1400, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1401, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1402, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1403, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1404, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1405, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1406, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1407, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1408, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1409, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1410, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1411, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1418, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1419, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1421, 2, 0, 0, 0, 0, 0, 0.999501, 0, 220.0, 0, 1.1, 0.9], [1422,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1423, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1424, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1425, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1426,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1427, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1428, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1429, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1431,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1432, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1433, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1434, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1435,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1436, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1437, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1438, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1439,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1440, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1443, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1444, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1445,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1446, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1447, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1448, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1449,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1450, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1451, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1452, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1453,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1454, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1455, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1456, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1457,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1458, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1459, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1460, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1461,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1462, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1463, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1464, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1465,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1466, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1467, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1468, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1469,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1470, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1471, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1472, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1473,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1474, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1475, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1476, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1477,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1483, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1484, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1485, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1486,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1489, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1490, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1491, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1492,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1493, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1494, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1495, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1497,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1498, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1501, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1503, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1504,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1505, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1506, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1507, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1510,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1511, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1512, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1513, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1518,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1519, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1520, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1521, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1522,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1523, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1524, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1525, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1526,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1527, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1528, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1529, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1530,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1531, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1532, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1534, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1535,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1536, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1537, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1538, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1539,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1540, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1541, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1542, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1543,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1544, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1545, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1546, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1547,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1548, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1549, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1550, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1551,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1552, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1553, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1554, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1555,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1556, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1557, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1558, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1559,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1560, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1561, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1562, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1563,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1564, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1565, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1566, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1567,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1568, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1569, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1570, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1571,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1572, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1573, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1574, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1575,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1576, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1577, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1578, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1579,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1580, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1581, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1582, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1583,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1584, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1585, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1586, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1587,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1588, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1589, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1590, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1591,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1592, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1593, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1594, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1595,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1596, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1597, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1598, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1599,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1600, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1601, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1602, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1603,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1604, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1605, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1606, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1607,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1608, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1609, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1610, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1611,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1612, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1613, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1614, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1615,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1616, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1617, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1618, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1619,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1620, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1621, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1622, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1623,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1624, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1625, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1626, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1627,\n 2, 0, 0, 0, 0, 0, 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1.0, 0, 380.0, 0, 1.1, 0.9], [1664, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1665, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1666, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1667,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1668, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1669, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1670, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1671,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1672, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1673, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1674, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1675,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1676, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1677, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1678, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1679,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1680, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1681, 2, 0, 0, 0, 0, 0, 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1.0, 0, 220.0, 0,\n 1.1, 0.9], [1718, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1719,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1720, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1721, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1722, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1723,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1724, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1725, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1726, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1727,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1728, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1729, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1730, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1731,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1732, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1733, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1734, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1735,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1736, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1737, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1738, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1739,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1740, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1741, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1742, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1743,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1744, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1745, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1746, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1747,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1748, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1749, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1750, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1751,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1752, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1753, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1754, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1755,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1756, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1757, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1758, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1759,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1760, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1761, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1762, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1763,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1764, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1765, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1766, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1767,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1768, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1769, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1770, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1771,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1772, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1773, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1774, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1775,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1776, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1777, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1778, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1779,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1780, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1781, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1782, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1783,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1784, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1785, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1786, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1787,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1788, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1789, 2, 0, 0, 0, 0, 0, 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1.0, 0, 220.0, 0, 1.1, 0.9], [1846, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1847, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1848, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1849,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1850, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1851, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1852, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1853,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1854, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1855, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1856, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1857,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1858, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1860, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1861, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1862,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1863, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1864, 2, 0, 0, 0, 0, 0, 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1.0, 0, 220.0, 0, 1.1, 0.9], [1883, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1884, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1885, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1886,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1887, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1888, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1889, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1890,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1891, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1892, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1893, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1894,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1895, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1896, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1897, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1898,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1899, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1900, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1901, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1902,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1903, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1904, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1905, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1906,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1907, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1908, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1909, 2, 0, 0, 0, 0, 0, 0.999501, 0, 220.0, 0, 1.1, 0.9], [\n 1910, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1911, 2, 0, 0, 0,\n 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1912, 2, 0, 0, 0, 0, 0, 1.0, 0, \n 380.0, 0, 1.1, 0.9], [1913, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, \n 0.9], [1914, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1915, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1916, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1917, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1918, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1919, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1920, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1921, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1922, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1923, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1924, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1925, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1926, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1927, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1928, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1929, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1930, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1931, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1932, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1933, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1934, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1935, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1936, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1937, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1938, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1939, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1940, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1941, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1942, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1943, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1944, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1945, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1946, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1947, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1948, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1949, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1950, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1951, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1952, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1953, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1954, 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2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1405, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1406, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1407, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1408, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1409, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1410, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1,\n 0.9], [1411, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1418, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1419, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 380.0, 0, 1.1, 0.9], [1421, 2, 0, 0, 0, 0, 0, 0.999501, 0, 220.0, 0,\n 1.1, 0.9], [1422, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1423,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1424, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1425, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1426, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1427,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1428, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], 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[1450,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1451, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1452, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1453, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1454,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1455, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1456, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1457, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1458,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1459, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1460, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1461, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1462,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1463, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1464, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1465, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1466,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1467, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1468, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1469, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1470,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1471, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1472, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1473, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1474,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1475, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1476, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1477, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1483,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1484, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1485, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1486, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1489,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1490, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1491, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1492, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1493,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1494, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1495, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1497, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1498,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1501, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1503, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1504, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1505,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1506, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1507, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1510, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1511,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1512, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1513, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1518, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1519,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1520, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1521, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1522, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1523,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1524, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1525, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1526, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1527,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1528, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1529, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1530, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1531,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1532, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1534, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1535, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1536,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1537, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1538, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1539, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1540,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1541, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1542, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1543, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1544,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1545, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1546, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1547, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1548,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1549, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1550, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1551, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1552,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1553, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1554, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1555, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1556,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1557, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1558, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1559, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1560,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1561, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1562, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1563, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1564,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1565, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1566, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1567, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1568,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1569, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1570, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1571, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1572,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1573, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1574, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1575, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1576,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1577, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1578, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1579, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1580,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1581, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1582, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0,\n 1.1, 0.9], [1583, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1584,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1585, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1586, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1587, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1588,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1589, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1590, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1591, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1592,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1593, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1594, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1595, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1596,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1597, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1598, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1599, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1600,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1601, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 220.0, 0, 1.1, 0.9], [1602, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1603, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1604,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1605, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1606, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1607, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1608,\n 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], [1609, 2, 0, 0, 0, 0, 0,\n 1.0, 0, 380.0, 0, 1.1, 0.9], [1610, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0,\n 1.1, 0.9], [1611, 2, 0, 0, 0, 0, 0, 1.0, 0, 380.0, 0, 1.1, 0.9], 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1.0, 0, 220.0, 0, 1.1, 0.9], [1993, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1994, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1995, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1996, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1997, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [1998, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [1999, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [2000, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [2001, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [2002, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [2003, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [2004, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [2005, 2, 0, 0, 0, 0, 0, 1.0, \n 0, 220.0, 0, 1.1, 0.9], [2006, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1,\n 0.9], [2007, 2, 0, 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [2008, 2, 0,\n 0, 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [1, 1, 331.244507, 66.248901, \n 0, 0, 0, 1.0, 0, 220.0, 0, 1.1, 0.9], [2, 1, 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0, 0, 0, 0], [650, \n 1324.5, 0, 9999, -9999, 1.0, 100, 1, 1324.5, 0.0, 0, 0, 0, 0, 0, 0, 0, \n 0, 0, 0, 0], [652, 46.9, 0, 9999, -9999, 1.0, 100, 1, 46.9, 0.0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0], [655, 61.5, 0, 9999, -9999, 1.0, 100, 1, \n 61.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [661, 32.7, 0, 9999, -9999,\n 1.0, 100, 1, 32.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [663, 15.0, 0,\n 9999, -9999, 1.0, 100, 1, 15.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [666, 28.9, 0, 9999, -9999, 1.0, 100, 1, 28.9, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [668, 766.0, 0, 9999, -9999, 1.0, 100, 1, 766.0, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [670, 24.0, 0, 9999, -9999, 1.0, 100, 1, \n 24.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [672, 33.1, 0, 9999, -9999,\n 1.0, 100, 1, 33.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [676, 370.0, \n 0, 9999, -9999, 1.0, 100, 1, 370.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n 0], [681, 40.1, 0, 9999, -9999, 1.0, 100, 1, 40.1, 0.0, 0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0], [683, 27.5, 0, 9999, -9999, 1.0, 100, 1, 27.5, 0.0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [687, 1329.0, 0, 9999, -9999, 1.0, \n 100, 1, 1329.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [689, 310.0, 0, \n 9999, -9999, 1.0, 100, 1, 310.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [691, 26.0, 0, 9999, -9999, 1.0, 100, 1, 26.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [693, 194.0, 0, 9999, -9999, 1.0, 100, 1, 194.0, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [694, 16.4, 0, 9999, -9999, 1.0, 100, 1, \n 16.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [695, 14.7, 0, 9999, -9999,\n 1.0, 100, 1, 14.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [696, 721.0, \n 0, 9999, -9999, 1.0, 100, 1, 721.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n 0], [697, 11.6, 0, 9999, -9999, 1.0, 100, 1, 11.6, 0.0, 0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0], [698, 24.0, 0, 9999, -9999, 1.0, 100, 1, 24.0, 0.0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [702, 73.4, 0, 9999, -9999, 1.0, 100,\n 1, 73.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [704, 508.0, 0, 9999, -\n 9999, 1.0, 100, 1, 508.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [705, \n 17.0, 0, 9999, -9999, 1.0, 100, 1, 17.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [707, 34.0, 0, 9999, -9999, 1.0, 100, 1, 34.0, 0.0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0], [708, 7.8, 0, 9999, -9999, 1.0, 100, 1, 7.8, 0.0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [711, 88.484779, 0, 9999, -9999, 1.0,\n 100, 1, 176.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [713, 13.4, 0, \n 9999, -9999, 1.0, 100, 1, 13.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [714, 15.0, 0, 9999, -9999, 1.0, 100, 1, 15.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [716, 0.1, 0, 9999, -9999, 1.0, 100, 1, 0.1, 0.0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0], [717, 11.0, 0, 9999, -9999, 1.0, 100, 1, 11.0,\n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [719, 1328.962186, 0, 9999, -\n 9999, 1.0, 100, 1, 1958.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [722,\n 20.7, 0, 9999, -9999, 1.0, 100, 1, 20.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [724, 12.1, 0, 9999, -9999, 1.0, 100, 1, 12.1, 0.0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0], [727, 61.5, 0, 9999, -9999, 1.0, 100, 1, 61.5, \n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [728, 510.0, 0, 9999, -9999, 1.0,\n 100, 1, 510.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [730, 633.2, 0, \n 9999, -9999, 1.0, 100, 1, 633.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [731, 540.174348, 0, 9999, -9999, 1.0, 100, 1, 895.0, 0.0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0], [732, 14.6, 0, 9999, -9999, 1.0, 100, 1, 14.6, \n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [735, 84.8, 0, 9999, -9999, 1.0,\n 100, 1, 84.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [737, 28.0, 0, \n 9999, -9999, 1.0, 100, 1, 28.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [738, 138.5, 0, 9999, -9999, 1.0, 100, 1, 138.5, 0.0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0], [741, 214.0, 0, 9999, -9999, 1.0, 100, 1, 214.0, 0.0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [742, 9.0, 0, 9999, -9999, 1.0, 100, 1, \n 9.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [743, 220.991864, 0, 9999, \n -9999, 1.0, 100, 1, 1410.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [746,\n 100.0, 0, 9999, -9999, 1.0, 100, 1, 100.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0], [747, 12.5, 0, 9999, -9999, 1.0, 100, 1, 12.5, 0.0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0], [748, 110.0, 0, 9999, -9999, 1.0, 100, 1, \n 110.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [749, 16.0, 0, 9999, -\n 9999, 1.0, 100, 1, 16.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [750, \n 90.8, 0, 9999, -9999, 1.0, 100, 1, 90.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [753, 116.851068, 0, 9999, -9999, 1.0, 100, 1, 311.8, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [758, 18.5, 0, 9999, -9999, 1.0, 100, 1, \n 18.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [760, 298.788806, 0, 9999,\n -9999, 1.0, 100, 1, 794.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [762,\n 700.835089, 0, 9999, -9999, 1.0, 100, 1, 1105.0, 0.0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0], [763, 20.3, 0, 9999, -9999, 1.0, 100, 1, 20.3, 0.0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [765, 59.0, 0, 9999, -9999, 1.0, 100, 1,\n 59.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [767, 11.2, 0, 9999, -9999,\n 1.0, 100, 1, 11.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [769, 43.3, 0,\n 9999, -9999, 1.0, 100, 1, 43.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [771, 690.0, 0, 9999, -9999, 1.0, 100, 1, 690.0, 0.0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0], [772, 18.8, 0, 9999, -9999, 1.0, 100, 1, 18.8, 0.0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [774, 33.5, 0, 9999, -9999, 1.0, 100, 1,\n 33.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [776, 54.222128, 0, 9999, \n -9999, 1.0, 100, 1, 56.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [777, \n 79.0, 0, 9999, -9999, 1.0, 100, 1, 79.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [778, 14.7, 0, 9999, -9999, 1.0, 100, 1, 14.7, 0.0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0], [781, 973.218708, 0, 9999, -9999, 1.0, 100, 1, \n 1310.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [784, 802.511044, 0, \n 9999, -9999, 1.0, 100, 1, 1275.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [785, 3.0, 0, 9999, -9999, 1.0, 100, 1, 3.0, 0.0, 0, 0, 0, 0, 0, 0, 0, \n 0, 0, 0, 0], [787, 778.0, 0, 9999, -9999, 1.0, 100, 1, 778.0, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [788, 875.0, 0, 9999, -9999, 1.0, 100, 1, \n 875.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [789, 77.4, 0, 9999, -\n 9999, 1.0, 100, 1, 77.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [791, \n 10.0, 0, 9999, -9999, 1.0, 100, 1, 10.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [792, 62.7, 0, 9999, -9999, 1.0, 100, 1, 62.7, 0.0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0], [795, 13.6, 0, 9999, -9999, 1.0, 100, 1, 13.6, \n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [801, 50.0, 0, 9999, -9999, 1.0,\n 100, 1, 50.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [802, 500.0, 0, \n 9999, -9999, 1.0, 100, 1, 500.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [805, 418.686348, 0, 9999, -9999, 1.0, 100, 1, 1410.0, 0.0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0], [806, 35.8, 0, 9999, -9999, 1.0, 100, 1, 35.8, \n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [808, 217.5, 0, 9999, -9999, 1.0,\n 100, 1, 217.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [809, 12.5, 0, \n 9999, -9999, 1.0, 100, 1, 12.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [811, 25.2, 0, 9999, -9999, 1.0, 100, 1, 25.2, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [814, 89.0, 0, 9999, -9999, 1.0, 100, 1, 89.0, 0.0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0], [816, 80.1, 0, 9999, -9999, 1.0, 100, 1, \n 80.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [817, 54.0, 0, 9999, -9999,\n 1.0, 100, 1, 54.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [821, 82.5, 0,\n 9999, -9999, 1.0, 100, 1, 82.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [822, 134.0, 0, 9999, -9999, 1.0, 100, 1, 134.0, 0.0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0], [826, 58.0, 0, 9999, -9999, 1.0, 100, 1, 58.0, 0.0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [829, 204.799653, 0, 9999, -9999, 1.0, \n 100, 1, 211.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [830, 89.0, 0, \n 9999, -9999, 1.0, 100, 1, 89.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [835, 63.7, 0, 9999, -9999, 1.0, 100, 1, 63.7, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [836, 25.5, 0, 9999, -9999, 1.0, 100, 1, 25.5, 0.0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0], [837, 472.0, 0, 9999, -9999, 1.0, 100, 1, \n 472.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [839, 73.3, 0, 9999, -\n 9999, 1.0, 100, 1, 73.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [841, \n 23.3, 0, 9999, -9999, 1.0, 100, 1, 23.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [843, 333.0, 0, 9999, -9999, 1.0, 100, 1, 333.0, 0.0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0], [844, 40.0, 0, 9999, -9999, 1.0, 100, 1, 40.0, \n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [845, 318.0, 0, 9999, -9999, 1.0,\n 100, 1, 318.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [849, 779.0, 0, \n 9999, -9999, 1.0, 100, 1, 779.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [850, 16.0, 0, 9999, -9999, 1.0, 100, 1, 16.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [851, 79.5, 0, 9999, -9999, 1.0, 100, 1, 79.5, 0.0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0], [853, 11.6, 0, 9999, -9999, 1.0, 100, 1, \n 11.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [854, 81.8, 0, 9999, -9999,\n 1.0, 100, 1, 81.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [855, 688.0, \n 0, 9999, -9999, 1.0, 100, 1, 688.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n 0], [856, 36.0, 0, 9999, -9999, 1.0, 100, 1, 36.0, 0.0, 0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0], [857, 1402.0, 0, 9999, -9999, 1.0, 100, 1, 1402.0, \n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [858, 56.8, 0, 9999, -9999, 1.0,\n 100, 1, 56.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [859, 85.0, 0, \n 9999, -9999, 1.0, 100, 1, 85.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [860, 25.0, 0, 9999, -9999, 1.0, 100, 1, 25.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [862, 725.0, 0, 9999, -9999, 1.0, 100, 1, 725.0, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [863, 0.6, 0, 9999, -9999, 1.0, 100, 1, 0.6,\n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [864, 875.0, 0, 9999, -9999, 1.0,\n 100, 1, 875.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [865, 11.0, 0, \n 9999, -9999, 1.0, 100, 1, 11.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [867, 769.0, 0, 9999, -9999, 1.0, 100, 1, 769.0, 0.0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0], [869, 1360.0, 0, 9999, -9999, 1.0, 100, 1, 1360.0, 0.0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [870, 58.4, 0, 9999, -9999, 1.0, 100,\n 1, 58.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [872, 22.5, 0, 9999, -\n 9999, 1.0, 100, 1, 22.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [873, \n 18.747266, 0, 9999, -9999, 1.0, 100, 1, 122.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [874, 20.7, 0, 9999, -9999, 1.0, 100, 1, 20.7, 0.0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0], [875, 24.4, 0, 9999, -9999, 1.0, 100, 1, \n 24.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [877, 24.8, 0, 9999, -9999,\n 1.0, 100, 1, 24.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [882, 17.4, 0,\n 9999, -9999, 1.0, 100, 1, 17.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [883, 18.0, 0, 9999, -9999, 1.0, 100, 1, 18.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [886, 2572.0, 0, 9999, -9999, 1.0, 100, 1, 2572.0, 0.0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [889, 9.5, 0, 9999, -9999, 1.0, 100, 1, \n 9.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [890, 48.0, 0, 9999, -9999,\n 1.0, 100, 1, 48.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [895, 19.0, 0,\n 9999, -9999, 1.0, 100, 1, 19.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [896, 24.0, 0, 9999, -9999, 1.0, 100, 1, 24.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [898, 84.6, 0, 9999, -9999, 1.0, 100, 1, 84.6, 0.0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0], [900, 112.6, 0, 9999, -9999, 1.0, 100, 1, \n 112.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [902, 19.5, 0, 9999, -\n 9999, 1.0, 100, 1, 19.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [903, \n 20.1, 0, 9999, -9999, 1.0, 100, 1, 20.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [905, 137.3, 0, 9999, -9999, 1.0, 100, 1, 137.3, 0.0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0], [906, 33.577454, 0, 9999, -9999, 1.0, 100, 1, \n 66.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [907, 67.3, 0, 9999, -9999,\n 1.0, 100, 1, 67.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [909, 36.8, 0,\n 9999, -9999, 1.0, 100, 1, 36.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [913, 74.0, 0, 9999, -9999, 1.0, 100, 1, 74.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [915, 12.0, 0, 9999, -9999, 1.0, 100, 1, 12.0, 0.0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0], [917, 17.0, 0, 9999, -9999, 1.0, 100, 1, \n 17.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [918, 38.5, 0, 9999, -9999,\n 1.0, 100, 1, 38.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [920, 12.8, 0,\n 9999, -9999, 1.0, 100, 1, 12.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [921, 124.0, 0, 9999, -9999, 1.0, 100, 1, 124.0, 0.0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0], [922, 164.0, 0, 9999, -9999, 1.0, 100, 1, 164.0, 0.0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [923, 146.0, 0, 9999, -9999, 1.0, 100, 1,\n 146.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [925, 26.0, 0, 9999, -\n 9999, 1.0, 100, 1, 26.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [928, \n 61.5, 0, 9999, -9999, 1.0, 100, 1, 61.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [931, 217.1, 0, 9999, -9999, 1.0, 100, 1, 217.1, 0.0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0], [934, 296.0, 0, 9999, -9999, 1.0, 100, 1, 296.0, \n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [935, 23.1, 0, 9999, -9999, 1.0,\n 100, 1, 23.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [936, 104.4, 0, \n 9999, -9999, 1.0, 100, 1, 104.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [937, 30.0, 0, 9999, -9999, 1.0, 100, 1, 30.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [939, 0.1, 0, 9999, -9999, 1.0, 100, 1, 0.1, 0.0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0], [940, 29.6, 0, 9999, -9999, 1.0, 100, 1, 29.6,\n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [942, 51.9, 0, 9999, -9999, 1.0,\n 100, 1, 51.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [944, 25.4, 0, \n 9999, -9999, 1.0, 100, 1, 25.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [945, 35.0, 0, 9999, -9999, 1.0, 100, 1, 35.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [950, 16.0, 0, 9999, -9999, 1.0, 100, 1, 16.0, 0.0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0], [952, 31.7, 0, 9999, -9999, 1.0, 100, 1, \n 31.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [958, 66.7, 0, 9999, -9999,\n 1.0, 100, 1, 66.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [959, 45.5, 0,\n 9999, -9999, 1.0, 100, 1, 45.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [960, 26.5, 0, 9999, -9999, 1.0, 100, 1, 26.5, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [963, 503.264337, 0, 9999, -9999, 1.0, 100, 1, 875.0, 0.0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [965, 352.0, 0, 9999, -9999, 1.0, 100,\n 1, 352.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [966, 66.0, 0, 9999, -\n 9999, 1.0, 100, 1, 66.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [967, \n 37.5, 0, 9999, -9999, 1.0, 100, 1, 37.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [968, 54.0, 0, 9999, -9999, 0.999501, 100, 1, 54.0, 0.0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0], [969, 56.9, 0, 9999, -9999, 0.999501, 100, 1, \n 56.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [971, 20.0, 0, 9999, -9999,\n 1.0, 100, 1, 20.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [973, 1347.0,\n 0, 9999, -9999, 1.0, 100, 1, 1347.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0], [976, 26.9, 0, 9999, -9999, 1.0, 100, 1, 26.9, 0.0, 0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0], [977, 324.0, 0, 9999, -9999, 1.0, 100, 1, 324.0, 0.0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [978, 4.6, 0, 9999, -9999, 1.0, 100, \n 1, 4.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [981, 119.0, 0, 9999, -\n 9999, 1.0, 100, 1, 119.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [982, \n 9.9, 0, 9999, -9999, 1.0, 100, 1, 9.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n 0, 0], [983, 44.0, 0, 9999, -9999, 1.0, 100, 1, 44.0, 0.0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0], [984, 465.0, 0, 9999, -9999, 1.0, 100, 1, 465.0, \n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [985, 22.0, 0, 9999, -9999, 1.0,\n 100, 1, 22.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [986, 11.2, 0, \n 9999, -9999, 1.0, 100, 1, 11.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [987, 164.5, 0, 9999, -9999, 1.0, 100, 1, 164.5, 0.0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0], [988, 5.1, 0, 9999, -9999, 1.0, 100, 1, 5.1, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [990, 238.511447, 0, 9999, -9999, 1.0, 100,\n 1, 300.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [993, 392.0, 0, 9999, \n -9999, 1.0, 100, 1, 392.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [994,\n 33.0, 0, 9999, -9999, 1.0, 100, 1, 33.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [995, 4.2, 0, 9999, -9999, 1.0, 100, 1, 4.2, 0.0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0], [997, 18.8, 0, 9999, -9999, 1.0, 100, 1, 18.8, 0.0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [999, 15.6, 0, 9999, -9999, 1.0, 100,\n 1, 15.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1000, 49.0, 0, 9999, -\n 9999, 1.0, 100, 1, 49.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1002, \n 9.9, 0, 9999, -9999, 1.0, 100, 1, 9.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n 0, 0], [1003, 900.0, 0, 9999, -9999, 1.0, 100, 1, 900.0, 0.0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0], [1007, 23.3, 0, 9999, -9999, 1.0, 100, 1, 23.3,\n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1008, 49.0, 0, 9999, 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1447.199962, 0, 9999, -9999, 1.0, 100, \n 1, 1447.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1032, 19.954769, 0, \n 9999, -9999, 1.0, 100, 1, 153.510391, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0], [1033, 3.129107, 0, 9999, -9999, 1.0, 100, 1, 50.164506, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [1034, 19.480611, 0, 9999, -9999, 1.0, 100,\n 1, 84.262779, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1035, 4.869691, 0,\n 9999, -9999, 1.0, 100, 1, 49.886469, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0], [1036, 4.299817, 0, 9999, -9999, 1.0, 100, 1, 67.223077, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [1037, 26.880386, 0, 9999, -9999, 1.0, 100,\n 1, 94.684044, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1038, 23.191978, \n 0, 9999, -9999, 1.0, 100, 1, 85.798525, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [1039, 14.50049, 0, 9999, -9999, 1.0, 100, 1, 132.724114, 0.0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1040, 4.2e-05, 0, 9999, -9999, 1.0, 100,\n 1, 0.064179, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1041, 23.612723, 0,\n 9999, -9999, 1.0, 100, 1, 204.187624, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0], [1042, 3.793945, 0, 9999, -9999, 1.0, 100, 1, 52.70053, 0.0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0], [1044, 13.8333, 0, 9999, -9999, 1.0, 100, 1,\n 36.163532, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1046, 72.936676, 0, \n 9999, -9999, 1.0, 100, 1, 106.787063, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0], [1047, 5.880677, 0, 9999, -9999, 1.0, 100, 1, 13.029581, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [1048, 38.915865, 0, 9999, -9999, 1.0, 100,\n 1, 71.656883, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1049, 57.66596, 0,\n 9999, -9999, 1.0, 100, 1, 293.755375, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0], [1050, 1.597255, 0, 9999, -9999, 1.0, 100, 1, 52.781606, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [1051, 11.031378, 0, 9999, -9999, 1.0, 100,\n 1, 304.42978, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1052, 12.527576, \n 0, 9999, -9999, 1.0, 100, 1, 20.66869, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n 0, 0], [1053, 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0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0], [683, 27.5, 0, 9999, -9999, 1.0, 100, 1, 27.5, 0.0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [687, 1329.0, 0, 9999, -9999, 1.0, \n 100, 1, 1329.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [689, 310.0, 0, \n 9999, -9999, 1.0, 100, 1, 310.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [691, 26.0, 0, 9999, -9999, 1.0, 100, 1, 26.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [693, 194.0, 0, 9999, -9999, 1.0, 100, 1, 194.0, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [694, 16.4, 0, 9999, -9999, 1.0, 100, 1, \n 16.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [695, 14.7, 0, 9999, -9999,\n 1.0, 100, 1, 14.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [696, 721.0, \n 0, 9999, -9999, 1.0, 100, 1, 721.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n 0], [697, 11.6, 0, 9999, -9999, 1.0, 100, 1, 11.6, 0.0, 0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0], [698, 24.0, 0, 9999, -9999, 1.0, 100, 1, 24.0, 0.0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [702, 73.4, 0, 9999, -9999, 1.0, 100,\n 1, 73.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [704, 508.0, 0, 9999, -\n 9999, 1.0, 100, 1, 508.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [705, \n 17.0, 0, 9999, -9999, 1.0, 100, 1, 17.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [707, 34.0, 0, 9999, -9999, 1.0, 100, 1, 34.0, 0.0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0], [708, 7.8, 0, 9999, -9999, 1.0, 100, 1, 7.8, 0.0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [711, 88.484779, 0, 9999, -9999, 1.0,\n 100, 1, 176.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [713, 13.4, 0, \n 9999, -9999, 1.0, 100, 1, 13.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [714, 15.0, 0, 9999, -9999, 1.0, 100, 1, 15.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [716, 0.1, 0, 9999, -9999, 1.0, 100, 1, 0.1, 0.0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0], [717, 11.0, 0, 9999, -9999, 1.0, 100, 1, 11.0,\n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [719, 1328.962186, 0, 9999, -\n 9999, 1.0, 100, 1, 1958.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [722,\n 20.7, 0, 9999, -9999, 1.0, 100, 1, 20.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [724, 12.1, 0, 9999, -9999, 1.0, 100, 1, 12.1, 0.0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0], [727, 61.5, 0, 9999, -9999, 1.0, 100, 1, 61.5, \n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [728, 510.0, 0, 9999, -9999, 1.0,\n 100, 1, 510.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [730, 633.2, 0, \n 9999, -9999, 1.0, 100, 1, 633.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [731, 540.174348, 0, 9999, -9999, 1.0, 100, 1, 895.0, 0.0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0], [732, 14.6, 0, 9999, -9999, 1.0, 100, 1, 14.6, \n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [735, 84.8, 0, 9999, -9999, 1.0,\n 100, 1, 84.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [737, 28.0, 0, \n 9999, -9999, 1.0, 100, 1, 28.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [738, 138.5, 0, 9999, -9999, 1.0, 100, 1, 138.5, 0.0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0], [741, 214.0, 0, 9999, -9999, 1.0, 100, 1, 214.0, 0.0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [742, 9.0, 0, 9999, -9999, 1.0, 100, 1, \n 9.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [743, 220.991864, 0, 9999, \n -9999, 1.0, 100, 1, 1410.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [746,\n 100.0, 0, 9999, -9999, 1.0, 100, 1, 100.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0], [747, 12.5, 0, 9999, -9999, 1.0, 100, 1, 12.5, 0.0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0], [748, 110.0, 0, 9999, -9999, 1.0, 100, 1, \n 110.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [749, 16.0, 0, 9999, -\n 9999, 1.0, 100, 1, 16.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [750, \n 90.8, 0, 9999, -9999, 1.0, 100, 1, 90.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [753, 116.851068, 0, 9999, -9999, 1.0, 100, 1, 311.8, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [758, 18.5, 0, 9999, -9999, 1.0, 100, 1, \n 18.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [760, 298.788806, 0, 9999,\n -9999, 1.0, 100, 1, 794.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [762,\n 700.835089, 0, 9999, -9999, 1.0, 100, 1, 1105.0, 0.0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0], [763, 20.3, 0, 9999, -9999, 1.0, 100, 1, 20.3, 0.0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [765, 59.0, 0, 9999, -9999, 1.0, 100, 1,\n 59.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [767, 11.2, 0, 9999, -9999,\n 1.0, 100, 1, 11.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [769, 43.3, 0,\n 9999, -9999, 1.0, 100, 1, 43.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [771, 690.0, 0, 9999, -9999, 1.0, 100, 1, 690.0, 0.0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0], [772, 18.8, 0, 9999, -9999, 1.0, 100, 1, 18.8, 0.0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [774, 33.5, 0, 9999, -9999, 1.0, 100, 1,\n 33.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [776, 54.222128, 0, 9999, \n -9999, 1.0, 100, 1, 56.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [777, \n 79.0, 0, 9999, -9999, 1.0, 100, 1, 79.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [778, 14.7, 0, 9999, -9999, 1.0, 100, 1, 14.7, 0.0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0], [781, 973.218708, 0, 9999, -9999, 1.0, 100, 1, \n 1310.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [784, 802.511044, 0, \n 9999, -9999, 1.0, 100, 1, 1275.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [785, 3.0, 0, 9999, -9999, 1.0, 100, 1, 3.0, 0.0, 0, 0, 0, 0, 0, 0, 0, \n 0, 0, 0, 0], [787, 778.0, 0, 9999, -9999, 1.0, 100, 1, 778.0, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [788, 875.0, 0, 9999, -9999, 1.0, 100, 1, \n 875.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [789, 77.4, 0, 9999, -\n 9999, 1.0, 100, 1, 77.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [791, \n 10.0, 0, 9999, -9999, 1.0, 100, 1, 10.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [792, 62.7, 0, 9999, -9999, 1.0, 100, 1, 62.7, 0.0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0], [795, 13.6, 0, 9999, -9999, 1.0, 100, 1, 13.6, \n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [801, 50.0, 0, 9999, -9999, 1.0,\n 100, 1, 50.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [802, 500.0, 0, \n 9999, -9999, 1.0, 100, 1, 500.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [805, 418.686348, 0, 9999, -9999, 1.0, 100, 1, 1410.0, 0.0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0], [806, 35.8, 0, 9999, -9999, 1.0, 100, 1, 35.8, \n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [808, 217.5, 0, 9999, -9999, 1.0,\n 100, 1, 217.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [809, 12.5, 0, \n 9999, -9999, 1.0, 100, 1, 12.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [811, 25.2, 0, 9999, -9999, 1.0, 100, 1, 25.2, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [814, 89.0, 0, 9999, -9999, 1.0, 100, 1, 89.0, 0.0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0], [816, 80.1, 0, 9999, -9999, 1.0, 100, 1, \n 80.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [817, 54.0, 0, 9999, -9999,\n 1.0, 100, 1, 54.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [821, 82.5, 0,\n 9999, -9999, 1.0, 100, 1, 82.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [822, 134.0, 0, 9999, -9999, 1.0, 100, 1, 134.0, 0.0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0], [826, 58.0, 0, 9999, -9999, 1.0, 100, 1, 58.0, 0.0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [829, 204.799653, 0, 9999, -9999, 1.0, \n 100, 1, 211.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [830, 89.0, 0, \n 9999, -9999, 1.0, 100, 1, 89.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [835, 63.7, 0, 9999, -9999, 1.0, 100, 1, 63.7, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [836, 25.5, 0, 9999, -9999, 1.0, 100, 1, 25.5, 0.0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0], [837, 472.0, 0, 9999, -9999, 1.0, 100, 1, \n 472.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [839, 73.3, 0, 9999, -\n 9999, 1.0, 100, 1, 73.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [841, \n 23.3, 0, 9999, -9999, 1.0, 100, 1, 23.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [843, 333.0, 0, 9999, -9999, 1.0, 100, 1, 333.0, 0.0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0], [844, 40.0, 0, 9999, -9999, 1.0, 100, 1, 40.0, \n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [845, 318.0, 0, 9999, -9999, 1.0,\n 100, 1, 318.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [849, 779.0, 0, \n 9999, -9999, 1.0, 100, 1, 779.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [850, 16.0, 0, 9999, -9999, 1.0, 100, 1, 16.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [851, 79.5, 0, 9999, -9999, 1.0, 100, 1, 79.5, 0.0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0], [853, 11.6, 0, 9999, -9999, 1.0, 100, 1, \n 11.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [854, 81.8, 0, 9999, -9999,\n 1.0, 100, 1, 81.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [855, 688.0, \n 0, 9999, -9999, 1.0, 100, 1, 688.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n 0], [856, 36.0, 0, 9999, -9999, 1.0, 100, 1, 36.0, 0.0, 0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0], [857, 1402.0, 0, 9999, -9999, 1.0, 100, 1, 1402.0, \n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [858, 56.8, 0, 9999, -9999, 1.0,\n 100, 1, 56.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [859, 85.0, 0, \n 9999, -9999, 1.0, 100, 1, 85.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [860, 25.0, 0, 9999, -9999, 1.0, 100, 1, 25.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [862, 725.0, 0, 9999, -9999, 1.0, 100, 1, 725.0, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [863, 0.6, 0, 9999, -9999, 1.0, 100, 1, 0.6,\n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [864, 875.0, 0, 9999, -9999, 1.0,\n 100, 1, 875.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [865, 11.0, 0, \n 9999, -9999, 1.0, 100, 1, 11.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [867, 769.0, 0, 9999, -9999, 1.0, 100, 1, 769.0, 0.0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0], [869, 1360.0, 0, 9999, -9999, 1.0, 100, 1, 1360.0, 0.0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [870, 58.4, 0, 9999, -9999, 1.0, 100,\n 1, 58.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [872, 22.5, 0, 9999, -\n 9999, 1.0, 100, 1, 22.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [873, \n 18.747266, 0, 9999, -9999, 1.0, 100, 1, 122.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [874, 20.7, 0, 9999, -9999, 1.0, 100, 1, 20.7, 0.0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0], [875, 24.4, 0, 9999, -9999, 1.0, 100, 1, \n 24.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [877, 24.8, 0, 9999, -9999,\n 1.0, 100, 1, 24.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [882, 17.4, 0,\n 9999, -9999, 1.0, 100, 1, 17.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [883, 18.0, 0, 9999, -9999, 1.0, 100, 1, 18.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [886, 2572.0, 0, 9999, -9999, 1.0, 100, 1, 2572.0, 0.0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [889, 9.5, 0, 9999, -9999, 1.0, 100, 1, \n 9.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [890, 48.0, 0, 9999, -9999,\n 1.0, 100, 1, 48.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [895, 19.0, 0,\n 9999, -9999, 1.0, 100, 1, 19.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [896, 24.0, 0, 9999, -9999, 1.0, 100, 1, 24.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [898, 84.6, 0, 9999, -9999, 1.0, 100, 1, 84.6, 0.0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0], [900, 112.6, 0, 9999, -9999, 1.0, 100, 1, \n 112.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [902, 19.5, 0, 9999, -\n 9999, 1.0, 100, 1, 19.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [903, \n 20.1, 0, 9999, -9999, 1.0, 100, 1, 20.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [905, 137.3, 0, 9999, -9999, 1.0, 100, 1, 137.3, 0.0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0], [906, 33.577454, 0, 9999, -9999, 1.0, 100, 1, \n 66.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [907, 67.3, 0, 9999, -9999,\n 1.0, 100, 1, 67.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [909, 36.8, 0,\n 9999, -9999, 1.0, 100, 1, 36.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [913, 74.0, 0, 9999, -9999, 1.0, 100, 1, 74.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [915, 12.0, 0, 9999, -9999, 1.0, 100, 1, 12.0, 0.0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0], [917, 17.0, 0, 9999, -9999, 1.0, 100, 1, \n 17.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [918, 38.5, 0, 9999, -9999,\n 1.0, 100, 1, 38.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [920, 12.8, 0,\n 9999, -9999, 1.0, 100, 1, 12.8, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [921, 124.0, 0, 9999, -9999, 1.0, 100, 1, 124.0, 0.0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0], [922, 164.0, 0, 9999, -9999, 1.0, 100, 1, 164.0, 0.0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [923, 146.0, 0, 9999, -9999, 1.0, 100, 1,\n 146.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [925, 26.0, 0, 9999, -\n 9999, 1.0, 100, 1, 26.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [928, \n 61.5, 0, 9999, -9999, 1.0, 100, 1, 61.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [931, 217.1, 0, 9999, -9999, 1.0, 100, 1, 217.1, 0.0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0], [934, 296.0, 0, 9999, -9999, 1.0, 100, 1, 296.0, \n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [935, 23.1, 0, 9999, -9999, 1.0,\n 100, 1, 23.1, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [936, 104.4, 0, \n 9999, -9999, 1.0, 100, 1, 104.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [937, 30.0, 0, 9999, -9999, 1.0, 100, 1, 30.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [939, 0.1, 0, 9999, -9999, 1.0, 100, 1, 0.1, 0.0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0], [940, 29.6, 0, 9999, -9999, 1.0, 100, 1, 29.6,\n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [942, 51.9, 0, 9999, -9999, 1.0,\n 100, 1, 51.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [944, 25.4, 0, \n 9999, -9999, 1.0, 100, 1, 25.4, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [945, 35.0, 0, 9999, -9999, 1.0, 100, 1, 35.0, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [950, 16.0, 0, 9999, -9999, 1.0, 100, 1, 16.0, 0.0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0], [952, 31.7, 0, 9999, -9999, 1.0, 100, 1, \n 31.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [958, 66.7, 0, 9999, -9999,\n 1.0, 100, 1, 66.7, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [959, 45.5, 0,\n 9999, -9999, 1.0, 100, 1, 45.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [960, 26.5, 0, 9999, -9999, 1.0, 100, 1, 26.5, 0.0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0], [963, 503.264337, 0, 9999, -9999, 1.0, 100, 1, 875.0, 0.0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [965, 352.0, 0, 9999, -9999, 1.0, 100,\n 1, 352.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [966, 66.0, 0, 9999, -\n 9999, 1.0, 100, 1, 66.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [967, \n 37.5, 0, 9999, -9999, 1.0, 100, 1, 37.5, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [968, 54.0, 0, 9999, -9999, 0.999501, 100, 1, 54.0, 0.0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0], [969, 56.9, 0, 9999, -9999, 0.999501, 100, 1, \n 56.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [971, 20.0, 0, 9999, -9999,\n 1.0, 100, 1, 20.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [973, 1347.0,\n 0, 9999, -9999, 1.0, 100, 1, 1347.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0], [976, 26.9, 0, 9999, -9999, 1.0, 100, 1, 26.9, 0.0, 0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0], [977, 324.0, 0, 9999, -9999, 1.0, 100, 1, 324.0, 0.0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [978, 4.6, 0, 9999, -9999, 1.0, 100, \n 1, 4.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [981, 119.0, 0, 9999, -\n 9999, 1.0, 100, 1, 119.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [982, \n 9.9, 0, 9999, -9999, 1.0, 100, 1, 9.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n 0, 0], [983, 44.0, 0, 9999, -9999, 1.0, 100, 1, 44.0, 0.0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0], [984, 465.0, 0, 9999, -9999, 1.0, 100, 1, 465.0, \n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [985, 22.0, 0, 9999, -9999, 1.0,\n 100, 1, 22.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [986, 11.2, 0, \n 9999, -9999, 1.0, 100, 1, 11.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [987, 164.5, 0, 9999, -9999, 1.0, 100, 1, 164.5, 0.0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0], [988, 5.1, 0, 9999, -9999, 1.0, 100, 1, 5.1, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [990, 238.511447, 0, 9999, -9999, 1.0, 100,\n 1, 300.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [993, 392.0, 0, 9999, \n -9999, 1.0, 100, 1, 392.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [994,\n 33.0, 0, 9999, -9999, 1.0, 100, 1, 33.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [995, 4.2, 0, 9999, -9999, 1.0, 100, 1, 4.2, 0.0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0], [997, 18.8, 0, 9999, -9999, 1.0, 100, 1, 18.8, 0.0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [999, 15.6, 0, 9999, -9999, 1.0, 100,\n 1, 15.6, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1000, 49.0, 0, 9999, -\n 9999, 1.0, 100, 1, 49.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1002, \n 9.9, 0, 9999, -9999, 1.0, 100, 1, 9.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n 0, 0], [1003, 900.0, 0, 9999, -9999, 1.0, 100, 1, 900.0, 0.0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0], [1007, 23.3, 0, 9999, -9999, 1.0, 100, 1, 23.3,\n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1008, 49.0, 0, 9999, -9999, 1.0,\n 100, 1, 49.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1010, 750.0, 0, \n 9999, -9999, 1.0, 100, 1, 750.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [1011, 18.7, 0, 9999, -9999, 1.0, 100, 1, 18.7, 0.0, 0, 0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0], [1012, 2835.0, 0, 9999, -9999, 1.0, 100, 1, 2835.0, 0.0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1018, 175.9, 0, 9999, -9999, 1.0, \n 100, 1, 175.9, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1023, 0.2, 0, \n 9999, -9999, 1.0, 100, 1, 0.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [\n 1026, 655.6, 0, 9999, -9999, 1.0, 100, 1, 655.6, 0.0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0], [1027, 17.423964, 0, 9999, -9999, 1.0, 100, 1, 48.3, \n 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1028, 400.0, 0, 9999, -9999, \n 1.0, 100, 1, 400.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1029, 60.0,\n 0, 9999, -9999, 1.0, 100, 1, 60.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n ], [1030, 541.076337, 0, 9999, -9999, 1.0, 100, 1, 1018.0, 0.0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0], [1031, 1447.199962, 0, 9999, -9999, 1.0, 100, \n 1, 1447.2, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1032, 19.954769, 0, \n 9999, -9999, 1.0, 100, 1, 153.510391, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0], [1033, 3.129107, 0, 9999, -9999, 1.0, 100, 1, 50.164506, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [1034, 19.480611, 0, 9999, -9999, 1.0, 100,\n 1, 84.262779, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1035, 4.869691, 0,\n 9999, -9999, 1.0, 100, 1, 49.886469, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0], [1036, 4.299817, 0, 9999, -9999, 1.0, 100, 1, 67.223077, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [1037, 26.880386, 0, 9999, -9999, 1.0, 100,\n 1, 94.684044, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1038, 23.191978, \n 0, 9999, -9999, 1.0, 100, 1, 85.798525, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [1039, 14.50049, 0, 9999, -9999, 1.0, 100, 1, 132.724114, 0.0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1040, 4.2e-05, 0, 9999, -9999, 1.0, 100,\n 1, 0.064179, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1041, 23.612723, 0,\n 9999, -9999, 1.0, 100, 1, 204.187624, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0], [1042, 3.793945, 0, 9999, -9999, 1.0, 100, 1, 52.70053, 0.0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0], [1044, 13.8333, 0, 9999, -9999, 1.0, 100, 1,\n 36.163532, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1046, 72.936676, 0, \n 9999, -9999, 1.0, 100, 1, 106.787063, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0], [1047, 5.880677, 0, 9999, -9999, 1.0, 100, 1, 13.029581, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [1048, 38.915865, 0, 9999, -9999, 1.0, 100,\n 1, 71.656883, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1049, 57.66596, 0,\n 9999, -9999, 1.0, 100, 1, 293.755375, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0], [1050, 1.597255, 0, 9999, -9999, 1.0, 100, 1, 52.781606, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [1051, 11.031378, 0, 9999, -9999, 1.0, 100,\n 1, 304.42978, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1052, 12.527576, \n 0, 9999, -9999, 1.0, 100, 1, 20.66869, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n 0, 0], [1053, 10.416727, 0, 9999, -9999, 1.0, 100, 1, 16.368087, 0.0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1054, 174.429944, 0, 9999, -9999, 1.0, \n 100, 1, 273.855776, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1055, \n 0.246667, 0, 9999, -9999, 1.0, 100, 1, 2.856069, 0.0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0], [1056, 64.106126, 0, 9999, -9999, 1.0, 100, 1, \n 603.943953, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1057, 55.944221, 0,\n 9999, -9999, 1.0, 100, 1, 426.979979, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0], [1058, 144.133643, 0, 9999, -9999, 1.0, 100, 1, 1055.735174, 0.0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1059, 53.908755, 0, 9999, -9999, 1.0, \n 100, 1, 414.871332, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1060, \n 0.772854, 0, 9999, -9999, 1.0, 100, 1, 10.351632, 0.0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0], [1061, 19.183871, 0, 9999, -9999, 1.0, 100, 1, \n 161.862597, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1062, 0.178546, 0, \n 9999, -9999, 1.0, 100, 1, 2.878561, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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100, 1, 236.871781, 0.0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0], [1971, 8.074517, 0, 9999, -9999, 1.0, 100, 1, \n 14.404409, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1972, 0.015521, 0, \n 9999, -9999, 1.0, 100, 1, 0.028378, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n 0], [1973, 0.292438, 0, 9999, -9999, 1.0, 100, 1, 0.534696, 0.0, 0, 0, \n 0, 0, 0, 0, 0, 0, 0, 0, 0], [1974, 1.504534, 0, 9999, -9999, 1.0, 100, \n 1, 2.750907, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1975, 36.887137, 0,\n 9999, -9999, 1.0, 100, 1, 81.92918, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n 0], [1976, 1.406463, 0, 9999, -9999, 1.0, 100, 1, 2.17499, 0.0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0], [1977, 122.592738, 0, 9999, -9999, 1.0, 100, 1,\n 226.383637, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1978, 0.706432, 0, \n 9999, -9999, 1.0, 100, 1, 1.331592, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, \n 0], [1979, 7.337064, 0, 9999, -9999, 1.0, 100, 1, 189.722792, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [1980, 52.239056, 0, 9999, -9999, 1.0, 100,\n 1, 100.61941, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1981, 79.320419, \n 0, 9999, -9999, 1.0, 100, 1, 144.682717, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [1982, 69.614382, 0, 9999, -9999, 1.0, 100, 1, 134.93778, 0.0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1983, 67.761201, 0, 9999, -9999, 1.0, \n 100, 1, 155.990147, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1984, \n 43.229883, 0, 9999, -9999, 1.0, 100, 1, 94.470611, 0.0, 0, 0, 0, 0, 0, \n 0, 0, 0, 0, 0, 0], [1985, 18.449298, 0, 9999, -9999, 1.0, 100, 1, \n 41.975835, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1986, 108.814425, 0,\n 9999, -9999, 1.0, 100, 1, 298.346979, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0], [1987, 189.251646, 0, 9999, -9999, 1.0, 100, 1, 393.914067, 0.0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1988, 144.661852, 0, 9999, -9999, 1.0, \n 100, 1, 251.944939, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1989, \n 6.390172, 0, 9999, -9999, 1.0, 100, 1, 10.378288, 0.0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0], [1990, 30.763561, 0, 9999, -9999, 1.0, 100, 1, \n 50.351426, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1991, 399.179592, 0,\n 9999, -9999, 1.0, 100, 1, 849.576944, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0], [1992, 125.280995, 0, 9999, -9999, 1.0, 100, 1, 233.477991, 0.0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1993, 121.464694, 0, 9999, -9999, 1.0, \n 100, 1, 242.698643, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1994, \n 45.014021, 0, 9999, -9999, 1.0, 100, 1, 255.834576, 0.0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0], [1995, 80.531008, 0, 9999, -9999, 1.0, 100, 1, \n 262.446698, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1996, 20.04469, 0, \n 9999, -9999, 1.0, 100, 1, 91.306832, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0], [1997, 9.226652, 0, 9999, -9999, 1.0, 100, 1, 26.592561, 0.0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0], [1998, 5.256357, 0, 9999, -9999, 1.0, 100, \n 1, 12.126511, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1999, 67.284228, \n 0, 9999, -9999, 1.0, 100, 1, 199.184531, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0], [2000, 253.175263, 0, 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[769, 293, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [771, 297, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [772, 3, 0, 1e-05, 0, 9999, 9999, 9999,\n 0, 0, 1, -360, 360], [774, 300, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1,\n -360, 360], [776, 300, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [777, 300, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 778, 300, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [781, 303,\n 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [784, 563, 0, 1e-05,\n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [785, 501, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [787, 308, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [788, 311, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [789, 565, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [791, 314, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [792, 316, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [795,\n 319, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [801, 327, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [802, 327, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [805, 328, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [806, 328, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [808, 329, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [809, 329, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [811, 568, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [814, 570, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [816,\n 335, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [817, 571, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [821, 338, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [822, 339, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [826, 339, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [829, 345, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [830, 345, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [835, 572, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [836, 572, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [837,\n 350, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [839, 350, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [841, 573, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [843, 352, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [844, 352, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [845, 356, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [849, 574, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [850, 574, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [851, 575, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [853,\n 362, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [854, 363, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [855, 363, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [856, 363, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [857, 365, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [858, 368, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [859, 368, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [860, 371, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [862, 372, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [863,\n 374, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [864, 374, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [865, 375, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [867, 376, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [869, 503, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [870, 503, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [872, 378, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [873, 576, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [874, 576, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [875,\n 381, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [877, 578, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [882, 388, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [883, 388, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [886, 394, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [889, 397, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [890, 40, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [895, 580, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [896, 581, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [898,\n 403, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [900, 405, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [902, 405, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [903, 406, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [905, 413, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [906, 414, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [907, 583, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [909, 417, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [913, 422, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [915,\n 423, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [917, 43, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [918, 424, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [920, 428, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [921, 428, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [922, 429, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [923, 432, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [925, 44, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360],\n [928, 435, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [931, \n 439, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [934, 45, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [935, 45, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [936, 445, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [937, 447, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [939, 450, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [940, 451, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [942, 458, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [944, 458, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [945,\n 459, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [950, 462, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [952, 47, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [958, 478, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [959, 478, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [960, 479, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [963, 481, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [965, 49, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360],\n [966, 49, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [967, 49,\n 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [968, 486, 0, 1e-05,\n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [969, 486, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [971, 51, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [973, 506, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [976, 58, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [977, 59, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360],\n [978, 491, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [981, 62,\n 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [982, 62, 0, 1e-05,\n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [983, 62, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [984, 63, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [985, 63, 0, 1e-05, 0, 9999, 9999, 9999, 0, \n 0, 1, -360, 360], [986, 64, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [987, 65, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360],\n [988, 66, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [990, 67,\n 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [993, 67, 0, 1e-05,\n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [994, 67, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [995, 509, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [997, 510, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [999, 70, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1000, 71, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1002, 71, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1003,\n 72, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1007, 511, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1008, 75, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1010, 79, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1011, 79, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1012, 81, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1018, 514, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1023, 515, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1026, 518, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1027, 218, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1028, \n 221, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1029, 268, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1030, 269, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1031, 498, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1032, 1, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1033, 3, 0, 1e-05, 0, 9999, 9999, 9999, 0, \n 0, 1, -360, 360], [1034, 4, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1035, 6, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360],\n [1036, 7, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1037, 8,\n 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1038, 9, 0, 1e-05,\n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1039, 11, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1040, 14, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1041, 16, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1042, 17, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1044, 21, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1046, 25, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1047,\n 27, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1048, 28, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1049, 29, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1050, 31, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1051, 33, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1052, 34, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1053, 35, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1054, 36, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1055, 38, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1056,\n 39, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1057, 40, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1058, 41, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1059, 43, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1060, 44, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1061, 45, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1062, 47, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1063, 48, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1064, 49, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1065,\n 50, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1066, 51, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1067, 53, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1072, 59, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1073, 60, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1074, 62, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1075, 63, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1076, 64, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1077, 65, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1078,\n 66, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1079, 67, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1080, 70, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1081, 71, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1082, 72, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1083, 73, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1084, 75, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1085, 76, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1086, 77, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1087,\n 79, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1088, 80, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1089, 81, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1090, 82, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1091, 83, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1092, 84, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1093, 85, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1094, 88, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1095, 89, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1096,\n 90, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1097, 91, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1098, 92, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1099, 93, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1101, 98, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1102, 101, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1103, 102, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1104, 103, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1105, 108, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1106, 109, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1107, \n 110, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1108, 111, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1109, 112, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1110, 113, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1111, 114, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1112, 115, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1113, 116, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1114, 118, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1115, 119, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1116, 121, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1117, \n 122, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1118, 126, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1119, 127, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1120, 130, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1121, 131, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1122, 132, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1123, 133, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1124, 134, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1125, 135, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1126, 136, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1127, \n 137, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1128, 139, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1129, 140, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1130, 141, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1131, 142, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1132, 144, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1133, 145, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1134, 146, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1135, 147, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1136, 148, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1137, \n 149, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1138, 150, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1139, 151, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1140, 152, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1141, 153, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1142, 154, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1143, 155, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1144, 158, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1145, 161, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1146, 162, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1147, \n 163, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1148, 164, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1149, 166, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1150, 167, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1151, 168, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1152, 169, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1153, 170, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1154, 171, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1155, 172, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1156, 173, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1157, \n 174, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1158, 175, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1159, 176, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1160, 177, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1161, 178, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1162, 179, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1163, 180, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1164, 181, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1165, 182, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1166, 183, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1167, \n 185, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1168, 186, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1169, 187, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1170, 188, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1171, 189, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1172, 190, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1173, 192, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1174, 193, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1175, 194, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1176, 196, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1177, \n 197, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1178, 198, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1179, 199, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1180, 200, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1181, 202, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1182, 203, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1183, 204, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1184, 205, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1185, 206, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1186, 207, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1187, \n 208, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1188, 209, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1189, 210, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1190, 211, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1191, 212, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1192, 213, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1196, 217, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1197, 218, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1198, 219, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1199, 221, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1200, \n 222, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1204, 226, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1206, 228, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1208, 230, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1211, 237, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1212, 238, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1213, 239, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1214, 240, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1215, 241, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1216, 242, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1217, \n 243, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1218, 244, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1219, 247, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1220, 251, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1221, 252, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1222, 253, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1224, 255, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1225, 256, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1226, 257, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1227, 258, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1229, \n 263, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1230, 264, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1231, 266, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1232, 267, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1233, 268, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1235, 271, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1236, 272, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1237, 273, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1238, 274, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1239, 275, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1240, \n 276, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1241, 278, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1242, 281, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1243, 282, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1244, 283, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1245, 284, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1246, 285, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1247, 286, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1248, 287, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1249, 288, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1250, \n 289, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1251, 291, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1252, 292, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1253, 293, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1254, 294, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1255, 295, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1256, 296, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1257, 297, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1258, 298, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1259, 299, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1260, \n 300, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1261, 302, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1264, 307, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1266, 309, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1267, 311, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1268, 312, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1269, 314, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1270, 316, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1274, 321, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1275, 322, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1276, \n 323, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1277, 324, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1278, 325, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1280, 327, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1281, 328, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1282, 329, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1283, 331, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1285, 335, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1286, 337, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1287, 338, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1288, \n 339, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1289, 340, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1290, 341, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1291, 342, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1292, 343, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1293, 344, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1294, 345, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1295, 346, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1296, 347, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1297, 348, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1298, \n 350, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1299, 352, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1300, 353, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1301, 354, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1302, 355, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1303, 356, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1306, 361, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1307, 362, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1308, 363, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1312, 367, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1316, \n 371, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1317, 372, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1319, 374, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1323, 378, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1326, 384, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1327, 385, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1328, 386, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1329, 387, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1331, 390, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1333, 392, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1336, \n 395, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1337, 396, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1339, 398, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1340, 399, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1345, 406, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1346, 407, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1348, 410, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1349, 411, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1356, 419, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1357, 420, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1359, \n 422, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1360, 423, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1361, 424, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1362, 425, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1366, 429, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1367, 430, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1372, 435, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1373, 436, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1374, 437, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1375, 438, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1376, \n 439, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1377, 440, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1378, 441, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1379, 442, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1380, 443, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1381, 445, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1382, 446, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1383, 447, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1384, 448, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1385, 449, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1386, \n 450, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1387, 451, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1388, 453, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1389, 454, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1390, 455, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1391, 456, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1392, 457, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1393, 458, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1394, 459, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1395, 460, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1396, \n 461, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1397, 462, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1398, 463, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1399, 464, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1400, 465, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1401, 466, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1402, 467, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1403, 468, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1404, 469, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1405, 470, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1406, \n 471, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1407, 472, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1408, 473, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1409, 474, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1410, 475, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1411, 476, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1418, 483, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1419, 484, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1421, 486, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1422, 487, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1423, \n 488, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1424, 489, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1425, 490, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1426, 491, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1427, 492, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1428, 493, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1429, 494, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1431, 496, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1432, 497, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1433, 498, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1434, \n 499, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1435, 500, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1436, 501, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1437, 502, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1438, 503, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1439, 504, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1440, 505, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1443, 508, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1444, 509, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1445, 510, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1446, \n 511, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1447, 512, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1448, 513, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1449, 514, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1450, 515, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1451, 516, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1452, 517, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1453, 518, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1454, 519, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1455, 520, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1456, \n 521, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1457, 522, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1458, 523, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1459, 524, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1460, 525, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1461, 526, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1462, 527, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1463, 528, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1464, 529, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1465, 530, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1466, \n 531, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1467, 532, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1468, 533, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1469, 534, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1470, 535, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1471, 536, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1472, 537, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1473, 538, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1474, 539, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1475, 540, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1476, \n 541, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1477, 542, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1483, 548, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1484, 549, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1485, 550, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1486, 551, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1489, 555, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1490, 556, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1491, 557, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1492, 558, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1493, \n 559, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1494, 560, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1495, 561, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1497, 563, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1498, 564, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1501, 567, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1503, 569, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1504, 570, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1505, 571, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1506, 572, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1507, \n 573, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1510, 576, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1511, 577, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1512, 578, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1513, 579, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1518, 584, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1519, 585, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1520, 1, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360],\n [1521, 3, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1522, 4,\n 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1523, 6, 0, 1e-05,\n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1524, 7, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1525, 8, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1526, 9, 0, 1e-05, 0, 9999, 9999, 9999, 0, \n 0, 1, -360, 360], [1527, 11, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1528, 14, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1529, 16, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1530,\n 17, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1531, 19, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1532, 21, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1534, 25, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1535, 27, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1536, 28, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1537, 29, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1538, 31, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1539, 33, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1540,\n 34, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1541, 35, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1542, 36, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1543, 38, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1544, 39, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1545, 40, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1546, 41, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1547, 43, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1548, 44, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1549,\n 45, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1550, 47, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1551, 48, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1552, 49, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1553, 50, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1554, 51, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1555, 53, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1556, 54, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1557, 55, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1558,\n 57, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1559, 58, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1560, 59, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1561, 60, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1562, 62, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1563, 63, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1564, 64, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1565, 65, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1566, 66, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1567,\n 67, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1568, 70, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1569, 71, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1570, 72, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1571, 73, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1572, 75, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1573, 76, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1574, 77, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1575, 79, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1576,\n 80, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1577, 81, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1578, 82, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1579, 83, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1580, 84, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1581, 85, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1582, 88, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1583, 89, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1584, 90, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1585,\n 91, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1586, 92, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1587, 93, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1588, 97, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1589, 98, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1590, 101, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1591, 102, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1592, 103, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1593, 108, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1594, 109, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1595, \n 110, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1596, 111, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1597, 112, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1598, 113, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1599, 114, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1600, 115, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1601, 116, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1602, 118, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1603, 119, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1604, 121, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1605, \n 122, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1606, 126, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1607, 127, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1608, 130, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1609, 131, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1610, 132, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1611, 133, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1612, 134, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1613, 135, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1614, 136, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1615, \n 137, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1616, 139, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1617, 140, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1618, 141, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1619, 142, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1620, 144, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1621, 145, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1622, 146, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1623, 147, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1624, 148, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1625, \n 149, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1626, 150, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1627, 151, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1628, 152, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1629, 153, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1630, 154, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1631, 155, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1632, 158, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1633, 161, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1634, 162, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1635, \n 163, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1636, 164, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1637, 166, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1638, 167, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1639, 168, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1640, 169, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1641, 170, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1642, 171, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1643, 172, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1644, 173, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1645, \n 174, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1646, 175, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1647, 176, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1648, 177, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1649, 178, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1650, 179, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1651, 180, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1652, 181, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1653, 182, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1654, 183, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1655, \n 185, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1656, 186, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1657, 187, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1658, 188, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1659, 189, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1660, 190, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1661, 192, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1662, 193, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1663, 194, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1664, 196, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1665, \n 197, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1666, 198, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1667, 199, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1668, 200, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1669, 202, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1670, 203, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1671, 204, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1672, 205, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1673, 206, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1674, 207, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1675, \n 208, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1676, 209, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1677, 210, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1678, 211, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1679, 212, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1680, 213, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1681, 214, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1682, 215, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1683, 216, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1684, 217, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1685, \n 218, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1686, 219, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1687, 221, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1688, 222, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1689, 223, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1690, 224, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1691, 225, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1692, 226, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1693, 227, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1694, 228, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1695, \n 229, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1696, 230, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1697, 234, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1698, 235, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1699, 237, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1700, 238, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1701, 239, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1702, 240, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1703, 241, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1704, 242, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1705, \n 243, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1706, 244, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1707, 247, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1708, 251, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1709, 252, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1710, 253, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1711, 254, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1712, 255, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1713, 256, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1714, 257, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1715, \n 258, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1716, 260, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1717, 263, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1718, 264, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1719, 266, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1720, 267, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1721, 268, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1722, 269, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1723, 271, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1724, 272, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1725, \n 273, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1726, 274, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1727, 275, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1728, 276, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1729, 278, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1730, 281, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1731, 282, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1732, 283, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1733, 284, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1734, 285, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1735, \n 286, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1736, 287, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1737, 288, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1738, 289, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1739, 291, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1740, 292, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1741, 293, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1742, 294, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1743, 295, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1744, 296, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1745, \n 297, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1746, 298, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1747, 299, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1748, 300, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1749, 302, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1750, 303, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1751, 304, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1752, 307, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1753, 308, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1754, 309, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1755, \n 311, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1756, 312, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1757, 314, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1758, 316, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1759, 317, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1760, 318, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1761, 319, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1762, 321, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1763, 322, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1764, 323, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1765, \n 324, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1766, 325, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1767, 326, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1768, 327, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1769, 328, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1770, 329, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1771, 331, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1772, 333, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1773, 335, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1774, 337, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1775, \n 338, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1776, 339, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1777, 340, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1778, 341, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1779, 342, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1780, 343, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1781, 344, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1782, 345, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1783, 346, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1784, 347, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1785, \n 348, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1786, 350, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1787, 352, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1788, 353, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1789, 354, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1790, 355, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1791, 356, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1792, 357, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1793, 359, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1794, 361, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1795, \n 362, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1796, 363, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1797, 364, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1798, 365, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1799, 366, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1800, 367, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1801, 368, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1802, 369, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1803, 370, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1804, 371, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1805, \n 372, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1806, 373, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1807, 374, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1808, 375, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1809, 376, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1810, 377, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1811, 378, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1812, 379, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1813, 381, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1814, 384, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1815, \n 385, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1816, 386, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1817, 387, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1818, 388, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1819, 390, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1820, 391, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1821, 392, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1822, 393, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1823, 394, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1824, 395, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1825, \n 396, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1826, 397, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1827, 398, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1828, 399, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1830, 403, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1831, 404, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1832, 405, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1833, 406, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1834, 407, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1836, 410, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1837, \n 411, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1838, 412, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1839, 413, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1840, 414, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1841, 416, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1842, 417, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1843, 418, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1844, 419, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1845, 420, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1846, 421, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1847, \n 422, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1848, 423, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1849, 424, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1850, 425, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1851, 426, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1852, 427, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1853, 428, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1854, 429, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1855, 430, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1856, 431, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1857, \n 432, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1858, 433, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1860, 435, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1861, 436, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1862, 437, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1863, 438, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1864, 439, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1865, 440, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1866, 441, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1867, 442, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1868, \n 443, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1869, 445, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1870, 446, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1871, 447, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1872, 448, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1873, 449, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1874, 450, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1875, 451, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1876, 453, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1877, 454, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1878, \n 455, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1879, 456, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1880, 457, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1881, 458, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1882, 459, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1883, 460, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1884, 461, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1885, 462, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1886, 463, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1887, 464, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1888, \n 465, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1889, 466, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1890, 467, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1891, 468, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1892, 469, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1893, 470, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1894, 471, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1895, 472, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1896, 473, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1897, 474, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1898, \n 475, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1899, 476, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1900, 477, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1901, 478, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1902, 479, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1903, 480, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1904, 481, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1905, 482, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1906, 483, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1907, 484, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1908, \n 485, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1909, 486, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1910, 487, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1911, 488, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1912, 489, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1913, 490, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1914, 491, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1915, 492, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1916, 493, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1917, 494, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1918, \n 495, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1919, 496, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1920, 497, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1921, 498, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1922, 499, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1923, 500, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1924, 501, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1925, 502, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1926, 503, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1927, 504, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1928, \n 505, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1929, 506, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1930, 507, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1931, 508, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1932, 509, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1933, 510, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1934, 511, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1935, 512, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1936, 513, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1937, 514, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1938, \n 515, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1939, 516, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1940, 517, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1941, 518, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1942, 519, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1943, 520, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1944, 521, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1945, 522, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1946, 523, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1947, 524, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1948, \n 525, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1949, 526, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1950, 527, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1951, 528, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1952, 529, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1953, 530, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1954, 531, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1955, 532, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1956, 533, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1957, 534, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1958, \n 535, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1959, 536, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1960, 537, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1961, 538, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1962, 539, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1963, 540, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1964, 541, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1965, 542, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1966, 543, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1967, 544, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1968, \n 545, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1969, 546, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1970, 547, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1971, 548, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1972, 549, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1973, 550, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1974, 551, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1975, 552, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1976, 553, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1977, 554, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1978, \n 555, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1979, 556, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1980, 557, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1981, 558, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1982, 559, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1983, 560, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1984, 561, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1985, 562, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1986, 563, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1987, 564, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1988, \n 565, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1989, 566, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1990, 567, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1991, 568, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1992, 569, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1993, 570, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1994, 571, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1995, 572, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1996, 573, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1997, 574, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1998, \n 575, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1999, 576, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [2000, 577, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [2001, 578, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [2002, 579, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [2003, 580, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [2004, 581, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [2005, 582, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [2006, 583, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 2007, 584, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [2008, \n 585, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1, 490, 0, \n 0.01433884297520661, 0.151691958358336, 991.0, 991.0, 991.0, 0, 2, 1, -\n 360, 43.375], [3, 4, 0, 0.006291637811634348, 0.903417549506624, 3423.0,\n 3423.0, 3423.0, 0, 2, 1, -360, 72.681], [491, 6, 0, \n 0.011200661157024791, 0.118492839955776, 991.0, 991.0, 991.0, 0, 2, 1, \n -360, 33.882], [7, 5, 0, 0.005794840720221606, 0.20802058859584005, \n 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 33.471], [8, 9, 0, \n 0.0024379328254847646, 0.350063268897336, 3423.0, 3423.0, 3423.0, 0, 1,\n 1, -360, 28.163], [492, 11, 0, 0.018224793388429753, 0.0482004476327704,\n 495.0, 495.0, 495.0, 0, 1, 1, -360, 27.565], [11, 493, 0, \n 0.030286942148760328, 0.08010209706571599, 495.0, 495.0, 495.0, 0, 1, 1,\n -360, 45.809], [492, 493, 0, 0.04521652892561983, 0.11958747011094399, \n 495.0, 495.0, 495.0, 0, 1, 1, -360, 68.39], [494, 14, 0, \n 0.012990743801652892, 0.137430291356512, 991.0, 991.0, 991.0, 0, 2, 1, \n -360, 39.297], [13, 15, 0, 0.007681959833795014, 0.27576354266704156, \n 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 44.371], [16, 5, 0, \n 0.006275623268698061, 0.22527950450957998, 1711.0, 1711.0, 1711.0, 0, 2,\n 1, -360, 36.248000000000005], [17, 18, 0, 0.04623522622347646, \n 0.9335989000302801, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 200.291], [\n 17, 12, 0, 0.0056020313942728535, 0.113118303398186, 1283.0, 1283.0, \n 1283.0, 0, 1, 1, -360, 24.268], [14, 495, 0, 0.0017957024793388433, \n 0.018996904156819597, 991.0, 991.0, 991.0, 0, 1, 1, -360, 5.432], [494,\n 19, 0, 0.010246611570247935, 0.10839986031771602, 991.0, 991.0, 991.0, \n 0, 1, 1, -360, 30.996], [20, 21, 0, 0.005415685595567867, \n 0.19440984828307922, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 31.281], [\n 20, 22, 0, 0.0049706544321329645, 0.713737278110032, 3423.0, 3423.0, \n 3423.0, 0, 2, 1, -360, 57.42100000000001], [497, 23, 0, \n 0.002190413223140496, 0.005793146490362, 495.0, 495.0, 495.0, 0, 1, 1, \n -360, 3.313], [23, 499, 0, 0.020799669421487598, 0.22004164444829602, \n 991.0, 991.0, 991.0, 0, 1, 1, -360, 62.919], [25, 26, 0, \n 0.00141845567867036, 0.050919084651523595, 1711.0, 1711.0, 1711.0, 0, 1,\n 1, -360, 8.193], [25, 22, 0, 0.0035578254847645433, 0.0319293051869808,\n 856.0, 856.0, 856.0, 0, 1, 1, -360, 10.275], [23, 27, 0, \n 0.027738181818181818, 0.073361203699828, 495.0, 495.0, 495.0, 0, 1, 1, \n -360, 41.95399999999999], [28, 23, 0, 0.012841652892561981, \n 0.0339632611780132, 495.0, 495.0, 495.0, 0, 1, 1, -360, 19.423], [8, 21,\n 0, 0.004948753462603878, 0.17764812836304802, 1711.0, 1711.0, 1711.0, 0,\n 2, 1, -360, 28.584], [9, 29, 0, 0.002212863573407202, \n 0.31774552934092004, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, \n 25.563000000000002], [30, 25, 0, 0.019958795013850415, \n 0.17911796401827998, 856.0, 856.0, 856.0, 0, 1, 1, -360, \n 57.641000000000005], [31, 32, 0, 0.0299776084949446, 0.605319030583196,\n 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 129.863], [32, 33, 0, \n 0.016762234533725762, 0.33846927983213604, 1283.0, 1283.0, 1283.0, 0, 1,\n 1, -360, 72.61399999999999], [34, 35, 0, 0.001931900826446281, \n 0.020437759184893597, 991.0, 991.0, 991.0, 0, 2, 1, -360, \n 5.843999999999999], [35, 36, 0, 0.0008730578512396695, \n 0.0092361605077588, 991.0, 991.0, 991.0, 0, 2, 1, -360, 2.641], [490, 6,\n 0, 0.049352066115702475, 0.130525028606764, 495.0, 495.0, 495.0, 0, 1, \n 1, -360, 74.645], [37, 10, 0, 0.02404639889196676, 0.485553838251812, \n 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 104.169], [10, 38, 0, \n 0.006848799630657894, 0.13829351176534158, 1283.0, 1283.0, 1283.0, 0, 1,\n 1, -360, 29.669], [37, 38, 0, 0.01437834718372576, 1.1613317560186958, \n 2567.0, 2567.0, 2567.0, 0, 1, 1, -360, 124.574], [39, 40, 0, \n 0.04521629732222991, 0.913024308337812, 1283.0, 1283.0, 1283.0, 0, 1, 1,\n -360, 195.877], [39, 41, 0, 0.017466989843005543, 0.35269996139852006, \n 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 75.667], [42, 41, 0, \n 0.031145429362880884, 0.6289001042979919, 1283.0, 1283.0, 1283.0, 0, 1,\n 1, -360, 134.922], [18, 42, 0, 0.03439750692520776, 0.6945672650962679,\n 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 149.01], [492, 43, 0, \n 0.01819173553719008, 0.192452068436848, 991.0, 991.0, 991.0, 0, 2, 1, -\n 360, 55.03], [44, 45, 0, 0.02562314049586777, 0.067767398802972, 495.0,\n 495.0, 495.0, 0, 1, 1, -360, 38.755], [44, 505, 0, 0.006061487603305785,\n 0.0160312607980052, 495.0, 495.0, 495.0, 0, 1, 1, -360, 9.168], [46, 12,\n 0, 0.0014741170360110802, 0.2116687641962416, 3423.0, 3423.0, 3423.0, 0,\n 2, 1, -360, 17.029], [47, 48, 0, 0.005344182825484765, \n 0.01199019212302604, 428.0, 428.0, 428.0, 0, 1, 1, -360, \n 7.7170000000000005], [49, 50, 0, 0.0019151662049861494, \n 0.0171874439892256, 856.0, 856.0, 856.0, 0, 1, 1, -360, \n 5.531000000000001], [31, 33, 0, 0.013475992613088641, \n 0.27211225959163604, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 58.378], [\n 31, 51, 0, 0.003518611495844875, 0.5052381383693519, 3423.0, 3423.0, \n 3423.0, 0, 1, 1, -360, 40.647], [52, 53, 0, 0.010464421745152355, \n 1.5025884408875438, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 120.885], [\n 52, 54, 0, 0.0076126500461911354, 0.1537174637168, 1283.0, 1283.0, \n 1283.0, 0, 1, 1, -360, 32.978], [506, 55, 0, 0.012634380165289257, \n 0.133660287181212, 991.0, 991.0, 991.0, 0, 1, 1, -360, 38.219], [506, \n 507, 0, 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9999, 9999, \n 9999, 0, 0, 1, -360, 360], [849, 574, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [850, 574, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [851, 575, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [853, 362, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [854,\n 363, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [855, 363, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [856, 363, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [857, 365, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [858, 368, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [859, 368, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [860, 371, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [862, 372, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [863, 374, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [864,\n 374, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [865, 375, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [867, 376, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [869, 503, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [870, 503, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [872, 378, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [873, 576, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [874, 576, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [875, 381, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [877,\n 578, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [882, 388, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [883, 388, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [886, 394, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [889, 397, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [890, 40, 0, 1e-05, 0, 9999, 9999, 9999, 0, \n 0, 1, -360, 360], [895, 580, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [896, 581, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [898, 403, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [900,\n 405, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [902, 405, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [903, 406, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [905, 413, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [906, 414, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [907, 583, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [909, 417, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [913, 422, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [915, 423, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [917,\n 43, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [918, 424, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [920, 428, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [921, 428, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [922, 429, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [923, 432, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [925, 44, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [928, 435, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [931, 439, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [934,\n 45, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [935, 45, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [936, 445, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [937, 447, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [939, 450, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [940, 451, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [942, 458, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [944, 458, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [945, 459, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [950,\n 462, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [952, 47, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [958, 478, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [959, 478, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [960, 479, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [963, 481, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [965, 49, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [966, 49, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360],\n [967, 49, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [968, 486,\n 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [969, 486, 0, 1e-05,\n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [971, 51, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [973, 506, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [976, 58, 0, 1e-05, 0, 9999, 9999, 9999, 0, \n 0, 1, -360, 360], [977, 59, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [978, 491, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [981, 62, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [982, \n 62, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [983, 62, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [984, 63, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [985, 63, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [986, 64, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [987, 65, 0, 1e-05, 0, 9999, 9999, 9999, 0, \n 0, 1, -360, 360], [988, 66, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [990, 67, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360],\n [993, 67, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [994, 67,\n 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [995, 509, 0, 1e-05,\n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [997, 510, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [999, 70, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1000, 71, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1002, 71, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1003, 72, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1007, 511, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1008, 75, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1010, 79,\n 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1011, 79, 0, 1e-05,\n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1012, 81, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1018, 514, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1023, 515, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1026, 518, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1027, 218, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1028, 221, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1029, 268, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1030, \n 269, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1031, 498, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1032, 1, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1033, 3, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1034, 4, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1035, 6, 0, 1e-05, 0, 9999, 9999, 9999, 0, \n 0, 1, -360, 360], [1036, 7, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1037, 8, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360],\n [1038, 9, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1039, 11,\n 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1040, 14, 0, 1e-05,\n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1041, 16, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1042, 17, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1044, 21, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1046, 25, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1047, 27, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1048, 28, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1049,\n 29, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1050, 31, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1051, 33, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1052, 34, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1053, 35, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1054, 36, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1055, 38, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1056, 39, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1057, 40, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1058,\n 41, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1059, 43, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1060, 44, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1061, 45, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1062, 47, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1063, 48, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1064, 49, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1065, 50, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1066, 51, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1067,\n 53, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1072, 59, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1073, 60, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1074, 62, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1075, 63, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1076, 64, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1077, 65, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1078, 66, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1079, 67, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1080,\n 70, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1081, 71, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1082, 72, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1083, 73, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1084, 75, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1085, 76, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1086, 77, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1087, 79, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1088, 80, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1089,\n 81, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1090, 82, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1091, 83, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1092, 84, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1093, 85, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1094, 88, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1095, 89, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1096, 90, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1097, 91, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1098,\n 92, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1099, 93, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1101, 98, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1102, 101, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1103, 102, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1104, 103, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1105, 108, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1106, 109, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1107, 110, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1108, 111, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1109, \n 112, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1110, 113, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1111, 114, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1112, 115, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1113, 116, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1114, 118, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1115, 119, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1116, 121, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1117, 122, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1118, 126, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1119, \n 127, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1120, 130, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1121, 131, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1122, 132, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1123, 133, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1124, 134, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1125, 135, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1126, 136, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1127, 137, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1128, 139, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1129, \n 140, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1130, 141, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1131, 142, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1132, 144, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1133, 145, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1134, 146, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1135, 147, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1136, 148, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1137, 149, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1138, 150, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1139, \n 151, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1140, 152, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1141, 153, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1142, 154, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1143, 155, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1144, 158, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1145, 161, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1146, 162, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1147, 163, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1148, 164, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1149, \n 166, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1150, 167, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1151, 168, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1152, 169, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1153, 170, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1154, 171, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1155, 172, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1156, 173, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1157, 174, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1158, 175, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1159, \n 176, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1160, 177, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1161, 178, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1162, 179, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1163, 180, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1164, 181, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1165, 182, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1166, 183, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1167, 185, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1168, 186, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1169, \n 187, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1170, 188, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1171, 189, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1172, 190, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1173, 192, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1174, 193, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1175, 194, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1176, 196, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1177, 197, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1178, 198, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1179, \n 199, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1180, 200, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1181, 202, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1182, 203, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1183, 204, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1184, 205, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1185, 206, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1186, 207, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1187, 208, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1188, 209, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1189, \n 210, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1190, 211, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1191, 212, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1192, 213, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1196, 217, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1197, 218, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1198, 219, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1199, 221, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1200, 222, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1204, 226, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1206, \n 228, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1208, 230, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1211, 237, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1212, 238, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1213, 239, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1214, 240, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1215, 241, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1216, 242, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1217, 243, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1218, 244, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1219, \n 247, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1220, 251, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1221, 252, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1222, 253, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1224, 255, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1225, 256, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1226, 257, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1227, 258, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1229, 263, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1230, 264, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1231, \n 266, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1232, 267, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1233, 268, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1235, 271, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1236, 272, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1237, 273, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1238, 274, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1239, 275, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1240, 276, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1241, 278, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1242, \n 281, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1243, 282, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1244, 283, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1245, 284, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1246, 285, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1247, 286, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1248, 287, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1249, 288, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1250, 289, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1251, 291, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1252, \n 292, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1253, 293, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1254, 294, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1255, 295, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1256, 296, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1257, 297, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1258, 298, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1259, 299, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1260, 300, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1261, 302, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1264, \n 307, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1266, 309, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1267, 311, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1268, 312, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1269, 314, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1270, 316, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1274, 321, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1275, 322, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1276, 323, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1277, 324, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1278, \n 325, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1280, 327, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1281, 328, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1282, 329, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1283, 331, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1285, 335, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1286, 337, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1287, 338, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1288, 339, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1289, 340, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1290, \n 341, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1291, 342, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1292, 343, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1293, 344, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1294, 345, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1295, 346, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1296, 347, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1297, 348, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1298, 350, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1299, 352, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1300, \n 353, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1301, 354, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1302, 355, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1303, 356, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1306, 361, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1307, 362, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1308, 363, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1312, 367, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1316, 371, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1317, 372, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1319, \n 374, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1323, 378, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1326, 384, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1327, 385, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1328, 386, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1329, 387, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1331, 390, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1333, 392, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1336, 395, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1337, 396, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1339, \n 398, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1340, 399, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1345, 406, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1346, 407, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1348, 410, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1349, 411, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1356, 419, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1357, 420, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1359, 422, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1360, 423, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1361, \n 424, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1362, 425, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1366, 429, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1367, 430, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1372, 435, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1373, 436, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1374, 437, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1375, 438, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1376, 439, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1377, 440, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1378, \n 441, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1379, 442, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1380, 443, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1381, 445, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1382, 446, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1383, 447, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1384, 448, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1385, 449, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1386, 450, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1387, 451, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1388, \n 453, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1389, 454, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1390, 455, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1391, 456, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1392, 457, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1393, 458, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1394, 459, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1395, 460, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1396, 461, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1397, 462, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1398, \n 463, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1399, 464, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1400, 465, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1401, 466, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1402, 467, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1403, 468, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1404, 469, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1405, 470, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1406, 471, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1407, 472, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1408, \n 473, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1409, 474, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1410, 475, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1411, 476, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1418, 483, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1419, 484, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1421, 486, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1422, 487, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1423, 488, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1424, 489, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1425, \n 490, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1426, 491, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1427, 492, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1428, 493, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1429, 494, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1431, 496, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1432, 497, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1433, 498, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1434, 499, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1435, 500, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1436, \n 501, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1437, 502, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1438, 503, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1439, 504, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1440, 505, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1443, 508, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1444, 509, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1445, 510, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1446, 511, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1447, 512, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1448, \n 513, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1449, 514, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1450, 515, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1451, 516, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1452, 517, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1453, 518, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1454, 519, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1455, 520, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1456, 521, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1457, 522, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1458, \n 523, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1459, 524, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1460, 525, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1461, 526, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1462, 527, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1463, 528, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1464, 529, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1465, 530, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1466, 531, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1467, 532, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1468, \n 533, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1469, 534, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1470, 535, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1471, 536, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1472, 537, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1473, 538, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1474, 539, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1475, 540, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1476, 541, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1477, 542, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1483, \n 548, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1484, 549, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1485, 550, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1486, 551, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1489, 555, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1490, 556, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1491, 557, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1492, 558, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1493, 559, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1494, 560, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1495, \n 561, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1497, 563, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1498, 564, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1501, 567, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1503, 569, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1504, 570, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1505, 571, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1506, 572, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1507, 573, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1510, 576, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1511, \n 577, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1512, 578, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1513, 579, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1518, 584, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1519, 585, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1520, 1, 0, 1e-05, 0, 9999, 9999, 9999, 0, \n 0, 1, -360, 360], [1521, 3, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1522, 4, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360],\n [1523, 6, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1524, 7,\n 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1525, 8, 0, 1e-05,\n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1526, 9, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1527, 11, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1528, 14, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1529, 16, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1530, 17, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1531, 19, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1532,\n 21, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1534, 25, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1535, 27, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1536, 28, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1537, 29, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1538, 31, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1539, 33, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1540, 34, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1541, 35, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1542,\n 36, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1543, 38, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1544, 39, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1545, 40, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1546, 41, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1547, 43, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1548, 44, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1549, 45, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1550, 47, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1551,\n 48, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1552, 49, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1553, 50, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1554, 51, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1555, 53, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1556, 54, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1557, 55, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1558, 57, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1559, 58, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1560,\n 59, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1561, 60, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1562, 62, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1563, 63, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1564, 64, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1565, 65, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1566, 66, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1567, 67, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1568, 70, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1569,\n 71, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1570, 72, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1571, 73, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1572, 75, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1573, 76, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1574, 77, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1575, 79, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1576, 80, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1577, 81, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1578,\n 82, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1579, 83, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1580, 84, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1581, 85, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1582, 88, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1583, 89, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1584, 90, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1585, 91, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360\n ], [1586, 92, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1587,\n 93, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1588, 97, 0, \n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1589, 98, 0, 1e-05, 0,\n 9999, 9999, 9999, 0, 0, 1, -360, 360], [1590, 101, 0, 1e-05, 0, 9999, \n 9999, 9999, 0, 0, 1, -360, 360], [1591, 102, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1592, 103, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1593, 108, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1594, 109, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1595, 110, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1596, 111, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1597, \n 112, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1598, 113, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1599, 114, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1600, 115, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1601, 116, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1602, 118, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1603, 119, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1604, 121, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1605, 122, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1606, 126, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1607, \n 127, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1608, 130, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1609, 131, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1610, 132, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1611, 133, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1612, 134, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1613, 135, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1614, 136, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1615, 137, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1616, 139, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1617, \n 140, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1618, 141, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1619, 142, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1620, 144, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1621, 145, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1622, 146, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1623, 147, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1624, 148, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1625, 149, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1626, 150, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1627, \n 151, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1628, 152, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1629, 153, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1630, 154, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1631, 155, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1632, 158, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1633, 161, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1634, 162, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1635, 163, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1636, 164, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1637, \n 166, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1638, 167, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1639, 168, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1640, 169, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1641, 170, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1642, 171, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1643, 172, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1644, 173, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1645, 174, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1646, 175, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1647, \n 176, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1648, 177, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1649, 178, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1650, 179, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1651, 180, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1652, 181, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1653, 182, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1654, 183, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1655, 185, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1656, 186, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1657, \n 187, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1658, 188, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1659, 189, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1660, 190, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1661, 192, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1662, 193, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1663, 194, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1664, 196, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1665, 197, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1666, 198, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1667, \n 199, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1668, 200, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1669, 202, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1670, 203, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1671, 204, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1672, 205, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1673, 206, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1674, 207, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1675, 208, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1676, 209, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1677, \n 210, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1678, 211, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1679, 212, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1680, 213, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1681, 214, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1682, 215, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1683, 216, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1684, 217, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1685, 218, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1686, 219, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1687, \n 221, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1688, 222, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1689, 223, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1690, 224, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1691, 225, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1692, 226, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1693, 227, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1694, 228, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1695, 229, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1696, 230, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1697, \n 234, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1698, 235, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1699, 237, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1700, 238, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1701, 239, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1702, 240, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1703, 241, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1704, 242, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1705, 243, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1706, 244, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1707, \n 247, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1708, 251, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1709, 252, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1710, 253, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1711, 254, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1712, 255, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1713, 256, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1714, 257, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1715, 258, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1716, 260, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1717, \n 263, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1718, 264, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1719, 266, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1720, 267, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1721, 268, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1722, 269, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1723, 271, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1724, 272, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1725, 273, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1726, 274, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1727, \n 275, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1728, 276, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1729, 278, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1730, 281, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1731, 282, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1732, 283, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1733, 284, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1734, 285, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1735, 286, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1736, 287, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1737, \n 288, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1738, 289, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1739, 291, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1740, 292, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1741, 293, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1742, 294, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1743, 295, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1744, 296, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1745, 297, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1746, 298, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1747, \n 299, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1748, 300, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1749, 302, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1750, 303, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1751, 304, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1752, 307, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1753, 308, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1754, 309, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1755, 311, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1756, 312, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1757, \n 314, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1758, 316, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1759, 317, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1760, 318, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1761, 319, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1762, 321, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1763, 322, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1764, 323, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1765, 324, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1766, 325, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1767, \n 326, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1768, 327, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1769, 328, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1770, 329, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1771, 331, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1772, 333, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1773, 335, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1774, 337, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1775, 338, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1776, 339, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1777, \n 340, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1778, 341, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1779, 342, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1780, 343, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1781, 344, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1782, 345, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1783, 346, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1784, 347, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1785, 348, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1786, 350, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1787, \n 352, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1788, 353, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1789, 354, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1790, 355, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1791, 356, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1792, 357, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1793, 359, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1794, 361, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1795, 362, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1796, 363, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1797, \n 364, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1798, 365, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1799, 366, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1800, 367, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1801, 368, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1802, 369, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1803, 370, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1804, 371, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1805, 372, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1806, 373, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1807, \n 374, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1808, 375, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1809, 376, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1810, 377, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1811, 378, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1812, 379, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1813, 381, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1814, 384, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1815, 385, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1816, 386, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1817, \n 387, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1818, 388, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1819, 390, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1820, 391, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1821, 392, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1822, 393, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1823, 394, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1824, 395, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1825, 396, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1826, 397, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1827, \n 398, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1828, 399, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1830, 403, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1831, 404, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1832, 405, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1833, 406, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1834, 407, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1836, 410, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1837, 411, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1838, 412, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1839, \n 413, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1840, 414, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1841, 416, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1842, 417, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1843, 418, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1844, 419, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1845, 420, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1846, 421, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1847, 422, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1848, 423, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1849, \n 424, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1850, 425, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1851, 426, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1852, 427, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1853, 428, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1854, 429, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1855, 430, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1856, 431, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1857, 432, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1858, 433, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1860, \n 435, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1861, 436, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1862, 437, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1863, 438, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1864, 439, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1865, 440, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1866, 441, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1867, 442, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1868, 443, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1869, 445, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1870, \n 446, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1871, 447, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1872, 448, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1873, 449, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1874, 450, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1875, 451, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1876, 453, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1877, 454, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1878, 455, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1879, 456, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1880, \n 457, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1881, 458, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1882, 459, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1883, 460, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1884, 461, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1885, 462, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1886, 463, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1887, 464, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1888, 465, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1889, 466, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1890, \n 467, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1891, 468, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1892, 469, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1893, 470, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1894, 471, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1895, 472, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1896, 473, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1897, 474, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1898, 475, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1899, 476, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1900, \n 477, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1901, 478, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1902, 479, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1903, 480, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1904, 481, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1905, 482, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1906, 483, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1907, 484, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1908, 485, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1909, 486, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1910, \n 487, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1911, 488, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1912, 489, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1913, 490, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1914, 491, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1915, 492, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1916, 493, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1917, 494, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1918, 495, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1919, 496, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1920, \n 497, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1921, 498, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1922, 499, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1923, 500, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1924, 501, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1925, 502, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1926, 503, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1927, 504, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1928, 505, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1929, 506, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1930, \n 507, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1931, 508, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1932, 509, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1933, 510, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1934, 511, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1935, 512, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1936, 513, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1937, 514, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1938, 515, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1939, 516, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1940, \n 517, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1941, 518, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1942, 519, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1943, 520, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1944, 521, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1945, 522, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1946, 523, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1947, 524, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1948, 525, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1949, 526, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1950, \n 527, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1951, 528, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1952, 529, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1953, 530, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1954, 531, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1955, 532, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1956, 533, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1957, 534, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1958, 535, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1959, 536, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1960, \n 537, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1961, 538, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1962, 539, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1963, 540, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1964, 541, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1965, 542, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1966, 543, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1967, 544, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1968, 545, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1969, 546, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1970, \n 547, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1971, 548, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1972, 549, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1973, 550, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1974, 551, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1975, 552, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1976, 553, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1977, 554, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1978, 555, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1979, 556, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1980, \n 557, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1981, 558, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1982, 559, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1983, 560, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1984, 561, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1985, 562, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1986, 563, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1987, 564, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1988, 565, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1989, 566, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1990, \n 567, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1991, 568, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1992, 569, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [1993, 570, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [1994, 571, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [1995, 572, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [1996, 573, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [1997, 574, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [1998, 575, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1999, 576, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [2000, \n 577, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [2001, 578, 0,\n 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [2002, 579, 0, 1e-05, \n 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [2003, 580, 0, 1e-05, 0, 9999,\n 9999, 9999, 0, 0, 1, -360, 360], [2004, 581, 0, 1e-05, 0, 9999, 9999, \n 9999, 0, 0, 1, -360, 360], [2005, 582, 0, 1e-05, 0, 9999, 9999, 9999, 0,\n 0, 1, -360, 360], [2006, 583, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -\n 360, 360], [2007, 584, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, \n 360], [2008, 585, 0, 1e-05, 0, 9999, 9999, 9999, 0, 0, 1, -360, 360], [\n 1, 490, 0, 0.01433884297520661, 0.151691958358336, 991.0, 991.0, 991.0,\n 0, 2, 1, -360, 43.375], [3, 4, 0, 0.006291637811634348, \n 0.903417549506624, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 72.681], [491,\n 6, 0, 0.011200661157024791, 0.118492839955776, 991.0, 991.0, 991.0, 0, \n 2, 1, -360, 33.882], [7, 5, 0, 0.005794840720221606, \n 0.20802058859584005, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 33.471], [8,\n 9, 0, 0.0024379328254847646, 0.350063268897336, 3423.0, 3423.0, 3423.0,\n 0, 1, 1, -360, 28.163], [492, 11, 0, 0.018224793388429753, \n 0.0482004476327704, 495.0, 495.0, 495.0, 0, 1, 1, -360, 27.565], [11, \n 493, 0, 0.030286942148760328, 0.08010209706571599, 495.0, 495.0, 495.0,\n 0, 1, 1, -360, 45.809], [492, 493, 0, 0.04521652892561983, \n 0.11958747011094399, 495.0, 495.0, 495.0, 0, 1, 1, -360, 68.39], [494, \n 14, 0, 0.012990743801652892, 0.137430291356512, 991.0, 991.0, 991.0, 0,\n 2, 1, -360, 39.297], [13, 15, 0, 0.007681959833795014, \n 0.27576354266704156, 1711.0, 1711.0, 1711.0, 0, 2, 1, -360, 44.371], [\n 16, 5, 0, 0.006275623268698061, 0.22527950450957998, 1711.0, 1711.0, \n 1711.0, 0, 2, 1, -360, 36.248000000000005], [17, 18, 0, \n 0.04623522622347646, 0.9335989000302801, 1283.0, 1283.0, 1283.0, 0, 1, \n 1, -360, 200.291], [17, 12, 0, 0.0056020313942728535, 0.113118303398186,\n 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 24.268], [14, 495, 0, \n 0.0017957024793388433, 0.018996904156819597, 991.0, 991.0, 991.0, 0, 1,\n 1, -360, 5.432], [494, 19, 0, 0.010246611570247935, 0.10839986031771602,\n 991.0, 991.0, 991.0, 0, 1, 1, -360, 30.996], [20, 21, 0, \n 0.005415685595567867, 0.19440984828307922, 1711.0, 1711.0, 1711.0, 0, 2,\n 1, -360, 31.281], [20, 22, 0, 0.0049706544321329645, 0.713737278110032,\n 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 57.42100000000001], [497, 23, 0,\n 0.002190413223140496, 0.005793146490362, 495.0, 495.0, 495.0, 0, 1, 1, \n -360, 3.313], [23, 499, 0, 0.020799669421487598, 0.22004164444829602, \n 991.0, 991.0, 991.0, 0, 1, 1, -360, 62.919], [25, 26, 0, \n 0.00141845567867036, 0.050919084651523595, 1711.0, 1711.0, 1711.0, 0, 1,\n 1, -360, 8.193], [25, 22, 0, 0.0035578254847645433, 0.0319293051869808,\n 856.0, 856.0, 856.0, 0, 1, 1, -360, 10.275], [23, 27, 0, \n 0.027738181818181818, 0.073361203699828, 495.0, 495.0, 495.0, 0, 1, 1, \n -360, 41.95399999999999], [28, 23, 0, 0.012841652892561981, \n 0.0339632611780132, 495.0, 495.0, 495.0, 0, 1, 1, -360, 19.423], [8, 21,\n 0, 0.004948753462603878, 0.17764812836304802, 1711.0, 1711.0, 1711.0, 0,\n 2, 1, -360, 28.584], [9, 29, 0, 0.002212863573407202, \n 0.31774552934092004, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, \n 25.563000000000002], [30, 25, 0, 0.019958795013850415, \n 0.17911796401827998, 856.0, 856.0, 856.0, 0, 1, 1, -360, \n 57.641000000000005], [31, 32, 0, 0.0299776084949446, 0.605319030583196,\n 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 129.863], [32, 33, 0, \n 0.016762234533725762, 0.33846927983213604, 1283.0, 1283.0, 1283.0, 0, 1,\n 1, -360, 72.61399999999999], [34, 35, 0, 0.001931900826446281, \n 0.020437759184893597, 991.0, 991.0, 991.0, 0, 2, 1, -360, \n 5.843999999999999], [35, 36, 0, 0.0008730578512396695, \n 0.0092361605077588, 991.0, 991.0, 991.0, 0, 2, 1, -360, 2.641], [490, 6,\n 0, 0.049352066115702475, 0.130525028606764, 495.0, 495.0, 495.0, 0, 1, \n 1, -360, 74.645], [37, 10, 0, 0.02404639889196676, 0.485553838251812, \n 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 104.169], [10, 38, 0, \n 0.006848799630657894, 0.13829351176534158, 1283.0, 1283.0, 1283.0, 0, 1,\n 1, -360, 29.669], [37, 38, 0, 0.01437834718372576, 1.1613317560186958, \n 2567.0, 2567.0, 2567.0, 0, 1, 1, -360, 124.574], [39, 40, 0, \n 0.04521629732222991, 0.913024308337812, 1283.0, 1283.0, 1283.0, 0, 1, 1,\n -360, 195.877], [39, 41, 0, 0.017466989843005543, 0.35269996139852006, \n 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 75.667], [42, 41, 0, \n 0.031145429362880884, 0.6289001042979919, 1283.0, 1283.0, 1283.0, 0, 1,\n 1, -360, 134.922], [18, 42, 0, 0.03439750692520776, 0.6945672650962679,\n 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 149.01], [492, 43, 0, \n 0.01819173553719008, 0.192452068436848, 991.0, 991.0, 991.0, 0, 2, 1, -\n 360, 55.03], [44, 45, 0, 0.02562314049586777, 0.067767398802972, 495.0,\n 495.0, 495.0, 0, 1, 1, -360, 38.755], [44, 505, 0, 0.006061487603305785,\n 0.0160312607980052, 495.0, 495.0, 495.0, 0, 1, 1, -360, 9.168], [46, 12,\n 0, 0.0014741170360110802, 0.2116687641962416, 3423.0, 3423.0, 3423.0, 0,\n 2, 1, -360, 17.029], [47, 48, 0, 0.005344182825484765, \n 0.01199019212302604, 428.0, 428.0, 428.0, 0, 1, 1, -360, \n 7.7170000000000005], [49, 50, 0, 0.0019151662049861494, \n 0.0171874439892256, 856.0, 856.0, 856.0, 0, 1, 1, -360, \n 5.531000000000001], [31, 33, 0, 0.013475992613088641, \n 0.27211225959163604, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 58.378], [\n 31, 51, 0, 0.003518611495844875, 0.5052381383693519, 3423.0, 3423.0, \n 3423.0, 0, 1, 1, -360, 40.647], [52, 53, 0, 0.010464421745152355, \n 1.5025884408875438, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 120.885], [\n 52, 54, 0, 0.0076126500461911354, 0.1537174637168, 1283.0, 1283.0, \n 1283.0, 0, 1, 1, -360, 32.978], [506, 55, 0, 0.012634380165289257, \n 0.133660287181212, 991.0, 991.0, 991.0, 0, 1, 1, -360, 38.219], [506, \n 507, 0, 0.044157355371900825, 0.11678619613628, 495.0, 495.0, 495.0, 0,\n 1, 1, -360, 66.788], [57, 506, 0, 0.004687272727272727, \n 0.049587095736244, 991.0, 991.0, 991.0, 0, 1, 1, -360, 14.179], [57, 58,\n 0, 0.014436363636363634, 0.0381809096340232, 495.0, 495.0, 495.0, 0, 1,\n 1, -360, 21.835], [58, 506, 0, 0.019797685950413223, 0.052360391943288,\n 495.0, 495.0, 495.0, 0, 1, 1, -360, 29.944000000000003], [59, 60, 0, \n 0.019407548476454296, 0.174170863885556, 856.0, 856.0, 856.0, 0, 1, 1, \n -360, 56.049], [508, 62, 0, 0.051111404958677685, 0.03379452026753001, \n 248.0, 248.0, 248.0, 0, 1, 1, -360, 38.653], [30, 61, 0, \n 0.03143698060941828, 0.28212765137935203, 856.0, 856.0, 856.0, 0, 1, 1,\n -360, 90.79], [63, 506, 0, 0.027457190082644623, 0.072618044249872, \n 495.0, 495.0, 495.0, 0, 1, 1, -360, 41.528999999999996], [13, 64, 0, \n 0.0014816481994459833, 0.2127501654814608, 3423.0, 3423.0, 3423.0, 0, 2,\n 1, -360, 17.116], [65, 66, 0, 0.03778185595567867, 0.7629053006222161, \n 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 163.671], [59, 67, 0, \n 0.0051880193905817175, 0.046559297286324804, 856.0, 856.0, 856.0, 0, 1,\n 1, -360, 14.982999999999999], [61, 67, 0, 0.012931440443213295, \n 0.1160517597580644, 856.0, 856.0, 856.0, 0, 1, 1, -360, 37.346], [68, \n 69, 0, 0.011149584487534626, 0.4002427745096039, 1711.0, 1711.0, 1711.0,\n 0, 1, 1, -360, 64.4], [70, 69, 0, 0.009625346260387812, \n 0.345526355460808, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, \n 55.596000000000004], [71, 72, 0, 0.008878635734072021, \n 0.318721276477736, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 51.283], [73,\n 74, 0, 0.012529547553116345, 0.253001288604392, 1283.0, 1283.0, 1283.0,\n 0, 1, 1, -360, 54.278], [37, 75, 0, 0.027459141274238225, \n 0.5544652029066119, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, \n 118.95299999999999], [72, 75, 0, 0.006688711911357341, \n 0.240108375006292, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 38.634], [37,\n 72, 0, 0.036222068328739615, 0.7314094881920841, 1283.0, 1283.0, 1283.0,\n 0, 1, 1, -360, 156.914], [76, 77, 0, 0.004683777700831025, \n 0.6725445900750401, 3423.0, 3423.0, 3423.0, 0, 2, 1, -360, 54.107], [77,\n 51, 0, 0.00363183864265928, 0.5214964473447999, 3423.0, 3423.0, 3423.0,\n 0, 2, 1, -360, 41.955], [73, 72, 0, 0.025475069252077563, \n 0.514402082018968, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, \n 110.35799999999999], [18, 40, 0, 0.01302770083102493, 0.26306018504072,\n 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 56.43600000000001], [492, 45, 0,\n 0.0308703030303719, 0.18370114733484796, 743.0, 743.0, 743.0, 0, 1, 1, \n -360, 70.03699999999999], [10, 74, 0, 0.030167359187465374, \n 0.609150547206812, 1283.0, 1283.0, 1283.0, 0, 1, 1, -360, 130.685], [45,\n 511, 0, 0.08203371900826446, 0.05424014819960001, 248.0, 248.0, 248.0, \n 0, 1, 1, -360, 62.038000000000004], [78, 32, 0, 0.013458795013850415, \n 0.48313777647302397, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 77.738], [\n 79, 80, 0, 0.0038086911357340715, 0.1367226831743568, 1711.0, 1711.0, \n 1711.0, 0, 2, 1, -360, 21.999000000000002], [81, 79, 0, \n 0.010767832409972299, 0.3865388099484561, 1711.0, 1711.0, 1711.0, 0, 2,\n 1, -360, 62.195], [34, 82, 0, 0.0015497520661157025, \n 0.00409874294399768, 495.0, 495.0, 495.0, 0, 1, 1, -360, 2.344], [83, \n 84, 0, 0.00902611570247934, 0.0238720301499152, 495.0, 495.0, 495.0, 0,\n 1, 1, -360, 13.652000000000001], [83, 499, 0, 0.04179570247933885, \n 0.0276350398834796, 248.0, 248.0, 248.0, 0, 1, 1, -360, 31.608], [85, \n 86, 0, 0.00802354570637119, 0.28802563884886, 1711.0, 1711.0, 1711.0, 0,\n 1, 1, -360, 46.343999999999994], [87, 86, 0, 0.01904968836565097, \n 0.683837154069184, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 110.031], [88,\n 89, 0, 0.00380297520661157, 0.010058007429140002, 495.0, 495.0, 495.0, \n 0, 1, 1, -360, 5.752000000000001], [90, 86, 0, 0.012097818559556786, \n 0.434282055192244, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 69.877], [91,\n 86, 0, 9.26246537396122e-05, 0.013299992817559201, 3423.0, 3423.0, \n 3423.0, 0, 2, 1, -360, 1.07], [86, 92, 0, 0.0001852493074792244, \n 0.0066499964087796005, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 1.07], [\n 86, 93, 0, 0.008152181440443215, 0.292643346635492, 1711.0, 1711.0, \n 1711.0, 0, 1, 1, -360, 47.086999999999996], [94, 86, 0, \n 0.012883829639889197, 0.46249792780547194, 1711.0, 1711.0, 1711.0, 0, 1,\n 1, -360, 74.417], [86, 95, 0, 0.010421052631578947, 0.37409026526870803,\n 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 60.192], [513, 517, 0, \n 0.0008733884297520661, 0.0023099144321748, 495.0, 495.0, 495.0, 0, 1, 1,\n -360, 1.321], [97, 66, 0, 0.03812777008310249, 0.34217338998058805, \n 856.0, 856.0, 856.0, 0, 1, 1, -360, 110.113], [42, 98, 0, \n 0.003091759002770083, 0.44394630230884, 3423.0, 3423.0, 3423.0, 0, 2, 1,\n -360, 35.716], [99, 100, 0, 0.016371537396121884, 0.587698093837988, \n 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 94.56200000000001], [42, 101, 0,\n 0.008165339335180054, 0.29311568282888, 1711.0, 1711.0, 1711.0, 0, 1, 1,\n -360, 47.163000000000004], [102, 42, 0, 0.012403047091412742, \n 0.44523901189173193, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 71.64], [\n 103, 87, 0, 0.007073060941828254, 0.25390556381756, 1711.0, 1711.0, \n 1711.0, 0, 1, 1, -360, 40.854], [104, 103, 0, 0.0028852146814404432, \n 0.1035721403291428, 1711.0, 1711.0, 1711.0, 0, 1, 1, -360, 16.665], [\n 105, 87, 0, 0.006406682825484765, 0.22998422159488002, 1711.0, 1711.0, \n 1711.0, 0, 1, 1, -360, 37.005], [106, 107, 0, 0.005714219759923823, \n 0.11538365264216799, 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0.0006057243836929574, 0.030286219184647873, 2.22, 61.69, \n 0.004502], [1849, 3, 0.002394906091011762, 0.1197453045505881, 2.22, \n 61.69, 0.004502], [1850, 3, 0.0030901892998593753, 0.1545094649929688, \n 2.22, 61.69, 0.004502], [1851, 3, 0.0005065229873089011, \n 0.025326149365445055, 2.22, 61.69, 0.004502], [1852, 3, \n 0.0023941306142429277, 0.11970653071214639, 2.22, 61.69, 0.004502], [\n 1853, 3, 0.001917289339589373, 0.09586446697946867, 2.22, 61.69, \n 0.004502], [1854, 3, 6.576971194700433e-05, 0.0032884855973502164, 2.22,\n 61.69, 0.004502], [1855, 2, 0.00774686590726215, 0.3873432953631076, 0,\n 0, 0], [1856, 2, 0.004052362315850483, 0.20261811579252417, 0, 0, 0], [\n 1857, 3, 0.0021783820758427335, 0.10891910379213668, 2.22, 61.69, \n 0.004502], [1858, 3, 0.0011079593636130858, 0.05539796818065428, 2.22, \n 61.69, 0.004502], [1860, 2, 0.005358021415770059, 0.26790107078850295, \n 0, 0, 0], [1861, 3, 0.0017100335257855066, 0.08550167628927534, 2.22, \n 61.69, 0.004502], [1862, 3, 0.0020698307768289865, 0.10349153884144933,\n 2.22, 61.69, 0.004502], [1863, 3, 0.00191391644363232, \n 0.095695822181616, 2.22, 61.69, 0.004502], [1864, 2, \n 0.008800397207769248, 0.44001986038846247, 0, 0, 0], [1865, 2, \n 0.0043352387888536065, 0.21676193944268035, 0, 0, 0], [1866, 2, \n 0.006257281052932708, 0.31286405264663536, 0, 0, 0], [1867, 3, \n 0.00012995244000252472, 0.006497622000126237, 2.22, 61.69, 0.004502], [\n 1868, 3, 0.00041083453079481484, 0.020541726539740745, 2.22, 61.69, \n 0.004502], [1869, 3, 0.00017567193669280263, 0.00878359683464013, 2.22,\n 61.69, 0.004502], [1870, 2, 0.003473694483557617, 0.1736847241778809, 0,\n 0, 0], [1871, 2, 0.0033517040413269606, 0.16758520206634805, 0, 0, 0],\n [1872, 3, 0.00010719744643252312, 0.005359872321626155, 2.22, 61.69, \n 0.004502], [1873, 3, 0.000574567390652452, 0.028728369532622606, 2.22, \n 61.69, 0.004502], [1874, 3, 0.00022628109654053896, \n 0.011314054827026949, 2.22, 61.69, 0.004502], [1875, 3, \n 0.0004989555693169999, 0.02494777846585, 2.22, 61.69, 0.004502], [1876,\n 3, 0.0003142782948794478, 0.015713914743972393, 2.22, 61.69, 0.004502],\n [1877, 3, 7.230196727588184e-05, 0.0036150983637940923, 2.22, 61.69, \n 0.004502], [1878, 3, 0.0005331263734578022, 0.026656318672890113, 2.22,\n 61.69, 0.004502], [1879, 3, 0.00011159186867635697, \n 0.005579593433817849, 2.22, 61.69, 0.004502], [1880, 3, \n 0.00244891520347455, 0.1224457601737275, 2.22, 61.69, 0.004502], [1881,\n 3, 0.0002887579564064166, 0.014437897820320829, 2.22, 61.69, 0.004502],\n [1882, 3, 0.00032599010771041975, 0.01629950538552099, 2.22, 61.69, \n 0.004502], [1883, 3, 0.00044187502678609845, 0.022093751339304926, 2.22,\n 61.69, 0.004502], [1884, 3, 0.00037340729038742344, \n 0.018670364519371176, 2.22, 61.69, 0.004502], [1885, 3, \n 0.0030245916943440585, 0.15122958471720294, 2.22, 61.69, 0.004502], [\n 1886, 3, 0.0003345690401481447, 0.016728452007407236, 2.22, 61.69, \n 0.004502], [1887, 3, 0.0010782856336352766, 0.053914281681763834, 2.22,\n 61.69, 0.004502], [1888, 3, 0.0002636376916630472, 0.01318188458315236,\n 2.22, 61.69, 0.004502], [1889, 2, 0.005814578382053085, \n 0.29072891910265425, 0, 0, 0], [1890, 3, 0.0015815352363784706, \n 0.07907676181892354, 2.22, 61.69, 0.004502], [1891, 3, \n 0.000992315529797541, 0.049615776489877056, 2.22, 61.69, 0.004502], [\n 1892, 3, 0.001259940613113676, 0.0629970306556838, 2.22, 61.69, \n 0.004502], [1893, 3, 0.001665887236274936, 0.0832943618137468, 2.22, \n 61.69, 0.004502], [1894, 3, 0.001079038206318279, 0.05395191031591395, \n 2.22, 61.69, 0.004502], [1895, 3, 8.952647871728964e-06, \n 0.00044763239358644815, 2.22, 61.69, 0.004502], [1896, 3, \n 0.0028811650323339066, 0.14405825161669536, 2.22, 61.69, 0.004502], [\n 1897, 3, 0.0009437630096352837, 0.04718815048176419, 2.22, 61.69, \n 0.004502], [1898, 3, 0.0006406905245851681, 0.03203452622925841, 2.22, \n 61.69, 0.004502], [1899, 3, 0.0007639753017939761, 0.0381987650896988, \n 2.22, 61.69, 0.004502], [1900, 2, 0.004972924117850934, \n 0.2486462058925467, 0, 0, 0], [1901, 2, 0.00850139874298526, \n 0.42506993714926306, 0, 0, 0], [1902, 2, 0.017941196935571776, \n 0.8970598467785887, 0, 0, 0], [1903, 2, 0.008625713146876468, \n 0.4312856573438233, 0, 0, 0], [1904, 2, 0.005041037225995458, \n 0.2520518612997729, 0, 0, 0], [1905, 3, 0.0005831823676327024, \n 0.02915911838163512, 2.22, 61.69, 0.004502], [1906, 2, \n 0.004606359297257753, 0.2303179648628877, 0, 0, 0], [1907, 3, \n 0.0018394260494774333, 0.09197130247387167, 2.22, 61.69, 0.004502], [\n 1908, 2, 0.0032135207686216495, 0.1606760384310825, 0, 0, 0], [1909, 3,\n 0.0012414089664561352, 0.062070448322806775, 2.22, 61.69, 0.004502], [\n 1910, 3, 0.0007671015575814698, 0.03835507787907349, 2.22, 61.69, \n 0.004502], [1911, 3, 0.0003087108567584627, 0.015435542837923134, 2.22,\n 61.69, 0.004502], [1912, 2, 0.0029895860721330676, 0.1494793036066534, \n 0, 0, 0], [1913, 3, 0.00021992506558906862, 0.010996253279453432, 2.22,\n 61.69, 0.004502], [1914, 3, 0.0005075021454898337, 0.025375107274491684,\n 2.22, 61.69, 0.004502], [1915, 2, 0.006010235457498342, \n 0.3005117728749171, 0, 0, 0], [1916, 2, 0.008326107867695528, \n 0.4163053933847764, 0, 0, 0], [1917, 2, 0.01186578896955475, \n 0.5932894484777375, 0, 0, 0], [1918, 2, 0.007670383184040397, \n 0.3835191592020199, 0, 0, 0], [1919, 2, 0.0038936492873901407, \n 0.19468246436950706, 0, 0, 0], [1920, 3, 0.000332549912725998, \n 0.0166274956362999, 2.22, 61.69, 0.004502], [1921, 2, \n 0.007214669119559851, 0.3607334559779926, 0, 0, 0], [1922, 2, \n 0.0021114418882092873, 0.10557209441046439, 0, 0, 0], [1923, 3, \n 0.0006974532752472191, 0.03487266376236096, 2.22, 61.69, 0.004502], [\n 1924, 3, 0.00125215478705234, 0.06260773935261701, 2.22, 61.69, \n 0.004502], [1925, 2, 0.008615016374090978, 0.430750818704549, 0, 0, 0],\n [1926, 2, 0.0061503949380010674, 0.3075197469000534, 0, 0, 0], [1927, 2,\n 0.0041806062493278656, 0.20903031246639325, 0, 0, 0], [1928, 3, \n 4.419749409205862e-05, 0.0022098747046029312, 2.22, 61.69, 0.004502], [\n 1929, 3, 0.00020680114039434865, 0.010340057019717434, 2.22, 61.69, \n 0.004502], [1930, 3, 0.0007005983174314458, 0.03502991587157229, 2.22, \n 61.69, 0.004502], [1931, 3, 0.0024239654405412703, 0.12119827202706351,\n 2.22, 61.69, 0.004502], [1932, 3, 0.002974438998844226, \n 0.1487219499422113, 2.22, 61.69, 0.004502], [1933, 3, \n 0.0028163541927531156, 0.1408177096376558, 2.22, 61.69, 0.004502], [\n 1934, 2, 0.02440916060463032, 1.220458030231516, 0, 0, 0], [1935, 2, \n 0.0039684102931149354, 0.19842051465574678, 0, 0, 0], [1936, 3, \n 0.000382479275998745, 0.01912396379993725, 2.22, 61.69, 0.004502], [\n 1937, 2, 0.008569267103180329, 0.42846335515901646, 0, 0, 0], [1938, 2,\n 0.00390989736605716, 0.19549486830285803, 0, 0, 0], [1939, 2, \n 0.006557418126204308, 0.3278709063102154, 0, 0, 0], [1940, 3, \n 0.001208357077306712, 0.0604178538653356, 2.22, 61.69, 0.004502], [1941,\n 2, 0.006652364482492468, 0.3326182241246234, 0, 0, 0], [1942, 2, \n 0.0045043949262709645, 0.22521974631354824, 0, 0, 0], [1943, 3, \n 0.00023252908320092636, 0.011626454160046318, 2.22, 61.69, 0.004502], [\n 1944, 2, 0.005929079599901607, 0.29645397999508033, 0, 0, 0], [1945, 3,\n 0.0006780918980905403, 0.03390459490452701, 2.22, 61.69, 0.004502], [\n 1946, 3, 8.336148919521743e-05, 0.004168074459760872, 2.22, 61.69, \n 0.004502], [1947, 3, 0.0011456765961899158, 0.05728382980949579, 2.22, \n 61.69, 0.004502], [1948, 2, 0.00528874503695904, 0.264437251847952, 0, \n 0, 0], [1949, 3, 0.0006489211057679636, 0.03244605528839819, 2.22, \n 61.69, 0.004502], [1950, 3, 5.516264040749443e-05, \n 0.0027581320203747214, 2.22, 61.69, 0.004502], [1951, 3, \n 0.0005040498585424706, 0.025202492927123534, 2.22, 61.69, 0.004502], [\n 1952, 2, 0.004311440642752593, 0.21557203213762965, 0, 0, 0], [1953, 3,\n 0.00056840953078036, 0.028420476539017997, 2.22, 61.69, 0.004502], [\n 1954, 3, 0.000810219119251976, 0.0405109559625988, 2.22, 61.69, \n 0.004502], [1955, 3, 0.00042177682050851135, 0.02108884102542557, 2.22,\n 61.69, 0.004502], [1956, 3, 0.002465302961964236, 0.12326514809821179, \n 2.22, 61.69, 0.004502], [1957, 2, 0.008383156986347735, \n 0.4191578493173868, 0, 0, 0], [1958, 2, 0.0038064615860352196, \n 0.190323079301761, 0, 0, 0], [1959, 3, 0.001895272146447572, \n 0.09476360732237861, 2.22, 61.69, 0.004502], [1960, 3, \n 0.0008645229684302137, 0.043226148421510686, 2.22, 61.69, 0.004502], [\n 1961, 3, 0.0011358239614237152, 0.05679119807118577, 2.22, 61.69, \n 0.004502], [1962, 3, 0.00020054662262724584, 0.010027331131362293, 2.22,\n 61.69, 0.004502], [1963, 3, 4.6561076539460124e-05, \n 0.002328053826973006, 2.22, 61.69, 0.004502], [1964, 2, \n 0.0022125463299369165, 0.11062731649684583, 0, 0, 0], [1965, 3, \n 0.0006342156106012513, 0.031710780530062564, 2.22, 61.69, 0.004502], [\n 1966, 3, 0.00017024481693603925, 0.008512240846801963, 2.22, 61.69, \n 0.004502], [1967, 2, 0.006307261687354099, 0.315363084367705, 0, 0, 0],\n [1968, 2, 0.01284277839703282, 0.6421389198516411, 0, 0, 0], [1969, 3, \n 0.0009579929272501334, 0.04789964636250667, 2.22, 61.69, 0.004502], [\n 1970, 2, 0.015079725927314894, 0.7539862963657448, 0, 0, 0], [1971, 3, \n 0.0009170131292254306, 0.04585065646127153, 2.22, 61.69, 0.004502], [\n 1972, 3, 1.8066254100367179e-06, 9.03312705018359e-05, 2.22, 61.69, \n 0.004502], [1973, 3, 3.403981706132528e-05, 0.001701990853066264, 2.22,\n 61.69, 0.004502], [1974, 3, 0.00017512817138826898, \n 0.008756408569413449, 2.22, 61.69, 0.004502], [1975, 2, \n 0.005215773570935892, 0.2607886785467946, 0, 0, 0], [1976, 3, \n 0.00013846418760463496, 0.006923209380231748, 2.22, 61.69, 0.004502], [\n 1977, 2, 0.01441202991758457, 0.7206014958792286, 0, 0, 0], [1978, 3, \n 8.477175778714879e-05, 0.0042385878893574395, 2.22, 61.69, 0.004502], [\n 1979, 2, 0.0057457235400009896, 0.2872861770000495, 0, 0, 0], [1980, 2,\n 0.006405630588486738, 0.32028152942433696, 0, 0, 0], [1981, 2, \n 0.009210787821818714, 0.46053939109093567, 0, 0, 0], [1982, 2, \n 0.008590405853146561, 0.4295202926573281, 0, 0, 0], [1983, 2, \n 0.009930641216311431, 0.49653206081557155, 0, 0, 0], [1984, 2, \n 0.0060141858887817045, 0.30070929443908523, 0, 0, 0], [1985, 3, \n 0.0026722646447263376, 0.13361323223631688, 2.22, 61.69, 0.004502], [\n 1986, 2, 0.018993358604279195, 0.9496679302139596, 0, 0, 0], [1987, 2, \n 0.02507734833641704, 1.2538674168208521, 0, 0, 0], [1988, 2, \n 0.01603931294702456, 0.801965647351228, 0, 0, 0], [1989, 3, \n 0.0006607023412943799, 0.033035117064719, 2.22, 61.69, 0.004502], [1990,\n 2, 0.0032054713137850705, 0.16027356568925352, 0, 0, 0], [1991, 2, \n 0.05408574806630115, 2.7042874033150572, 0, 0, 0], [1992, 2, \n 0.014863670563732221, 0.743183528186611, 0, 0, 0], [1993, 2, \n 0.015450675484657526, 0.7725337742328763, 0, 0, 0], [1994, 2, \n 0.01457937125804357, 0.7289685629021785, 0, 0, 0], [1995, 2, \n 0.016707875705152985, 0.8353937852576492, 0, 0, 0], [1996, 2, \n 0.005812773436471257, 0.2906386718235629, 0, 0, 0], [1997, 3, \n 0.0016929350309317515, 0.08464675154658759, 2.22, 61.69, 0.004502], [\n 1998, 3, 0.0007719976652252124, 0.038599883261260626, 2.22, 61.69, \n 0.004502], [1999, 2, 0.012680481108039163, 0.6340240554019583, 0, 0, 0],\n [2000, 2, 0.03691344580491312, 1.845672290245656, 0, 0, 0], [2001, 2, \n 0.007786859497473928, 0.3893429748736964, 0, 0, 0], [2002, 3, \n 0.001170905360798366, 0.05854526803991831, 2.22, 61.69, 0.004502], [\n 2003, 3, 0.0015052919810963758, 0.07526459905481879, 2.22, 61.69, \n 0.004502], [2004, 3, 0.0011289420570764744, 0.05644710285382372, 2.22, \n 61.69, 0.004502], [2005, 2, 0.004588211407678609, 0.22941057038393042, \n 0, 0, 0], [2006, 2, 0.003798130062159873, 0.18990650310799362, 0, 0, 0],\n [2007, 3, 0.00010704803006198773, 0.005352401503099387, 2.22, 61.69, \n 0.004502], [2008, 3, 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3, \n 9.793425792119684e-05, 0.004896712896059842, 2.22, 61.69, 0.004502], [\n 1820, 2, 0.005074724090318581, 0.253736204515929, 0, 0, 0], [1821, 2, \n 0.012520998202122803, 0.6260499101061401, 0, 0, 0], [1822, 2, \n 0.010875476397094096, 0.5437738198547047, 0, 0, 0], [1823, 2, \n 0.008368758642072162, 0.41843793210360813, 0, 0, 0], [1824, 2, \n 0.0021616183697018014, 0.10808091848509005, 0, 0, 0], [1825, 2, \n 0.0025970025810079836, 0.12985012905039917, 0, 0, 0], [1826, 2, \n 0.004717432235769138, 0.23587161178845692, 0, 0, 0], [1827, 3, \n 0.0010473607763716022, 0.05236803881858012, 2.22, 61.69, 0.004502], [\n 1828, 3, 0.001360469510306804, 0.0680234755153402, 2.22, 61.69, \n 0.004502], [1830, 3, 0.001765013532013441, 0.08825067660067205, 2.22, \n 61.69, 0.004502], [1831, 2, 0.004449336207686825, 0.2224668103843413, 0,\n 0, 0], [1832, 3, 0.001690901876552968, 0.08454509382764841, 2.22, 61.69,\n 0.004502], [1833, 2, 0.005179663380052178, 0.2589831690026089, 0, 0, 0],\n [1834, 2, 0.006527235089427834, 0.32636175447139176, 0, 0, 0], [1836, 3,\n 0.00021912289073146074, 0.010956144536573037, 2.22, 61.69, 0.004502], [\n 1837, 3, 0.0004122879579140169, 0.020614397895700843, 2.22, 61.69, \n 0.004502], [1838, 3, 0.001628531485618897, 0.08142657428094485, 2.22, \n 61.69, 0.004502], [1839, 2, 0.011697833115635535, 0.5848916557817768, 0,\n 0, 0], [1840, 2, 0.008465463985539544, 0.42327319927697715, 0, 0, 0], [\n 1841, 3, 0.0014631197849879433, 0.07315598924939716, 2.22, 61.69, \n 0.004502], [1842, 3, 0.0004754679394685904, 0.023773396973429523, 2.22,\n 61.69, 0.004502], [1843, 3, 0.0012264279861417988, 0.06132139930708994,\n 2.22, 61.69, 0.004502], [1844, 3, 0.002061648212488373, \n 0.10308241062441864, 2.22, 61.69, 0.004502], [1845, 3, \n 0.0020012780505250503, 0.10006390252625251, 2.22, 61.69, 0.004502], [\n 1846, 3, 0.0002387222436177512, 0.01193611218088756, 2.22, 61.69, \n 0.004502], [1847, 2, 0.007653161133263645, 0.38265805666318226, 0, 0, 0\n ], [1848, 3, 0.0006057243836929574, 0.030286219184647873, 2.22, 61.69, \n 0.004502], [1849, 3, 0.002394906091011762, 0.1197453045505881, 2.22, \n 61.69, 0.004502], [1850, 3, 0.0030901892998593753, 0.1545094649929688, \n 2.22, 61.69, 0.004502], [1851, 3, 0.0005065229873089011, \n 0.025326149365445055, 2.22, 61.69, 0.004502], [1852, 3, \n 0.0023941306142429277, 0.11970653071214639, 2.22, 61.69, 0.004502], [\n 1853, 3, 0.001917289339589373, 0.09586446697946867, 2.22, 61.69, \n 0.004502], [1854, 3, 6.576971194700433e-05, 0.0032884855973502164, 2.22,\n 61.69, 0.004502], [1855, 2, 0.00774686590726215, 0.3873432953631076, 0,\n 0, 0], [1856, 2, 0.004052362315850483, 0.20261811579252417, 0, 0, 0], [\n 1857, 3, 0.0021783820758427335, 0.10891910379213668, 2.22, 61.69, \n 0.004502], [1858, 3, 0.0011079593636130858, 0.05539796818065428, 2.22, \n 61.69, 0.004502], [1860, 2, 0.005358021415770059, 0.26790107078850295, \n 0, 0, 0], [1861, 3, 0.0017100335257855066, 0.08550167628927534, 2.22, \n 61.69, 0.004502], [1862, 3, 0.0020698307768289865, 0.10349153884144933,\n 2.22, 61.69, 0.004502], [1863, 3, 0.00191391644363232, \n 0.095695822181616, 2.22, 61.69, 0.004502], [1864, 2, \n 0.008800397207769248, 0.44001986038846247, 0, 0, 0], [1865, 2, \n 0.0043352387888536065, 0.21676193944268035, 0, 0, 0], [1866, 2, \n 0.006257281052932708, 0.31286405264663536, 0, 0, 0], [1867, 3, \n 0.00012995244000252472, 0.006497622000126237, 2.22, 61.69, 0.004502], [\n 1868, 3, 0.00041083453079481484, 0.020541726539740745, 2.22, 61.69, \n 0.004502], [1869, 3, 0.00017567193669280263, 0.00878359683464013, 2.22,\n 61.69, 0.004502], [1870, 2, 0.003473694483557617, 0.1736847241778809, 0,\n 0, 0], [1871, 2, 0.0033517040413269606, 0.16758520206634805, 0, 0, 0],\n [1872, 3, 0.00010719744643252312, 0.005359872321626155, 2.22, 61.69, \n 0.004502], [1873, 3, 0.000574567390652452, 0.028728369532622606, 2.22, \n 61.69, 0.004502], [1874, 3, 0.00022628109654053896, \n 0.011314054827026949, 2.22, 61.69, 0.004502], [1875, 3, \n 0.0004989555693169999, 0.02494777846585, 2.22, 61.69, 0.004502], [1876,\n 3, 0.0003142782948794478, 0.015713914743972393, 2.22, 61.69, 0.004502],\n [1877, 3, 7.230196727588184e-05, 0.0036150983637940923, 2.22, 61.69, \n 0.004502], [1878, 3, 0.0005331263734578022, 0.026656318672890113, 2.22,\n 61.69, 0.004502], [1879, 3, 0.00011159186867635697, \n 0.005579593433817849, 2.22, 61.69, 0.004502], [1880, 3, \n 0.00244891520347455, 0.1224457601737275, 2.22, 61.69, 0.004502], [1881,\n 3, 0.0002887579564064166, 0.014437897820320829, 2.22, 61.69, 0.004502],\n [1882, 3, 0.00032599010771041975, 0.01629950538552099, 2.22, 61.69, \n 0.004502], [1883, 3, 0.00044187502678609845, 0.022093751339304926, 2.22,\n 61.69, 0.004502], [1884, 3, 0.00037340729038742344, \n 0.018670364519371176, 2.22, 61.69, 0.004502], [1885, 3, \n 0.0030245916943440585, 0.15122958471720294, 2.22, 61.69, 0.004502], [\n 1886, 3, 0.0003345690401481447, 0.016728452007407236, 2.22, 61.69, \n 0.004502], [1887, 3, 0.0010782856336352766, 0.053914281681763834, 2.22,\n 61.69, 0.004502], [1888, 3, 0.0002636376916630472, 0.01318188458315236,\n 2.22, 61.69, 0.004502], [1889, 2, 0.005814578382053085, \n 0.29072891910265425, 0, 0, 0], [1890, 3, 0.0015815352363784706, \n 0.07907676181892354, 2.22, 61.69, 0.004502], [1891, 3, \n 0.000992315529797541, 0.049615776489877056, 2.22, 61.69, 0.004502], [\n 1892, 3, 0.001259940613113676, 0.0629970306556838, 2.22, 61.69, \n 0.004502], [1893, 3, 0.001665887236274936, 0.0832943618137468, 2.22, \n 61.69, 0.004502], [1894, 3, 0.001079038206318279, 0.05395191031591395, \n 2.22, 61.69, 0.004502], [1895, 3, 8.952647871728964e-06, \n 0.00044763239358644815, 2.22, 61.69, 0.004502], [1896, 3, \n 0.0028811650323339066, 0.14405825161669536, 2.22, 61.69, 0.004502], [\n 1897, 3, 0.0009437630096352837, 0.04718815048176419, 2.22, 61.69, \n 0.004502], [1898, 3, 0.0006406905245851681, 0.03203452622925841, 2.22, \n 61.69, 0.004502], [1899, 3, 0.0007639753017939761, 0.0381987650896988, \n 2.22, 61.69, 0.004502], [1900, 2, 0.004972924117850934, \n 0.2486462058925467, 0, 0, 0], [1901, 2, 0.00850139874298526, \n 0.42506993714926306, 0, 0, 0], [1902, 2, 0.017941196935571776, \n 0.8970598467785887, 0, 0, 0], [1903, 2, 0.008625713146876468, \n 0.4312856573438233, 0, 0, 0], [1904, 2, 0.005041037225995458, \n 0.2520518612997729, 0, 0, 0], [1905, 3, 0.0005831823676327024, \n 0.02915911838163512, 2.22, 61.69, 0.004502], [1906, 2, \n 0.004606359297257753, 0.2303179648628877, 0, 0, 0], [1907, 3, \n 0.0018394260494774333, 0.09197130247387167, 2.22, 61.69, 0.004502], [\n 1908, 2, 0.0032135207686216495, 0.1606760384310825, 0, 0, 0], [1909, 3,\n 0.0012414089664561352, 0.062070448322806775, 2.22, 61.69, 0.004502], [\n 1910, 3, 0.0007671015575814698, 0.03835507787907349, 2.22, 61.69, \n 0.004502], [1911, 3, 0.0003087108567584627, 0.015435542837923134, 2.22,\n 61.69, 0.004502], [1912, 2, 0.0029895860721330676, 0.1494793036066534, \n 0, 0, 0], [1913, 3, 0.00021992506558906862, 0.010996253279453432, 2.22,\n 61.69, 0.004502], [1914, 3, 0.0005075021454898337, 0.025375107274491684,\n 2.22, 61.69, 0.004502], [1915, 2, 0.006010235457498342, \n 0.3005117728749171, 0, 0, 0], [1916, 2, 0.008326107867695528, \n 0.4163053933847764, 0, 0, 0], [1917, 2, 0.01186578896955475, \n 0.5932894484777375, 0, 0, 0], [1918, 2, 0.007670383184040397, \n 0.3835191592020199, 0, 0, 0], [1919, 2, 0.0038936492873901407, \n 0.19468246436950706, 0, 0, 0], [1920, 3, 0.000332549912725998, \n 0.0166274956362999, 2.22, 61.69, 0.004502], [1921, 2, \n 0.007214669119559851, 0.3607334559779926, 0, 0, 0], [1922, 2, \n 0.0021114418882092873, 0.10557209441046439, 0, 0, 0], [1923, 3, \n 0.0006974532752472191, 0.03487266376236096, 2.22, 61.69, 0.004502], [\n 1924, 3, 0.00125215478705234, 0.06260773935261701, 2.22, 61.69, \n 0.004502], [1925, 2, 0.008615016374090978, 0.430750818704549, 0, 0, 0],\n [1926, 2, 0.0061503949380010674, 0.3075197469000534, 0, 0, 0], [1927, 2,\n 0.0041806062493278656, 0.20903031246639325, 0, 0, 0], [1928, 3, \n 4.419749409205862e-05, 0.0022098747046029312, 2.22, 61.69, 0.004502], [\n 1929, 3, 0.00020680114039434865, 0.010340057019717434, 2.22, 61.69, \n 0.004502], [1930, 3, 0.0007005983174314458, 0.03502991587157229, 2.22, \n 61.69, 0.004502], [1931, 3, 0.0024239654405412703, 0.12119827202706351,\n 2.22, 61.69, 0.004502], [1932, 3, 0.002974438998844226, \n 0.1487219499422113, 2.22, 61.69, 0.004502], [1933, 3, \n 0.0028163541927531156, 0.1408177096376558, 2.22, 61.69, 0.004502], [\n 1934, 2, 0.02440916060463032, 1.220458030231516, 0, 0, 0], [1935, 2, \n 0.0039684102931149354, 0.19842051465574678, 0, 0, 0], [1936, 3, \n 0.000382479275998745, 0.01912396379993725, 2.22, 61.69, 0.004502], [\n 1937, 2, 0.008569267103180329, 0.42846335515901646, 0, 0, 0], [1938, 2,\n 0.00390989736605716, 0.19549486830285803, 0, 0, 0], [1939, 2, \n 0.006557418126204308, 0.3278709063102154, 0, 0, 0], [1940, 3, \n 0.001208357077306712, 0.0604178538653356, 2.22, 61.69, 0.004502], [1941,\n 2, 0.006652364482492468, 0.3326182241246234, 0, 0, 0], [1942, 2, \n 0.0045043949262709645, 0.22521974631354824, 0, 0, 0], [1943, 3, \n 0.00023252908320092636, 0.011626454160046318, 2.22, 61.69, 0.004502], [\n 1944, 2, 0.005929079599901607, 0.29645397999508033, 0, 0, 0], [1945, 3,\n 0.0006780918980905403, 0.03390459490452701, 2.22, 61.69, 0.004502], [\n 1946, 3, 8.336148919521743e-05, 0.004168074459760872, 2.22, 61.69, \n 0.004502], [1947, 3, 0.0011456765961899158, 0.05728382980949579, 2.22, \n 61.69, 0.004502], [1948, 2, 0.00528874503695904, 0.264437251847952, 0, \n 0, 0], [1949, 3, 0.0006489211057679636, 0.03244605528839819, 2.22, \n 61.69, 0.004502], [1950, 3, 5.516264040749443e-05, \n 0.0027581320203747214, 2.22, 61.69, 0.004502], [1951, 3, \n 0.0005040498585424706, 0.025202492927123534, 2.22, 61.69, 0.004502], [\n 1952, 2, 0.004311440642752593, 0.21557203213762965, 0, 0, 0], [1953, 3,\n 0.00056840953078036, 0.028420476539017997, 2.22, 61.69, 0.004502], [\n 1954, 3, 0.000810219119251976, 0.0405109559625988, 2.22, 61.69, \n 0.004502], [1955, 3, 0.00042177682050851135, 0.02108884102542557, 2.22,\n 61.69, 0.004502], [1956, 3, 0.002465302961964236, 0.12326514809821179, \n 2.22, 61.69, 0.004502], [1957, 2, 0.008383156986347735, \n 0.4191578493173868, 0, 0, 0], [1958, 2, 0.0038064615860352196, \n 0.190323079301761, 0, 0, 0], [1959, 3, 0.001895272146447572, \n 0.09476360732237861, 2.22, 61.69, 0.004502], [1960, 3, \n 0.0008645229684302137, 0.043226148421510686, 2.22, 61.69, 0.004502], [\n 1961, 3, 0.0011358239614237152, 0.05679119807118577, 2.22, 61.69, \n 0.004502], [1962, 3, 0.00020054662262724584, 0.010027331131362293, 2.22,\n 61.69, 0.004502], [1963, 3, 4.6561076539460124e-05, \n 0.002328053826973006, 2.22, 61.69, 0.004502], [1964, 2, \n 0.0022125463299369165, 0.11062731649684583, 0, 0, 0], [1965, 3, \n 0.0006342156106012513, 0.031710780530062564, 2.22, 61.69, 0.004502], [\n 1966, 3, 0.00017024481693603925, 0.008512240846801963, 2.22, 61.69, \n 0.004502], [1967, 2, 0.006307261687354099, 0.315363084367705, 0, 0, 0],\n [1968, 2, 0.01284277839703282, 0.6421389198516411, 0, 0, 0], [1969, 3, \n 0.0009579929272501334, 0.04789964636250667, 2.22, 61.69, 0.004502], [\n 1970, 2, 0.015079725927314894, 0.7539862963657448, 0, 0, 0], [1971, 3, \n 0.0009170131292254306, 0.04585065646127153, 2.22, 61.69, 0.004502], [\n 1972, 3, 1.8066254100367179e-06, 9.03312705018359e-05, 2.22, 61.69, \n 0.004502], [1973, 3, 3.403981706132528e-05, 0.001701990853066264, 2.22,\n 61.69, 0.004502], [1974, 3, 0.00017512817138826898, \n 0.008756408569413449, 2.22, 61.69, 0.004502], [1975, 2, \n 0.005215773570935892, 0.2607886785467946, 0, 0, 0], [1976, 3, \n 0.00013846418760463496, 0.006923209380231748, 2.22, 61.69, 0.004502], [\n 1977, 2, 0.01441202991758457, 0.7206014958792286, 0, 0, 0], [1978, 3, \n 8.477175778714879e-05, 0.0042385878893574395, 2.22, 61.69, 0.004502], [\n 1979, 2, 0.0057457235400009896, 0.2872861770000495, 0, 0, 0], [1980, 2,\n 0.006405630588486738, 0.32028152942433696, 0, 0, 0], [1981, 2, \n 0.009210787821818714, 0.46053939109093567, 0, 0, 0], [1982, 2, \n 0.008590405853146561, 0.4295202926573281, 0, 0, 0], [1983, 2, \n 0.009930641216311431, 0.49653206081557155, 0, 0, 0], [1984, 2, \n 0.0060141858887817045, 0.30070929443908523, 0, 0, 0], [1985, 3, \n 0.0026722646447263376, 0.13361323223631688, 2.22, 61.69, 0.004502], [\n 1986, 2, 0.018993358604279195, 0.9496679302139596, 0, 0, 0], [1987, 2, \n 0.02507734833641704, 1.2538674168208521, 0, 0, 0], [1988, 2, \n 0.01603931294702456, 0.801965647351228, 0, 0, 0], [1989, 3, \n 0.0006607023412943799, 0.033035117064719, 2.22, 61.69, 0.004502], [1990,\n 2, 0.0032054713137850705, 0.16027356568925352, 0, 0, 0], [1991, 2, \n 0.05408574806630115, 2.7042874033150572, 0, 0, 0], [1992, 2, \n 0.014863670563732221, 0.743183528186611, 0, 0, 0], [1993, 2, \n 0.015450675484657526, 0.7725337742328763, 0, 0, 0], [1994, 2, \n 0.01457937125804357, 0.7289685629021785, 0, 0, 0], [1995, 2, \n 0.016707875705152985, 0.8353937852576492, 0, 0, 0], [1996, 2, \n 0.005812773436471257, 0.2906386718235629, 0, 0, 0], [1997, 3, \n 0.0016929350309317515, 0.08464675154658759, 2.22, 61.69, 0.004502], [\n 1998, 3, 0.0007719976652252124, 0.038599883261260626, 2.22, 61.69, \n 0.004502], [1999, 2, 0.012680481108039163, 0.6340240554019583, 0, 0, 0],\n [2000, 2, 0.03691344580491312, 1.845672290245656, 0, 0, 0], [2001, 2, \n 0.007786859497473928, 0.3893429748736964, 0, 0, 0], [2002, 3, \n 0.001170905360798366, 0.05854526803991831, 2.22, 61.69, 0.004502], [\n 2003, 3, 0.0015052919810963758, 0.07526459905481879, 2.22, 61.69, \n 0.004502], [2004, 3, 0.0011289420570764744, 0.05644710285382372, 2.22, \n 61.69, 0.004502], [2005, 2, 0.004588211407678609, 0.22941057038393042, \n 0, 0, 0], [2006, 2, 0.003798130062159873, 0.18990650310799362, 0, 0, 0],\n [2007, 3, 0.00010704803006198773, 0.005352401503099387, 2.22, 61.69, \n 0.004502], [2008, 3, 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300, 0], [1749, 302, 0], [1750, 303, 0],\n [1751, 304, 0], [1752, 307, 0], [1753, 308, 0], [1754, 309, 0], [1755, \n 311, 0], [1756, 312, 0], [1757, 314, 0], [1758, 316, 0], [1759, 317, 0],\n [1760, 318, 0], [1761, 319, 0], [1762, 321, 0], [1763, 322, 0], [1764, \n 323, 0], [1765, 324, 0], [1766, 325, 0], [1767, 326, 0], [1768, 327, 0],\n [1769, 328, 0], [1770, 329, 0], [1771, 331, 0], [1772, 333, 0], [1773, \n 335, 0], [1774, 337, 0], [1775, 338, 0], [1776, 339, 0], [1777, 340, 0],\n [1778, 341, 0], [1779, 342, 0], [1780, 343, 0], [1781, 344, 0], [1782, \n 345, 0], [1783, 346, 0], [1784, 347, 0], [1785, 348, 0], [1786, 350, 0],\n [1787, 352, 0], [1788, 353, 0], [1789, 354, 0], [1790, 355, 0], [1791, \n 356, 0], [1792, 357, 0], [1793, 359, 0], [1794, 361, 0], [1795, 362, 0],\n [1796, 363, 0], [1797, 364, 0], [1798, 365, 0], [1799, 366, 0], [1800, \n 367, 0], [1801, 368, 0], [1802, 369, 0], [1803, 370, 0], [1804, 371, 0],\n [1805, 372, 0], [1806, 373, 0], [1807, 374, 0], [1808, 375, 0], [1809, \n 376, 0], [1810, 377, 0], [1811, 378, 0], [1812, 379, 0], [1813, 381, 0],\n [1814, 384, 0], [1815, 385, 0], [1816, 386, 0], [1817, 387, 0], [1818, \n 388, 0], [1819, 390, 0], [1820, 391, 0], [1821, 392, 0], [1822, 393, 0],\n [1823, 394, 0], [1824, 395, 0], [1825, 396, 0], [1826, 397, 0], [1827, \n 398, 0], [1828, 399, 0], [1830, 403, 0], [1831, 404, 0], [1832, 405, 0],\n [1833, 406, 0], [1834, 407, 0], [1836, 410, 0], [1837, 411, 0], [1838, \n 412, 0], [1839, 413, 0], [1840, 414, 0], [1841, 416, 0], [1842, 417, 0],\n [1843, 418, 0], [1844, 419, 0], [1845, 420, 0], [1846, 421, 0], [1847, \n 422, 0], [1848, 423, 0], [1849, 424, 0], [1850, 425, 0], [1851, 426, 0],\n [1852, 427, 0], [1853, 428, 0], [1854, 429, 0], [1855, 430, 0], [1856, \n 431, 0], [1857, 432, 0], [1858, 433, 0], [1860, 435, 0], [1861, 436, 0],\n [1862, 437, 0], [1863, 438, 0], [1864, 439, 0], [1865, 440, 0], [1866, \n 441, 0], [1867, 442, 0], [1868, 443, 0], [1869, 445, 0], [1870, 446, 0],\n [1871, 447, 0], [1872, 448, 0], [1873, 449, 0], [1874, 450, 0], [1875, \n 451, 0], [1876, 453, 0], [1877, 454, 0], [1878, 455, 0], [1879, 456, 0],\n [1880, 457, 0], [1881, 458, 0], [1882, 459, 0], [1883, 460, 0], [1884, \n 461, 0], [1885, 462, 0], [1886, 463, 0], [1887, 464, 0], [1888, 465, 0],\n [1889, 466, 0], [1890, 467, 0], [1891, 468, 0], [1892, 469, 0], [1893, \n 470, 0], [1894, 471, 0], [1895, 472, 0], [1896, 473, 0], [1897, 474, 0],\n [1898, 475, 0], [1899, 476, 0], [1900, 477, 0], [1901, 478, 0], [1902, \n 479, 0], [1903, 480, 0], [1904, 481, 0], [1905, 482, 0], [1906, 483, 0],\n [1907, 484, 0], [1908, 485, 0], [1909, 486, 0], [1910, 487, 0], [1911, \n 488, 0], [1912, 489, 0], [1913, 490, 0], [1914, 491, 0], [1915, 492, 0],\n [1916, 493, 0], [1917, 494, 0], [1918, 495, 0], [1919, 496, 0], [1920, \n 497, 0], [1921, 498, 0], [1922, 499, 0], [1923, 500, 0], [1924, 501, 0],\n [1925, 502, 0], [1926, 503, 0], [1927, 504, 0], [1928, 505, 0], [1929, \n 506, 0], [1930, 507, 0], [1931, 508, 0], [1932, 509, 0], [1933, 510, 0],\n [1934, 511, 0], [1935, 512, 0], [1936, 513, 0], [1937, 514, 0], [1938, \n 515, 0], [1939, 516, 0], [1940, 517, 0], [1941, 518, 0], [1942, 519, 0],\n [1943, 520, 0], [1944, 521, 0], [1945, 522, 0], [1946, 523, 0], [1947, \n 524, 0], [1948, 525, 0], [1949, 526, 0], [1950, 527, 0], [1951, 528, 0],\n [1952, 529, 0], [1953, 530, 0], [1954, 531, 0], [1955, 532, 0], [1956, \n 533, 0], [1957, 534, 0], [1958, 535, 0], [1959, 536, 0], [1960, 537, 0],\n [1961, 538, 0], [1962, 539, 0], [1963, 540, 0], [1964, 541, 0], [1965, \n 542, 0], [1966, 543, 0], [1967, 544, 0], [1968, 545, 0], [1969, 546, 0],\n [1970, 547, 0], [1971, 548, 0], [1972, 549, 0], [1973, 550, 0], [1974, \n 551, 0], [1975, 552, 0], [1976, 553, 0], [1977, 554, 0], [1978, 555, 0],\n [1979, 556, 0], [1980, 557, 0], [1981, 558, 0], [1982, 559, 0], [1983, \n 560, 0], [1984, 561, 0], [1985, 562, 0], [1986, 563, 0], [1987, 564, 0],\n [1988, 565, 0], [1989, 566, 0], [1990, 567, 0], [1991, 568, 0], [1992, \n 569, 0], [1993, 570, 0], [1994, 571, 0], [1995, 572, 0], [1996, 573, 0],\n [1997, 574, 0], [1998, 575, 0], [1999, 576, 0], [2000, 577, 0], [2001, \n 578, 0], [2002, 579, 0], [2003, 580, 0], [2004, 581, 0], [2005, 582, 0],\n [2006, 583, 0], [2007, 584, 0], [2008, 585, 0], [1, 490, 0], [3, 4, 1],\n [491, 6, 0], [7, 5, 0], [8, 9, 0], [492, 11, 0], [11, 493, 0], [492, \n 493, 1], [494, 14, 0], [13, 15, 0], [16, 5, 0], [17, 18, 1], [17, 12, 0\n ], [14, 495, 0], [494, 19, 0], [20, 21, 0], [20, 22, 1], [497, 23, 0],\n [23, 499, 1], [25, 26, 0], [25, 22, 0], [23, 27, 0], [28, 23, 0], [8, \n 21, 0], [9, 29, 0], [30, 25, 1], [31, 32, 1], [32, 33, 1], [34, 35, 0],\n [35, 36, 0], [490, 6, 1], [37, 10, 1], [10, 38, 0], [37, 38, 1], [39, \n 40, 1], [39, 41, 1], [42, 41, 1], [18, 42, 1], [492, 43, 1], [44, 45, 0\n ], [44, 505, 0], [46, 12, 0], [47, 48, 0], [49, 50, 0], [31, 33, 1], [\n 31, 51, 0], [52, 53, 1], [52, 54, 0], [506, 55, 0], [506, 507, 1], [57,\n 506, 0], [57, 58, 0], [58, 506, 0], [59, 60, 1], [508, 62, 0], [30, 61,\n 1], [63, 506, 0], [13, 64, 0], [65, 66, 1], [59, 67, 0], [61, 67, 0], [\n 68, 69, 1], [70, 69, 1], [71, 72, 1], [73, 74, 1], [37, 75, 1], [72, 75,\n 0], [37, 72, 1], [76, 77, 1], [77, 51, 0], [73, 72, 1], [18, 40, 1], [\n 492, 45, 1], [10, 74, 1], [45, 511, 1], [78, 32, 1], [79, 80, 0], [81, \n 79, 1], [34, 82, 0], [83, 84, 0], [83, 499, 0], [85, 86, 0], [87, 86, 1\n ], [88, 89, 0], [90, 86, 1], [91, 86, 0], [86, 92, 0], [86, 93, 0], [94,\n 86, 1], [86, 95, 1], [513, 517, 0], [97, 66, 1], [42, 98, 0], [99, 100,\n 1], [42, 101, 0], [102, 42, 1], [103, 87, 0], [104, 103, 0], [105, 87, \n 0], [106, 107, 0], [108, 107, 0], [109, 106, 0], [110, 111, 1], [87, \n 112, 0], [113, 87, 0], [87, 85, 1], [110, 114, 1], [115, 116, 0], [117,\n 118, 0], [117, 119, 0], [117, 120, 1], [121, 122, 0], [123, 124, 0], [\n 125, 126, 0], [127, 119, 0], [118, 128, 0], [121, 119, 0], [530, 527, 0\n ], [125, 130, 0], [125, 123, 0], [131, 132, 0], [133, 123, 0], [524, \n 134, 0], [135, 136, 0], [123, 131, 0], [117, 128, 1], [137, 521, 0], [\n 531, 514, 0], [139, 521, 0], [140, 514, 0], [522, 141, 0], [142, 523, 0\n ], [530, 526, 0], [140, 532, 0], [142, 144, 0], [140, 522, 0], [145, \n 146, 0], [147, 523, 0], [144, 523, 0], [139, 523, 0], [140, 141, 0], [\n 528, 526, 0], [528, 148, 0], [149, 150, 0], [145, 528, 0], [530, 151, 0\n ], [524, 152, 0], [149, 525, 1], [139, 514, 0], [126, 120, 1], [530, \n 153, 0], [528, 147, 1], [528, 154, 0], [130, 120, 1], [528, 155, 1], [\n 524, 533, 0], [524, 149, 0], [154, 150, 0], [157, 110, 1], [119, 158, 0\n ], [159, 60, 0], [536, 161, 0], [115, 151, 0], [162, 134, 0], [115, 526,\n 0], [138, 87, 0], [123, 163, 0], [112, 164, 0], [112, 165, 0], [166, \n 165, 0], [167, 537, 0], [168, 104, 0], [531, 520, 0], [139, 520, 0], [\n 520, 169, 0], [168, 105, 0], [520, 170, 0], [171, 89, 0], [521, 172, 0],\n [123, 173, 0], [521, 174, 0], [37, 39, 0], [530, 175, 0], [530, 176, 0],\n [88, 530, 0], [177, 496, 1], [178, 525, 0], [179, 493, 1], [180, 181, 1\n ], [182, 180, 0], [179, 181, 0], [180, 493, 1], [183, 30, 0], [183, 21,\n 0], [538, 185, 0], [538, 89, 0], [184, 186, 0], [184, 187, 0], [520, \n 172, 0], [89, 175, 0], [185, 89, 0], [89, 188, 0], [189, 190, 0], [539,\n 172, 0], [504, 192, 0], [105, 186, 0], [105, 187, 0], [539, 193, 0], [\n 187, 194, 0], [539, 540, 0], [539, 196, 0], [197, 540, 0], [110, 198, 0\n ], [197, 539, 0], [199, 537, 0], [134, 526, 0], [200, 193, 0], [4, 201,\n 1], [202, 86, 0], [85, 203, 0], [147, 204, 0], [147, 205, 0], [123, 206,\n 0], [537, 207, 0], [165, 208, 0], [4, 94, 1], [4, 2, 0], [209, 4, 0], [\n 119, 163, 0], [210, 3, 0], [99, 211, 0], [99, 69, 1], [212, 99, 0], [\n 213, 214, 0], [510, 215, 0], [128, 69, 1], [216, 69, 1], [217, 98, 0],\n [504, 218, 0], [177, 504, 1], [219, 209, 0], [219, 220, 0], [94, 95, 1],\n [159, 221, 1], [34, 161, 0], [222, 221, 0], [211, 52, 1], [215, 223, 1],\n [224, 215, 0], [225, 224, 1], [224, 223, 0], [226, 6, 0], [7, 3, 1], [\n 216, 227, 1], [228, 229, 0], [227, 230, 0], [231, 53, 1], [544, 545, 0],\n [234, 235, 1], [546, 214, 1], [233, 227, 0], [237, 238, 0], [212, 100, \n 0], [519, 239, 0], [238, 519, 0], [213, 240, 0], [241, 242, 1], [70, \n 241, 0], [509, 213, 0], [68, 243, 0], [243, 244, 0], [68, 244, 0], [544,\n 547, 1], [245, 227, 1], [246, 208, 0], [112, 208, 0], [165, 247, 0], [\n 537, 549, 0], [537, 550, 0], [537, 551, 0], [110, 251, 0], [510, 252, 1\n ], [529, 253, 1], [237, 239, 1], [254, 238, 1], [69, 255, 0], [510, 225,\n 1], [256, 257, 0], [258, 190, 0], [258, 259, 0], [260, 261, 1], [554, \n 553, 1], [515, 263, 0], [14, 264, 1], [116, 555, 0], [151, 116, 0], [\n 111, 114, 1], [77, 111, 0], [266, 525, 0], [267, 120, 1], [268, 269, 0],\n [556, 271, 0], [556, 272, 0], [529, 273, 0], [128, 274, 0], [34, 275, 0\n ], [503, 276, 0], [503, 504, 1], [177, 218, 1], [277, 278, 1], [557, \n 558, 1], [557, 559, 1], [559, 558, 1], [277, 78, 1], [277, 279, 1], [78,\n 279, 0], [281, 282, 0], [283, 161, 1], [268, 161, 1], [256, 284, 0], [\n 515, 516, 1], [263, 516, 0], [516, 285, 0], [63, 286, 0], [287, 516, 0],\n [8, 102, 1], [8, 101, 1], [80, 288, 0], [80, 289, 0], [276, 560, 0], [\n 37, 290, 0], [290, 74, 1], [512, 291, 0], [78, 292, 1], [199, 548, 0],\n [491, 293, 0], [4, 294, 0], [490, 541, 1], [491, 295, 0], [491, 296, 0],\n [295, 297, 0], [508, 161, 0], [117, 123, 0], [133, 117, 0], [71, 74, 1],\n [74, 278, 1], [298, 515, 0], [5, 299, 0], [32, 292, 1], [5, 29, 1], [\n 503, 560, 0], [300, 301, 1], [51, 300, 0], [244, 302, 1], [31, 302, 1],\n [51, 282, 1], [303, 304, 0], [305, 304, 0], [305, 259, 0], [306, 307, 1\n ], [305, 308, 0], [305, 309, 0], [310, 309, 1], [306, 309, 1], [311, \n 280, 0], [280, 278, 1], [311, 32, 1], [13, 312, 1], [313, 314, 0], [312,\n 313, 1], [547, 566, 1], [245, 315, 1], [312, 316, 0], [312, 314, 0], [\n 554, 546, 1], [262, 216, 1], [317, 233, 0], [318, 317, 0], [231, 52, 1],\n [319, 567, 0], [557, 321, 0], [277, 65, 1], [322, 288, 1], [322, 323, 0\n ], [277, 324, 1], [324, 325, 0], [277, 325, 0], [326, 327, 0], [328, \n 326, 1], [328, 327, 1], [326, 329, 0], [568, 329, 1], [568, 326, 0], [\n 332, 78, 1], [333, 306, 0], [332, 333, 0], [332, 334, 0], [66, 334, 1],\n [330, 335, 1], [336, 66, 0], [330, 336, 1], [68, 70, 0], [509, 337, 1],\n [324, 288, 0], [338, 559, 0], [339, 559, 0], [339, 340, 1], [559, 340, \n 1], [341, 292, 0], [557, 342, 0], [558, 343, 0], [502, 340, 1], [72, 32,\n 1], [344, 345, 0], [346, 47, 0], [46, 47, 0], [346, 345, 0], [347, 328,\n 0], [347, 348, 1], [571, 348, 1], [347, 572, 0], [571, 570, 1], [14, \n 350, 0], [350, 573, 0], [15, 351, 1], [352, 15, 0], [15, 335, 1], [232,\n 227, 0], [565, 544, 1], [235, 567, 1], [567, 286, 0], [353, 519, 0], [\n 354, 353, 0], [355, 354, 0], [354, 356, 0], [357, 358, 0], [574, 359, 0\n ], [235, 575, 0], [167, 361, 0], [528, 362, 0], [363, 344, 0], [259, \n 364, 1], [54, 56, 0], [365, 364, 0], [231, 366, 0], [30, 367, 0], [61, \n 367, 1], [254, 368, 0], [254, 369, 0], [254, 370, 0], [99, 358, 0], [\n 354, 519, 0], [571, 371, 0], [207, 372, 0], [57, 373, 0], [209, 374, 0],\n [375, 376, 0], [376, 377, 0], [16, 49, 0], [318, 377, 0], [378, 297, 0],\n [562, 379, 0], [576, 563, 0], [576, 381, 0], [577, 576, 1], [244, 383, \n 0], [244, 306, 1], [383, 306, 1], [380, 306, 0], [252, 225, 0], [220, \n 76, 0], [542, 384, 0], [385, 384, 0], [542, 385, 0], [386, 385, 0], [\n 387, 578, 0], [332, 388, 1], [382, 332, 1], [382, 388, 0], [579, 578, 0\n ], [577, 387, 1], [144, 390, 0], [37, 49, 0], [391, 233, 0], [392, 310,\n 0], [260, 393, 0], [394, 230, 0], [395, 282, 1], [395, 244, 0], [25, \n 396, 1], [81, 74, 0], [278, 80, 1], [81, 278, 1], [569, 570, 0], [397, \n 552, 0], [542, 398, 0], [398, 385, 0], [399, 499, 0], [83, 399, 0], [\n 498, 400, 0], [518, 239, 1], [575, 543, 0], [401, 360, 0], [580, 581, 0\n ], [401, 402, 0], [403, 231, 0], [189, 360, 1], [234, 404, 0], [235, \n 404, 1], [235, 580, 0], [216, 259, 0], [405, 259, 0], [405, 318, 0], [\n 406, 230, 0], [542, 407, 0], [23, 408, 0], 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136, 0], [1615, 137, 0], [1616, 139, 0], [1617, 140, 0], [1618, 141, 0],\n [1619, 142, 0], [1620, 144, 0], [1621, 145, 0], [1622, 146, 0], [1623, \n 147, 0], [1624, 148, 0], [1625, 149, 0], [1626, 150, 0], [1627, 151, 0],\n [1628, 152, 0], [1629, 153, 0], [1630, 154, 0], [1631, 155, 0], [1632, \n 158, 0], [1633, 161, 0], [1634, 162, 0], [1635, 163, 0], [1636, 164, 0],\n [1637, 166, 0], [1638, 167, 0], [1639, 168, 0], [1640, 169, 0], [1641, \n 170, 0], [1642, 171, 0], [1643, 172, 0], [1644, 173, 0], [1645, 174, 0],\n [1646, 175, 0], [1647, 176, 0], [1648, 177, 0], [1649, 178, 0], [1650, \n 179, 0], [1651, 180, 0], [1652, 181, 0], [1653, 182, 0], [1654, 183, 0],\n [1655, 185, 0], [1656, 186, 0], [1657, 187, 0], [1658, 188, 0], [1659, \n 189, 0], [1660, 190, 0], [1661, 192, 0], [1662, 193, 0], [1663, 194, 0],\n [1664, 196, 0], [1665, 197, 0], [1666, 198, 0], [1667, 199, 0], [1668, \n 200, 0], [1669, 202, 0], [1670, 203, 0], [1671, 204, 0], [1672, 205, 0],\n [1673, 206, 0], [1674, 207, 0], 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286, 0],\n [1736, 287, 0], [1737, 288, 0], [1738, 289, 0], [1739, 291, 0], [1740, \n 292, 0], [1741, 293, 0], [1742, 294, 0], [1743, 295, 0], [1744, 296, 0],\n [1745, 297, 0], [1746, 298, 0], [1747, 299, 0], [1748, 300, 0], [1749, \n 302, 0], [1750, 303, 0], [1751, 304, 0], [1752, 307, 0], [1753, 308, 0],\n [1754, 309, 0], [1755, 311, 0], [1756, 312, 0], [1757, 314, 0], [1758, \n 316, 0], [1759, 317, 0], [1760, 318, 0], [1761, 319, 0], [1762, 321, 0],\n [1763, 322, 0], [1764, 323, 0], [1765, 324, 0], [1766, 325, 0], [1767, \n 326, 0], [1768, 327, 0], [1769, 328, 0], [1770, 329, 0], [1771, 331, 0],\n [1772, 333, 0], [1773, 335, 0], [1774, 337, 0], [1775, 338, 0], [1776, \n 339, 0], [1777, 340, 0], [1778, 341, 0], [1779, 342, 0], [1780, 343, 0],\n [1781, 344, 0], [1782, 345, 0], [1783, 346, 0], [1784, 347, 0], [1785, \n 348, 0], [1786, 350, 0], [1787, 352, 0], [1788, 353, 0], [1789, 354, 0],\n [1790, 355, 0], [1791, 356, 0], [1792, 357, 0], [1793, 359, 0], [1794, \n 361, 0], [1795, 362, 0], [1796, 363, 0], [1797, 364, 0], [1798, 365, 0],\n [1799, 366, 0], [1800, 367, 0], [1801, 368, 0], [1802, 369, 0], [1803, \n 370, 0], [1804, 371, 0], [1805, 372, 0], [1806, 373, 0], [1807, 374, 0],\n [1808, 375, 0], [1809, 376, 0], [1810, 377, 0], [1811, 378, 0], [1812, \n 379, 0], [1813, 381, 0], [1814, 384, 0], [1815, 385, 0], [1816, 386, 0],\n [1817, 387, 0], [1818, 388, 0], [1819, 390, 0], [1820, 391, 0], [1821, \n 392, 0], [1822, 393, 0], [1823, 394, 0], [1824, 395, 0], [1825, 396, 0],\n [1826, 397, 0], [1827, 398, 0], [1828, 399, 0], [1830, 403, 0], [1831, \n 404, 0], [1832, 405, 0], [1833, 406, 0], [1834, 407, 0], [1836, 410, 0],\n [1837, 411, 0], [1838, 412, 0], [1839, 413, 0], [1840, 414, 0], [1841, \n 416, 0], [1842, 417, 0], [1843, 418, 0], [1844, 419, 0], [1845, 420, 0],\n [1846, 421, 0], [1847, 422, 0], [1848, 423, 0], [1849, 424, 0], [1850, \n 425, 0], [1851, 426, 0], [1852, 427, 0], [1853, 428, 0], [1854, 429, 0],\n [1855, 430, 0], [1856, 431, 0], [1857, 432, 0], [1858, 433, 0], [1860, \n 435, 0], [1861, 436, 0], [1862, 437, 0], [1863, 438, 0], [1864, 439, 0],\n [1865, 440, 0], [1866, 441, 0], [1867, 442, 0], [1868, 443, 0], [1869, \n 445, 0], [1870, 446, 0], [1871, 447, 0], [1872, 448, 0], [1873, 449, 0],\n [1874, 450, 0], [1875, 451, 0], [1876, 453, 0], [1877, 454, 0], [1878, \n 455, 0], [1879, 456, 0], [1880, 457, 0], [1881, 458, 0], [1882, 459, 0],\n [1883, 460, 0], [1884, 461, 0], [1885, 462, 0], [1886, 463, 0], [1887, \n 464, 0], [1888, 465, 0], [1889, 466, 0], [1890, 467, 0], [1891, 468, 0],\n [1892, 469, 0], [1893, 470, 0], [1894, 471, 0], [1895, 472, 0], [1896, \n 473, 0], [1897, 474, 0], [1898, 475, 0], [1899, 476, 0], [1900, 477, 0],\n [1901, 478, 0], [1902, 479, 0], [1903, 480, 0], [1904, 481, 0], [1905, \n 482, 0], [1906, 483, 0], [1907, 484, 0], [1908, 485, 0], [1909, 486, 0],\n [1910, 487, 0], [1911, 488, 0], [1912, 489, 0], [1913, 490, 0], [1914, \n 491, 0], [1915, 492, 0], [1916, 493, 0], [1917, 494, 0], [1918, 495, 0],\n [1919, 496, 0], [1920, 497, 0], [1921, 498, 0], [1922, 499, 0], [1923, \n 500, 0], [1924, 501, 0], [1925, 502, 0], [1926, 503, 0], [1927, 504, 0],\n [1928, 505, 0], [1929, 506, 0], [1930, 507, 0], [1931, 508, 0], [1932, \n 509, 0], [1933, 510, 0], [1934, 511, 0], [1935, 512, 0], [1936, 513, 0],\n [1937, 514, 0], [1938, 515, 0], [1939, 516, 0], [1940, 517, 0], [1941, \n 518, 0], [1942, 519, 0], [1943, 520, 0], [1944, 521, 0], [1945, 522, 0],\n [1946, 523, 0], [1947, 524, 0], [1948, 525, 0], [1949, 526, 0], [1950, \n 527, 0], [1951, 528, 0], [1952, 529, 0], [1953, 530, 0], [1954, 531, 0],\n [1955, 532, 0], [1956, 533, 0], [1957, 534, 0], [1958, 535, 0], [1959, \n 536, 0], [1960, 537, 0], [1961, 538, 0], [1962, 539, 0], [1963, 540, 0],\n [1964, 541, 0], [1965, 542, 0], [1966, 543, 0], [1967, 544, 0], [1968, \n 545, 0], [1969, 546, 0], [1970, 547, 0], [1971, 548, 0], [1972, 549, 0],\n [1973, 550, 0], [1974, 551, 0], [1975, 552, 0], [1976, 553, 0], [1977, \n 554, 0], [1978, 555, 0], [1979, 556, 0], [1980, 557, 0], [1981, 558, 0],\n [1982, 559, 0], [1983, 560, 0], [1984, 561, 0], [1985, 562, 0], [1986, \n 563, 0], [1987, 564, 0], [1988, 565, 0], [1989, 566, 0], [1990, 567, 0],\n [1991, 568, 0], [1992, 569, 0], [1993, 570, 0], [1994, 571, 0], [1995, \n 572, 0], [1996, 573, 0], [1997, 574, 0], [1998, 575, 0], [1999, 576, 0],\n [2000, 577, 0], [2001, 578, 0], [2002, 579, 0], [2003, 580, 0], [2004, \n 581, 0], [2005, 582, 0], [2006, 583, 0], [2007, 584, 0], [2008, 585, 0],\n [1, 490, 0], [3, 4, 1], [491, 6, 0], [7, 5, 0], [8, 9, 0], [492, 11, 0],\n [11, 493, 0], [492, 493, 1], [494, 14, 0], [13, 15, 0], [16, 5, 0], [17,\n 18, 1], [17, 12, 0], [14, 495, 0], [494, 19, 0], [20, 21, 0], [20, 22, \n 1], [497, 23, 0], [23, 499, 1], [25, 26, 0], [25, 22, 0], [23, 27, 0],\n [28, 23, 0], [8, 21, 0], [9, 29, 0], [30, 25, 1], [31, 32, 1], [32, 33,\n 1], [34, 35, 0], [35, 36, 0], [490, 6, 1], [37, 10, 1], [10, 38, 0], [\n 37, 38, 1], [39, 40, 1], [39, 41, 1], [42, 41, 1], [18, 42, 1], [492, \n 43, 1], [44, 45, 0], [44, 505, 0], [46, 12, 0], [47, 48, 0], [49, 50, 0\n ], [31, 33, 1], [31, 51, 0], [52, 53, 1], [52, 54, 0], [506, 55, 0], [\n 506, 507, 1], [57, 506, 0], [57, 58, 0], [58, 506, 0], [59, 60, 1], [\n 508, 62, 0], [30, 61, 1], [63, 506, 0], [13, 64, 0], [65, 66, 1], [59, \n 67, 0], [61, 67, 0], [68, 69, 1], [70, 69, 1], [71, 72, 1], [73, 74, 1],\n [37, 75, 1], [72, 75, 0], [37, 72, 1], [76, 77, 1], [77, 51, 0], [73, \n 72, 1], [18, 40, 1], [492, 45, 1], [10, 74, 1], [45, 511, 1], [78, 32, \n 1], [79, 80, 0], [81, 79, 1], [34, 82, 0], [83, 84, 0], [83, 499, 0], [\n 85, 86, 0], [87, 86, 1], [88, 89, 0], [90, 86, 1], [91, 86, 0], [86, 92,\n 0], [86, 93, 0], [94, 86, 1], [86, 95, 1], [513, 517, 0], [97, 66, 1],\n [42, 98, 0], [99, 100, 1], [42, 101, 0], [102, 42, 1], [103, 87, 0], [\n 104, 103, 0], [105, 87, 0], [106, 107, 0], [108, 107, 0], [109, 106, 0],\n [110, 111, 1], [87, 112, 0], [113, 87, 0], [87, 85, 1], [110, 114, 1],\n [115, 116, 0], [117, 118, 0], [117, 119, 0], [117, 120, 1], [121, 122, \n 0], [123, 124, 0], [125, 126, 0], [127, 119, 0], [118, 128, 0], [121, \n 119, 0], [530, 527, 0], [125, 130, 0], [125, 123, 0], [131, 132, 0], [\n 133, 123, 0], [524, 134, 0], [135, 136, 0], [123, 131, 0], [117, 128, 1\n ], [137, 521, 0], [531, 514, 0], [139, 521, 0], [140, 514, 0], [522, \n 141, 0], [142, 523, 0], [530, 526, 0], [140, 532, 0], [142, 144, 0], [\n 140, 522, 0], [145, 146, 0], [147, 523, 0], [144, 523, 0], [139, 523, 0\n ], [140, 141, 0], [528, 526, 0], [528, 148, 0], [149, 150, 0], [145, \n 528, 0], [530, 151, 0], [524, 152, 0], [149, 525, 1], [139, 514, 0], [\n 126, 120, 1], [530, 153, 0], [528, 147, 1], [528, 154, 0], [130, 120, 1\n ], [528, 155, 1], [524, 533, 0], [524, 149, 0], [154, 150, 0], [157, \n 110, 1], [119, 158, 0], [159, 60, 0], [536, 161, 0], [115, 151, 0], [\n 162, 134, 0], [115, 526, 0], [138, 87, 0], [123, 163, 0], [112, 164, 0],\n [112, 165, 0], [166, 165, 0], [167, 537, 0], [168, 104, 0], [531, 520, \n 0], [139, 520, 0], [520, 169, 0], [168, 105, 0], [520, 170, 0], [171, \n 89, 0], [521, 172, 0], [123, 173, 0], [521, 174, 0], [37, 39, 0], [530,\n 175, 0], [530, 176, 0], [88, 530, 0], [177, 496, 1], [178, 525, 0], [\n 179, 493, 1], [180, 181, 1], [182, 180, 0], [179, 181, 0], [180, 493, 1\n ], [183, 30, 0], [183, 21, 0], [538, 185, 0], [538, 89, 0], [184, 186, \n 0], [184, 187, 0], [520, 172, 0], [89, 175, 0], [185, 89, 0], [89, 188,\n 0], [189, 190, 0], [539, 172, 0], [504, 192, 0], [105, 186, 0], [105, \n 187, 0], [539, 193, 0], [187, 194, 0], [539, 540, 0], [539, 196, 0], [\n 197, 540, 0], [110, 198, 0], [197, 539, 0], [199, 537, 0], [134, 526, 0\n ], [200, 193, 0], [4, 201, 1], [202, 86, 0], [85, 203, 0], [147, 204, 0\n ], [147, 205, 0], [123, 206, 0], [537, 207, 0], [165, 208, 0], [4, 94, \n 1], [4, 2, 0], [209, 4, 0], [119, 163, 0], [210, 3, 0], [99, 211, 0], [\n 99, 69, 1], [212, 99, 0], [213, 214, 0], [510, 215, 0], [128, 69, 1], [\n 216, 69, 1], [217, 98, 0], [504, 218, 0], [177, 504, 1], [219, 209, 0],\n [219, 220, 0], [94, 95, 1], [159, 221, 1], [34, 161, 0], [222, 221, 0],\n [211, 52, 1], [215, 223, 1], [224, 215, 0], [225, 224, 1], [224, 223, 0\n ], [226, 6, 0], [7, 3, 1], [216, 227, 1], [228, 229, 0], [227, 230, 0],\n [231, 53, 1], [544, 545, 0], [234, 235, 1], [546, 214, 1], [233, 227, 0\n ], [237, 238, 0], [212, 100, 0], [519, 239, 0], [238, 519, 0], [213, \n 240, 0], [241, 242, 1], [70, 241, 0], [509, 213, 0], [68, 243, 0], [243,\n 244, 0], [68, 244, 0], [544, 547, 1], [245, 227, 1], [246, 208, 0], [\n 112, 208, 0], [165, 247, 0], [537, 549, 0], [537, 550, 0], [537, 551, 0\n ], [110, 251, 0], [510, 252, 1], [529, 253, 1], [237, 239, 1], [254, \n 238, 1], [69, 255, 0], [510, 225, 1], [256, 257, 0], [258, 190, 0], [\n 258, 259, 0], [260, 261, 1], [554, 553, 1], [515, 263, 0], [14, 264, 1],\n [116, 555, 0], [151, 116, 0], [111, 114, 1], [77, 111, 0], [266, 525, 0\n ], [267, 120, 1], [268, 269, 0], [556, 271, 0], [556, 272, 0], [529, \n 273, 0], [128, 274, 0], [34, 275, 0], [503, 276, 0], [503, 504, 1], [\n 177, 218, 1], [277, 278, 1], [557, 558, 1], [557, 559, 1], [559, 558, 1\n ], [277, 78, 1], [277, 279, 1], [78, 279, 0], [281, 282, 0], [283, 161,\n 1], [268, 161, 1], [256, 284, 0], [515, 516, 1], [263, 516, 0], [516, \n 285, 0], [63, 286, 0], [287, 516, 0], [8, 102, 1], [8, 101, 1], [80, \n 288, 0], [80, 289, 0], [276, 560, 0], [37, 290, 0], [290, 74, 1], [512,\n 291, 0], [78, 292, 1], [199, 548, 0], [491, 293, 0], [4, 294, 0], [490,\n 541, 1], [491, 295, 0], [491, 296, 0], [295, 297, 0], [508, 161, 0], [\n 117, 123, 0], [133, 117, 0], [71, 74, 1], [74, 278, 1], [298, 515, 0],\n [5, 299, 0], [32, 292, 1], [5, 29, 1], [503, 560, 0], [300, 301, 1], [\n 51, 300, 0], [244, 302, 1], [31, 302, 1], [51, 282, 1], [303, 304, 0],\n [305, 304, 0], [305, 259, 0], [306, 307, 1], [305, 308, 0], [305, 309, \n 0], [310, 309, 1], [306, 309, 1], [311, 280, 0], [280, 278, 1], [311, \n 32, 1], [13, 312, 1], [313, 314, 0], [312, 313, 1], [547, 566, 1], 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359, 0], [235, 575, 0], [167, 361, 0], [\n 528, 362, 0], [363, 344, 0], [259, 364, 1], [54, 56, 0], [365, 364, 0],\n [231, 366, 0], [30, 367, 0], [61, 367, 1], [254, 368, 0], [254, 369, 0],\n [254, 370, 0], [99, 358, 0], [354, 519, 0], [571, 371, 0], [207, 372, 0\n ], [57, 373, 0], [209, 374, 0], [375, 376, 0], [376, 377, 0], [16, 49, \n 0], [318, 377, 0], [378, 297, 0], [562, 379, 0], [576, 563, 0], [576, \n 381, 0], [577, 576, 1], [244, 383, 0], [244, 306, 1], [383, 306, 1], [\n 380, 306, 0], [252, 225, 0], [220, 76, 0], [542, 384, 0], [385, 384, 0],\n [542, 385, 0], [386, 385, 0], [387, 578, 0], [332, 388, 1], [382, 332, \n 1], [382, 388, 0], [579, 578, 0], [577, 387, 1], [144, 390, 0], [37, 49,\n 0], [391, 233, 0], [392, 310, 0], [260, 393, 0], [394, 230, 0], [395, \n 282, 1], [395, 244, 0], [25, 396, 1], [81, 74, 0], [278, 80, 1], [81, \n 278, 1], [569, 570, 0], [397, 552, 0], [542, 398, 0], [398, 385, 0], [\n 399, 499, 0], [83, 399, 0], [498, 400, 0], [518, 239, 1], [575, 543, 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#!/usr/bin/env python # -*- coding:utf-8 -*- """ Created on 18/10/11 16:32:52 @author: <NAME> """ from collections import Counter from typing import List import numpy as np from antNRE.lib.k_means import KMeans class DataLoader: def __init__(self, datasets: List, n_bkts: int) -> None: len_counter = Counter() for instance in datasets: len_counter[len(instance['tokens'])] += 1 self._bucket_sizes = KMeans(n_bkts, len_counter).splits self._buckets = [[] for i in range(n_bkts)] len2bkt = {} prev_size = -1 for bkt_idx, size in enumerate(self._bucket_sizes): len2bkt.update(zip(range(prev_size+1, size+1), [bkt_idx] * (size - prev_size))) prev_size = size self._record = [] for instance in datasets: bkt_idx = len2bkt[len(instance['tokens'])] self._buckets[bkt_idx].append(instance) idx = len(self._buckets[bkt_idx]) - 1 self._record.append((bkt_idx, idx)) def get_batches(self, batch_size: int, shuffle: bool = True) -> List: batches = [] for bkt_idx, bucket in enumerate(self._buckets): bucket_len = len(bucket) print(bucket_len, self._bucket_sizes[bkt_idx]) n_tokens = bucket_len * self._bucket_sizes[bkt_idx] n_splits = max(n_tokens // batch_size, 1) range_func = np.random.permutation if shuffle else np.arange for bkt_batch in np.array_split(range_func(bucket_len), n_splits): batches.append((bkt_idx, bkt_batch)) if shuffle: np.random.shuffle(batches) return batches def get_batch_instance(self, batch) -> List: bkt_idx, bkt_instance_idxes = batch return [self._buckets[bkt_idx][bkt_instance_idx] for bkt_instance_idx in bkt_instance_idxes] def get_datasets(self) -> List: return [self._buckets[bkt_idx][bkt_instance_idx] for bkt_idx, bkt_instance_idx in self._record]
[ "collections.Counter", "antNRE.lib.k_means.KMeans", "numpy.random.shuffle" ]
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#!/usr/bin/env python # coding: utf-8 # In[3]: from sklearn import svm, datasets from sklearn.model_selection import train_test_split import numpy as np import pandas as pd url = "C:/Users/USUARIO/Desktop/Tesis/centroides-Apriori4.csv" datos = pd.read_csv(url, sep=",") # In[5]: iris = datos # Add noisy features random_state = np.random.RandomState(0) n_samples, n_features = X.shape X = np.c_[X, random_state.randn(n_samples, 200 * n_features)] # Limit to the two first classes, and split into training and test X_train, X_test, y_train, y_test = train_test_split(X[y < 2], y[y < 2], test_size=.5, random_state=random_state) # Create a simple classifier classifier = svm.LinearSVC(random_state=random_state) classifier.fit(X_train, y_train) y_score = classifier.decision_function(X_test) print(y) print(X) # In[6]: from sklearn.metrics import average_precision_score average_precision = average_precision_score(y_test, y_score) print('Average precision-recall score: {0:0.2f}'.format( average_precision)) # In[7]: from sklearn.metrics import precision_recall_curve from sklearn.metrics import plot_precision_recall_curve import matplotlib.pyplot as plt disp = plot_precision_recall_curve(classifier, X_test, y_test) disp.ax_.set_title('2-class Precision-Recall curve: ' 'AP={0:0.2f}'.format(average_precision)) # In[8]: from sklearn.preprocessing import label_binarize # Use label_binarize to be multi-label like settings Y = label_binarize(y, classes=[0, 1, 2]) n_classes = Y.shape[1] # Split into training and test X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.5, random_state=random_state) # We use OneVsRestClassifier for multi-label prediction from sklearn.multiclass import OneVsRestClassifier # Run classifier classifier = OneVsRestClassifier(svm.LinearSVC(random_state=random_state)) classifier.fit(X_train, Y_train) y_score = classifier.decision_function(X_test) # In[9]: from sklearn.metrics import precision_recall_curve from sklearn.metrics import average_precision_score # For each class precision = dict() recall = dict() average_precision = dict() for i in range(n_classes): precision[i], recall[i], _ = precision_recall_curve(Y_test[:, i], y_score[:, i]) average_precision[i] = average_precision_score(Y_test[:, i], y_score[:, i]) # A "micro-average": quantifying score on all classes jointly precision["micro"], recall["micro"], _ = precision_recall_curve(Y_test.ravel(), y_score.ravel()) average_precision["micro"] = average_precision_score(Y_test, y_score, average="micro") print('Average precision score, micro-averaged over all classes: {0:0.2f}' .format(average_precision["micro"])) # In[10]: plt.figure() plt.step(recall['micro'], precision['micro'], where='post') plt.xlabel('Recall') plt.ylabel('Precision') plt.ylim([0.0, 1.05]) plt.xlim([0.0, 1.0]) plt.title( 'Average precision score, micro-averaged over all classes: AP={0:0.2f}' .format(average_precision["micro"])) # In[11]: from itertools import cycle # setup plot details colors = cycle(['navy', 'turquoise', 'darkorange', 'cornflowerblue', 'teal']) plt.figure(figsize=(7, 8)) f_scores = np.linspace(0.2, 0.8, num=4) lines = [] labels = [] for f_score in f_scores: x = np.linspace(0.01, 1) y = f_score * x / (2 * x - f_score) l, = plt.plot(x[y >= 0], y[y >= 0], color='gray', alpha=0.2) plt.annotate('f1={0:0.1f}'.format(f_score), xy=(0.9, y[45] + 0.02)) lines.append(l) labels.append('iso-f1 curves') l, = plt.plot(recall["micro"], precision["micro"], color='gold', lw=2) lines.append(l) labels.append('micro-average Precision-recall (area = {0:0.2f})' ''.format(average_precision["micro"])) for i, color in zip(range(n_classes), colors): l, = plt.plot(recall[i], precision[i], color=color, lw=2) lines.append(l) labels.append('Precision-recall for class {0} (area = {1:0.2f})' ''.format(i, average_precision[i])) fig = plt.gcf() fig.subplots_adjust(bottom=0.25) plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('Recall') plt.ylabel('Precision') plt.title('Extension of Precision-Recall curve to multi-class') plt.legend(lines, labels, loc=(0, -.38), prop=dict(size=14)) plt.show() # In[ ]:
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'plt.plot', (['recall[i]', 'precision[i]'], {'color': 'color', 'lw': '(2)'}), '(recall[i], precision[i], color=color, lw=2)\n', (4059, 4103), True, 'import matplotlib.pyplot as plt\n')]
# Hacktoberfest 2021 # Problem: House Prices # # You are given an array that represents house prices. # Calculate and output the percentage of houses that are within one standard deviation from the mean. # To calculate the percentage, divide the number of houses that satisfy the condition by the total number of houses, and multiply the result by 100. import numpy as np data = np.array([150000, 125000, 320000, 540000, 200000, 120000, 160000, 230000, 280000, 290000, 300000, 500000, 420000, 100000, 150000, 280000]) m = np.mean(data) d = np.std(data) y1 = m-d y2 = m+d s = len(data [(data > y1) & (data < y2)]) r = (s/len(data))*100 print(r)
[ "numpy.array", "numpy.mean", "numpy.std" ]
[((391, 534), 'numpy.array', 'np.array', (['[150000, 125000, 320000, 540000, 200000, 120000, 160000, 230000, 280000, \n 290000, 300000, 500000, 420000, 100000, 150000, 280000]'], {}), '([150000, 125000, 320000, 540000, 200000, 120000, 160000, 230000, \n 280000, 290000, 300000, 500000, 420000, 100000, 150000, 280000])\n', (399, 534), True, 'import numpy as np\n'), ((537, 550), 'numpy.mean', 'np.mean', (['data'], {}), '(data)\n', (544, 550), True, 'import numpy as np\n'), ((556, 568), 'numpy.std', 'np.std', (['data'], {}), '(data)\n', (562, 568), True, 'import numpy as np\n')]
#xyz Sep 2017 ''' Data preparation for datsets: stanford_indoor, scannet, ETH_semantic3D Core idea: store all the information in hdf5 file itself # The workflow to use this tool: Raw_H5f -> Sorted_H5f -> merge block to get new block size -> randomnly select n points -> Normed_H5f -> Net_Provider ## Raw_H5f store the raw data of dataset, which contains several datasets: xyz, label, color.... Each dataset stores the whole data for one dtype data. (.rh5) ## Sorted_H5f contains lots of lots of dataset. Each dataset stores all types of data within a spacial block. The point number of each block/dataset can be fix or not. (.sh5) Use class Sort_RawH5f to generate sorted file with unfixed point num in each block, and a small stride / step size. Then merge .sh5 file with small stride/step size to get larger size block. (.rsh5) Randomly sampling .sh5 file to get Sorted_H5f file with fixed point number in each block. ## Normed_H5f includes 4 datasets: data, label, raw_xyz, pred_logit (.sph5) This file is directly used to feed data for deep learning models. .sph5 file is generated by Sorted_H5f.file_normalize_to_NormedH5F() ## For all three files, show_h5f_summary_info() can use to show the info summary. ## scannet_block_sample.py is the basic usage for these classes. ''' from __future__ import print_function import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = os.path.dirname(BASE_DIR) sys.path.append(BASE_DIR) sys.path.append(os.path.join(ROOT_DIR,'utils')) #from plyfile import (PlyData, PlyElement, make2d, PlyParseError, PlyProperty) import math import numpy as np import h5py import glob import time import multiprocessing as mp import itertools import ply_util #from global_para import GLOBAL_PARA sys.path.append(BASE_DIR+'/MATTERPORT_util') sys.path.append(BASE_DIR+'/KITTI_util') from MATTERPORT_util import get_cat40_from_rawcat sys.path.append(BASE_DIR+'/all_datasets_meta') from datasets_meta import DatasetsMeta import csv,pickle from configs import get_gsbb_config, NETCONFIG import magic ''' Def key words list Search with "name:" to find the definition. rootb_split_idxmap bxmh5 flatten_bidxmap sg_bidxmap baseb_exact_flat_num global_step ''' ''' Important functions get_blockids_of_dif_stride_step get_bidxmap get_all_bidxmaps gsbb naming: get_pyramid_flag file_saveas_pyramid_feed ''' ''' step, stride Configuration (1) set_whole_scene_stride_step: limit stride, step of every cascade by whole scene scope. By calling update_align_scope_by_stridetoalign_ (2) IsLimitStrideStepCascades_Inbxmap : Always limit step and stride larger than last cascade in bxmh5 ''' SHOW_ONLY_ERR = False DEBUGTMP = False ENABLECHECK = False START_T = time.time() g_h5_num_row_1M = 5*1000 ROOT_DIR = os.path.dirname(BASE_DIR) UPER_DIR = os.path.dirname(ROOT_DIR) DATA_DIR = os.path.join(ROOT_DIR,'data') DATA_SOURCE_NAME_LIST = ['ETH','STANFORD_INDOOR3D','SCANNET','MATTERPORT','KITTI', 'MODELNET40'] FLOAT_BIAS = 1e-8 def isin_sorted( a,v ): i = np.searchsorted(a,v) if i>=a.size: return False r = a[i] == v return r def get_stride_step_name(block_stride,block_step): if not block_step[0] == block_step[1]: import pdb; pdb.set_trace() # XXX BREAKPOINT pass assert block_stride[0] == block_stride[1] #assert (block_step[0] == block_step[2] and block_stride[0] == block_stride[2]) or (block_step[2]<0 and block_stride[2]<0) def get_str(v): return str(v).replace('.','d') #assert (v*100) % 1 < 1e-8, "v=%s"%(str(v)) #if v%1!=0: # if (v*10)%1 < 1e-8: return '%dd%d'%(v,v%1*10) # else: return '%dd%d%d'%(v,v%1*10, v*10%1*10) #else: return str(int(v)) if block_stride[2] == -1: return 'stride-%s-step-%s'%(get_str(block_stride[0]),get_str(block_step[0])) else: return 'stride_%s_step_%s'%(get_str(block_stride[0]),get_str(block_step[0])) def rm_file_name_midpart(fn,rm_part): base_name = os.path.basename(fn) parts = base_name.split(rm_part) if len(parts)>1: new_bn = parts[0] + parts[1] else: new_bn = parts[0] new_fn = os.path.join(os.path.dirname(fn),new_bn) return new_fn def copy_h5f_attrs(h5f_attrs): attrs = {} for e in h5f_attrs: attrs[e] = h5f_attrs[e] return attrs def get_mean_sg_sample_rate(sum_sg_bidxmap_sample_num): global_block_num = sum_sg_bidxmap_sample_num[0,4] subblock_num = sum_sg_bidxmap_sample_num[:,-1] mean_sg_bidxmap_sample_num = np.copy(sum_sg_bidxmap_sample_num) for i in range(sum_sg_bidxmap_sample_num.shape[0]): mean_sg_bidxmap_sample_num[i,0:5] /= mean_sg_bidxmap_sample_num[i,4] mean_sg_bidxmap_sample_num[i,5:8] /= mean_sg_bidxmap_sample_num[i,7] return mean_sg_bidxmap_sample_num,global_block_num,subblock_num def get_mean_flatten_sample_rate(sum_flatten_bmap_sample_num): global_block_num = sum_flatten_bmap_sample_num[0,2] mean_flatten_bmap_sample_num = np.copy(sum_flatten_bmap_sample_num) for i in range(sum_flatten_bmap_sample_num.shape[0]): mean_flatten_bmap_sample_num[i,0:3] /= mean_flatten_bmap_sample_num[i,2] return mean_flatten_bmap_sample_num,global_block_num def get_attrs_str(attrs): attrs_str = '' for a in attrs: elenames = '' if type(attrs[a])==str: a_str = attrs[a] else: a_val = attrs[a] if a == "sum_sg_bidxmap_sample_num": a_val,global_block_num,subblock_num = get_mean_sg_sample_rate(a_val) elenames = str(GlobalSubBaseBLOCK.get_sg_bidxmap_sample_num_elename()) + '\n' + 'global_block_num: %d'%(global_block_num) + '\tsubblock_num: %s'%(subblock_num) + '\n' if a == "sum_flatten_bmap_sample_num": a_val,global_block_num = get_mean_flatten_sample_rate(a_val) elenames = str(GlobalSubBaseBLOCK.get_flatten_bidxmaps_sample_num_elename()) +'\n' + 'global_block_num: %d'%(global_block_num) + '\n' a_str = np.array2string(a_val,precision=2,separator=',',suppress_small=True) attrs_str += ( a+':\n'+elenames+a_str+'\n' ) return attrs_str def show_h5f_summary_info(h5f): root_attrs = [attr for attr in h5f.attrs] summary_str = '' summary_str += '--------------------------------------------------------------------------\n' summary_str += 'The root_attr: %s'%(root_attrs) + '\n' summary_str += get_attrs_str(h5f.attrs) + '\n' summary_str += '\n--------------------------------------------------------------------------\n' summary_str += 'The elements in h5f\n' def show_dset(dset_name,id): dset_str = '' if id>10: return dset_str dset = h5f[dset_name] dset_str += '# dataset %d: %s shape=%s\n'%(id,dset_name,dset.shape) if id>6: return dset_str dset_str += get_attrs_str(dset.attrs) + '\n' if len(dset.shape)==2: dset_str += str( dset[0:min(10,dset.shape[0]),:]) + '\n' if len(dset.shape)==3: dset_str += str( dset[0:min(2,dset.shape[0]),:] ) + '\n' elif len(dset.shape)==4: var = dset[0:min(1,dset.shape[0]),0,0:min(2,dset.shape[2]),:] dset_str += np.array2string(var,formatter={'float_kind':lambda var:"%0.2f"%var}) + '\n' dset_str += '\n' return dset_str def show_root_ele(ele_name,id): root_ele_str = '' ele = h5f[ele_name] if type(ele) == h5py._hl.group.Group: root_ele_str += 'The group: %s'%(ele_name) + '\n' root_ele_str += get_attrs_str(ele.attrs) + '\n' for dset_name in ele: root_ele_str += show_dset(ele_name+'/'+dset_name,id) else: root_ele_str += show_dset(ele_name,id) return root_ele_str k = -1 for k, ele_name in enumerate(h5f): if ele_name == 'xyz': summary_str += show_dset(ele_name,k) continue summary_str += show_root_ele(ele_name,k) summary_str += '%d datasets totally'%(k+1)+'\n' print( summary_str ) return summary_str def get_sample_choice(org_N,sample_N,random_sampl_pro=None): ''' all replace with random_choice laer ''' sample_method='random' if sample_method == 'random': if org_N == sample_N: sample_choice = np.arange(sample_N) elif org_N > sample_N: sample_choice = np.random.choice(org_N,sample_N,replace=False,p=random_sampl_pro) else: #sample_choice = np.arange(org_N) new_samp = np.random.choice(org_N,sample_N-org_N) sample_choice = np.concatenate( (np.arange(org_N),new_samp) ) reduced_num = org_N - sample_N #str = '%d -> %d %d%%'%(org_N,sample_N,100.0*sample_N/org_N) #print(str) return sample_choice,reduced_num def random_choice(org_vector,sample_N,random_sampl_pro=None, keeporder=True, only_tile_last_one=False): assert org_vector.ndim == 1 org_N = org_vector.size if org_N == sample_N: sampled_vector = org_vector elif org_N > sample_N: sampled_vector = np.random.choice(org_vector,sample_N,replace=False,p=random_sampl_pro) if keeporder: sampled_vector = np.sort(sampled_vector) else: if only_tile_last_one: new_vector = np.array( [ org_vector[-1] ]*(sample_N-org_N) ).astype(org_vector.dtype) else: new_vector = np.random.choice(org_vector,sample_N-org_N,replace=True) sampled_vector = np.concatenate( [org_vector,new_vector] ) #str = '%d -> %d %d%%'%(org_N,sample_N,100.0*sample_N/org_N) #print(str) return sampled_vector def index_in_sorted(sorted_vector,values): if values.ndim==0: values = np.array([values]) assert values.ndim<=1 and sorted_vector.ndim==1 #values_valid = values[np.isin(values,sorted_vector)] indexs = np.searchsorted(sorted_vector,values) indexs_valid = [] for j,index in enumerate(indexs): if index<sorted_vector.size and sorted_vector[index] == values[j]: indexs_valid.append( index ) indexs_valid = np.array(indexs_valid) assert indexs_valid.size <= values.size #assert indexs.size==0 or np.max(indexs) < sorted_vector.size, 'err in index_in_sorted' return indexs_valid def check_h5fs_intact(file_name): if not os.path.exists(file_name): return False,"file not exist: %s"%(file_name) f_format = os.path.splitext(file_name)[-1] if f_format == '.rh5': return Raw_H5f.check_rh5_intact(file_name) elif f_format == '.sh5' or f_format == '.rsh5': return Sorted_H5f.check_sh5_intact(file_name) elif f_format == '.sph5' or f_format == '.prh5': return Normed_H5f.check_sph5_intact(file_name) elif f_format == '.bmh5': return GlobalSubBaseBLOCK.check_bmh5_intact(file_name) else: return False, "file format not recognized %s"%(f_format) def float_exact_division( A, B ): C = A / B r = np.isclose( C, np.rint(C) ) R = r.all() return R def my_fix(orgvar): # why do not use np.fix() directly: np.fix(2.999999) = 2.0 assert orgvar.ndim == 1 rint_var = np.rint(orgvar) zero_gap = rint_var - orgvar fix_var = np.copy(orgvar).astype(np.int64) for i in range(orgvar.size): if np.isclose(zero_gap[i],0): fix_var[i] = rint_var[i].astype(np.int64) else: fix_var[i] = np.fix(orgvar[i]).astype(np.int64) return fix_var def my_ceil(orgvar): # why do not use np.ceil: np.ceil(12.0000000000000001)=13 assert orgvar.ndim == 1 rint_var = np.rint(orgvar) zero_gap = rint_var - orgvar ceil_var = np.copy(orgvar).astype(np.int64) for i in range(orgvar.size): if np.isclose(zero_gap[i],0): ceil_var[i] = rint_var[i].astype(np.int64) else: try: ceil_var[i] = np.ceil(orgvar[i]).astype(np.int64) except: import pdb; pdb.set_trace() # XXX BREAKPOINT pass return ceil_var class Raw_H5f(): ''' * raw data:unsorted points,all the time in one dataset * Each data type as a hdf5 dataset: xyz, intensity, label, color * class "Sorted_H5f" will sort data to blocks based on this class ''' file_flag = 'RAW_H5F' h5_num_row_1M = 50*1000 dtypes = { 'xyz':np.float32, 'nxnynz':np.float32, 'intensity':np.int32, \ 'color':np.uint8,'label_category':np.uint32,'label_instance':np.int32,\ 'label_material':np.int32, 'label_mesh':np.int32, 'label_raw_category':np.int32 } num_channels = {'xyz':3,'nxnynz':3,'intensity':1,'color':3,'label_category':1,\ 'label_instance':1,'label_material':1,'label_mesh':1, 'label_raw_category':1} def __init__(self,raw_h5_f,file_name,datasource_name=None): self.h5f = raw_h5_f if datasource_name == None: assert 'datasource_name' in self.h5f.attrs else: self.h5f.attrs['datasource_name'] = datasource_name assert self.h5f.attrs['datasource_name'] in DATA_SOURCE_NAME_LIST self.datasource_name = self.h5f.attrs['datasource_name'] self.dataset_meta = DatasetsMeta(self.datasource_name) self.get_summary_info() self.file_name = file_name self.num_default_row = 0 def show_h5f_summary_info(self): print('\n\nsummary of file: ',self.file_name) return show_h5f_summary_info(self.h5f) def set_num_default_row(self,N): self.num_default_row = N def get_dataset(self,data_name): if data_name in self.h5f: return self.h5f[data_name] assert(data_name in self.dtypes) nc = self.num_channels[data_name] dset = self.h5f.create_dataset(data_name,shape=(self.num_default_row,nc),\ maxshape=(None,nc),dtype=self.dtypes[data_name],\ chunks = (self.h5_num_row_1M,nc),\ compression = "gzip") dset.attrs['valid_num'] = 0 setattr(self,data_name+'_dset',dset) if 'element_names' not in self.h5f.attrs: self.h5f.attrs['element_names'] = [data_name] else: self.h5f.attrs['element_names'] = [data_name]+[e for e in self.h5f.attrs['element_names']] return dset def get_total_num_channels_name_list(self): total_num_channels = 0 data_name_list = [str(dn) for dn in self.h5f] for dn in data_name_list: total_num_channels += self.num_channels[dn] return total_num_channels,data_name_list def append_to_dset(self,dset_name,new_data): self.add_to_dset(dset_name,new_data,None,None) def get_all_dsets(self,start_idx,end_idx): out_dset_order = ['xyz','color','label','intensity'] data_list = [] for dset_name in out_dset_order: if dset_name in self.h5f: data_k = self.h5f[dset_name][start_idx:end_idx,:] data_list.append(data_k) data = np.concatenate(data_list,1) return data def add_to_dset(self,dset_name,new_data,start,end): dset = self.get_dataset(dset_name) assert dset.ndim == new_data.ndim valid_n = dset.attrs['valid_num'] if start == None: start = valid_n end = start + new_data.shape[0] if dset.shape[0] < end: dset.resize((end,)+dset.shape[1:]) if valid_n < end: dset.attrs['valid_num'] = end if new_data.ndim==1 and dset.ndim==2 and dset.shape[1]==1: new_data = np.expand_dims(new_data,1) dset[start:end,:] = new_data def rm_invalid(self): for dset_name in self.h5f: dset = self.h5f[dset_name] if 'valid_num' in dset.attrs: valid_num = dset.attrs['valid_num'] if valid_num < dset.shape[0]: dset.resize( (valid_num,)+dset.shape[1:] ) def get_summary_info(self): for dset_name in self.h5f: setattr(self,dset_name+'_dset',self.h5f[dset_name]) if 'xyz' in self.h5f: self.total_row_N = self.xyz_dset.shape[0] self.xyz_max = self.xyz_dset.attrs['max'] self.xyz_min = self.xyz_dset.attrs['min'] self.xyz_scope = self.xyz_max - self.xyz_min def generate_objfile(self,obj_file_name=None,IsLabelColor=False,xyz_cut_rate=None): if obj_file_name==None: base_fn = os.path.basename(self.file_name) base_fn = os.path.splitext(base_fn)[0] folder_path = os.path.dirname(self.file_name) obj_folder = os.path.join(folder_path,'obj/'+base_fn) print('obj_folder:',obj_folder) obj_file_name_nocolor = os.path.join(obj_folder,base_fn+'_xyz.obj') if IsLabelColor: base_fn = base_fn + '_TrueLabel' obj_file_name = os.path.join(obj_folder,base_fn+'.obj') if not os.path.exists(obj_folder): os.makedirs(obj_folder) print('automatic obj file name: %s'%(obj_file_name)) with open(obj_file_name,'w') as out_obj_file: with open(obj_file_name_nocolor,'w') as xyz_obj_file: xyz_dset = self.xyz_dset if 'color' in self.h5f: color_dset = self.color_dset else: if 'label_category' in self.h5f: IsLabelColor = True if IsLabelColor: label_category_dset = self.label_category_dset if xyz_cut_rate != None: # when rate < 0.5: cut small # when rate >0.5: cut big xyz_max = np.array([ np.max(xyz_dset[:,i]) for i in range(3) ]) xyz_min = np.array([ np.min(xyz_dset[:,i]) for i in range(3) ]) xyz_scope = xyz_max - xyz_min xyz_thres = xyz_scope * xyz_cut_rate + xyz_min print('xyz_thres = ',str(xyz_thres)) cut_num = 0 row_step = self.h5_num_row_1M * 10 row_N = xyz_dset.shape[0] for k in range(0,row_N,row_step): end = min(k+row_step,row_N) xyz_buf_k = xyz_dset[k:end,:] if 'color' in self.h5f: color_buf_k = color_dset[k:end,:] buf_k = np.hstack((xyz_buf_k,color_buf_k)) else: buf_k = xyz_buf_k if IsLabelColor: label_k = label_category_dset[k:end,0] for j in range(0,buf_k.shape[0]): is_cut_this_point = False if xyz_cut_rate!=None: # cut by position for xyz_j in range(3): if (xyz_cut_rate[xyz_j] >0.5 and buf_k[j,xyz_j] > xyz_thres[xyz_j]) or \ (xyz_cut_rate[xyz_j]<=0.5 and buf_k[j,xyz_j] < xyz_thres[xyz_j]): is_cut_this_point = True if is_cut_this_point: cut_num += 1 continue if not IsLabelColor: str_j = 'v ' + '\t'.join( ['%0.5f'%(d) for d in buf_k[j,0:3]]) + ' \t'\ + '\t'.join( ['%d'%(d) for d in buf_k[j,3:6]]) + '\n' else: label = label_k[j] label_color = self.dataset_meta.label2color[label] str_j = 'v ' + '\t'.join( ['%0.5f'%(d) for d in buf_k[j,0:3]]) + ' \t'\ + '\t'.join( ['%d'%(d) for d in label_color ]) + '\n' nocolor_str_j = 'v ' + '\t'.join( ['%0.5f'%(d) for d in buf_k[j,0:3]]) + ' \n' out_obj_file.write(str_j) xyz_obj_file.write(nocolor_str_j) print('gen raw obj: %s'%(obj_file_name,)) def rh5_create_done(self): self.rm_invalid() self.add_geometric_scope() self.write_raw_summary() #self.show_h5f_summary_info() def write_raw_summary(self): summary_fn = os.path.splitext( self.file_name )[0]+'.txt' with open(summary_fn,'w') as summary_f: summary_f.write( self.show_h5f_summary_info() ) def add_geometric_scope(self,line_num_limit=None): ''' calculate the geometric scope of raw h5 data, and add the result to attrs of dset''' #begin = time.time() max_xyz = -np.ones((3))*1e10 min_xyz = np.ones((3))*1e10 xyz_dset = self.xyz_dset row_step = self.h5_num_row_1M print('File: %s %d lines'\ %(os.path.basename(self.file_name),xyz_dset.shape[0]) ) #print('read row step = %d'%(row_step)) for k in range(0,xyz_dset.shape[0],row_step): end = min(k+row_step,xyz_dset.shape[0]) xyz_buf = xyz_dset[k:end,:] xyz_buf_max = xyz_buf.max(axis=0) xyz_buf_min = xyz_buf.min(axis=0) max_xyz = np.maximum(max_xyz,xyz_buf_max) min_xyz = np.minimum(min_xyz,xyz_buf_min) if line_num_limit!=None and k > line_num_limit: print('break at k = ',line_num_limit) break xyz_dset.attrs['max'] = max_xyz xyz_dset.attrs['min'] = min_xyz self.h5f.attrs['xyz_max'] = max_xyz self.h5f.attrs['xyz_min'] = min_xyz max_str = ' '.join([ str(e) for e in max_xyz ]) min_str = ' '.join([ str(e) for e in min_xyz ]) print('max_str=%s\tmin_str=%s'%(max_str,min_str) ) #print('T=',time.time()-begin) @staticmethod def check_rh5_intact( file_name ): f_format = os.path.splitext(file_name)[-1] assert f_format == '.rh5' if not os.path.exists(file_name): return False, "%s not exist"%(file_name) #if os.path.getsize( file_name ) / 1000.0 < 100: # return False,"file too small < 20 K" file_type = magic.from_file(file_name) if "Hierarchical Data Format" not in file_type: return False,"File signature err" with h5py.File(file_name,'r') as h5f: attrs_to_check = ['xyz_max','xyz_min'] for attrs in attrs_to_check: if attrs not in h5f.attrs: return False, "%s not in %s"%(attrs,file_name) return True,"" def Write_all_file_accuracies(normed_h5f_file_list=None,out_path=None,pre_out_fn=''): if normed_h5f_file_list == None: normed_h5f_file_list = glob.glob( GLOBAL_PARA.stanford_indoor3d_globalnormedh5_stride_0d5_step_1_4096 + '/Area_2_office_1*' ) if out_path == None: out_path = os.path.join(GLOBAL_PARA.stanford_indoor3d_globalnormedh5_stride_0d5_step_1_4096, 'pred_accuracy') if not os.path.exists(out_path): os.makedirs(out_path) all_acc_fn = os.path.join(out_path,pre_out_fn+'accuracies.txt') all_ave_acc_fn = os.path.join(out_path,pre_out_fn+'average_accuracies.txt') class_TP = class_FN = class_FP = np.zeros(shape=(len(Normed_H5f.g_class2label))) total_num = 0 average_class_accu_ls = [] with open(all_acc_fn,'w') as all_acc_f,open(all_ave_acc_fn,'w') as all_ave_acc_f: for i,fn in enumerate(normed_h5f_file_list): h5f = h5py.File(fn,'r') norm_h5f = Normed_H5f(h5f,fn) class_TP_i,class_FN_i,class_FP_i,total_num_i,acc_str_i,ave_acc_str_i = norm_h5f.Get_file_accuracies( IsWrite=False, out_path = out_path) class_TP = class_TP_i + class_TP class_FN = class_FN_i + class_FN class_FP = class_FP_i + class_FP total_num = total_num_i + total_num if acc_str_i != '': all_acc_f.write('File: '+os.path.basename(fn)+'\n') all_acc_f.write(acc_str_i+'\n') all_ave_acc_f.write(ave_acc_str_i+'\t: '+os.path.basename(fn)+'\n') acc_str,ave_acc_str = Normed_H5f.cal_accuracy(class_TP,class_FN,class_FP,total_num) ave_str = 'Throughout All %d files.\n'%(i+1) + acc_str all_acc_f.write('\n'+ave_str) all_ave_acc_f.write('\n'+ave_str) print('accuracy file: '+all_acc_fn) print('average accuracy file: '+all_ave_acc_fn) return ave_str,out_path,class_TP,class_FN,class_FP,total_num def Write_Area_accuracies(): ave_str_areas = '' class_TP = class_FN = class_FP = np.zeros(shape=(len(Normed_H5f.g_class2label))) total_num = 0 for i in range(6): glob_i = 'Area_%d'%(i+1) normed_h5f_file_list = glob.glob( os.path.join(GLOBAL_PARA.stanford_indoor3d_globalnormedh5_stride_0d5_step_1_4096, glob_i+'*') ) ave_str,out_path,class_TP_i,class_FN_i,class_FP_i,total_num_i = Write_all_file_accuracies(normed_h5f_file_list,pre_out_fn=glob_i+'_') class_TP = class_TP_i + class_TP class_FN = class_FN_i + class_FN class_FP = class_FP_i + class_FP total_num = total_num_i + total_num ave_str_areas += '\nArea%d\n'%i ave_str_areas += ave_str acc_str,ave_acc_str = Normed_H5f.cal_accuracy(class_TP,class_FN,class_FP,total_num) all_area_str = '\nThrough %d areas.\n'%(i+1)+acc_str with open(os.path.join(out_path,'areas_accuracies.txt'),'w' ) as area_acc_f: area_acc_f.write(ave_str_areas) area_acc_f.write(all_area_str) #------------------------------------------------------------------------------- # Test above codes #------------------------------------------------------------------------------- def main(file_list): outdoor_prep = MAIN_DATA_PREP() actions = ['merge','sample_merged','obj_sampled_merged','norm_sampled_merged'] actions = ['merge','sample_merged','norm_sampled_merged'] outdoor_prep.main(file_list,actions,sample_num=4096,sample_method='random',\ stride=[8,8,-1],step=[8,8,-1]) #outdoor_prep.Do_sort_to_blocks() #Do_extract_part_area() #outdoor_prep.test_sub_block_ks() #outdoor_prep.DO_add_geometric_scope_file() #outdoor_prep.DO_gen_rawETH_to_h5() def show_h5f_file(): fn = '/home/y/Research/dynamic_pointnet/data/Matterport3D_H5F/v1/scans/17DRP5sb8fy/stride_0d1_step_0d1/region2.sh5' fn = '/home/y/DS/Matterport3D/Matterport3D_H5F/v1/scans/17DRP5sb8fy/stride_0d1_step_0d1_pyramid-1_2-512_128_64_16-0d2_0d4_0d8_16/region2.prh5' with h5py.File(fn,'r') as h5f: show_h5f_summary_info(h5f) if __name__ == '__main__': START_T = time.time() Do_extract_part_area() T = time.time() - START_T print('exit main, T = ',T)
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# -*- coding: utf-8 -*- """Supports Kp index values. Downloads data from ftp.gfz-potsdam.de or SWPC. Parameters ---------- platform 'sw' name 'kp' tag - '' : Standard Kp data - 'forecast' : Grab forecast data from SWPC (next 3 days) - 'recent' : Grab last 30 days of Kp data from SWPC Note ---- Standard Kp files are stored by the first day of each month. When downloading use kp.download(start, stop, freq='MS') to only download days that could possibly have data. 'MS' gives a monthly start frequency. The forecast data is stored by generation date, where each file contains the forecast for the next three days. Forecast data downloads are only supported for the current day. When loading forecast data, the date specified with the load command is the date the forecast was generated. The data loaded will span three days. To always ensure you are loading the most recent data, load the data with tomorrow's date. :: kp = pysat.Instrument('sw', 'kp', tag='recent') kp.download() kp.load(date=kp.tomorrow()) Recent data is also stored by the generation date from the SWPC. Each file contains 30 days of Kp measurements. The load date issued to pysat corresponds to the generation date. The recent and forecast data should not be used with the data padding option available from pysat.Instrument objects. Warnings -------- The 'forecast' Kp data loads three days at a time. The data padding feature and multi_file_day feature available from the pyast.Instrument object is not appropriate for Kp 'forecast' data. This material is based upon work supported by the National Science Foundation under Grant Number 1259508. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Custom Functions ---------------- filter_geoquiet Filters pysat.Instrument data for given time after Kp drops below gate. """ import functools import numpy as np import os import pandas as pds import pysat import logging logger = logging.getLogger(__name__) platform = 'sw' name = 'kp' tags = {'': '', 'forecast': 'SWPC Forecast data next (3 days)', 'recent': 'SWPC provided Kp for past 30 days'} sat_ids = {'': ['', 'forecast', 'recent']} # generate todays date to support loading forecast data now = pysat.datetime.now() today = pysat.datetime(now.year, now.month, now.day) # set test dates _test_dates = {'': {'': pysat.datetime(2009, 1, 1), 'forecast': today + pds.DateOffset(days=1)}} def load(fnames, tag=None, sat_id=None): """Load Kp index files Parameters ------------ fnames : pandas.Series Series of filenames tag : str or NoneType tag or None (default=None) sat_id : str or NoneType satellite id or None (default=None) Returns --------- data : pandas.DataFrame Object containing satellite data meta : pysat.Meta Object containing metadata such as column names and units Notes ----- Called by pysat. Not intended for direct use by user. """ from pysat.utils.time import parse_date meta = pysat.Meta() if tag == '': # Kp data stored monthly, need to return data daily # the daily date is attached to filename # parse off the last date, load month of data, downselect to desired # day data = pds.DataFrame() # set up fixed width format for these files colspec = [(0, 2), (2, 4), (4, 6), (7, 10), (10, 13), (13, 16), (16, 19), (19, 23), (23, 26), (26, 29), (29, 32), (32, 50)] for filename in fnames: # the daily date is attached to filename # parse off the last date, load month of data, downselect to the # desired day fname = filename[0:-11] date = pysat.datetime.strptime(filename[-10:], '%Y-%m-%d') temp = pds.read_fwf(fname, colspecs=colspec, skipfooter=4, header=None, parse_dates=[[0, 1, 2]], date_parser=parse_date, index_col='0_1_2') idx, = np.where((temp.index >= date) & (temp.index < date + pds.DateOffset(days=1))) temp = temp.iloc[idx, :] data = pds.concat([data, temp], axis=0) # drop last column as it has data I don't care about data = data.iloc[:, 0:-1] # each column increments UT by three hours # produce a single data series that has Kp value monotonically # increasing in time with appropriate datetime indices s = pds.Series() for i in np.arange(8): temp = pds.Series(data.iloc[:, i].values, index=data.index+pds.DateOffset(hours=int(3*i))) s = s.append(temp) s = s.sort_index() s.index.name = 'time' # now, Kp comes in non-user friendly values # 2-, 2o, and 2+ relate to 1.6, 2.0, 2.3 # will convert for user friendliness first = np.array([float(x[0]) for x in s]) flag = np.array([x[1] for x in s]) ind, = np.where(flag == '+') first[ind] += 1.0 / 3.0 ind, = np.where(flag == '-') first[ind] -= 1.0 / 3.0 result = pds.DataFrame(first, columns=['Kp'], index=s.index) fill_val = np.nan elif tag == 'forecast': # load forecast data result = pds.read_csv(fnames[0], index_col=0, parse_dates=True) fill_val = -1 elif tag == 'recent': # load recent Kp data result = pds.read_csv(fnames[0], index_col=0, parse_dates=True) fill_val = -1 # Initalize the meta data for kk in result.keys(): initialize_kp_metadata(meta, kk, fill_val) return result, meta def list_files(tag=None, sat_id=None, data_path=None, format_str=None): """Return a Pandas Series of every file for chosen satellite data Parameters ----------- tag : string or NoneType Denotes type of file to load. (default=None) sat_id : string or NoneType Specifies the satellite ID for a constellation. Not used. (default=None) data_path : string or NoneType Path to data directory. If None is specified, the value previously set in Instrument.files.data_path is used. (default=None) format_str : string or NoneType User specified file format. If None is specified, the default formats associated with the supplied tags are used. (default=None) Returns -------- pysat.Files.from_os : pysat._files.Files A class containing the verified available files Notes ----- Called by pysat. Not intended for direct use by user. """ if data_path is not None: if tag == '': # files are by month, going to add date to monthly filename for # each day of the month. The load routine will load a month of # data and use the appended date to select out appropriate data. if format_str is None: format_str = 'kp{year:2d}{month:02d}.tab' out = pysat.Files.from_os(data_path=data_path, format_str=format_str, two_digit_year_break=94) if not out.empty: out.loc[out.index[-1] + pds.DateOffset(months=1) - pds.DateOffset(days=1)] = out.iloc[-1] out = out.asfreq('D', 'pad') out = out + '_' + out.index.strftime('%Y-%m-%d') return out elif tag == 'forecast': format_str = 'kp_forecast_{year:04d}-{month:02d}-{day:02d}.txt' files = pysat.Files.from_os(data_path=data_path, format_str=format_str) # pad list of files data to include most recent file under tomorrow if not files.empty: pds_offset = pds.DateOffset(days=1) files.loc[files.index[-1] + pds_offset] = files.values[-1] files.loc[files.index[-1] + pds_offset] = files.values[-1] return files elif tag == 'recent': format_str = 'kp_recent_{year:04d}-{month:02d}-{day:02d}.txt' files = pysat.Files.from_os(data_path=data_path, format_str=format_str) # pad list of files data to include most recent file under tomorrow if not files.empty: pds_offset = pds.DateOffset(days=1) files.loc[files.index[-1] + pds_offset] = files.values[-1] files.loc[files.index[-1] + pds_offset] = files.values[-1] return files else: raise ValueError('Unrecognized tag name for Space Weather Index ' + 'Kp') else: raise ValueError('A data_path must be passed to the loading routine ' + 'for Kp') def download(date_array, tag, sat_id, data_path, user=None, password=None): """Routine to download Kp index data Parameters ----------- tag : string or NoneType Denotes type of file to load. Accepted types are '' and 'forecast'. (default=None) sat_id : string or NoneType Specifies the satellite ID for a constellation. Not used. (default=None) data_path : string or NoneType Path to data directory. If None is specified, the value previously set in Instrument.files.data_path is used. (default=None) Note ---- Called by pysat. Not intended for direct use by user. Warnings -------- Only able to download current forecast data, not archived forecasts. """ # download standard Kp data if tag == '': import ftplib from ftplib import FTP import sys ftp = FTP('ftp.gfz-potsdam.de') # connect to host, default port ftp.login() # user anonymous, passwd anonymous@ ftp.cwd('/pub/home/obs/kp-ap/tab') dnames = list() for date in date_array: fname = 'kp{year:02d}{month:02d}.tab' fname = fname.format(year=(date.year - date.year//100*100), month=date.month) local_fname = fname saved_fname = os.path.join(data_path, local_fname) if not fname in dnames: try: logger.info('Downloading file for '+date.strftime('%b %Y')) sys.stdout.flush() ftp.retrbinary('RETR '+fname, open(saved_fname, 'wb').write) dnames.append(fname) except ftplib.error_perm as exception: if str(exception.args[0]).split(" ", 1)[0] != '550': # leaving a bare raise below so that ftp errors # are properly reported as coming from ftp # and gives the correct line number. # We aren't expecting any 'normal' ftp errors # here, other than a 550 'no file' error, thus # accurately raising FTP issues is the way to go raise else: # file isn't actually there, just let people know # then continue on os.remove(saved_fname) logger.info('File not available for '+date.strftime('%x')) ftp.close() elif tag == 'forecast': import requests logger.info('This routine can only download the current forecast, ' + 'not archived forecasts') # download webpage furl = 'https://services.swpc.noaa.gov/text/3-day-geomag-forecast.txt' r = requests.get(furl) # parse text to get the date the prediction was generated date_str = r.text.split(':Issued: ')[-1].split(' UTC')[0] date = pysat.datetime.strptime(date_str, '%Y %b %d %H%M') # data is the forecast value for the next three days raw_data = r.text.split('NOAA Kp index forecast ')[-1] # get date of the forecasts date_str = raw_data[0:6] + ' ' + str(date.year) forecast_date = pysat.datetime.strptime(date_str, '%d %b %Y') # strings we will use to parse the downloaded text lines = ['00-03UT', '03-06UT', '06-09UT', '09-12UT', '12-15UT', '15-18UT', '18-21UT', '21-00UT'] # storage for daily forecasts # get values for each day, then combine together day1 = [] day2 = [] day3 = [] for line in lines: raw = raw_data.split(line)[-1].split('\n')[0] day1.append(int(raw[0:10])) day2.append(int(raw[10:20])) day3.append(int(raw[20:])) times = pds.date_range(forecast_date, periods=24, freq='3H') day = [] for dd in [day1, day2, day3]: day.extend(dd) # put data into nicer DataFrame data = pds.DataFrame(day, index=times, columns=['Kp']) # write out as a file data.to_csv(os.path.join(data_path, 'kp_forecast_' + date.strftime('%Y-%m-%d') + '.txt'), header=True) elif tag == 'recent': import requests logger.info('This routine can only download the current webpage, not ' + 'archived forecasts') # download webpage rurl = 'https://services.swpc.noaa.gov/text/' + \ 'daily-geomagnetic-indices.txt' r = requests.get(rurl) # parse text to get the date the prediction was generated date_str = r.text.split(':Issued: ')[-1].split('\n')[0] date = pysat.datetime.strptime(date_str, '%H%M UT %d %b %Y') # data is the forecast value for the next three days raw_data = r.text.split('# Date ')[-1] # keep only the middle bits that matter raw_data = raw_data.split('\n')[1:-1] # hold times from the file kp_time = [] # holds Kp value for each station sub_kps = [[], [], []] # iterate through file lines and parse out the info we want for line in raw_data: kp_time.append(pysat.datetime.strptime(line[0:10], '%Y %m %d')) # pick out Kp values for each of the three columns sub_lines = [line[17:33], line[40:56], line[63:]] for sub_line, sub_kp in zip(sub_lines, sub_kps): for i in np.arange(8): sub_kp.append(int(sub_line[i*2:(i+1)*2])) # create times on 3 hour cadence times = pds.date_range(kp_time[0], periods=8*30, freq='3H') # put into DataFrame data = pds.DataFrame({'mid_lat_Kp': sub_kps[0], 'high_lat_Kp': sub_kps[1], 'Kp': sub_kps[2]}, index=times) # write out as a file data.to_csv(os.path.join(data_path, 'kp_recent_' + date.strftime('%Y-%m-%d') + '.txt'), header=True) return def filter_geoquiet(sat, maxKp=None, filterTime=None, kpData=None, kp_inst=None): """Filters pysat.Instrument data for given time after Kp drops below gate. Parameters ---------- sat : pysat.Instrument Instrument to be filtered maxKp : float Maximum Kp value allowed. Kp values above this trigger sat.data filtering. filterTime : int Number of hours to filter data after Kp drops below maxKp kpData : pysat.Instrument (optional) Kp pysat.Instrument object with data already loaded kp_inst : pysat.Instrument (optional) Kp pysat.Instrument object ready to load Kp data.Overrides kpData. Notes ----- Loads Kp data for the same timeframe covered by sat and sets sat.data to NaN for times when Kp > maxKp and for filterTime after Kp drops below maxKp. This routine is written for standard Kp data, not the forecast or recent data. """ if kp_inst is not None: kp_inst.load(date=sat.date, verifyPad=True) kpData = kp_inst elif kpData is None: kp = pysat.Instrument('sw', 'Kp', pad=pds.DateOffset(days=1)) kp.load(date=sat.date, verifyPad=True) kpData = kp if maxKp is None: maxKp = 3 + 1./3. if filterTime is None: filterTime = 24 # now the defaults are ensured, let's do some filtering # date of satellite data date = sat.date selData = kpData[date-pds.DateOffset(days=1):date+pds.DateOffset(days=1)] ind, = np.where(selData['Kp'] >= maxKp) for lind in ind: sind = selData.index[lind] eind = sind + pds.DateOffset(hours=filterTime) sat.data[sind:eind] = np.NaN sat.data = sat.data.dropna(axis=0, how='all') return def initialize_kp_metadata(meta, data_key, fill_val=-1): """ Initialize the Kp meta data using our knowledge of the index Parameters ---------- meta : pysat._meta.Meta Pysat Metadata data_key : str String denoting the data key fill_val : int or float File-specific fill value (default=-1) """ data_label = data_key.replace("_", " ") format_label = data_label[0].upper() + data_label[1:] meta[data_key] = {meta.units_label: '', meta.name_label: data_key, meta.desc_label: "Planetary K-index", meta.plot_label: format_label, meta.axis_label: format_label, meta.scale_label: 'linear', meta.min_label: 0, meta.max_label: 9, meta.fill_label: fill_val} return def convert_3hr_kp_to_ap(kp_inst): """ Calculate 3 hour ap from 3 hour Kp index Parameters ---------- kp_inst : pysat.Instrument Pysat instrument containing Kp data Returns ------- Void : Updates kp_inst with '3hr_ap' Notes ----- Conversion between ap and Kp indices is described at: https://www.ngdc.noaa.gov/stp/GEOMAG/kp_ap.html """ # Kp are keys, where n.3 = n+ and n.6 = (n+1)-. E.g., 0.6 = 1- kp_to_ap = {0: 0, 0.3: 2, 0.6: 3, 1: 4, 1.3: 5, 1.6: 6, 2: 7, 2.3: 9, 2.6: 12, 3: 15, 3.3: 18, 3.6: 22, 4: 27, 4.3: 32, 4.6: 39, 5: 48, 5.3: 56, 5.6: 67, 6: 80, 6.3: 94, 6.6: 111, 7: 132, 7.3: 154, 7.6: 179, 8: 207, 8.3: 236, 8.6: 300, 9: 400} def ap(kk): return kp_to_ap[np.floor(kk*10.0) / 10.0] \ if np.isfinite(kk) else np.nan # Test the input if 'Kp' not in kp_inst.data.columns: raise ValueError('unable to locate Kp data') # Convert from Kp to ap fill_val = kp_inst.meta['Kp'][kp_inst.meta.fill_label] ap_data = np.array([ap(kp) if kp != fill_val else fill_val for kp in kp_inst['Kp']]) # Append the output to the pysat instrument kp_inst['3hr_ap'] = pds.Series(ap_data, index=kp_inst.index) # Add metadata meta_dict = {kp_inst.meta.units_label: '', kp_inst.meta.name_label: 'ap', kp_inst.meta.desc_label: "3-hour ap (equivalent range) index", kp_inst.meta.plot_label: "ap", kp_inst.meta.axis_label: "ap", kp_inst.meta.scale_label: 'linear', kp_inst.meta.min_label: 0, kp_inst.meta.max_label: 400, kp_inst.meta.fill_label: fill_val, kp_inst.meta.notes_label: 'ap converted from Kp as described ' 'at: https://www.ngdc.noaa.gov/stp/GEOMAG/kp_ap.html'} kp_inst.meta.__setitem__('3hr_ap', meta_dict)
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from sklearn.preprocessing import MinMaxScaler, StandardScaler import numpy as np import pandas as pd import torch from torch import nn import matplotlib.pyplot as plt import pickle from IPython import display from .useful import ts as src from .useful import iterators from .models import lstm as models from . import generate_residuals from . import stastics class AnomalyDetection(): """ Pipeline Time Series Anomaly Detection based on SOTA deep learning forecasting algorithms. Данный пайплайн избавит вас от проблем написания кода для: \n 1) формирование выборок для подачи в sequence модели \n 2) обучения моделей \n 3) поиска аномалий в невязках \n Данный пайплайн позволяет: \n 1) пронгозировать временные ряды, в том числе многомерные. \n 2) вычислять невязку между прогнозом и настоящими значениями \n 3) анализировать невязку, и возращать разметку аномалиями \n Parameters ---------- preproc : object, default = sklearn.preprocessing.MinMaxScaler() Объект предобратки значений временного ряда. Требования к классу по методами атрибутам одинаковы с default. generate_res_func : func, default = generate_residuals.abs Функция генерация невязки. На вход y_pred, y_true. В default это абсолютная разница значений. Требования к функциям описаны в generate_residuals.py. res_analys_alg : object, default=stastics.Hotelling(). Объект поиска аномалий в остатках. В default это статистика Хоттелинга.Требования к классам описаны в generate_residuals.py. Attributes ---------- Return ---------- object : object Объект этого класса DL_AD References ---------- Links to the papers Examples -------- https://github.com/waico/tsad/tree/main/examples """ def _get_Train_Test_sets(self, dfs, len_seq, points_ahead, gap, shag, intersection, test_size, train_size, random_state, shuffle, stratify, ): """ Вспомогательная функция, избавляющая от дубляжа """ if (type(dfs) == pd.core.series.Series) | (type(dfs) == pd.core.frame.DataFrame): df = dfs.copy() if type(dfs) == pd.core.frame.DataFrame else pd.DataFrame(dfs) self.columns = df.columns self.index = df.index if self._init_preproc: new_df = pd.DataFrame(self.preproc.fit_transform(df), index=df.index, columns=df.columns) self._init_preproc = False else: new_df = pd.DataFrame(self.preproc.transform(df), index=df.index, columns=df.columns) assert len_seq + points_ahead + gap - 1 <= len(df) X_train, X_test, y_train, y_test = src.ts_train_test_split(df=new_df, len_seq=len_seq, points_ahead=points_ahead, gap=gap, shag=shag, intersection=intersection, test_size=test_size, train_size=train_size, random_state=random_state, shuffle=False, # потому что потом нужно в основном итераторе stratify=stratify) elif type(dfs) == type(list()): # уже все pd.DataFrame _df = pd.concat(dfs, ignore_index=True) if self._init_preproc: self.preproc.fit(_df) self._init_preproc = False self.columns = _df.columns self.index = _df.index X_train, X_test, y_train, y_test = [], [], [], [] _k = 0 for df in dfs: if ((type(df) == pd.core.series.Series) | (type(df) == pd.core.frame.DataFrame)) == False: raise NameError('Type of dfs is unsupported') if not (len_seq + points_ahead + gap + 1 <= len(df)): _k += 1 continue new_df = pd.DataFrame(self.preproc.transform(df), index=df.index, columns=df.columns) _X_train, _X_test, _y_train, _y_test = src.ts_train_test_split(new_df, len_seq, points_ahead=points_ahead, gap=gap, shag=shag, intersection=intersection, test_size=test_size, train_size=train_size, random_state=random_state, shuffle=False, stratify=stratify) X_train += _X_train X_test += _X_test y_train += _y_train y_test += _y_test print( f'Пропущено {_k} датастов, из-за того что saples слишком малов в датасете. (len_seq + points_ahead + gap -1 <= len(df))') else: raise NameError('Type of dfs is unsupported') return X_train, X_test, y_train, y_test def _get_anomaly_timestamps(self, dfs, Loader, len_seq, points_ahead, gap, shag, intersection, test_size, random_state, shuffle, stratify, device, point_ahead_for_residuals=0): """ Вспомогательная функция для генерации всего """ X, _, y_true, _ = self._get_Train_Test_sets(dfs=dfs, len_seq=len_seq, points_ahead=points_ahead, # 1 это default, так с остатками лучше не шутить до сих пор gap=gap, shag=shag, intersection=intersection, test_size=test_size, train_size=None, random_state=random_state, shuffle=shuffle, stratify=stratify) all_data_iterator = Loader(X, y_true, self.batch_size, shuffle=False) y_pred = self.model.run_epoch(all_data_iterator, None, None, phase='forecast', points_ahead=points_ahead, device=device) residuals = self.generate_res_func(y_pred, np.array(y_true)) point_ahead_for_residuals = 0 # мы иногда прогнозим на 10 точек вперед, ну интересует все равно на одну точку впреред res_indices = [y_true[i].index[point_ahead_for_residuals] for i in range(len(y_true))] df_residuals = pd.DataFrame(residuals[:, point_ahead_for_residuals, :], columns=self.columns, index=res_indices).sort_index() return df_residuals # ----------------------------------------------------------------------------------------- # Формирование сутевой части класса # ----------------------------------------------------------------------------------------- def __init__(self, preproc=None, generate_res_func=None, res_analys_alg=None, ): self.preproc = MinMaxScaler() if preproc is None else preproc self.generate_res_func = generate_residuals.abs if generate_res_func is None else generate_res_func self.res_analys_alg = stastics.Hotelling() if res_analys_alg is None else res_analys_alg def fit(self, dfs, targets=None, # for RUL task. model=None, encod_decode_model=False, # ужас, нужно это править, особенность encod_decode модели. Попытаться вообще еубрать эту переменную criterion=None, optimiser=None, batch_size=64, len_seq=10, points_ahead=5, n_epochs=100, gap=0, shag=1, intersection=True, test_size=0.2, train_size=None, random_state=None, shuffle=False, show_progress=True, show_figures=True, best_model_file='./best_model.pt', stratify=None, Loader=None, ): """ Обучение модели как для задачи прогнозирования так и для задачи anomaly detection на имеющихся данных. fit = fit_predict_anmaloy Parameters ---------- dfs : {{df*,ts*}, list of {df*,ts*}} df*,ts* are pd.core.series.Seriesor or pd.core.frame.DataFrame data type. Исходные данные. Данные не долнжны содержать np.nan вовсе, иметь постоянную и одинковую частоту of df.index и при этом не иметь пропусков. Проблему с пропуском решают дробление одно df на list of df. model : object of torch.nn.Module class, default=models.SimpleLSTM() Используемая модель нейронной сети. criterion : object of torch.nn class, default=nn.MSELoss() Критерий подсчета ошибки для оптмизации. optimiser : tuple = (torch.optim class ,default = torch.optim.Adam, dict (dict of arguments without params models) , default=default) Example of optimiser : optimiser=(torch.optim.Adam,{'lr':0.001}) Метод оптимизации нейронной сети и его параметры, указанные в документации к torch. batch_size : int, default=64 Размер батча (Число сэмплов по которым усредняется градиент) len_seq : int, default=10 Размер окна (количество последовательных точек ряда), на котором модель реально работает. По сути аналог порядка в авторегрессии. points_ahead : int, default=5 Горизонт прогнозирования. n_epochs : int, default=100 Количество эпох. >>> train_test_split vars gap : int, default=0 Сколько точек между трейном и тестом. Условно говоря, если крайняя точка train а это t, то первая точка теста t + gap +1. Параметр создан, чтобы можно было прогнозировать одну точку через большой дополнительный интервал времени. shag : int, default=1. Шаг генерации выборки. Если первая точка была t у 1-ого сэмпла трейна, то у 2-ого сэмла трейна она будет t + shag, если intersection=True, иначе тоже самое но без пересечений значений ряда. intersection : bool, default=True Наличие значений ряда (одного момента времени) в различных сэмплах выборки. test_size : float or int, default=None If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is set to the complement of the train size. If ``train_size`` is also None, it will be set to 0.25. * *https://github.com/scikit-learn/scikit-learn/blob/95119c13a/sklearn/model_selection/_split.py#L2076 Может быть 0, тогда вернет значения X,y train_size : float or int, default=None If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size. * *https://github.com/scikit-learn/scikit-learn/blob/95119c13a/sklearn/model_selection/_split.py#L2076 random_state : int, RandomState instance or None, default=None Controls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`.* *https://github.com/scikit-learn/scikit-learn/blob/95119c13a/sklearn/model_selection/_split.py#L2076 shuffle : bool, default=True Whether or not to shuffle the data before splitting. If shuffle=False then stratify must be None. * show_progress : bool, default=True Показывать или нет прогресс обучения с детализацией по эпохам. show_figures : bool, default=True Показывать или нет результаты решения задачии anomaly detection и кривую трейна и валидации по эпохам. best_model_file : string, './best_model.pt' Путь до файла, где будет хранится лучшие веса модели Loader : class, default=ufesul.iterators.Loader. Тип загрузчика, которую будет использовать как итератор в будущем, благодаря которому, есть возможность бить на бачи. Attributes ---------- Return ---------- list of pd.datetime anomalies on initial dataset """ self._init_preproc = True # это кастыль для _get_Train_Test_sets self.points_ahead = points_ahead self.len_seq = len_seq self.batch_size = batch_size dfs = dfs.copy() self.best_model_file = best_model_file self.encod_decode_model = encod_decode_model if show_progress: show_progress_text = "" # ----------------------------------------------------------------------------------------- # Формирование train_iterator и val_iteraror # ----------------------------------------------------------------------------------------- if Loader is None: Loader = iterators.Loader X_train, X_test, y_train, y_test = self._get_Train_Test_sets(dfs=dfs, len_seq=len_seq, points_ahead=points_ahead, gap=gap, shag=shag, intersection=intersection, test_size=test_size, train_size=train_size, random_state=random_state, shuffle=shuffle, stratify=stratify) train_iterator = Loader(X_train, y_train, batch_size, shuffle=shuffle) val_iterator = Loader(X_test, y_test, batch_size, shuffle=shuffle) # ----------------------------------------------------------------------------------------- # Обучение моделей # ----------------------------------------------------------------------------------------- device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") if criterion is None: criterion = nn.MSELoss() if model is None: model = models.SimpleLSTM(len(self.columns), len(self.columns), seed=random_state) self.model = model if optimiser is None: optimiser = torch.optim.Adam optimiser = optimiser(self.model.parameters()) else: args = optimiser[1] args['params'] = self.model.parameters() optimiser = optimiser[0](**args) history_train = [] history_val = [] best_val_loss = float('+inf') for epoch in range(n_epochs): train_loss = self.model.run_epoch(train_iterator, optimiser, criterion, phase='train', points_ahead=points_ahead, encod_decode_model=self.encod_decode_model, device=device) # , writer=writer) val_loss = self.model.run_epoch(val_iterator, None, criterion, phase='val', points_ahead=points_ahead, encod_decode_model=self.encod_decode_model, device=device) # , writer=writer) history_train.append(train_loss) history_val.append(val_loss) if val_loss < best_val_loss: best_val_loss = val_loss torch.save(self.model.state_dict(), self.best_model_file) if show_figures: display.clear_output(wait=True) plt.figure() plt.plot(history_train, label='Train') plt.plot(history_val, label='Val') plt.xlabel('Epoch') plt.ylabel('MSE') plt.legend() plt.show() if show_progress: show_progress_text = f'Epoch: {epoch + 1:02} \n' + \ f'\tTrain Loss: {train_loss:.3f} \n' + \ f'\t Val. Loss: {val_loss:.3f} \n\n' + \ show_progress_text print(show_progress_text) self.model.load_state_dict(torch.load(self.best_model_file)) if show_progress: print("After choosing the best model:") try: test_iterator = Loader(X_test, y_test, len(X_test), shuffle=False) test_loss = self.model.run_epoch(test_iterator, None, criterion, phase='val', encod_decode_model=self.encod_decode_model, device=device) print(f'Test Loss: {test_loss:.3f}') except: print('Весь X_test не помещается в память, тестим усреднением по батчам') test_iterator = Loader(X_test, y_test, batch_size, shuffle=False) test_loss = [] for epoch in range(n_epochs): test_loss.append(self.model.run_epoch(test_iterator, None, criterion, phase='val', encod_decode_model=self.encod_decode_model, device=device)) print(f'Test Loss: {np.mean(test_loss):.3f}') # ----------------------------------------------------------------------------------------- # Генерация остатков # ----------------------------------------------------------------------------------------- df_residuals = self._get_anomaly_timestamps(dfs=dfs, Loader=Loader, len_seq=len_seq, points_ahead=1, gap=gap, shag=shag, intersection=intersection, test_size=0, random_state=None, shuffle=False, stratify=stratify, device=device, point_ahead_for_residuals=0) self.anomaly_timestamps = self.res_analys_alg.fit_predict(df_residuals, show_figure=show_figures) self.statistic = self.res_analys_alg.statistic self.ucl = self.res_analys_alg.ucl self.lcl = self.res_analys_alg.lcl return self.anomaly_timestamps # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # накосячил тут с прогнозом на одну точку вперед. Могут быть проблемы если ahead !=1 def predict_anomaly(self, dfs, Loader=None, gap=0, shag=1, intersection=True, train_size=None, random_state=None, shuffle=False, stratify=None, show_progress=True, show_figures=True ): """ Поиск аномалий в новом наборе данных Parameters ---------- см self.fit() dockstring Return ---------- anomaly_timestamps : list of df.index.dtype Возвращает список временных меток аномалий Attributes ---------- """ len_seq = self.len_seq if Loader is None: Loader = iterators.Loader device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # ----------------------------------------------------------------------------------------- # Генерация остатков # ----------------------------------------------------------------------------------------- df_residuals = self._get_anomaly_timestamps(dfs=dfs, Loader=Loader, len_seq=len_seq, points_ahead=1, gap=gap, shag=shag, intersection=intersection, test_size=0, random_state=None, shuffle=False, stratify=stratify, device=device, point_ahead_for_residuals=0) self.anomaly_timestamps = self.res_analys_alg.predict(df_residuals, show_figure=show_figures) self.statistic = self.res_analys_alg.statistic return self.anomaly_timestamps def forecast(self, df, points_ahead=None, Loader=None, show_figures=True): """ Прогнозирование временного ряда, в том числе векторного. Parameters ---------- df : pd.core.series.Series or pd.core.frame.DataFrame data type Исходные данные. Данные не долнжны содержать np.nan вовсе, иметь постоянную и одинковую частоту of df.index и при этом не иметь пропусков. points_ahead : int, default=5 Горизонт прогнозирования. show_figures : bool, default=True Показывать или нет результаты решения задачии anomaly detection и кривую трейна и валидации по эпохам. Loader : class, default=iterators.Loader. Тип загрузчика, которую будет использовать как итератор в будущем, благодаря которому, есть возможность бить на бачи. Attributes ---------- """ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") if Loader is None: Loader = iterators.Loader df = df.copy() points_ahead = points_ahead if points_ahead is not None else self.points_ahead len_seq = self.len_seq batch_size = self.batch_size assert (type(df) == pd.core.series.Series) | (type(df) == pd.core.frame.DataFrame) df = df.copy() if type(df) == pd.core.frame.DataFrame else pd.DataFrame(df) df = df[-len_seq:] assert not self._init_preproc preproc_values = self.preproc.transform(df) iterator = Loader(np.expand_dims(preproc_values, 0), np.expand_dims(preproc_values, 0), # ничего страшного, 'y' все равно не используется batch_size, shuffle=False) y_pred = self.model.run_epoch(iterator, None, None, phase='forecast', points_ahead=points_ahead, device=device)[ 0] y_pred = self.preproc.inverse_transform(y_pred) t_last = np.datetime64(df.index[-1]) delta_dime = np.timedelta64(df.index[-1] - df.index[-2]) new_index = pd.to_datetime(t_last + np.arange(1, points_ahead + 1) * delta_dime) y_pred = pd.DataFrame(y_pred, index=new_index, columns=df.columns) if show_figures: pd.concat([df, y_pred])[-3 * points_ahead:].plot() plt.axvspan(t_last, y_pred.index[-1], alpha=0.2, color='green', label='forecast') plt.xlabel('Datetime') plt.ylabel('Value') plt.legend() plt.show() return y_pred def save(self, path='./pipeline.pcl'): """ Method for saving pipeline. It may be required for example after training. CPU. Parameters ---------- path : str Путь до файла, для сохранения пайплайна. Пайлайн сохраняется в формате pickle """ self.model.run_epoch(iterators.Loader(torch.zeros((1, self.len_seq, self.model.in_features), dtype=float), torch.zeros((1, self.len_seq, self.model.in_features), dtype=float), batch_size=1), None, None, phase='forecast', points_ahead=1, device=torch.device("cpu")) with open(path, 'wb') as f: pickle.dump(self, f) def load(self, path='./pipeline.pcl'): """ Method for loading pipeline. It may be required for example after training. Parameters ---------- path : str Путь до сохраненного файла пайплайна. Пайлайн должен быть в формате pickle """ with open(path, 'rb') as f: pipeline = pickle.load(f) self.__dict__.update(pipeline.__dict__)
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## Linear Regression Channel ## Ref https://medium.com/coinmonks/trading-bitcoin-with-linear-regression-channels-b84e7e43d984 from google.protobuf import symbol_database from st_stock.stocky import get_assets from typing import List from datetime import datetime import seaborn as sns from matplotlib import pyplot as plt import numpy as np import streamlit as st from st_stock.misc import load_cypto_symbols, load_config def std_line_plot(x, y, ax, sigma=1, color='r', **kwargs): ''' plot strand dev lines for X sigma ''' sns.lineplot( x = x, y = (y + (sigma * np.std(y,))), color=color, ax=ax, **kwargs) sns.lineplot( x = x, y = (y - (sigma * np.std(y))), color=color, ax=ax, **kwargs) return ax def plot_linear_regression_channel(symbol: str, period: str='ytd', interval: str='1d', y: str='Close',): ''' Plot linear regression channel for a given asset ''' df = get_assets(symbol, period, interval=interval) df = df.reset_index() df.loc[:,'idx'] = range(len(df)) df['pct'] = df.Close.pct_change(fill_method='ffill') df['log'] = np.log(df.Close) if 'Date' in df: df = df.rename(columns={'Date': 'Datetime'}) # # df['epoch'] = (df.Datetime - datetime(1970,1,1)).dt.total_seconds() # df[''] # else: # df['epoch'] = (df.Date - datetime(1970,1,1)).dt.total_seconds() sns.set(font_scale=1.5) fig, ax = plt.subplots(figsize=(15, 8)) ## using build in the regression plot reg_plot = sns.regplot( 'idx', y, data=df, ci=95, marker='.', ax=ax, ) ## extract the regression list y_mean = reg_plot.get_lines()[0].get_ydata() x_mean = reg_plot.get_lines()[0].get_xdata() ## plot the channel std_line_plot(x_mean,y_mean, ax=ax, sigma=1, color='r', label='1 std ' ) std_line_plot(x_mean,y_mean, ax=ax, sigma=2, color='r', linestyle='--', label='2 std' ) # ax.set_xticklabels([datetime.fromtimestamp(x) for x in ax.get_xticks()], rotation =90) # print([x for x in ax.get_xticks()]) # ax.set_xticklabels([df.loc[int(x), 'Datetime'] if x >=0 else df.loc[0,'Datetime'] for x in ax.get_xticks() ], rotation =90) ax.set(xlabel='') ax.legend() return (fig, ax, df) def st_linear_regression_channel(): config = load_config() symbol = st.sidebar.selectbox('symbols', load_cypto_symbols()) period = st.sidebar.selectbox('Period', config['cypto']['periods'], index=config['cypto']['periods'].index('ytd') ) interval = st.sidebar.selectbox('Interval', config['cypto']['intervals'], index=config['cypto']['intervals'].index('1d')) y = st.sidebar.selectbox('Y axis', ['Close', 'pct', 'log'], index=0) st.header('Linear Regression Channels for cypto') fig, ax, data = plot_linear_regression_channel(symbol, period, interval, y=y) st.write(fig) st.dataframe(data)
[ "seaborn.set", "seaborn.regplot", "numpy.log", "streamlit.write", "st_stock.misc.load_config", "st_stock.misc.load_cypto_symbols", "streamlit.dataframe", "st_stock.stocky.get_assets", "streamlit.sidebar.selectbox", "numpy.std", "streamlit.header", "matplotlib.pyplot.subplots" ]
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''' Author <NAME> Date 5/21/2021 utility functions for train and predict data generators ''' import cv2 import numpy as np import re from joblib import Parallel, delayed def sort_frame_crop_filenames(filenames): def find_frame_crop_number(filename): filename = filename.split('/')[-1] regex = re.compile(r'.*_c') frame_id = regex.findall(filename)[0] regex = re.compile(r'_c\d+_') crop_id = regex.findall(filename)[0] # example: '_c40_' crop_id = int(crop_id.replace('_c', '').replace('_', '')) return frame_id, crop_id # For instance, parse ../crop/generated/crop_even_input256_FNA_valid_fold0/mask_repeat0/pk2777-third-0620_c9_even_input256.png filenames.sort(key=find_frame_crop_number) assert_crop_increment_filenames(filenames) return filenames def assert_crop_increment_filenames(filenames): prev_crop_id = 0 prev_max_crop_id = 0 for filename in filenames: filename = filename.split('/')[-1] regex = re.compile(r'_c\d+_') crop_id = regex.findall(filename)[0] # example: '_c40_' crop_id = int(crop_id.replace('_c', '').replace('_', '')) if crop_id >= prev_crop_id: # assume crop id increment one by one prev_crop_id = crop_id elif prev_max_crop_id == 0: prev_max_crop_id = crop_id # first time setting prev_max_crop_id prev_crop_id = 0 elif prev_max_crop_id == crop_id: # check the next time crop id reaches the prev_max_crop_id prev_crop_id = 0 else: raise ValueError('crop id is not incrementing correctly') def assert_same_two_filenames(first_filenames, second_filenames): for first_filename, second_filename in zip(first_filenames, second_filenames): assert first_filename.split('/')[-1] == second_filename.split('/')[-1] def threshold_mask_area_list(image_height, image_width, mask_area_list, threshold_percentage): min_mask_area_threshold = image_height * image_width * threshold_percentage * 0.01 return [mask_area > min_mask_area_threshold for mask_area in mask_area_list] def convert_masks_to_areas(mask_list): mask_area_list = np.zeros(mask_list.shape[0]) for i, mask in enumerate(mask_list): mask_area_list[i] = np.sum(mask > 0) return mask_area_list def convert_masks_to_classes(image_height, image_width, mask_list): min_mask_area_threshold = image_height * image_width * 0.01 mask_class_list = np.zeros(mask_list.shape[0]) for i, mask in enumerate(mask_list): mask_class_list[i] = np.sum(mask > 0) > min_mask_area_threshold return mask_class_list def read_images(image_path_list): # https://stackoverflow.com/questions/33778155/python-parallelized-image-reading-and-preprocessing-using-multiprocessing images = Parallel(n_jobs=4, verbose=1)( delayed(cv2.imread)(image_path, cv2.IMREAD_GRAYSCALE) for image_path in image_path_list ) return images def read_color_images(image_path_list): images = Parallel(n_jobs=4, verbose=1)( delayed(cv2.imread)(image_path, cv2.IMREAD_COLOR) for image_path in image_path_list ) return images def unison_shuffle_lists(a, b): assert len(a) == len(b) p = np.random.permutation(len(a)) a = [a[i] for i in p] b = [b[i] for i in p] return a, b def unison_shuffle_ndarrays(a, b): assert len(a) == len(b) shuffler = np.random.permutation(len(a)) a_shuffled = a[shuffler] b_shuffled = b[shuffler] return a_shuffled, b_shuffled # def unison_shuffle_multiple_ndarrays(*args): # assert len(args[0]) == len(args[0]) # assert len(args[0]) == len(args[-1]) # # shuffler = np.random.permutation(len(args[0])) # shuffled_args = [] # for i in range(len(args)): # shuffled_args.append(args[i][shuffler]) def unison_shuffle_multiple_ndarrays(a,b,c,d): assert len(a) == len(b) assert len(a) == len(c) shuffler = np.random.permutation(len(a)) a_shuffled = a[shuffler] b_shuffled = b[shuffler] c_shuffled = c[shuffler] d_shuffled = d[shuffler] return a_shuffled, b_shuffled, c_shuffled, d_shuffled def regex_find_crop_id(filename): regex = re.compile(r'_c\d+_') crop_id = regex.findall(filename)[0] # example: '/_c40' return crop_id def regex_find_frame_id(filename): regex = re.compile(r'/f\d+_c') frame_id = regex.findall(filename)[0] # example: '/f040_c' return frame_id def regex_find_prev_filenames(cur_filename, max_prev_frame_num): # For the given current frame, get n previous frames # cur_frame_id_string: '/f040_c', crop_id_string: 'c0' cur_frame_id_string = regex_find_frame_id(cur_filename) crop_id_string = regex_find_crop_id(cur_filename) cur_frame_id = int(cur_frame_id_string.replace('/f', '').replace('_c', '')) if cur_frame_id - max_prev_frame_num < 0: return None else: prev_filenames = [] for prev_counter in range(1, max_prev_frame_num+1): prev_frame_id = f"/f{(cur_frame_id - prev_counter):03d}{crop_id_string}" prev_filenames.append(cur_filename.replace(f"{cur_frame_id_string.replace('_c', '')}{crop_id_string}", prev_frame_id)) return prev_filenames
[ "re.compile", "joblib.Parallel", "numpy.sum", "numpy.zeros", "joblib.delayed" ]
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import glob, os, pickle, datetime, time, re, pprint import matplotlib.pyplot as plt import numpy as np from src import plotter, graphs from src.mltoolbox.metrics import METRICS from src.utils import * from shutil import copyfile, rmtree from src.plotter import Plotter def main(): # SETUP BEGIN test_folder_path = './test_log/test_rslo100_sloreg_100n_exp[1]_mtrT0all_sgC1e-05alpha_52000samp_INFtime_400iter' target_x0 = 100 target_x = 300 logs, setup = load_test_logs(test_folder_path, return_setup=True) objfunc = METRICS[setup['obj_function']] degrees = {} for graph in setup['graphs']: degrees[graph] = degree_from_adjacency_matrix(setup['graphs'][graph]) graph_filter = [ '*' ] pred_ratios = [] real_slopes = [] # equal to None so that if it will not be reassigned then it will raise exception clique_slope = None clique_spectral_gap = None for graph, graph_objfunc_log in dict(logs['metrics'][objfunc.id]).items(): if graph not in graph_filter and '*' not in graph_filter: del logs['metrics'][objfunc.id][graph] continue y0 = logs['metrics'][objfunc.id][graph][target_x0] y = logs['metrics'][objfunc.id][graph][target_x] """if degrees[graph] == 2: y0 = logs['metrics'][objfunc.id][graph][3000] y = logs['metrics'][objfunc.id][graph][3600]""" slope = y - y0 print(slope) real_slopes.append(slope) pred_ratios.append(1 / math.sqrt(uniform_weighted_Pn_spectral_gap_from_adjacency_matrix(setup['graphs'][graph]))) if 'clique' in graph: clique_slope = slope clique_spectral_gap = uniform_weighted_Pn_spectral_gap_from_adjacency_matrix(setup['graphs'][graph]) real_ratios = clique_slope / np.array(real_slopes) pred_ratios = np.array(pred_ratios) print(real_ratios) plt.figure(1, figsize=(12, 6)) plt.suptitle(test_folder_path) plt.subplot(1, 2, 1) plt.title("Ratio comparison", loc='left') plt.title("({})".format(setup['time_distr_class'].name), loc='right') plt.xlabel("prediction") plt.ylabel("simulation") plt.yscale('linear') plt.plot( pred_ratios, real_ratios, color='blue', markersize=5, marker='o', ) for i in range(len(pred_ratios)): plt.text( pred_ratios[i], real_ratios[i], 'd={}'.format(degrees[list(logs['metrics'][objfunc.id].keys())[i]]), size='xx-small' ) colors = Plotter.generate_rainbow_color_dict_from_graph_keys( list(setup['graphs'].keys()), setup['n'] ) # objfunc - AVG ITER SUBPLOT plt.subplot(1, 2, 2) plt.title("{} over iteration".format(objfunc.fullname), loc='left') plt.title("({})".format(setup['time_distr_class'].name), loc='right') plt.xlabel('iter') plt.ylabel(objfunc.fullname) plt.yscale('linear') for graph, graph_objfunc_log in dict(logs['metrics'][objfunc.id]).items(): plt.plot( list(range(len(graph_objfunc_log))), graph_objfunc_log, label=graph, color=colors[graph] ) plt.legend() plt.subplots_adjust( top=0.88, bottom=0.08, left=0.06, right=0.96, hspace=0.2, wspace=0.17 ) plt.show() plt.close() if __name__ == '__main__': main()
[ "matplotlib.pyplot.subplots_adjust", "matplotlib.pyplot.ylabel", "matplotlib.pyplot.xlabel", "matplotlib.pyplot.plot", "matplotlib.pyplot.close", "numpy.array", "matplotlib.pyplot.figure", "matplotlib.pyplot.title", "matplotlib.pyplot.yscale", "matplotlib.pyplot.subplot", "matplotlib.pyplot.supt...
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import matplotlib.pyplot as plt import numpy as np from rich.prompt import Confirm from loguru import logger from fcutils.progress import track from slam import Environment, Agent # create env env = Environment() # create agent agent = Agent(env, x=20, y=10, angle=np.random.uniform(10, 80)) agent.update_map_every = 1000 # check intial conditions f, ax = plt.subplots(figsize=(10, 10)) env.draw(ax) agent.draw(ax) print("Is the starting configuration OK?") plt.show() if Confirm.ask("Continue?"): # run simulation for i in track(range(500)): # move/update agent agent.update() # check termination conditions if env.out_of_bounds(agent.COM): logger.warning("Agent out of bounds") break elif env.is_point_in_obstacle(agent.COM): logger.warning("Agent is in an obstacle") break agent.slam() # draw environment f, axes = plt.subplots(figsize=(20, 10), ncols=2) env.draw(axes[0]) # draw agent and map agent.draw(axes[0]) agent.map.draw(axes[1]) agent.planner.draw(axes[1]) axes[0].axis("equal") axes[1].axis("equal") axes[1].legend() axes[0].set(title="world view") axes[1].set(title="agent view") f.tight_layout() plt.show()
[ "rich.prompt.Confirm.ask", "slam.Environment", "loguru.logger.warning", "numpy.random.uniform", "matplotlib.pyplot.subplots", "matplotlib.pyplot.show" ]
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import pandas as pd import houghtest from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score from sklearn.metrics import confusion_matrix import cv2 import numpy as np import pickle from multiprocessing import Process import time def thread1(): global h, w, trained_model, copy1 newcopy1 = copy1.copy() for y in range((h/2)-1): for c in range((w/2)-1): b = newcopy1.item(y, c, 0) g = newcopy1.item(y, c, 1) r = newcopy1.item(y, c, 2) bl = newcopy1.item(y, c - 1, 0) gl = newcopy1.item(y, c - 1, 1) rl = newcopy1.item(y, c - 1, 2) br = newcopy1.item(y, c + 1, 0) gr = newcopy1.item(y, c + 1, 1) rr = newcopy1.item(y, c + 1, 2) bu = newcopy1.item(y - 1, c, 0) gu = newcopy1.item(y - 1, c, 1) ru = newcopy1.item(y - 1, c, 2) bul = newcopy1.item(y - 1, c - 1, 0) gul = newcopy1.item(y - 1, c - 1, 1) rul = newcopy1.item(y - 1, c - 1, 2) bur = newcopy1.item(y - 1, c + 1, 0) gur = newcopy1.item(y - 1, c + 1, 1) rur = newcopy1.item(y - 1, c + 1, 2) bdl = newcopy1.item(y + 1, c - 1, 0) gdl = newcopy1.item(y + 1, c - 1, 1) rdl = newcopy1.item(y + 1, c - 1, 2) bdr = newcopy1.item(y + 1, c + 1, 0) gdr = newcopy1.item(y + 1, c + 1, 1) rdr = newcopy1.item(y + 1, c + 1, 2) bd = newcopy1.item(y + 1, c, 0) gd = newcopy1.item(y + 1, c, 1) rd = newcopy1.item(y + 1, c, 2) new_prediction = trained_model.predict(np.array([[b, g, r, bl, gl, rl, br, gr, rr, bu, gu, ru, bul, gul, rul, bur, gur, rur, bdl, gdl, rdl, bdr, gdr, rdr, bd, gd, rd]])) if new_prediction > 0.5: copy1[y, c] = (255, 255, 0) cv2.imwrite("copy1.png",copy1) def thread2(): global h, w, trained_model, copy2 newcopy2 = copy2.copy() for y in range((h/2)-1): for c in range((w/2)-1): b = newcopy2.item(y, c, 0) g = newcopy2.item(y, c, 1) r = newcopy2.item(y, c, 2) bl = newcopy2.item(y, c - 1, 0) gl = newcopy2.item(y, c - 1, 1) rl = newcopy2.item(y, c - 1, 2) br = newcopy2.item(y, c + 1, 0) gr = newcopy2.item(y, c + 1, 1) rr = newcopy2.item(y, c + 1, 2) bu = newcopy2.item(y - 1, c, 0) gu = newcopy2.item(y - 1, c, 1) ru = newcopy2.item(y - 1, c, 2) bul = newcopy2.item(y - 1, c - 1, 0) gul = newcopy2.item(y - 1, c - 1, 1) rul = newcopy2.item(y - 1, c - 1, 2) bur = newcopy2.item(y - 1, c + 1, 0) gur = newcopy2.item(y - 1, c + 1, 1) rur = newcopy2.item(y - 1, c + 1, 2) bdl = newcopy2.item(y + 1, c - 1, 0) gdl = newcopy2.item(y + 1, c - 1, 1) rdl = newcopy2.item(y + 1, c - 1, 2) bdr = newcopy2.item(y + 1, c + 1, 0) gdr = newcopy2.item(y + 1, c + 1, 1) rdr = newcopy2.item(y + 1, c + 1, 2) bd = newcopy2.item(y + 1, c, 0) gd = newcopy2.item(y + 1, c, 1) rd = newcopy2.item(y + 1, c, 2) new_prediction = trained_model.predict(np.array([[b, g, r, bl, gl, rl, br, gr, rr, bu, gu, ru, bul, gul, rul, bur, gur, rur, bdl, gdl, rdl, bdr, gdr, rdr, bd, gd, rd]])) if new_prediction > 0.5: copy2[y, c-(w/2)] = (255, 255, 0) cv2.imwrite("copy2.png", copy2) def thread3(): global h, w, trained_model, copy3 newcopy3 = copy3.copy() for y in range((h/2)-1): for c in range((w/2)-1): b = newcopy3.item(y, c, 0) g = newcopy3.item(y, c, 1) r = newcopy3.item(y, c, 2) bl = newcopy3.item(y, c - 1, 0) gl = newcopy3.item(y, c - 1, 1) rl = newcopy3.item(y, c - 1, 2) br = newcopy3.item(y, c + 1, 0) gr = newcopy3.item(y, c + 1, 1) rr = newcopy3.item(y, c + 1, 2) bu = newcopy3.item(y - 1, c, 0) gu = newcopy3.item(y - 1, c, 1) ru = newcopy3.item(y - 1, c, 2) bul = newcopy3.item(y - 1, c - 1, 0) gul = newcopy3.item(y - 1, c - 1, 1) rul = newcopy3.item(y - 1, c - 1, 2) bur = newcopy3.item(y - 1, c + 1, 0) gur = newcopy3.item(y - 1, c + 1, 1) rur = newcopy3.item(y - 1, c + 1, 2) bdl = newcopy3.item(y + 1, c - 1, 0) gdl = newcopy3.item(y + 1, c - 1, 1) rdl = newcopy3.item(y + 1, c - 1, 2) bdr = newcopy3.item(y + 1, c + 1, 0) gdr = newcopy3.item(y + 1, c + 1, 1) rdr = newcopy3.item(y + 1, c + 1, 2) bd = newcopy3.item(y + 1, c, 0) gd = newcopy3.item(y + 1, c, 1) rd = newcopy3.item(y + 1, c, 2) new_prediction = trained_model.predict(np.array([[b, g, r, bl, gl, rl, br, gr, rr, bu, gu, ru, bul, gul, rul, bur, gur, rur, bdl, gdl, rdl, bdr, gdr, rdr, bd, gd, rd]])) if new_prediction > 0.5: copy3[y-(h/2), c] = (255, 255, 0) cv2.imwrite("copy3.png", copy3) def thread4(): global h, w, trained_model, copy4 newcopy4 = copy4.copy() for y in range((h/2)-1): for c in range((w/2)-1): b = newcopy4.item(y, c, 0) g = newcopy4.item(y, c, 1) r = newcopy4.item(y, c, 2) bl = newcopy4.item(y, c - 1, 0) gl = newcopy4.item(y, c - 1, 1) rl = newcopy4.item(y, c - 1, 2) br = newcopy4.item(y, c + 1, 0) gr = newcopy4.item(y, c + 1, 1) rr = newcopy4.item(y, c + 1, 2) bu = newcopy4.item(y - 1, c, 0) gu = newcopy4.item(y - 1, c, 1) ru = newcopy4.item(y - 1, c, 2) bul = newcopy4.item(y - 1, c - 1, 0) gul = newcopy4.item(y - 1, c - 1, 1) rul = newcopy4.item(y - 1, c - 1, 2) bur = newcopy4.item(y - 1, c + 1, 0) gur = newcopy4.item(y - 1, c + 1, 1) rur = newcopy4.item(y - 1, c + 1, 2) bdl = newcopy4.item(y + 1, c - 1, 0) gdl = newcopy4.item(y + 1, c - 1, 1) rdl = newcopy4.item(y + 1, c - 1, 2) bdr = newcopy4.item(y + 1, c + 1, 0) gdr = newcopy4.item(y + 1, c + 1, 1) rdr = newcopy4.item(y + 1, c + 1, 2) bd = newcopy4.item(y + 1, c, 0) gd = newcopy4.item(y + 1, c, 1) rd = newcopy4.item(y + 1, c, 2) new_prediction = trained_model.predict(np.array([[b, g, r, bl, gl, rl, br, gr, rr, bu, gu, ru, bul, gul, rul, bur, gur, rur, bdl, gdl, rdl, bdr, gdr, rdr, bd, gd, rd]])) if new_prediction > 0.5: copy4[y-(h/2), c-(w/2)] = (255, 255, 0) cv2.imwrite("copy4.png", copy4) def main(img_path_or): global trained_model, copy1, copy2, copy3, copy4, h, w start = time.time() print('Unpacking model') trained_model = pickle.load(open("trained_model_25509_wo_verbose.sav",'rb')) img = cv2.imread(img_path_or) h, w = img.shape[:2] copy1 = img[0:(h/2), 0:(w/2)] copy2 = img[0:(h/2), (w/2):w] copy3 = img[(h/2):h, 0:(w/2)] copy4 = img[(h/2):h, (w/2):w] print('Pocessing') p1 = Process(target=thread1) p2 = Process(target=thread2) p3 = Process(target=thread3) p4 = Process(target=thread4) p1.start() p2.start() p3.start() p4.start() p1.join() p2.join() p3.join() p4.join() out1 = np.zeros((320, 480, 3)) out1[0:(h/2), 0:(w/2)] = cv2.imread('copy1.png') out1[0:(h/2), (w/2):w] = cv2.imread('copy2.png') out1[(h/2):h, 0:(w/2)] = cv2.imread('copy3.png') out1[(h/2):h, (w/2):w] = cv2.imread('copy4.png') cv2.imwrite('images/out1.png', out1) length = houghtest.main("images/out1.png",img_path_or) print('finished') end = time.time() print('Took '+str(round(((end - start)/60), 2))+' mins to process') return length if __name__ == '__main__': main(img_path_or)
[ "cv2.imwrite", "multiprocessing.Process", "houghtest.main", "numpy.array", "numpy.zeros", "time.time", "cv2.imread" ]
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import sys, re import PyQt5 from PyQt5.QtWidgets import QApplication, QLineEdit, QMainWindow, QMessageBox, QDialog, QPushButton, QLabel, QTableWidget, QTableWidgetItem, QVBoxLayout, QHeaderView, QSpinBox, QDoubleSpinBox, QGraphicsView, QTableWidgetItem from PyQt5 import uic from PyQt5.QtCore import pyqtSlot, QDate, Qt from PyQt5.QtGui import QIcon, QPixmap, QFont, QImage import shutil, os from reportlab.pdfgen import canvas from reportlab.lib import colors from reportlab.lib.utils import ImageReader #For logo import matplotlib.pyplot as plt import numpy as np import datetime from pydrive.auth import GoogleAuth from pydrive.drive import GoogleDrive import urllib.request import sqlite3 import os.path now = datetime.datetime.now() day = now.day month = now.month year = now.year current_datetime = '%s/%s/%s' % (day, month, year) mesos = ['Gener', 'Febrer', 'Març', 'Abril', 'Maig', 'Juny', 'Juliol', 'Agost', 'Setembre', 'Octubre', 'Novembre', 'Desembre'] mesos_minus = ['gener', 'febrer', 'març', 'abril', 'maig', 'juny', 'juliol', 'agost', 'setembre', 'octubre', 'novembre', 'desembre'] clear = lambda: os.system('cls') dir_principal = os.getcwd() carpeta_data = dir_principal + '\Data' carpeta_factures = dir_principal + '\Factures' carpeta_abonos = dir_principal + '\Abonos' carpeta_albaranes = dir_principal + '\Albaranes' current_month_factures = carpeta_factures + '\%s_%s' % (mesos[int(month)-1], year) if not os.path.exists(carpeta_data): os.makedirs(carpeta_data) if not os.path.exists(carpeta_factures): os.makedirs(carpeta_factures) if not os.path.exists(carpeta_abonos): os.makedirs(carpeta_abonos) ##################################################################### # # # FUNCTIONS # # # ##################################################################### #DRIVE def fitxer_drive(name): count = False file_list = drive.ListFile({'q': "'root' in parents and trashed=false"}).GetList() for file1 in file_list: if file1['title'] == name: file = drive.CreateFile({'id': file1['id']}) count = True if count == False: file = drive.CreateFile({'title': name}) file.SetContentFile(name) file.Upload() def carpeta_drive(folder_name): count = False file_list = drive.ListFile({'q': "'root' in parents and trashed=false"}).GetList() for file1 in file_list: if file1['title'] == folder_name: #La carpeta ja està creada Factures_id = file1['id'] count = True if count == False: #Crear la carpeta # Create folder. folder_metadata = { 'title' : folder_name, # The mimetype defines this new file as a folder, so don't change this. 'mimeType' : 'application/vnd.google-apps.folder' } folder = drive.CreateFile(folder_metadata) folder.Upload() file_list = drive.ListFile({'q': "'root' in parents and trashed=false"}).GetList() for file1 in file_list: if file1['title'] == folder_name: Factures_id = file1['id'] return Factures_id def file_to_folder(folder_id, filename): file = drive.CreateFile({"parents": [{"kind": "drive#fileLink", "id": folder_id}]}) file.SetContentFile(filename) file.Upload() def delete_file_in_folder(folder_id, filename): file_list = drive.ListFile({'q': "'%s' in parents and trashed=false" % folder_id}).GetList() for file1 in file_list: if file1['title'] == filename: #L'arxiu ja està creat file1.Delete() def internet_on(): try: urllib.request.urlopen('http://172.16.17.32', timeout=1) return True except urllib.request.URLError as err: return False def upload_to_drive_database(name): global drive os.chdir(carpeta_data) ##################################### LOG IN GOOGLE DRIVE ############################################# int_connexion = internet_on() if int_connexion == True: gauth = GoogleAuth() gauth.LocalWebserverAuth() drive = GoogleDrive(gauth) ######################################################################################################## #Pujar base de dades clients if int_connexion == True: folder_id = carpeta_drive('Data') #Pujar a google drive la factura filename = '%s.db' % name delete_file_in_folder(folder_id,filename) file_to_folder(folder_id, filename) def upload_to_drive_factura(dirr, NAME, NAME_2, num_fact, data, NOMCOM, num_client, filename_ventes, filename_facturacio_clients, filename_facturacio_total, filename_factures_emeses): global drive mes = mesos[int(data[3:5])-1] ano = str(data[6:]).zfill(4) current_month_factures = dirr + '\%s_%s' % (mes, ano) ##################################### LOG IN GOOGLE DRIVE ############################################# int_connexion = internet_on() if int_connexion == True: previous_directory = os.getcwd() os.chdir(carpeta_data) gauth = GoogleAuth() gauth.LocalWebserverAuth() drive = GoogleDrive(gauth) os.chdir(previous_directory) ######################################################################################################## #Guardar la factura al drive os.chdir(current_month_factures) if int_connexion == True: folder_id = carpeta_drive('%s_%s_%s' % (NAME, mes, str(year))) #Pujar a google drive la factura filename = '%s_%s_%s_%s.pdf' % (NAME_2, str(num_fact).zfill(4), NOMCOM, str(num_client).zfill(4)) delete_file_in_folder(folder_id,filename) file_to_folder(folder_id, filename) folder_id = carpeta_drive('Data') #Pujar bases de dades os.chdir(carpeta_data) #Ventes delete_file_in_folder(folder_id,filename_ventes) file_to_folder(folder_id, filename_ventes) #Facturació clients delete_file_in_folder(folder_id,filename_facturacio_clients) file_to_folder(folder_id, filename_facturacio_clients) #Facturació total delete_file_in_folder(folder_id,filename_facturacio_total) file_to_folder(folder_id, filename_facturacio_total) #Factures emeses delete_file_in_folder(folder_id,filename_factures_emeses) file_to_folder(folder_id, filename_factures_emeses) #FACTURES def assignar_numero_factura(table, year): tablas = [ """ CREATE TABLE IF NOT EXISTS numero_factura( any TEXT NOT NULL, num TEXT NOT NULL ); """ ] create_database('CompanyName', tablas) tablas = [ """ CREATE TABLE IF NOT EXISTS numero_abono( any TEXT NOT NULL, num TEXT NOT NULL ); """ ] create_database('CompanyName', tablas) tablas = [ """ CREATE TABLE IF NOT EXISTS numero_albaran( any TEXT NOT NULL, num TEXT NOT NULL ); """ ] create_database('CompanyName', tablas) database = sqlite3.connect('CompanyName.db') cursor = database.cursor() sentencia = "SELECT * FROM %s WHERE any LIKE %s" % (table, year) cursor.execute(sentencia) lines = cursor.fetchall() if len(lines) == 0: sentencia = "INSERT INTO '%s'(any, num) VALUES (?,?)" % (table) cursor.execute(sentencia, [year, 1]) database.commit() return 1 else: num_fact = int(lines[0][1]) + 1 sentencia = "DELETE FROM '%s' WHERE any LIKE ?;" % table cursor.execute(sentencia, [ "%{}%".format(year)]) database.commit() sentencia = "INSERT INTO '%s'(any, num) VALUES (?,?)" % (table) cursor.execute(sentencia, [year, num_fact]) database.commit() return num_fact def factura(dirr, NAME, num_client, NOMCOM, NOMFIS, DIR, NIF, TEL, POBLACIO, num_fact, date, form_pag, dim, array_ref, array_concept, array_U, array_PU, array_BI, SUMA, IVA, TOTAL): global c CompanyName = 'COMPANY NAME' CompanyDirection = 'C/ street, nº fºfª' PC = 'CP' CITY = 'CITY' EMAIL = 'companyemail' FISCAL_NAME = 'Fiscal Name' ID = 'ID_number' FISCAL_DIRECTION = 'Fiscal direction' FISCAL_CITY = 'Fiscal city' FISCAL_PC = 'Fiscal P.C.' mes = mesos[int(date[3:5])-1] ano = str(date[6:]).zfill(4) current_month_factures = dirr + '\%s_%s' % (mes, ano) if not os.path.exists(current_month_factures): os.makedirs(current_month_factures) os.chdir(current_month_factures) c = canvas.Canvas("%s_%s_%s_%s.pdf" % (NAME, str(num_fact).zfill(4), NOMCOM, str(num_client).zfill(4))) x_CompanyName = 40 x_customer = 350 x_doc = 350 y_CompanyName = 690 y_customer = 810 y_doc = 680 y_title = 780 c.setFont('Helvetica', 20) #Title (may be substituted by a logo) logo = ImageReader(carpeta_data + '\logo.png') c.drawImage(logo, x_CompanyName + 50, y_CompanyName + 50, width=50, height=50, mask='auto') c.setFont('Helvetica', 10) #CompanyName data c.drawString(x_CompanyName, y_CompanyName, CompanyName) c.drawString(x_CompanyName, y_CompanyName - 15, CompanyDirection) c.drawString(x_CompanyName, y_CompanyName - 15*2, PC + '' + CITY) c.drawString(x_CompanyName, y_CompanyName - 15*3, 'EMAIL: %s' % EMAIL) #c.drawString(x_CompanyName, y_CompanyName - 15*4, 'TELF: 640087843-678230059') #Customer data c.drawString(x_customer, y_customer, 'CUSTOMER') c.line(x_customer, y_customer - 5, x_customer + 35, y_customer - 5) c.drawString(x_customer, y_customer - 5 - 15, str(num_client).zfill(4) + ' ' + NOMCOM) c.drawString(x_customer, y_customer - 5 - 15*2, NOMFIS) c.drawString(x_customer, y_customer - 5 - 15*3, DIR) c.drawString(x_customer, y_customer - 5 - 15*4, POBLACIO) c.drawString(x_customer, y_customer - 5 - 15*5, 'NIF: ' + NIF) c.drawString(x_customer, y_customer - 5 -15*6, 'TEL: ' + TEL) #Document data c.drawString(x_doc, y_doc, 'DOCUMENT') c.line(x_doc, y_doc-5, x_doc + 60, y_doc-5) c.drawString(x_doc, y_doc-5-15*1, '%s: ' % NAME + str(num_fact).zfill(4)) c.drawString(x_doc, y_doc-5-15*2, 'DATE: ' + date) c.drawString(x_doc, y_doc-5-15*3, 'WAY TO PAY: ' + form_pag) #Make the table x_ref = 50 x_concepte = 90 x_U = 400 x_PU = 450 x_BI = 500 y_ref = 580 y_concepte = 580 y_U = 580 y_PU = 580 y_BI = 580 x_final_tabla = 550 y_final_tabla = 200 c.drawString(x_ref, y_ref, 'REF') c.drawString(x_concepte+5, y_concepte, 'CONCEPT') c.drawString(x_U+5, y_U, 'Units') c.drawString(x_PU+10, y_PU, 'U.P.') c.drawString(x_BI + 5, y_BI, 'T.B.') c.line(x_ref-10, y_ref-5, x_final_tabla, y_BI-5) #linea sota el encabezado c.line(x_ref-10, y_ref-5, x_ref-10, y_final_tabla) #linea vertical inicial c.line(x_concepte-10, y_ref-5, x_concepte-10, y_final_tabla) #linea vertical despres de ref c.line(x_U-3, y_U-5, x_U-3, y_final_tabla) #linea vertical dsps de concepte c.line(x_PU-5, y_PU-5, x_PU-5, y_final_tabla) #linea dsps de unitats c.line(x_BI-7, y_BI-5, x_BI-7, y_final_tabla) #linea vertical dsps de P.U. c.line(x_final_tabla, y_ref-5, x_final_tabla, y_final_tabla) #ultima linea vertical #c.line(x_ref-10, y_final_tabla+20, x_final_tabla, y_final_tabla+20) #penultima linea horitzontal c.line(x_ref-10, y_final_tabla, x_final_tabla, y_final_tabla ) #ultima linea horitzontal # Taula de resultats finals x_0 = x_ref-10 y_0 = y_final_tabla - 30 x_f = x_final_tabla y_f = y_0 - 25 c.line(x_0, y_0, x_f, y_0) #primera linea horitzontal c.line(x_0, y_f, x_f, y_f) #ultima linea horitzontal x_1 = (x_f-x_0)/3 x_2 = 2*x_1 c.line(x_0, y_0, x_0, y_f) #linia vertical inicial c.line(x_1,y_0, x_1, y_f) #primera linea vertical c.line(x_2,y_0,x_2,y_f) #ultima linea vertical c.line(x_f,y_0,x_f,y_f) #linea vertical final #Introduir referencies, conceptes, unitats, preu unitats, base imponible, suma total, iva i suma final y_new = y_ref-20 sep = 15 for i in range(0, dim): c.drawString(x_ref, y_new -sep*i, array_ref[i]) c.drawString(x_concepte + 5, y_new -sep*i, array_concept[i]) c.drawString(x_U + 15, y_new -sep*i, str(array_U[i])) c.drawString(x_PU + 5, y_new -sep*i, str(array_PU[i])) c.drawString(x_BI + 5, y_new -sep*i, str(array_BI[i])) #c.drawString(x_BI + 5, y_final_tabla + 7, str(SUMA)) c.drawString(x_0 + 20, y_0-15, 'T.B.: ' + str(SUMA)) c.drawString(x_1 + 50, y_0 - 15, 'V.A.T. 21%: ' + str(IVA)) c.setFont('Helvetica-Bold', 10) c.drawString(x_2 + 50, y_0 - 15, 'TOTAL: ' + str(TOTAL) + '\u20ac') x_dades_personals = x_0 y_dades_personals = y_f - 100 c.setFont('Helvetica', 8) c.drawString(x_dades_personals, y_dades_personals, '%s, %s, %s, %s, %s' % (FISCAL_NAME, ID, FISCAL_DIRECTION, FISCAL_PC, FISCAL_CITY)) c.save() def factura_de_albaranes(dirr, NAME, num_client, NOMCOM, NOMFIS, DIR, NIF, TEL, POBLACIO, num_fact, date, form_pag, array_num, array_data, array_bi, array_iva, array_total, SUMA, IVA, TOTAL): global c CompanyName = 'COMPANY NAME' CompanyDirection = 'C/ street, nº fºfª' PC = 'CP' CITY = 'CITY' EMAIL = 'companyemail' FISCAL_NAME = 'Fiscal Name' ID = 'ID_number' FISCAL_DIRECTION = 'Fiscal direction' FISCAL_CITY = 'Fiscal city' FISCAL_PC = 'Fiscal P.C.' mes = mesos[int(date[3:5])-1] ano = str(date[6:]).zfill(4) current_month_factures = dirr + '\%s_%s' % (mes, ano) if not os.path.exists(current_month_factures): os.makedirs(current_month_factures) os.chdir(current_month_factures) c = canvas.Canvas("%s_%s_%s_%s.pdf" % (NAME, str(num_fact).zfill(4), NOMCOM, str(num_client).zfill(4))) x_CompanyName = 40 x_customer = 350 x_doc = 350 y_CompanyName = 690 y_customer = 810 y_doc = 680 y_title = 780 c.setFont('Helvetica', 20) #Title (may be substituted by a logo) logo = ImageReader(carpeta_data + '\logo.png') c.drawImage(logo, x_amilcar + 50, y_amilcar + 50, width=50, height=50, mask='auto') c.setFont('Helvetica', 10) #CompanyName data c.drawString(x_CompanyName, y_CompanyName, CompanyName) c.drawString(x_CompanyName, y_CompanyName - 15, CompanyDirection) c.drawString(x_CompanyName, y_CompanyName - 15*2, PC + '' + CITY) c.drawString(x_CompanyName, y_CompanyName - 15*3, 'EMAIL: %s' % EMAIL) #c.drawString(x_CompanyName, y_CompanyName - 15*4, 'TELF: 640087843-678230059') #Customer data c.drawString(x_customer, y_customer, 'CUSTOMER') c.line(x_customer, y_customer - 5, x_customer + 35, y_customer - 5) c.drawString(x_customer, y_customer - 5 - 15, str(num_client).zfill(4) + ' ' + NOMCOM) c.drawString(x_customer, y_customer - 5 - 15*2, NOMFIS) c.drawString(x_customer, y_customer - 5 - 15*3, DIR) c.drawString(x_customer, y_customer - 5 - 15*4, POBLACIO) c.drawString(x_customer, y_customer - 5 - 15*5, 'NIF: ' + NIF) c.drawString(x_customer, y_customer - 5 -15*6, 'TEL: ' + TEL) #Document data c.drawString(x_doc, y_doc, 'DOCUMENT') c.line(x_doc, y_doc-5, x_doc + 60, y_doc-5) c.drawString(x_doc, y_doc-5-15*1, '%s: ' % NAME + str(num_fact).zfill(4)) c.drawString(x_doc, y_doc-5-15*2, 'DATE: ' + date) c.drawString(x_doc, y_doc-5-15*3, 'WAY TO PAY: ' + form_pag) #Make the table x_ref = 50 x_concepte = 90 x_U = 400 x_PU = 450 x_BI = 500 y_ref = 580 y_concepte = 580 y_U = 580 y_PU = 580 y_BI = 580 x_final_tabla = 550 y_final_tabla = 200 c.drawString(x_ref, y_ref, 'Nº') c.drawString(x_concepte+5, y_concepte, 'DATE') c.drawString(x_U+5, y_U, 'T.B.') c.drawString(x_PU+10, y_PU, 'V.A.T.') c.drawString(x_BI + 5, y_BI, 'TOTAL') c.line(x_ref-10, y_ref-5, x_final_tabla, y_BI-5) #linea sota el encabezado c.line(x_ref-10, y_ref-5, x_ref-10, y_final_tabla) #linea vertical inicial c.line(x_concepte-10, y_ref-5, x_concepte-10, y_final_tabla) #linea vertical despres de ref c.line(x_U-3, y_U-5, x_U-3, y_final_tabla) #linea vertical dsps de concepte c.line(x_PU-5, y_PU-5, x_PU-5, y_final_tabla) #linea dsps de unitats c.line(x_BI-7, y_BI-5, x_BI-7, y_final_tabla) #linea vertical dsps de P.U. c.line(x_final_tabla, y_ref-5, x_final_tabla, y_final_tabla) #ultima linea vertical #c.line(x_ref-10, y_final_tabla+20, x_final_tabla, y_final_tabla+20) #penultima linea horitzontal c.line(x_ref-10, y_final_tabla, x_final_tabla, y_final_tabla ) #ultima linea horitzontal # Taula de resultats finals x_0 = x_ref-10 y_0 = y_final_tabla - 30 x_f = x_final_tabla y_f = y_0 - 25 c.line(x_0, y_0, x_f, y_0) #primera linea horitzontal c.line(x_0, y_f, x_f, y_f) #ultima linea horitzontal x_1 = (x_f-x_0)/3 x_2 = 2*x_1 c.line(x_0, y_0, x_0, y_f) #linia vertical inicial c.line(x_1,y_0, x_1, y_f) #primera linea vertical c.line(x_2,y_0,x_2,y_f) #ultima linea vertical c.line(x_f,y_0,x_f,y_f) #linea vertical final #Introduir referencies, conceptes, unitats, preu unitats, base imponible, suma total, iva i suma final y_new = y_ref-20 sep = 15 for i in range(len(array_bi)): c.drawString(x_ref, y_new -sep*i, array_num[i]) c.drawString(x_concepte + 5, y_new -sep*i, array_data[i]) c.drawString(x_U + 15, y_new -sep*i, str(array_bi[i])) c.drawString(x_PU + 5, y_new -sep*i, str(array_iva[i])) c.drawString(x_BI + 5, y_new -sep*i, str(array_total[i])) #c.drawString(x_BI + 5, y_final_tabla + 7, str(SUMA)) c.drawString(x_0 + 20, y_0-15, 'TOTAL T.B..: ' + str(SUMA)) c.drawString(x_1 + 50, y_0 - 15, 'TOTAL V.A.T.: ' + str(IVA)) c.setFont('Helvetica-Bold', 10) c.drawString(x_2 + 50, y_0 - 15, 'FINAL TOTAL: ' + str(TOTAL) + '\u20ac') x_dades_personals = x_0 y_dades_personals = y_f - 100 c.setFont('Helvetica', 8) c.drawString(x_dades_personals, y_dades_personals, '%s, %s, %s, %s, %s' % (FISCAL_NAME, ID, FISCAL_DIRECTION, FISCAL_PC, FISCAL_CITY)) c.save() #BASEES DE DADES def create_database_client(name): #Create a database database = sqlite3.connect("%s.db" % name) #Create a data table in the database cursor = database.cursor() tablas = [ """ CREATE TABLE IF NOT EXISTS data( num_client TEXT NOT NULL, nom_comercial TEXT NOT NULL, nom_fiscal TEXT NOT NULL, adreça TEXT NOT NULL, poblacio TEXT NOT NULL, nif TEXT NOT NULL, telf TEXT NOT NULL, forma_pago TEXT NOT NULL ); """ ] for tabla in tablas: cursor.execute(tabla); def fill_database(name, numclient, nomcom, nomfis, adreça, poblacio, nif, telf, formapago): database = sqlite3.connect('%s.db' % name) cursor = database.cursor() sentencia = "INSERT INTO data(num_client, nom_comercial, nom_fiscal, adreça, poblacio, nif, telf, forma_pago) VALUES (?,?,?,?,?,?,?,?)" cursor.execute(sentencia, [numclient, nomcom, nomfis, adreça, poblacio, nif, telf, formapago]) database.commit() def read_database(name, table, name_2, order): database = sqlite3.connect('%s.db' % name) cursor = database.cursor() sentencia = "SELECT * FROM '%s' ORDER BY %s %s;" % (table, name_2, order) cursor.execute(sentencia) lines = cursor.fetchall() cursor.close() return lines def select_from_database(name, busqueda, name_2): os.chdir(carpeta_data) database = sqlite3.connect('%s.db' % name) cursor=database.cursor() sentencia = "SELECT * FROM data WHERE nom_comercial LIKE ? ORDER BY %s ASC;" % name_2 cursor.execute(sentencia, [ "%{}%".format(busqueda)]) matches = cursor.fetchall() if len(matches) != 0: return matches, True else: sentencia = "SELECT * FROM data WHERE num_client LIKE ?;" cursor.execute(sentencia, [ "%{}%".format(busqueda)]) matches = cursor.fetchall() if len(matches) != 0: return matches, True else: return matches, False def select_from_database_general(name, table, busqueda, name_2, order, order_2): os.chdir(carpeta_data) database = sqlite3.connect('%s.db' % name) cursor=database.cursor() sentencia = "SELECT * FROM '%s' WHERE %s LIKE ? ORDER BY %s %s;" % (table, name_2, order, order_2) cursor.execute(sentencia, ["%{}%".format(busqueda)]) matches = cursor.fetchall() return matches def delete_from_database(name, name_2, num): os.chdir(carpeta_data) database = sqlite3.connect('%s.db' % name) cursor=database.cursor() sentencia = "DELETE FROM data WHERE %s LIKE ?;" % name_2 cursor.execute(sentencia, [ "%{}%".format(num)]) database.commit() def delete_from_database_general(name, table, name_2, num): os.chdir(carpeta_data) database = sqlite3.connect('%s.db' % name) cursor=database.cursor() sentencia = "DELETE FROM '%s' WHERE %s LIKE ?;" % (table, name_2) cursor.execute(sentencia, [ "%{}%".format(num)]) database.commit() def create_database_ventes(name, table): database = sqlite3.connect("%s.db" % name) cursor = database.cursor() tablas = [ """ CREATE TABLE IF NOT EXISTS '%s'( ref TEXT NOT NULL, gener REAL NOT NULL, febrer REAL NOT NULL, març REAL NOT NULL, abril REAL NOT NULL, maig REAL NOT NULL, juny REAL NOT NULL, juliol REAL NOT NULL, agost REAL NOT NULL, setembre REAL NOT NULL, octubre REAL NOT NULL, novembre REAL NOT NULL, desembre REAL NOT NULL ); """ % table ] for tabla in tablas: cursor.execute(tabla); def create_database_cataleg(name): #Create a database database = sqlite3.connect("%s.db" % name) #Create a data table in the database cursor = database.cursor() tablas = [ """ CREATE TABLE IF NOT EXISTS data( ref TEXT NOT NULL, prod TEXT NOT NULL, preu REAL NOT NULL ); """ ] for tabla in tablas: cursor.execute(tabla); def fill_database_cataleg(name, ref, prod, preu): database = sqlite3.connect('%s.db' % name) cursor = database.cursor() sentencia = "INSERT INTO data(ref, prod, preu) VALUES (?,?,?)" cursor.execute(sentencia, [ref, prod, preu]) database.commit() def fill_database_ventes(name, tabla, ref, month, ventes, zfill_ref): database = sqlite3.connect('%s.db' % name) cursor = database.cursor() llista = [str(ref).zfill(zfill_ref), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] #Tenir en compte les unitats venudes prèviament sentencia = "SELECT * FROM '%s' WHERE ref LIKE ?;" % tabla cursor.execute(sentencia, ["%{}%".format(ref)]) matches = cursor.fetchall() if len(matches) != 0: llista = [] for i in range(len(matches[0])): llista.append(matches[0][i]) nou = llista[month] + ventes llista[month] = nou #Eliminar la fila actual sentencia = "DELETE FROM '%s' WHERE ref LIKE ?;" % tabla cursor.execute(sentencia, [ "%{}%".format(ref)]) #Escriure les ventes actualitzades sentencia = "INSERT INTO '%s'(ref, gener, febrer, març, abril, maig, juny, juliol, agost, setembre, octubre, novembre, desembre) VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?)" % tabla cursor.execute(sentencia, llista) else: #Escriure les ventes actualitzades llista[month] = ventes sentencia = "INSERT INTO '%s'(ref, gener, febrer, març, abril, maig, juny, juliol, agost, setembre, octubre, novembre, desembre) VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?)" % tabla cursor.execute(sentencia, llista) database.commit() def create_database_preus(name): database = sqlite3.connect("%s.db" % name) cursor = database.cursor() tablas = [ """ CREATE TABLE IF NOT EXISTS data( num_client TEXT NOT NULL, ref TEXT NOT NULL, preu REAL NOT NULL ); """ ] for tabla in tablas: cursor.execute(tabla); def fill_database_preus(name, num, ref, prod, preu): database = sqlite3.connect('%s.db' % name) cursor = database.cursor() lines = select_from_database_preus(name, num, ref) if len(lines) != 0: return False else: sentencia = "INSERT INTO data(num_client, ref, prod, preu) VALUES (?,?,?,?)" cursor.execute(sentencia, [num, ref, prod, preu]) database.commit() return True def modificar_database_preus(name, num, ref, prod, preu): database = sqlite3.connect('%s.db' % name) cursor = database.cursor() lines = select_from_database_preus(name, num, ref) if len(lines) == 0: return False else: sentencia = "DELETE FROM data WHERE num_client LIKE ? AND ref LIKE ?;" cursor.execute(sentencia, [ "%{}%".format(num), "%{}%".format(ref)]) sentencia = "INSERT INTO data(num_client, ref, prod, preu) VALUES (?,?,?,?)" cursor.execute(sentencia, [num, ref, prod, preu]) database.commit() return True def select_from_database_preus(name, num, ref): database = sqlite3.connect('%s.db' % name) cursor=database.cursor() sentencia = "SELECT * FROM data WHERE num_client LIKE ? AND ref LIKE ?;" cursor.execute(sentencia, ["%{}%".format(num), "%{}%".format(ref)]) matches = cursor.fetchall() return matches def create_database_factures(name): #Create a database database = sqlite3.connect("%s.db" % name) #Create a data table in the database cursor = database.cursor() tablas = [ """ CREATE TABLE IF NOT EXISTS data( dia TEXT NOT NULL, mes TEXT NOT NULL, any TEXT NOT NULL, nom TEXT NOT NULL, base_imp REAL NOT NULL, iva REAL NOT NULL, total REAL NOT NULL ); """ ] for tabla in tablas: cursor.execute(tabla); def fill_database_factures(name, dia, mes, any, nom, base, iva, total): database = sqlite3.connect('%s.db' % name) cursor = database.cursor() sentencia = "INSERT INTO data(dia, mes, any, nom, base_imp, iva, total) VALUES (?,?,?,?,?,?,?)" cursor.execute(sentencia, [dia, mes, any, nom, base, iva, total]) database.commit() def delete_database_factures(name, dia, mes, any, base, iva, total): database = sqlite3.connect('%s.db' % name) cursor = database.cursor() sentencia = "DELETE FROM data WHERE dia LIKE ? AND mes LIKE ? AND any LIKE ? AND base_imp LIKE ? AND iva LIKE ? AND total LIKE ? ;" cursor.execute(sentencia, [ "%{}%".format(dia), "%{}%".format(mes), "%{}%".format(any), "%{}%".format(base), "%{}%".format(iva), "%{}%".format(total)]) database.commit() def read_database_factures(name, order): database = sqlite3.connect('%s.db' % name) cursor = database.cursor() sentencia = "SELECT * FROM data ORDER BY any, mes, dia %s" % order cursor.execute(sentencia) lines = cursor.fetchall() return lines def select_from_database_factures(name, dia, mes, ano): database = sqlite3.connect('%s.db' % name) cursor=database.cursor() sentencia = "SELECT * FROM data WHERE dia LIKE ? AND mes LIKE ? AND any LIKE ? ;" cursor.execute(sentencia, ["%{}%".format(dia), "%{}%".format(mes), "%{}%".format(ano)]) matches = cursor.fetchall() return matches def check_table_exists(name, table): database = sqlite3.connect('%s.db' % name) cursor = database.cursor() sentencia = "SELECT name FROM sqlite_master WHERE type='table' AND name = '%s' ;" % table cursor.execute(sentencia) lines = cursor.fetchall() if len(lines) == 0: return False else: return True def create_database(name, tablas): #Create a database database = sqlite3.connect("%s.db" % name) #Create a data table in the database cursor = database.cursor() for tabla in tablas: cursor.execute(tabla); def fill_table_stock(name, items_list): database = sqlite3.connect('%s.db' % name) cursor = database.cursor() sentencia = "DELETE FROM stock WHERE REF LIKE ?;" cursor.execute(sentencia, ["%{}%".format(items_list[0])]) sentencia = "INSERT INTO stock(REF, NAME, QUANTITY, UNIT_PRICE, TOTAL_PRICE) VALUES (?,?,?,?,?)" cursor.execute(sentencia, items_list) database.commit() def fill_database_general(name, table, interrogants, values): database = sqlite3.connect('%s.db' % name) cursor = database.cursor() sentencia = "INSERT INTO %s VALUES %s" % (table, interrogants) cursor.execute(sentencia, values) database.commit() #DATE def change_date_format(date): return datetime.datetime.strptime(date, "%Y-%m-%d").strftime("%d-%m-%Y") ##################################################################### # # # CLASSES # # # ##################################################################### #CLIENTS class Nou_client(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('NouClient.ui', self) os.chdir(dir_principal) self.nomcom.textChanged.connect(self.validar_nom_com) #Executa la funció validar_nom_com al clicar sobre el camp self.nomfis.textChanged.connect(self.validar_nom_fis) self.direccio.textChanged.connect(self.validar_direccio) self.poblacio.textChanged.connect(self.validar_poblacio) self.nif.textChanged.connect(self.validar_nif) self.telf.textChanged.connect(self.validar_telf) self.formapago.textChanged.connect(self.validar_forma_pago) self.accept_botton.clicked.connect(self.validar_dades) self.pujar_drive.clicked.connect(self.upload_database) def assignar_numero_client(self, name): database = sqlite3.connect('%s.db' % name) cursor = database.cursor() sentencia = "SELECT * FROM data;" cursor.execute(sentencia) clients = cursor.fetchall() num = len(clients) + 1 return num def validar_nom_com(self): nom_com = self.nomcom.text() validar = re.match("^[a-z\sáéíóúàèìòùäëïöüñç0123456789'.-]+$", nom_com, re.I) #Permetre lletres a-z, espais, accents, numeros if nom_com == '': #Si esta buit bordes grocs #re.I ignora majuscules i minuscules self.nomcom.setStyleSheet('border: 1px solid yellow;') return False, nom_com elif not validar:#Si no es valid bordes vermells self.nomcom.setStyleSheet('border: 1px solid red;') return False, nom_com else: self.nomcom.setStyleSheet('border: 1px solid green;') return True, nom_com def validar_nom_fis(self): nom_fis = self.nomfis.text() validar = re.match("^[a-z\sáéíóúàèìòùäëïöüñç0123456789'.-]+$", nom_fis, re.I) #Permetre lletres a-z, espais, accents, numeros if nom_fis == '': #Si esta buit bordes grocs #re.I ignora majuscules i minuscules self.nomfis.setStyleSheet('border: 1px solid yellow;') return False, nom_fis elif not validar:#Si no es valid bordes vermells self.nomfis.setStyleSheet('border: 1px solid red;') return False, nom_fis else: self.nomfis.setStyleSheet('border: 1px solid green;') return True, nom_fis def validar_direccio(self): direccio = self.direccio.text() validar = re.match("^[a-z\sáéíóúàèìòùäëïöüñç0123456789'/,ªº.-]+$", direccio, re.I) #Permetre lletres a-z, espais, accents, numeros if direccio == '': #Si esta buit bordes grocs self.direccio.setStyleSheet('border: 1px solid yellow;') return False, direccio elif not validar:#Si no es valid bordes vermells self.direccio.setStyleSheet('border: 1px solid red;') return False, direccio else: self.direccio.setStyleSheet('border: 1px solid green;') return True, direccio def validar_poblacio(self): poblacio = self.poblacio.text() validar = re.match("^[a-z\sáéíóúàèìòùäëïöüñç0123456789',.-]+$", poblacio, re.I) #Permetre lletres a-z, espais, accents, numeros if poblacio == '': #Si esta buit bordes grocs self.poblacio.setStyleSheet('border: 1px solid yellow;') return False, poblacio elif not validar:#Si no es valid bordes vermells self.poblacio.setStyleSheet('border: 1px solid red;') return False, poblacio else: self.poblacio.setStyleSheet('border: 1px solid green;') return True, poblacio def validar_nif(self): nif = self.nif.text() validar = re.match('^[a-zñç0123456789]+$', nif, re.I) #Permetre lletres a-z, numeros if nif == '': #Si esta buit bordes grocs self.nif.setStyleSheet('border: 1px solid yellow;') return False, nif elif not validar:#Si no es valid bordes vermells self.nif.setStyleSheet('border: 1px solid red;') return False, nif else: self.nif.setStyleSheet('border: 1px solid green;') return True, nif def validar_telf(self): telf = self.telf.text() validar = re.match('^[0123456789]+$', telf, re.I) #Permetre numeros if telf == '': #Si esta buit bordes grocs self.nif.setStyleSheet('border: 1px solid yellow;') return False, telf elif not validar:#Si no es valid bordes vermells self.telf.setStyleSheet('border: 1px solid red;') return False, telf else: self.telf.setStyleSheet('border: 1px solid green;') return True, telf def validar_forma_pago(self): forma_pago = self.formapago.text() validar = re.match('^[a-zñç.-]+$', forma_pago, re.I) #Permetre lletres a-z #re.I ignora majuscules i minuscules if forma_pago == '': #Si esta buit bordes grocs self.nif.setStyleSheet('border: 1px solid yellow;') return False, forma_pago elif not validar:#Si no es valid bordes vermells self.formapago.setStyleSheet('border: 1px solid red;') return False, forma_pago else: self.formapago.setStyleSheet('border: 1px solid green;') return True, forma_pago def validar_dades(self): bool_1, NOMCOM = self.validar_nom_com() bool_2, NOMFIS = self.validar_nom_fis() bool_3, DIR = self.validar_direccio() bool_4, POBLACIO = self.validar_poblacio() bool_5, NIF = self.validar_nif() bool_6, TEL = self.validar_telf() bool_7, forma_pago = self.validar_forma_pago() if bool_1 and bool_2 and bool_3 and bool_4 and bool_5 and bool_6 and bool_7: os.chdir(carpeta_data) create_database_client('clients') #Just if the database doesn't exist num_client = self.assignar_numero_client('clients') fill_database('clients', str(num_client).zfill(4), str(NOMCOM), str(NOMFIS), str(DIR), str(POBLACIO), str(NIF), str(TEL), str(forma_pago)) if self.pujar_drive_check.isChecked(): upload_to_drive_database('clients') QMessageBox.information(self, 'Dades correctes' , 'El client ha estat registrat a la base de dades amb número de client %s' % str(num_client).zfill(4), QMessageBox.Discard) self.reinit_dialog() else: QMessageBox.warning(self, 'Dades incorrectes' , 'Comprova que tots els camps estan omplerts correctament', QMessageBox.Discard) def upload_database(self): upload_to_drive_database('clients') QMessageBox.information(self, 'Information', 'Dades pujades correctament') def reinit_dialog(self): self.nomcom.setText('') self.nomfis.setText('') self.direccio.setText('') self.poblacio.setText('') self.nif.setText('') self.telf.setText('') self.formapago.setText('') self.nomcom.setStyleSheet('') self.nomfis.setStyleSheet('') self.direccio.setStyleSheet('') self.poblacio.setStyleSheet('') self.nif.setStyleSheet('') self.telf.setStyleSheet('') self.formapago.setStyleSheet('') def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() self.pujar_drive_check.setChecked(True) else: event.ignore() class Modificar_client(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('ModClient.ui', self) self.numclient.textChanged.connect(self.validar_num_client) self.searchbotton.clicked.connect(self.search) self.nomcom.textChanged.connect(self.validar_nom_com) #Executa la funció validar_nom_com al clicar sobre el camp self.nomfis.textChanged.connect(self.validar_nom_fis) self.direccio.textChanged.connect(self.validar_direccio) self.poblacio.textChanged.connect(self.validar_poblacio) self.nif.textChanged.connect(self.validar_nif) self.telf.textChanged.connect(self.validar_telf) self.formapago.textChanged.connect(self.validar_forma_pago) self.accept_botton.clicked.connect(self.validar_dades) self.pujar_drive.clicked.connect(self.upload_database) def search(self): os.chdir(carpeta_data) control, num = self.validar_num_client() if control == True: if os.path.exists('clients.db'): client = select_from_database_general('clients', 'data', num, 'num_client', 'num_client', 'ASC') if len(client) == 0: QMessageBox.warning(self, 'Warning!', 'Client no registrat!', QMessageBox.Discard) self.reinit_dialog() else: self.nomcom.setText(client[0][1]) self.nomfis.setText(client[0][2]) self.direccio.setText(client[0][3]) self.poblacio.setText(client[0][4]) self.nif.setText(client[0][5]) self.telf.setText(client[0][6]) self.formapago.setText(client[0][7]) else: QMessageBox.warning(self, 'Warning!', 'Encara no has registrat cap client!') else: QMessageBox.warning(self, 'Warning', 'Número de client no vàlid!') def validar_num_client(self): num_client = self.numclient.text() validar = re.match('^[0123456789]+$', num_client) if num_client == '': #Si esta buit bordes grocs self.numclient.setStyleSheet('border: 1px solid yellow;') return False, num_client elif not validar:#Si no es valid bordes vermells self.numclient.setStyleSheet('border: 1px solid red;') return False, num_client else: self.numclient.setStyleSheet('border: 1px solid green;') return True, num_client def validar_nom_com(self): nom_com = self.nomcom.text() validar = re.match("^[a-z\sáéíóúàèìòùäëïöüñç0123456789'.-]+$", nom_com, re.I) #Permetre lletres a-z, espais, accents, numeros if nom_com == '': #Si esta buit bordes grocs #re.I ignora majuscules i minuscules self.nomcom.setStyleSheet('border: 1px solid yellow;') return False, nom_com elif not validar:#Si no es valid bordes vermells self.nomcom.setStyleSheet('border: 1px solid red;') return False, nom_com else: self.nomcom.setStyleSheet('border: 1px solid green;') return True, nom_com def validar_nom_fis(self): nom_fis = self.nomfis.text() validar = re.match("^[a-z\sáéíóúàèìòùäëïöüñç0123456789'.-]+$", nom_fis, re.I) #Permetre lletres a-z, espais, accents, numeros if nom_fis == '': #Si esta buit bordes grocs #re.I ignora majuscules i minuscules self.nomfis.setStyleSheet('border: 1px solid yellow;') return False, nom_fis elif not validar:#Si no es valid bordes vermells self.nomfis.setStyleSheet('border: 1px solid red;') return False, nom_fis else: self.nomfis.setStyleSheet('border: 1px solid green;') return True, nom_fis def validar_direccio(self): direccio = self.direccio.text() validar = re.match("^[a-z\sáéíóúàèìòùäëïöüñç0123456789'/,ªº.-]+$", direccio, re.I) #Permetre lletres a-z, espais, accents, numeros if direccio == '': #Si esta buit bordes grocs self.direccio.setStyleSheet('border: 1px solid yellow;') return False, direccio elif not validar:#Si no es valid bordes vermells self.direccio.setStyleSheet('border: 1px solid red;') return False, direccio else: self.direccio.setStyleSheet('border: 1px solid green;') return True, direccio def validar_poblacio(self): poblacio = self.poblacio.text() validar = re.match("^[a-z\sáéíóúàèìòùäëïöüñç0123456789',.-]+$", poblacio, re.I) #Permetre lletres a-z, espais, accents, numeros if poblacio == '': #Si esta buit bordes grocs self.poblacio.setStyleSheet('border: 1px solid yellow;') return False, poblacio elif not validar:#Si no es valid bordes vermells self.poblacio.setStyleSheet('border: 1px solid red;') return False, poblacio else: self.poblacio.setStyleSheet('border: 1px solid green;') return True, poblacio def validar_nif(self): nif = self.nif.text() validar = re.match('^[a-zñç0123456789]+$', nif, re.I) #Permetre lletres a-z, numeros if nif == '': #Si esta buit bordes grocs self.nif.setStyleSheet('border: 1px solid yellow;') return False, nif elif not validar:#Si no es valid bordes vermells self.nif.setStyleSheet('border: 1px solid red;') return False, nif else: self.nif.setStyleSheet('border: 1px solid green;') return True, nif def validar_telf(self): telf = self.telf.text() validar = re.match('^[0123456789]+$', telf, re.I) #Permetre numeros if telf == '': #Si esta buit bordes grocs self.nif.setStyleSheet('border: 1px solid yellow;') return False, telf elif not validar:#Si no es valid bordes vermells self.telf.setStyleSheet('border: 1px solid red;') return False, telf else: self.telf.setStyleSheet('border: 1px solid green;') return True, telf def validar_forma_pago(self): forma_pago = self.formapago.text() validar = re.match('^[a-zñç.]+$', forma_pago, re.I) #Permetre lletres a-z #re.I ignora majuscules i minuscules if forma_pago == '': #Si esta buit bordes grocs self.nif.setStyleSheet('border: 1px solid yellow;') return False, forma_pago elif not validar:#Si no es valid bordes vermells self.formapago.setStyleSheet('border: 1px solid red;') return False, forma_pago else: self.formapago.setStyleSheet('border: 1px solid green;') return True, forma_pago def validar_dades(self): bool_1, NOMCOM = self.validar_nom_com() bool_2, NOMFIS = self.validar_nom_fis() bool_3, DIR = self.validar_direccio() bool_4, POBLACIO = self.validar_poblacio() bool_5, NIF = self.validar_nif() bool_6, TEL = self.validar_telf() bool_7, forma_pago = self.validar_forma_pago() bool_8, num_client = self.validar_num_client() if bool_1 and bool_2 and bool_3 and bool_4 and bool_5 and bool_6 and bool_7 and bool_8 : os.chdir(carpeta_data) delete_from_database('clients', 'num_client', str(num_client).zfill(4)) fill_database('clients', str(num_client).zfill(4), str(NOMCOM), str(NOMFIS), str(DIR), str(POBLACIO), str(NIF), str(TEL), str(forma_pago)) if self.pujar_drive_check.isChecked(): upload_to_drive_database('clients') QMessageBox.information(self, 'Information' , 'Client modificat', QMessageBox.Discard) self.reinit_dialog() else: QMessageBox.warning(self, 'Warning!' , 'Dades incorrectes, comprova si el número de client està registrat', QMessageBox.Discard) def upload_database(self): upload_to_drive_database('clients') QMessageBox.information(self, 'Information', 'Dades pujades correctament') def reinit_dialog(self): self.numclient.setText('') self.nomcom.setText('') self.direccio.setText('') self.nomfis.setText('') self.poblacio.setText('') self.nif.setText('') self.telf.setText('') self.formapago.setText('') self.numclient.setStyleSheet('') self.nomcom.setStyleSheet('') self.nomfis.setStyleSheet('') self.direccio.setStyleSheet('') self.poblacio.setStyleSheet('') self.nif.setStyleSheet('') self.telf.setStyleSheet('') self.formapago.setStyleSheet('') def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() self.pujar_drive_check.setChecked(True) else: event.ignore() class Registre_clients(QDialog): def __init__(self): QDialog.__init__(self) uic.loadUi('RegistreClients.ui', self) self.show_table() def show_table(self): os.chdir(carpeta_data) if os.path.exists('clients.db'): lines = read_database('clients', 'data', 'num_client', 'ASC') self.table.setRowCount(len(lines)) self.table.setColumnCount(8) self.table.setHorizontalHeaderLabels(['Nº CLIENT', 'NOM COMERCIAL', 'NOM FISCAL', 'ADREÇA', 'POBLACIÓ', 'NIF', 'TEL', 'FORMA PAGO']) font = QFont() font.setFamily('Segoe UI Black') font.setPointSize(9) for i in range(8): self.table.horizontalHeaderItem(i).setFont(font) llista = [] for i in range(len(lines)): llista.append(lines[i][0]) for j in range(8): if j == 0: self.table.setItem(i,j, QTableWidgetItem(lines[i][0])) #num_client elif j == 1: self.table.setItem(i,j, QTableWidgetItem(lines[i][1])) #nom_com elif j == 2: self.table.setItem(i,j, QTableWidgetItem(lines[i][2])) #nom_fis elif j == 3: self.table.setItem(i,j, QTableWidgetItem(lines[i][3])) #adreça elif j == 4: self.table.setItem(i,j, QTableWidgetItem(lines[i][4])) #poblacio elif j == 5: self.table.setItem(i,j, QTableWidgetItem(lines[i][5])) #nif elif j == 6: self.table.setItem(i,j, QTableWidgetItem(lines[i][6])) #tel else: self.table.setItem(i,j, QTableWidgetItem(lines[i][7])) #forma pago self.table.setVerticalHeaderLabels(llista) header = self.table.horizontalHeader() for i in range(8): header.setSectionResizeMode(i, QHeaderView.ResizeToContents) for j in range(len(lines)): self.table.verticalHeaderItem(j).setFont(font) class Buscar_client(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('BuscarClient.ui', self) self.buscador.textChanged.connect(self.validar_buscador) self.accepted.connect(self.accept) self.rejected.connect(self.reinit_dialog) def validar_buscador(self): entrada = self.buscador.text() validar = re.match("^[a-z\sáéíóúàèìòùäëïöüñç0123456789']+$", entrada, re.I) if entrada == '': #Si esta buit bordes grocs self.buscador.setStyleSheet('border: 1px solid yellow;') return False, entrada elif not validar:#Si no es valid bordes vermells self.buscador.setStyleSheet('border: 1px solid red;') return False, entrada else: self.buscador.setStyleSheet('border: 1px solid green;') return True, entrada def accept(self): os.chdir(carpeta_data) bool1, entrada = self.validar_buscador() CONTROL = False if bool1 == True: if os.path.exists('clients.db'): matches, CONTROL = select_from_database('clients', entrada, 'num_client') if CONTROL == True: self.table.setRowCount(len(matches)) self.table.setColumnCount(8) self.table.setHorizontalHeaderLabels(['Nº CLIENT', 'NOM COMERCIAL', 'NOM FISCAL', 'ADREÇA', 'POBLACIÓ', 'NIF', 'TEL', 'FORMA PAGO']) font = QFont() font.setFamily('Segoe UI Black') font.setPointSize(9) for i in range(8): self.table.horizontalHeaderItem(i).setFont(font) llista = [] for i in range(len(matches)): llista.append('') for j in range(8): if j == 0: self.table.setItem(i,j, QTableWidgetItem(matches[i][0])) #num_client elif j == 1: self.table.setItem(i,j, QTableWidgetItem(matches[i][1])) #nom_com elif j == 2: self.table.setItem(i,j, QTableWidgetItem(matches[i][2])) #nom_fis elif j == 3: self.table.setItem(i,j, QTableWidgetItem(matches[i][3])) #adreça elif j == 4: self.table.setItem(i,j, QTableWidgetItem(matches[i][4])) #poblacio elif j == 5: self.table.setItem(i,j, QTableWidgetItem(matches[i][5])) #nif elif j == 6: self.table.setItem(i,j, QTableWidgetItem(matches[i][6])) #tel else: self.table.setItem(i,j, QTableWidgetItem(matches[i][7])) #forma pago self.table.setVerticalHeaderLabels(llista) header = self.table.horizontalHeader() for i in range(8): header.setSectionResizeMode(i, QHeaderView.ResizeToContents) else: QMessageBox.warning(self, 'Warning!' , 'Cap coincidència', QMessageBox.Discard) else: QMessageBox.warning(self, 'Dades incorrectes', 'Encara no has registrat cap client!', QMessageBox.Discard) else: QMessageBox.warning(self, 'Dades incorrectes', 'Algun caràcter introduït al marcador no és vàlid', QMessageBox.Discard) def reinit_dialog(self): self.buscador.setText('') self.table.setRowCount(0) self.table.setColumnCount(0) self.buscador.setStyleSheet('') def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() else: event.ignore() #PRODUCTES class Nou_producte(QDialog): def __init__(self): QDialog.__init__(self) uic.loadUi('NouProducte.ui', self) self.ref_modificar.textChanged.connect(self.validar_ref_mod) self.ref_eliminar.textChanged.connect(self.validar_ref_elim) self.nom.textChanged.connect(self.validar_nom_prod) self.veure_nom_mod.clicked.connect(self.veure_nom_modificar) self.veure_nom_elim.clicked.connect(self.veure_nom_eliminar) self.registrar.clicked.connect(self.registrar_producte) self.modificar.clicked.connect(self.modificar_producte) self.eliminar.clicked.connect(self.eliminar_producte) self.pujar_drive.clicked.connect(self.upload_database) def validar_nom_prod(self): nom = self.nom.text() validar = re.match('^[a-z\sáéíóúàèìòùäëïöüñç0123456789.-]+$', nom, re.I) #Permetre lletres a-z, espais, accents, numeros if nom == '': #Si esta buit bordes grocs #re.I ignora majuscules i minuscules self.nom.setStyleSheet('border: 1px solid yellow;') return False, nom else: self.nom.setStyleSheet('border: 1px solid green;') return True, nom ''' elif not validar:#Si no es valid bordes vermells self.nom.setStyleSheet('border: 1px solid red;') return False, nom ''' def validar_ref_mod(self): ref = self.ref_modificar.text() validar = re.match('^[0123456789]+$', ref) if ref == '': #Si esta buit bordes grocs self.ref_modificar.setStyleSheet('border: 1px solid yellow;') return False, ref elif not validar:#Si no es valid bordes vermells self.ref_modificar.setStyleSheet('border: 1px solid red;') return False, ref else: self.ref_modificar.setStyleSheet('border: 1px solid green;') return True, ref def validar_ref_elim(self): ref = self.ref_eliminar.text() validar = re.match('^[0123456789]+$', ref) if ref == '': #Si esta buit bordes grocs self.ref_eliminar.setStyleSheet('border: 1px solid yellow;') return False, ref elif not validar:#Si no es valid bordes vermells self.ref_eliminar.setStyleSheet('border: 1px solid red;') return False, ref else: self.ref_eliminar.setStyleSheet('border: 1px solid green;') return True, ref def veure_nom_modificar(self): os.chdir(carpeta_data) control, ref = self.validar_ref_mod() if not os.path.exists('cataleg.db'): QMessageBox.warning(self, 'Warning!', 'No existeix catàleg!') elif control == False: QMessageBox.warning(self, 'Warning!', 'Referència no vàlida!') else: prod = select_from_database_general('cataleg', 'data', ref, 'ref', 'ref', 'ASC') if len(prod) != 0: self.nom_modificar.setText(prod[0][1]) self.preu_mod_prod.setValue(prod[0][2]) else: QMessageBox.warning(self, 'Warning!', 'Referència no registrada') def veure_nom_eliminar(self): os.chdir(carpeta_data) control, ref = self.validar_ref_elim() if not os.path.exists('cataleg.db'): QMessageBox.warning(self, 'Warning!', 'No existeix catàleg!') elif control == False: QMessageBox.warning(self, 'Warning!', 'Referència no vàlida!') else: prod = select_from_database_general('cataleg', 'data', ref, 'ref', 'ref', 'ASC') if len(prod) != 0: self.nom_eliminar.setText(prod[0][1]) self.preu_elim_prod.setValue(prod[0][2]) else: QMessageBox.warning(self, 'Warning!', 'Referència no registrada') def eliminar_producte(self): os.chdir(carpeta_data) control, ref = self.validar_ref_elim() if not os.path.exists('cataleg.db'): QMessageBox.warning(self, 'Warning!', 'No existeix catàleg!') elif control == False: QMessageBox.warning(self, 'Warning!', 'Referència no vàlida!') else: delete_from_database('cataleg', 'ref', ref) if self.pujar_drive_check.isChecked(): upload_to_drive_database('cataleg') QMessageBox.information(self, 'Information', 'Producte eliminat correctament!') self.reinit_dialog() def modificar_producte(self): os.chdir(carpeta_data) control, ref = self.validar_ref_mod() prod = self.nom_modificar.text() preu = self.preu_mod_prod.value() if self.nom_modificar.text() == '': os.chdir(carpeta_data) control, ref = self.validar_ref_mod() if not os.path.exists('cataleg.db'): QMessageBox.warning(self, 'Warning!', 'No existeix catàleg!') elif control == False: QMessageBox.warning(self, 'Warning!', 'Referència no vàlida!') else: prod = select_from_database_general('cataleg', 'data', ref, 'ref', 'ref', 'ASC') if len(prod) != 0: prod = prod[0][1] else: QMessageBox.warning(self, 'Warning!', 'Referència no registrada') if not os.path.exists('cataleg.db'): QMessageBox.warning(self, 'Warning!', 'No existeix catàleg!') elif control == False: QMessageBox.warning(self, 'Warning!', 'Referència no vàlida!') elif preu == 0: QMessageBox.warning(self, 'Warning!', 'El preu de compra no pot ser 0!') else: delete_from_database('cataleg', 'ref', ref) fill_database_cataleg('cataleg', str(ref).zfill(3), prod, preu) if self.pujar_drive_check.isChecked(): upload_to_drive_database('cataleg') QMessageBox.information(self, 'Information', 'Producte modificat correctament!') self.reinit_dialog() def registrar_producte(self): os.chdir(carpeta_data) create_database_cataleg('cataleg') lines = read_database('cataleg', 'data', 'ref', 'ASC') control_nom, prod = self.validar_nom_prod() ref = int(lines[len(lines)-1][0]) + 1 preu = self.preu_nou_prod.value() control = True for i in range(len(lines)): if lines[i][1] == prod: control = False control_ref = lines[i][0] if control == False: QMessageBox.warning(self, 'Warning!', 'Aquest producte ja esta registrat amb número de referència %s' % control_ref) elif preu == 0: QMessageBox.warning(self, 'Warning!', 'El preu del producte no pot ser 0!') elif control_nom == False: QMessageBox.warning(self, 'Warning!', 'El nom del producte no pot estar buit!') else: fill_database_cataleg('cataleg', str(ref).zfill(3), prod, preu) if self.pujar_drive_check.isChecked(): upload_to_drive_database('cataleg') QMessageBox.information(self, 'Information', 'Producte ja registrat amb número de referència %s' % ref) self.reinit_dialog() def upload_database(self): upload_to_drive_database('cataleg') QMessageBox.information(self, 'Information', 'Dades pujades correctament') def reinit_dialog(self): self.nom.setText('') self.ref_modificar.setText('') self.ref_eliminar.setText('') self.nom_modificar.setText('') self.nom_eliminar.setText('') self.preu_mod_prod.setValue(0) self.preu_nou_prod.setValue(0) self.preu_elim_prod.setValue(0) def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() self.ref_modificar.setStyleSheet('') self.ref_eliminar.setStyleSheet('') self.pujar_drive_check.setChecked(True) else: event.ignore() class Introduir_preu_producte(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('IntroduirPreu.ui', self) self.referencia.textChanged.connect(self.validar_ref) self.numclient.textChanged.connect(self.validar_num_client) self.seleccionar.clicked.connect(self.validar_dades) self.guardar.clicked.connect(self.guardar_preu) self.modificar.clicked.connect(self.modificar_preu) self.pujar_drive.clicked.connect(self.upload_database) def validar_ref(self): ref = self.referencia.text() validar = re.match('^[0123456789]+$', ref) if ref == '': #Si esta buit bordes grocs self.referencia.setStyleSheet('border: 1px solid yellow;') return False, ref elif not validar:#Si no es valid bordes vermells self.referencia.setStyleSheet('border: 1px solid red;') return False, ref else: self.referencia.setStyleSheet('border: 1px solid green;') return True, ref def validar_dades(self): control, ref = self.validar_ref() os.chdir(carpeta_data) if os.path.exists('cataleg.db') and control == True: lines = select_from_database_general('cataleg', 'data', ref, 'ref', 'ref', 'ASC') if len(lines) != 0: nom_ref = lines[0][1] self.nom.setText(nom_ref) else: QMessageBox.warning(self, 'Warning!', 'Referència incorrecta o no registrada al catàleg!') elif control == False: QMessageBox.warning(self, 'Warning!', 'Dades incorrectes!') else: QMessageBox.warning(self, 'Warning!', 'Encara no has registrat cap producte!') def validar_num_client(self): num_client = self.numclient.text() validar = re.match('^[0123456789]+$', num_client) if num_client == '': #Si esta buit bordes grocs self.numclient.setStyleSheet('border: 1px solid yellow;') return False, num_client elif not validar:#Si no es valid bordes vermells self.numclient.setStyleSheet('border: 1px solid red;') return False, num_client else: self.numclient.setStyleSheet('border: 1px solid green;') return True, num_client def guardar_preu(self): control, num_client = self.validar_num_client() control_2, ref = self.validar_ref() os.chdir(carpeta_data) if os.path.exists('clients.db'): lines = select_from_database_general('clients', 'data', num_client, 'num_client', 'num_client', 'ASC') if control == True and control_2 == True and len(lines) != 0: preu_ref = self.preu.value() prod = self.nom.text() if preu_ref > 0: create_database_preus('preus') control = fill_database_preus('preus', num_client, ref, prod, preu_ref) if control == False: QMessageBox.warning(self, 'Warning!', 'El preu per aquest producte i client ja existeix a la base de dades! Si vols modificar-lo, clica el botó \"Modificar preu\"') else: if self.pujar_drive_check.isChecked(): upload_to_drive_database('preus') QMessageBox.information(self, 'Information', 'Dades guardades correctament') self.reinit_dialog() else: QMessageBox.warning(self, 'Warning!', 'Selecciona un preu diferent de 0 per al producte!') else: QMessageBox.warning(self, 'Warning!', 'Número de client no registrat!') else: QMessageBox.warning(self, 'Warning!', 'Encara no has reistrat cap client!') def upload_database(self): upload_to_drive_database('preus') QMessageBox.Information(self, 'Information', 'Dades pujades correctament') def modificar_preu(self): control, num_client = self.validar_num_client() control_2, ref = self.validar_ref() os.chdir(carpeta_data) if os.path.exists('clients.db'): lines = select_from_database_general('clients', 'data', num_client, 'num_client', 'num_client', 'ASC') if control == True and control_2 == True and len(lines) != 0: preu_ref = self.preu.value() prod = self.nom.text() if preu_ref > 0: create_database_preus('preus') control = modificar_database_preus('preus', num_client, ref, prod, preu_ref) if control == False: QMessageBox.warning(self, 'Warning!', 'El preu per aquest producte i client no existeix a la base de dades, per introduir un preu nou clica el botó \"Guardar preu\".') else: if self.pujar_drive_check.isChecked(): upload_to_drive_database('preus') QMessageBox.information(self, 'Information', 'Dades modificades correctament') self.reinit_dialog() else: QMessageBox.warning(self, 'Warning!', 'Selecciona un preu diferent de 0 per al producte!') else: QMessageBox.warning(self, 'Warning!', 'Dades incorrectes!') else: QMessageBox.warning(self, 'Warning!', 'Encara no has registrat cap client!') def reinit_dialog(self): self.referencia.setText('') self.numclient.setText('') self.nom.setText('') self.preu.setValue(0.) def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() self.referencia.setStyleSheet('') self.numclient.setStyleSheet('') self.pujar_drive_check.setChecked(True) else: event.ignore() class Cataleg(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('Cataleg.ui', self) self.show.clicked.connect(self.createTable) def createTable(self): os.chdir(carpeta_data) if os.path.exists('cataleg.db'): if self.order.currentText() == 'Ordre alfabètic': lines = read_database('cataleg', 'data', 'prod', 'ASC') elif self.order.currentText() == 'Referència asc': lines = read_database('cataleg', 'data', 'ref', 'ASC') else: lines = read_database('cataleg', 'data', 'ref', 'DESC') self.table.setRowCount(len(lines)) self.table.setColumnCount(3) header = self.table.horizontalHeader() header.setSectionResizeMode(1, QHeaderView.Stretch) header.setSectionResizeMode(0, QHeaderView.ResizeToContents) header.setSectionResizeMode(2, QHeaderView.ResizeToContents) self.table.setHorizontalHeaderLabels(['REF', 'PRODUCTE', 'PREU DE COMPRA']) font = QFont() font.setFamily('Segoe UI Black') font.setPointSize(9) for i in range(3): self.table.horizontalHeaderItem(i).setFont(font) for i in range(len(lines)): for j in range(3): self.table.setItem(i,j, QTableWidgetItem(str(lines[i][j]))) llista = [] for i in range(len(lines)): llista.append('') self.table.setVerticalHeaderLabels(llista) else: QMessageBox.warning(self, 'Warning!', 'Encara no has registrat cap producte!') def reinit_dialog(self): self.table.clearContents() def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() else: event.ignore() class Veure_preus(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('VeurePreus.ui', self) self.show_table() self.numclient.textChanged.connect(self.validar_num_client) self.referencia.textChanged.connect(self.validar_ref) self.seleccionar.clicked.connect(self.show_price_ref) def validar_num_client(self): num_client = self.numclient.text() validar = re.match('^[0123456789]+$', num_client) if num_client == '': #Si esta buit bordes grocs self.numclient.setStyleSheet('border: 1px solid yellow;') return False, num_client elif not validar:#Si no es valid bordes vermells self.numclient.setStyleSheet('border: 1px solid red;') return False, num_client else: self.numclient.setStyleSheet('border: 1px solid green;') return True, num_client def validar_ref(self): ref = self.referencia.text() validar = re.match('^[0123456789]+$', ref) if ref == '': #Si esta buit bordes grocs self.referencia.setStyleSheet('border: 1px solid yellow;') return False, ref elif not validar:#Si no es valid bordes vermells self.referencia.setStyleSheet('border: 1px solid red;') return False, ref else: self.referencia.setStyleSheet('border: 1px solid green;') return True, ref def show_table(self): os.chdir(carpeta_data) if os.path.exists('cataleg.db') and os.path.exists('preus.db'): referencies = read_database('cataleg', 'data', 'ref', 'ASC') clients = read_database('clients', 'data', 'num_client', 'ASC') preus = read_database('preus', 'data', 'ref', 'ASC') self.table.setRowCount(len(clients)) self.table.setColumnCount(len(referencies) + 1) horitzontal_labels = ['NUM. CLIENT / REF'] vertical_labels = [] for i in range(len(referencies)): horitzontal_labels.append(str(i+1).zfill(3)) for i in range(len(clients)): vertical_labels.append(clients[i][0]) for j in range(len(referencies)+1): if j == 0: self.table.setItem(i,j, QTableWidgetItem(clients[i][0])) else: current_price_customer = select_from_database_preus('preus', str(i+1).zfill(4), str(j).zfill(3)) if len(current_price_customer) == 0: self.table.setItem(i,j, QTableWidgetItem('NULL')) else: self.table.setItem(i,j, QTableWidgetItem(str(current_price_customer[0][3]))) self.table.setHorizontalHeaderLabels(horitzontal_labels) self.table.setVerticalHeaderLabels(vertical_labels) header = self.table.horizontalHeader() header.setSectionResizeMode(0, QHeaderView.ResizeToContents) font = QFont() font.setFamily('Segoe UI Black') font.setPointSize(9) for i in range(len(referencies)+1): self.table.horizontalHeaderItem(i).setFont(font) for i in range(len(clients)): self.table.verticalHeaderItem(i).setFont(font) else: #QMessageBox.warning(self, 'Warning!', 'No existeixen les bases de dades del catàleg o preus! ') pass def show_price_ref(self): control, num_client = self.validar_num_client() control_ref, ref = self.validar_ref() os.chdir(carpeta_data) if os.path.exists('preus.db'): price_ref_customer = select_from_database_preus('preus', str(num_client).zfill(4), str(ref).zfill(3)) if control == True and control_ref == True and len(price_ref_customer) != 0: self.preu.setText(str(price_ref_customer[0][3])) elif control == True and control_ref == True and len(price_ref_customer) == 0: self.preu.setText('NULL') else: QMessageBox.warning(self, 'Warning!', 'Número de client o referència incorrecte') else: QMessageBox.warning(self, 'Warning!', 'Encara no has registrat cap preu!') def reinit_dialog(self): self.numclient.setText('') self.referencia.setText('') self.preu.setText('') self.referencia.setStyleSheet('') self.numclient.setStyleSheet('') def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() else: event.ignore() #FACTURACIÓ class Factura(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('Factura.ui', self) self.numclient.textChanged.connect(self.validar_num_client) self.seleccionar.clicked.connect(self.search) self.facturar.clicked.connect(self.fer_factura) def show_table(self, num_client): os.chdir(carpeta_data) if not os.path.exists('preus.db'): QMessageBox.warning(self, 'Warning!', 'No has registrat cap preu!') else: if self.comboBox.currentText() == 'Referència ascendent': lines = select_from_database_general('preus', 'data', str(num_client).zfill(4), 'num_client', 'ref', 'ASC') elif self.comboBox.currentText() == 'Referència descendent': lines = select_from_database_general('preus', 'data', str(num_client).zfill(4), 'num_client', 'ref', 'DESC') elif self.comboBox.currentText() == 'Alfabètic': lines = select_from_database_general('preus', 'data', str(num_client).zfill(4), 'num_client', 'prod', 'ASC') if len(lines) == 0: QMessageBox.warning(self, 'Warning!', 'Aquest client no té cap preu registrat!') else: self.table.setRowCount(len(lines)) self.table.setColumnCount(4) llista = [] for i in range(len(lines)): llista.append('') for j in range(4): if j == 0: #UNITS sp = QSpinBox() sp.setMaximum(9999) self.table.setCellWidget(i,j, sp) elif j == 1: self.table.setItem(i,j, QTableWidgetItem(lines[i][1])) #REF elif j == 2: self.table.setItem(i,j, QTableWidgetItem(lines[i][2])) elif j == 3:#PRICE sp = QDoubleSpinBox() sp.setDecimals(3) sp.setValue(float(lines[i][3])) sp.setMaximum(float(lines[i][3])) sp.setMinimum(float(lines[i][3])) self.table.setCellWidget(i,j, sp) header = self.table.horizontalHeader() header.setSectionResizeMode(0, QHeaderView.ResizeToContents) header.setSectionResizeMode(1, QHeaderView.ResizeToContents) header.setSectionResizeMode(2, QHeaderView.Stretch) self.table.setHorizontalHeaderLabels(['UNITATS', 'REF', 'PRODUCTE', 'PREU']) self.table.setVerticalHeaderLabels(llista) font = QFont() font.setFamily('Segoe UI Black') font.setPointSize(9) for i in range(4): self.table.horizontalHeaderItem(i).setFont(font) def search(self): control, num = self.validar_num_client() if control == True: os.chdir(carpeta_data) if os.path.exists('clients.db') and os.path.exists('preus.db'): dades = select_from_database_general('clients', 'data', num, 'num_client', 'num_client', 'ASC') if len(dades) == 0: QMessageBox.warning(self, 'Warning!', 'Client no registrat!', QMessageBox.Discard) return False, 0 else: self.show_table(num) self.nomcom.setText(dades[0][1]) self.nomfis.setText(dades[0][2]) self.direccio.setText(dades[0][3]) self.poblacio.setText(dades[0][4]) self.nif.setText(dades[0][5]) self.telf.setText(dades[0][6]) self.formapago.setText(dades[0][7]) return True, num else: QMessageBox.warning(self, 'Warning!', 'Encara no has registrat cap client no has registrat cap preu!') return False, 0 else: QMessageBox.warning(self, 'Warning!', 'Dades incorrectes!', QMessageBox.Discard) return False, 0 def validar_num_client(self): num_client = self.numclient.text() validar = re.match('^[0123456789]+$', num_client) if num_client == '': #Si esta buit bordes grocs self.numclient.setStyleSheet('border: 1px solid yellow;') return False, num_client elif not validar:#Si no es valid bordes vermells self.numclient.setStyleSheet('border: 1px solid red;') return False, num_client else: self.numclient.setStyleSheet('border: 1px solid green;') return True, num_client def validar_client(self): control, num = self.validar_num_client() if control == True: os.chdir(carpeta_data) if os.path.exists('clients.db'): dades = select_from_database_general('clients', 'data', num, 'num_client', 'num_client', 'ASC') if len(dades) == 0: QMessageBox.warning(self, 'Warning!', 'Client no registrat!', QMessageBox.Discard) return False, 0 else: return True, num else: QMessageBox.warning(self, 'Warning!', 'Encara no has registrat cap client!') return False, 0 else: QMessageBox.warning(self, 'Warning!', 'Dades incorrectes!', QMessageBox.Discard) return False, 0 def fer_factura(self): control, num_client = self.validar_client() if control == True: data = self.calendar.selectedDate().toString("dd/MM/yyyy") dia = str(data[0:2]).zfill(2) mes = str(data[3:5]).zfill(2) ano = str(data[6:]).zfill(4) nom_mes = mesos[int(data[3:5])-1] ref = [] prod = [] units = [] preu = [] base_imponible = [] create_database_ventes('ventes', str(ano)) create_database_ventes('facturacio_ref', str(ano)) lines = self.table.rowCount() for i in range(lines): current_units = self.table.cellWidget(i,0).value() current_ref = self.table.item(i,1).text() current_prod = self.table.item(i,2).text().strip('\n') if current_units != 0 : ref.append(current_ref) prod.append(current_prod) units.append(current_units) #Obtenir el preu a partir de la base de dades if os.path.exists('preus.db'): control_2 = True lines = select_from_database_preus('preus', num_client, current_ref) if len(lines) != 0: current_price = lines[0][3] preu.append(current_price) base = round(current_units*current_price, 2) base_imponible.append(base) #Guardar les ventes a la base de dades fill_database_ventes('ventes', str(ano), int(current_ref), int(data[3:5]), current_units, 3) fill_database_ventes('facturacio_ref', str(ano), int(current_ref), int(data[3:5]), base, 3) else : control = False ref_control = current_ref break else: control_2 = False if len(prod) == 0: QMessageBox.warning(self, 'Warning!', 'No has seleccionat cap producte!', QMessageBox.Discard) elif np.any(np.array(preu) == 0): QMessageBox.warning(self, 'Warning!', 'No has indicat el preu d\'algun dels productes seleccionats!', QMessageBox.Discard) elif control == False: QMessageBox.warning(self, 'Warning!', 'El preu per a la referència %s i pel número de client %s no està guardat a la base de dades' % (ref_control, num_client)) elif control_2 == False: QMessageBox.warning(self, 'Warning', 'No hi ha preus reistrats per cap client!') else: #Calcular import total suma = 0 for i in range(len(base_imponible)): suma = suma + base_imponible[i] suma = round(suma, 2) iva = round(0.21 * suma, 2) total = round(suma + iva, 2) #Fer factura i pujar al drive NOMCOM = self.nomcom.text() NOMFIS = self.nomfis.text() DIR = self.direccio.text() NIF = self.nif.text() POBLACIO = self.poblacio.text() TEL = self.telf.text() forma_pago = self.formapago.text() dim = len(prod) num_fact = assignar_numero_factura('numero_factura', ano) factura(carpeta_factures, 'FACTURA', num_client, NOMCOM, NOMFIS, DIR, NIF, TEL, POBLACIO, num_fact, data, forma_pago, dim, ref, prod, units, preu, base_imponible, suma, iva, total) #Plantilla de la factura per al client seleccionat os.chdir(carpeta_data) #Factura a la base de dades (nom comercial, B.I., I.V.A., total) create_database_factures('factures_emeses') fill_database_factures('factures_emeses', dia, mes, ano, str(num_fact).zfill(4), suma, 21, total) #Facturació per client base de dades create_database_ventes('facturacio_clients', ano) fill_database_ventes('facturacio_clients', ano, int(num_client), int(data[3:5]), suma, 4) #Facturació total base de dades create_database_ventes('facturacio_total', 'data') fill_database_ventes('facturacio_total', 'data', int(data[6:]), int(data[3:5]), suma, 3) upload_to_drive_factura(carpeta_factures, 'Factures', 'FACTURA', num_fact, data, NOMCOM, num_client, 'ventes.db', 'facturacio_clients.db', 'facturacio_total.db', 'factures_emeses.db') QMessageBox.information(self, 'Information', 'Factura realitzada correctament!') self.reinit_dialog() def reinit_dialog(self): self.numclient.setText('') self.nomcom.setText('') self.direccio.setText('') self.nomfis.setText('') self.poblacio.setText('') self.nif.setText('') self.telf.setText('') self.formapago.setText('') self.table.clearContents() self.numclient.setStyleSheet('') def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() else: event.ignore() class Substituir_factura(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('SubstituirFactura.ui', self) self.numclient.textChanged.connect(self.validar_num_client) self.numfact.textChanged.connect(self.validar_num_factura) self.seleccionar.clicked.connect(self.search) self.facturar.clicked.connect(self.fer_factura) def show_table(self, num_client): os.chdir(carpeta_data) if not os.path.exists('preus.db'): QMessageBox.warning(self, 'Warning!', 'No has registrat cap preu!') else: if self.comboBox.currentText() == 'Referència ascendent': lines = select_from_database_general('preus', 'data', str(num_client).zfill(4), 'num_client', 'ref', 'ASC') elif self.comboBox.currentText() == 'Referència descendent': lines = select_from_database_general('preus', 'data', str(num_client).zfill(4), 'num_client', 'ref', 'DESC') elif self.comboBox.currentText() == 'Alfabètic': lines = select_from_database_general('preus', 'data', str(num_client).zfill(4), 'num_client', 'prod', 'ASC') if len(lines) == 0: QMessageBox.warning(self, 'Warning!', 'Aquest client no té cap preu registrat!') else: self.table.setRowCount(len(lines)) self.table.setColumnCount(4) llista = [] for i in range(len(lines)): llista.append('') for j in range(4): if j == 0: #UNITS sp = QSpinBox() sp.setMaximum(9999) self.table.setCellWidget(i,j, sp) elif j == 1: self.table.setItem(i,j, QTableWidgetItem(lines[i][1])) #REF elif j == 2: self.table.setItem(i,j, QTableWidgetItem(lines[i][2])) elif j == 3:#PRICE sp = QDoubleSpinBox() sp.setDecimals(3) sp.setValue(float(lines[i][3])) sp.setMaximum(float(lines[i][3])) sp.setMinimum(float(lines[i][3])) self.table.setCellWidget(i,j, sp) header = self.table.horizontalHeader() header.setSectionResizeMode(0, QHeaderView.ResizeToContents) header.setSectionResizeMode(1, QHeaderView.ResizeToContents) header.setSectionResizeMode(2, QHeaderView.Stretch) self.table.setHorizontalHeaderLabels(['UNITATS', 'REF', 'PRODUCTE', 'PREU']) self.table.setVerticalHeaderLabels(llista) font = QFont() font.setFamily('Segoe UI Black') font.setPointSize(9) for i in range(4): self.table.horizontalHeaderItem(i).setFont(font) def search(self): control_client, num = self.validar_num_client() control_factura, num_fact = self.validar_num_factura() if control_client == True and control_factura == True: os.chdir(carpeta_data) if os.path.exists('clients.db') and os.path.exists('preus.db'): dades = select_from_database_general('clients', 'data', num, 'num_client', 'num_client', 'ASC') if len(dades) == 0: QMessageBox.warning(self, 'Warning!', 'Client no registrat!', QMessageBox.Discard) return False, 0 else: self.show_table(num) self.nomcom.setText(dades[0][1]) self.nomfis.setText(dades[0][2]) self.direccio.setText(dades[0][3]) self.poblacio.setText(dades[0][4]) self.nif.setText(dades[0][5]) self.telf.setText(dades[0][6]) self.formapago.setText(dades[0][7]) return True, num else: QMessageBox.warning(self, 'Warning!', 'Encara no has registrat cap client o no has registrat cap preu!') return False, 0 else: QMessageBox.warning(self, 'Warning!', 'Dades incorrectes!', QMessageBox.Discard) return False, 0 def validar_num_client(self): num_client = self.numclient.text() validar = re.match('^[0123456789]+$', num_client) if num_client == '': #Si esta buit bordes grocs self.numclient.setStyleSheet('border: 1px solid yellow;') return False, num_client elif not validar:#Si no es valid bordes vermells self.numclient.setStyleSheet('border: 1px solid red;') return False, num_client else: self.numclient.setStyleSheet('border: 1px solid green;') return True, num_client def validar_num_factura(self): os.chdir(carpeta_factures) num_fact = self.numfact.text() validar = re.match('^[0123456789]+$', num_fact) if num_fact == '': #Si esta buit bordes grocs self.numfact.setStyleSheet('border: 1px solid yellow;') return False, num_fact elif not validar:#Si no es valid bordes vermells self.numfact.setStyleSheet('border: 1px solid red;') return False, num_fact else: self.numfact.setStyleSheet('border: 1px solid green;') return True, num_fact def validar_client(self): control, num = self.validar_num_client() if control == True: os.chdir(carpeta_data) if os.path.exists('clients.db'): dades = select_from_database_general('clients', 'data', num, 'num_client', 'num_client', 'ASC') if len(dades) == 0: QMessageBox.warning(self, 'Warning!', 'Client no registrat!', QMessageBox.Discard) return False, 0 else: return True, num else: QMessageBox.warning(self, 'Warning!', 'Encara no has registrat cap client!') else: QMessageBox.warning(self, 'Warning!', 'Dades incorrectes!', QMessageBox.Discard) return False, 0 def fer_factura(self): control_client, num_client = self.validar_client() data = self.calendar.selectedDate().toString("dd/MM/yyyy") num_fact = self.numfact.text() dia = str(data[0:2]).zfill(2) mes_numero = str(data[3:5]).zfill(2) ano = str(data[6:]).zfill(4) name_fact = 'FACTURA_%s' % num_fact mes = mesos[int(data[3:5])-1] año = data[6:] if not os.path.exists(carpeta_factures + '\%s_%s' % (mes, año)): QMessageBox.warning(self, 'Warning!', 'No has realitzat cap factura pel mes i any de la data seleccionada!') else: os.chdir(carpeta_factures + '\%s_%s' % (mes, año)) list_files = os.listdir() control_factura = False for file in list_files: if file[0:12] == name_fact: control_factura = True os.remove(file) break else: control_factura = False if control_factura == False: QMessageBox.warning(self, 'Warning!', 'Aquest número de factura no existeix!') elif control_client == True: ref = [] prod = [] units = [] preu = [] base_imponible = [] lines = self.table.rowCount() for i in range(lines): current_units = self.table.cellWidget(i,0).value() current_ref = self.table.item(i,1).text() current_prod = self.table.item(i,2).text().strip('\n') if current_units != 0 : ref.append(current_ref) prod.append(current_prod) units.append(current_units) #Obtenir el preu a partir de la base de dades os.chdir(carpeta_data) lines = select_from_database_preus('preus', num_client, current_ref) if len(lines) != 0: current_price = lines[0][3] preu.append(current_price) base_imponible.append(round(current_units*current_price, 2)) control = True else : control = False ref_control = current_ref break if len(prod)== 0: QMessageBox.warning(self, 'Warning!', 'No has seleccionat cap producte!', QMessageBox.Discard) elif np.any(np.array(preu) == 0): QMessageBox.warning(self, 'Warning!', 'No has indicat el preu d\'algun dels productes seleccionats!', QMessageBox.Discard) elif control == False: QMessageBox.warning(self, 'Warning!', 'El preu per a la referència %s i pel número de client %s no està guardat a la base de dades' % (ref_control, num_client)) else: #Calcular import total suma = 0 for i in range(len(base_imponible)): suma = suma + base_imponible[i] suma = round(suma, 2) iva = round(0.21 * suma, 2) total = round(suma + iva, 2) #Fer factura i pujar al drive NOMCOM = self.nomcom.text() NOMFIS = self.nomfis.text() DIR = self.direccio.text() NIF = self.nif.text() POBLACIO = self.poblacio.text() TEL = self.telf.text() forma_pago = self.formapago.text() dim = len(prod) factura(carpeta_factures, 'FACTURA', num_client, NOMCOM, NOMFIS, DIR, NIF, TEL, POBLACIO, num_fact, data, forma_pago, dim, ref, prod, units, preu, base_imponible, suma, iva, total) #Plantilla de la factura per al client seleccionat #Factura a la base de dades (nom comercial, B.I., I.V.A., total) os.chdir(carpeta_data) create_database_factures('factures_emeses') delete_from_database('factures_emeses', 'nom', str(num_fact).zfill(4)) fill_database_factures('factures_emeses', dia, mes_numero, ano, str(num_fact).zfill(4), suma, 21, total) upload_to_drive_factura(carpeta_factures, 'Factures', 'FACTURA', num_fact, data, NOMCOM, num_client, 'ventes.db', 'facturacio_clients.db', 'facturacio_total.db', 'factures_emeses.db') QMessageBox.information(self, 'Information', 'Factura modificada correctament!') self.reinit_dialog() def reinit_dialog(self): self.numclient.setText('') self.nomcom.setText('') self.direccio.setText('') self.nomfis.setText('') self.poblacio.setText('') self.nif.setText('') self.telf.setText('') self.formapago.setText('') self.numfact.setText('') self.table.clearContents() self.numclient.setStyleSheet('') self.numfact.setStyleSheet('') def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() else: event.ignore() class Abonos(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('Abono.ui', self) self.setWindowTitle('Abonos') self.facturar.setText('Realitzar abono') self.numclient.textChanged.connect(self.validar_num_client) self.seleccionar.clicked.connect(self.search) self.facturar.clicked.connect(self.fer_factura) def show_table(self, num_client): os.chdir(carpeta_data) if not os.path.exists('preus.db'): QMessageBox.warning(self, 'Warning!', 'No has registrat cap preu!') else: if self.comboBox.currentText() == 'Referència ascendent': lines = select_from_database_general('preus', 'data', str(num_client).zfill(4), 'num_client', 'ref', 'ASC') elif self.comboBox.currentText() == 'Referència descendent': lines = select_from_database_general('preus', 'data', str(num_client).zfill(4), 'num_client', 'ref', 'DESC') elif self.comboBox.currentText() == 'Alfabètic': lines = select_from_database_general('preus', 'data', str(num_client).zfill(4), 'num_client', 'prod', 'ASC') if len(lines) == 0: QMessageBox.warning(self, 'Warning!', 'Aquest client no té cap preu registrat!') else: self.table.setRowCount(len(lines)) self.table.setColumnCount(4) llista = [] for i in range(len(lines)): llista.append('') for j in range(4): if j == 0: #UNITS sp = QSpinBox() sp.setMaximum(9999) self.table.setCellWidget(i,j, sp) elif j == 1: self.table.setItem(i,j, QTableWidgetItem(lines[i][1])) #REF elif j == 2: self.table.setItem(i,j, QTableWidgetItem(lines[i][2])) elif j == 3:#PRICE sp = QDoubleSpinBox() sp.setDecimals(3) sp.setValue(float(lines[i][3])) sp.setMaximum(float(lines[i][3])) sp.setMinimum(float(lines[i][3])) self.table.setCellWidget(i,j, sp) header = self.table.horizontalHeader() header.setSectionResizeMode(0, QHeaderView.ResizeToContents) header.setSectionResizeMode(1, QHeaderView.ResizeToContents) header.setSectionResizeMode(2, QHeaderView.Stretch) self.table.setHorizontalHeaderLabels(['UNITATS', 'REF', 'PRODUCTE', 'PREU']) self.table.setVerticalHeaderLabels(llista) font = QFont() font.setFamily('Segoe UI Black') font.setPointSize(9) for i in range(4): self.table.horizontalHeaderItem(i).setFont(font) def search(self): control, num = self.validar_num_client() if control == True: os.chdir(carpeta_data) if os.path.exists('clients.db') and os.path.exists('preus.db'): dades = select_from_database_general('clients', 'data', num, 'num_client', 'num_client', 'ASC') if len(dades) == 0: QMessageBox.warning(self, 'Warning!', 'Client no registrat!', QMessageBox.Discard) return False, 0 else: self.show_table(num) self.nomcom.setText(dades[0][1]) self.nomfis.setText(dades[0][2]) self.direccio.setText(dades[0][3]) self.poblacio.setText(dades[0][4]) self.nif.setText(dades[0][5]) self.telf.setText(dades[0][6]) self.formapago.setText(dades[0][7]) return True, num else: QMessageBox.warning(self, 'Warning!', 'Encara no has registrat cap client o encara no has registrat cap preu!') return False, 0 else: QMessageBox.warning(self, 'Warning!', 'Dades incorrectes!', QMessageBox.Discard) return False, 0 def validar_num_client(self): num_client = self.numclient.text() validar = re.match('^[0123456789]+$', num_client) if num_client == '': #Si esta buit bordes grocs self.numclient.setStyleSheet('border: 1px solid yellow;') return False, num_client elif not validar:#Si no es valid bordes vermells self.numclient.setStyleSheet('border: 1px solid red;') return False, num_client else: self.numclient.setStyleSheet('border: 1px solid green;') return True, num_client def validar_client(self): control, num = self.validar_num_client() if control == True: os.chdir(carpeta_data) if os.path.exists('clients.db'): dades = select_from_database_general('clients', 'data', num, 'num_client', 'num_client', 'ASC') if len(dades) == 0: QMessageBox.warning(self, 'Warning!', 'Client no registrat!', QMessageBox.Discard) return False, 0 else: return True, num else: QMessageBox.warning(self, 'Warning!', 'Encara no has registrat cap client!') return False, 0 else: QMessageBox.warning(self, 'Warning!', 'Dades incorrectes!', QMessageBox.Discard) return False, 0 def fer_factura(self): control, num_client = self.validar_client() if control == True: data = self.calendar.selectedDate().toString("dd/MM/yyyy") dia = str(data[0:2]).zfill(2) mes = str(data[3:5]).zfill(2) ano = str(data[6:]).zfill(4) nom_mes = mesos[int(data[3:5])-1] ref = [] prod = [] units = [] preu = [] base_imponible = [] create_database_ventes('ventes', data[6:]) create_database_ventes('facturacio_ref', data[6:]) lines = self.table.rowCount() for i in range(lines): current_units = self.table.cellWidget(i,0).value() current_ref = self.table.item(i,1).text() current_prod = self.table.item(i,2).text().strip('\n') if current_units != 0 : ref.append(current_ref) prod.append(current_prod) units.append(current_units) #Obtenir el preu a partir de la base de dades lines = select_from_database_preus('preus', num_client, current_ref) if len(lines) != 0: current_price = -lines[0][3] preu.append(current_price) base = round(current_units*current_price, 2) base_imponible.append(base) #Guardar les ventes a la base de dades fill_database_ventes('ventes', data[6:], int(current_ref), int(data[3:5]), -current_units, 3) fill_database_ventes('facturacio_ref', str(ano), int(current_ref), int(data[3:5]), base, 3) else : control = False ref_control = current_ref break if len(prod) == 0: QMessageBox.warning(self, 'Warning!', 'No has seleccionat cap producte!', QMessageBox.Discard) elif np.any(np.array(preu) == 0): QMessageBox.warning(self, 'Warning!', 'No has indicat el preu d\'algun dels productes seleccionats!', QMessageBox.Discard) elif control == False: QMessageBox.warning(self, 'Warning!', 'El preu per a la referència %s i pel número de client %s no està guardat a la base de dades' % (ref_control, num_client)) elif not os.path.exists('factures_emeses.db'): QMessageBox.warning(self, 'Warning!', 'No pots fer un abono si no has realitzat encara cap factura!') else: #Calcular import total suma = 0 for i in range(len(base_imponible)): suma = suma + base_imponible[i] suma = round(suma, 2) iva = round(0.21 * suma, 2) total = round(suma + iva, 2) #Fer factura i pujar al drive NOMCOM = self.nomcom.text() NOMFIS = self.nomfis.text() DIR = self.direccio.text() NIF = self.nif.text() POBLACIO = self.poblacio.text() TEL = self.telf.text() forma_pago = self.formapago.text() dim = len(prod) num_fact = assignar_numero_factura('numero_abono', ano) if self.tipo_abono.currentText() == 'Factura': tipo = 'ABONO_FACTURA' os.chdir(carpeta_data) #SUMA JA ÉS NEATIVA!!! #Factura a la base de dades (nom comercial, B.I., I.V.A., total) delete_database_factures('factures_emeses', dia, mes, ano, -suma, 21, -total) #Facturació per client base de dades fill_database_ventes('facturacio_clients', data[6:], int(num_client), int(data[3:5]), suma, 4) #Facturació total base de dades fill_database_ventes('facturacio_total', 'data', int(data[6:]), int(data[3:5]), suma, 3) else: tipo = 'ABONO_ALBARAN' fill_database_general('CompanyName', 'albaranes(num_client, num_albaran, data, base_imp, iva, total)', '(?,?,?,?,?,?)', [str(num_client).zfill(4), str(num_fact).zfill(4), '%s-%s-%s' % (ano, mes, dia), suma, iva, total]) factura(carpeta_abonos, tipo, num_client, NOMCOM, NOMFIS, DIR, NIF, TEL, POBLACIO, num_fact, data, forma_pago, dim, ref, prod, units, preu, base_imponible, suma, iva, total) #Plantilla de la factura per al client seleccionat upload_to_drive_factura(carpeta_abonos, 'Abonos', tipo, num_fact, data, NOMCOM, num_client, 'ventes.db', 'facturacio_clients.db', 'facturacio_total.db', 'factures_emeses.db') QMessageBox.information(self, 'Information', 'Abono realitzat correctament!') self.reinit_dialog() def reinit_dialog(self): self.numclient.setText('') self.nomcom.setText('') self.direccio.setText('') self.nomfis.setText('') self.poblacio.setText('') self.nif.setText('') self.telf.setText('') self.formapago.setText('') self.table.clearContents() self.numclient.setStyleSheet('') self.nomcom.setStyleSheet('') self.direccio.setStyleSheet('') self.nomfis.setStyleSheet('') self.poblacio.setStyleSheet('') self.nif.setStyleSheet('') self.telf.setStyleSheet('') self.formapago.setStyleSheet('') def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() else: event.ignore() class Albaran(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('Factura.ui', self) self.setWindowTitle('Albaran') self.facturar.setText('Realitzar albaran') self.numclient.textChanged.connect(self.validar_num_client) self.seleccionar.clicked.connect(self.search) self.facturar.clicked.connect(self.fer_factura) def show_table(self, num_client): os.chdir(carpeta_data) if not os.path.exists('preus.db'): QMessageBox.warning(self, 'Warning!', 'No has registrat cap preu!') else: if self.comboBox.currentText() == 'Referència ascendent': lines = select_from_database_general('preus', 'data', str(num_client).zfill(4), 'num_client', 'ref', 'ASC') elif self.comboBox.currentText() == 'Referència descendent': lines = select_from_database_general('preus', 'data', str(num_client).zfill(4), 'num_client', 'ref', 'DESC') elif self.comboBox.currentText() == 'Alfabètic': lines = select_from_database_general('preus', 'data', str(num_client).zfill(4), 'num_client', 'prod', 'ASC') if len(lines) == 0: QMessageBox.warning(self, 'Warning!', 'Aquest client no té cap preu registrat!') else: self.table.setRowCount(len(lines)) self.table.setColumnCount(4) llista = [] for i in range(len(lines)): llista.append('') for j in range(4): if j == 0: #UNITS sp = QSpinBox() sp.setMaximum(9999) self.table.setCellWidget(i,j, sp) elif j == 1: self.table.setItem(i,j, QTableWidgetItem(lines[i][1])) #REF elif j == 2: self.table.setItem(i,j, QTableWidgetItem(lines[i][2])) elif j == 3:#PRICE sp = QDoubleSpinBox() sp.setDecimals(3) sp.setValue(float(lines[i][3])) sp.setMaximum(float(lines[i][3])) sp.setMinimum(float(lines[i][3])) self.table.setCellWidget(i,j, sp) header = self.table.horizontalHeader() header.setSectionResizeMode(0, QHeaderView.ResizeToContents) header.setSectionResizeMode(1, QHeaderView.ResizeToContents) header.setSectionResizeMode(2, QHeaderView.Stretch) self.table.setHorizontalHeaderLabels(['UNITATS', 'REF', 'PRODUCTE', 'PREU']) self.table.setVerticalHeaderLabels(llista) font = QFont() font.setFamily('Segoe UI Black') font.setPointSize(9) for i in range(4): self.table.horizontalHeaderItem(i).setFont(font) def search(self): control, num = self.validar_num_client() if control == True: os.chdir(carpeta_data) if os.path.exists('clients.db') and os.path.exists('preus.db'): dades = select_from_database_general('clients', 'data', num, 'num_client', 'num_client', 'ASC') if len(dades) == 0: QMessageBox.warning(self, 'Warning!', 'Client no registrat!', QMessageBox.Discard) return False, 0 else: self.show_table(num) self.nomcom.setText(dades[0][1]) self.nomfis.setText(dades[0][2]) self.direccio.setText(dades[0][3]) self.poblacio.setText(dades[0][4]) self.nif.setText(dades[0][5]) self.telf.setText(dades[0][6]) self.formapago.setText(dades[0][7]) return True, num else: QMessageBox.warning(self, 'Warning!', 'Encara no has registrat cap client no has registrat cap preu!') return False, 0 else: QMessageBox.warning(self, 'Warning!', 'Dades incorrectes!', QMessageBox.Discard) return False, 0 def validar_num_client(self): num_client = self.numclient.text() validar = re.match('^[0123456789]+$', num_client) if num_client == '': #Si esta buit bordes grocs self.numclient.setStyleSheet('border: 1px solid yellow;') return False, num_client elif not validar:#Si no es valid bordes vermells self.numclient.setStyleSheet('border: 1px solid red;') return False, num_client else: self.numclient.setStyleSheet('border: 1px solid green;') return True, num_client def validar_client(self): control, num = self.validar_num_client() if control == True: os.chdir(carpeta_data) if os.path.exists('clients.db'): dades = select_from_database_general('clients', 'data', num, 'num_client', 'num_client', 'ASC') if len(dades) == 0: QMessageBox.warning(self, 'Warning!', 'Client no registrat!', QMessageBox.Discard) return False, 0 else: return True, num else: QMessageBox.warning(self, 'Warning!', 'Encara no has registrat cap client!') return False, 0 else: QMessageBox.warning(self, 'Warning!', 'Dades incorrectes!', QMessageBox.Discard) return False, 0 def fer_factura(self): control, num_client = self.validar_client() if control == True: data = self.calendar.selectedDate().toString("dd/MM/yyyy") dia = str(data[0:2]).zfill(2) mes = str(data[3:5]).zfill(2) ano = str(data[6:]).zfill(4) nom_mes = mesos[int(data[3:5])-1] ref = [] prod = [] units = [] preu = [] base_imponible = [] create_database_ventes('ventes', str(ano)) create_database_ventes('facturacio_ref', str(ano)) lines = self.table.rowCount() for i in range(lines): current_units = self.table.cellWidget(i,0).value() current_ref = self.table.item(i,1).text() current_prod = self.table.item(i,2).text().strip('\n') if current_units != 0 : ref.append(current_ref) prod.append(current_prod) units.append(current_units) #Obtenir el preu a partir de la base de dades if os.path.exists('preus.db'): control_2 = True lines = select_from_database_preus('preus', num_client, current_ref) if len(lines) != 0: current_price = lines[0][3] preu.append(current_price) base = round(current_units*current_price, 2) base_imponible.append(base) #Guardar les ventes a la base de dades fill_database_ventes('ventes', str(ano), int(current_ref), int(data[3:5]), current_units, 3) fill_database_ventes('facturacio_ref', str(ano), int(current_ref), int(data[3:5]), base, 3) else : control = False ref_control = current_ref break else: control_2 = False if len(prod) == 0: QMessageBox.warning(self, 'Warning!', 'No has seleccionat cap producte!', QMessageBox.Discard) elif np.any(np.array(preu) == 0): QMessageBox.warning(self, 'Warning!', 'No has indicat el preu d\'algun dels productes seleccionats!', QMessageBox.Discard) elif control == False: QMessageBox.warning(self, 'Warning!', 'El preu per a la referència %s i pel número de client %s no està guardat a la base de dades' % (ref_control, num_client)) elif control_2 == False: QMessageBox.warning(self, 'Warning', 'No hi ha preus reistrats per cap client!') else: #Calcular import total suma = 0 for i in range(len(base_imponible)): suma = suma + base_imponible[i] suma = round(suma, 2) iva = round(0.21 * suma, 2) total = round(suma + iva, 2) #Fer factura i pujar al drive NOMCOM = self.nomcom.text() NOMFIS = self.nomfis.text() DIR = self.direccio.text() NIF = self.nif.text() POBLACIO = self.poblacio.text() TEL = self.telf.text() forma_pago = self.formapago.text() dim = len(prod) num_fact = assignar_numero_factura('numero_albaran', ano) factura(carpeta_albaranes, 'ALBARAN', num_client, NOMCOM, NOMFIS, DIR, NIF, TEL, POBLACIO, num_fact, data, forma_pago, dim, ref, prod, units, preu, base_imponible, suma, iva, total) #Plantilla de la factura per al client seleccionat #Albaran a la base de dades (nom comercial, B.I., I.V.A., total) os.chdir(carpeta_data) tablas = [ """ CREATE TABLE IF NOT EXISTS albaranes( num_client TEXT NOT NULL, num_albaran TEXT NOT NULL, data TEXT NOT NULL, base_imp REAL NOT NULL, iva REAL NOT NULL, total REAL NOT NULL ); """ ] #Albaran a la base de dades create_database('CompanyName', tablas) fill_database_general('CompanyName', 'albaranes(num_client, num_albaran, data, base_imp, iva, total)', '(?,?,?,?,?,?)', [str(num_client).zfill(4), str(num_fact).zfill(4), '%s-%s-%s' % (ano, mes, dia), suma, iva, total]) upload_to_drive_factura(carpeta_albaranes, 'Albaranes', 'ALBARAN', num_fact, data, NOMCOM, num_client, 'ventes.db', 'facturacio_clients.db', 'facturacio_total.db', 'factures_emeses.db') QMessageBox.information(self, 'Information', 'Albaran realitzat correctament!') self.reinit_dialog() def reinit_dialog(self): self.numclient.setText('') self.nomcom.setText('') self.direccio.setText('') self.nomfis.setText('') self.poblacio.setText('') self.nif.setText('') self.telf.setText('') self.formapago.setText('') self.table.clearContents() self.numclient.setStyleSheet('') def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() else: event.ignore() class Substituir_albaran(QDialog): #Creating! def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('SubstituirFactura.ui', self) self.setWindowTitle('Substituir albaran') self.facturar.setText('Substituir') self.label_num_fact.setText('Número albaran') self.numclient.textChanged.connect(self.validar_num_client) self.numfact.textChanged.connect(self.validar_num_factura) self.seleccionar.clicked.connect(self.search) self.facturar.clicked.connect(self.fer_factura) def show_table(self, num_client): os.chdir(carpeta_data) if not os.path.exists('preus.db'): QMessageBox.warning(self, 'Warning!', 'No has registrat cap preu!') else: if self.comboBox.currentText() == 'Referència ascendent': lines = select_from_database_general('preus', 'data', str(num_client).zfill(4), 'num_client', 'ref', 'ASC') elif self.comboBox.currentText() == 'Referència descendent': lines = select_from_database_general('preus', 'data', str(num_client).zfill(4), 'num_client', 'ref', 'DESC') elif self.comboBox.currentText() == 'Alfabètic': lines = select_from_database_general('preus', 'data', str(num_client).zfill(4), 'num_client', 'prod', 'ASC') if len(lines) == 0: QMessageBox.warning(self, 'Warning!', 'Aquest client no té cap preu registrat!') else: self.table.setRowCount(len(lines)) self.table.setColumnCount(4) llista = [] for i in range(len(lines)): llista.append('') for j in range(4): if j == 0: #UNITS sp = QSpinBox() sp.setMaximum(9999) self.table.setCellWidget(i,j, sp) elif j == 1: self.table.setItem(i,j, QTableWidgetItem(lines[i][1])) #REF elif j == 2: self.table.setItem(i,j, QTableWidgetItem(lines[i][2])) elif j == 3:#PRICE sp = QDoubleSpinBox() sp.setDecimals(3) sp.setValue(float(lines[i][3])) sp.setMaximum(float(lines[i][3])) sp.setMinimum(float(lines[i][3])) self.table.setCellWidget(i,j, sp) header = self.table.horizontalHeader() header.setSectionResizeMode(0, QHeaderView.ResizeToContents) header.setSectionResizeMode(1, QHeaderView.ResizeToContents) header.setSectionResizeMode(2, QHeaderView.Stretch) self.table.setHorizontalHeaderLabels(['UNITATS', 'REF', 'PRODUCTE', 'PREU']) self.table.setVerticalHeaderLabels(llista) font = QFont() font.setFamily('Segoe UI Black') font.setPointSize(9) for i in range(4): self.table.horizontalHeaderItem(i).setFont(font) def search(self): control_client, num = self.validar_num_client() control_factura, num_fact = self.validar_num_factura() if control_client == True and control_factura == True: os.chdir(carpeta_data) if os.path.exists('clients.db') and os.path.exists('preus.db'): dades = select_from_database_general('clients', 'data', num, 'num_client', 'num_client', 'ASC') if len(dades) == 0: QMessageBox.warning(self, 'Warning!', 'Client no registrat!', QMessageBox.Discard) return False, 0 else: self.show_table(num) self.nomcom.setText(dades[0][1]) self.nomfis.setText(dades[0][2]) self.direccio.setText(dades[0][3]) self.poblacio.setText(dades[0][4]) self.nif.setText(dades[0][5]) self.telf.setText(dades[0][6]) self.formapago.setText(dades[0][7]) return True, num else: QMessageBox.warning(self, 'Warning!', 'Encara no has registrat cap client o no has registrat cap preu!') return False, 0 else: QMessageBox.warning(self, 'Warning!', 'Dades incorrectes!', QMessageBox.Discard) return False, 0 def validar_num_client(self): num_client = self.numclient.text() validar = re.match('^[0123456789]+$', num_client) if num_client == '': #Si esta buit bordes grocs self.numclient.setStyleSheet('border: 1px solid yellow;') return False, num_client elif not validar:#Si no es valid bordes vermells self.numclient.setStyleSheet('border: 1px solid red;') return False, num_client else: self.numclient.setStyleSheet('border: 1px solid green;') return True, num_client def validar_num_factura(self): os.chdir(carpeta_albaranes) num_fact = self.numfact.text() validar = re.match('^[0123456789]+$', num_fact) if num_fact == '': #Si esta buit bordes grocs self.numfact.setStyleSheet('border: 1px solid yellow;') return False, num_fact elif not validar:#Si no es valid bordes vermells self.numfact.setStyleSheet('border: 1px solid red;') return False, num_fact else: self.numfact.setStyleSheet('border: 1px solid green;') return True, num_fact def validar_client(self): control, num = self.validar_num_client() if control == True: os.chdir(carpeta_data) if os.path.exists('clients.db'): dades = select_from_database_general('clients', 'data', num, 'num_client', 'num_client', 'ASC') if len(dades) == 0: QMessageBox.warning(self, 'Warning!', 'Client no registrat!', QMessageBox.Discard) return False, 0 else: return True, num else: QMessageBox.warning(self, 'Warning!', 'Encara no has registrat cap client!') else: QMessageBox.warning(self, 'Warning!', 'Dades incorrectes!', QMessageBox.Discard) return False, 0 def fer_factura(self): control_client, num_client = self.validar_client() data = self.calendar.selectedDate().toString("dd/MM/yyyy") num_fact = self.numfact.text() dia = str(data[0:2]).zfill(2) mes_numero = str(data[3:5]).zfill(2) ano = str(data[6:]).zfill(4) name_fact = 'ALBARAN_%s' % num_fact mes = mesos[int(data[3:5])-1] año = data[6:] if not os.path.exists(carpeta_albaranes + '\%s_%s' % (mes, año)): QMessageBox.warning(self, 'Warning!', 'No has realitzat cap albaran pel mes i any de la data seleccionada!') else: os.chdir(carpeta_albaranes + '\%s_%s' % (mes, año)) list_files = os.listdir() control_factura = False for file in list_files: if file[0:12] == name_fact: control_factura = True os.remove(file) break else: control_factura = False if control_factura == False: QMessageBox.warning(self, 'Warning!', 'Aquest número d\'albaran no existeix!') elif control_client == True: ref = [] prod = [] units = [] preu = [] base_imponible = [] lines = self.table.rowCount() for i in range(lines): current_units = self.table.cellWidget(i,0).value() current_ref = self.table.item(i,1).text() current_prod = self.table.item(i,2).text().strip('\n') if current_units != 0 : ref.append(current_ref) prod.append(current_prod) units.append(current_units) #Obtenir el preu a partir de la base de dades os.chdir(carpeta_data) lines = select_from_database_preus('preus', num_client, current_ref) if len(lines) != 0: current_price = lines[0][3] preu.append(current_price) base_imponible.append(round(current_units*current_price, 2)) control = True else : control = False ref_control = current_ref break if len(prod)== 0: QMessageBox.warning(self, 'Warning!', 'No has seleccionat cap producte!', QMessageBox.Discard) elif np.any(np.array(preu) == 0): QMessageBox.warning(self, 'Warning!', 'No has indicat el preu d\'algun dels productes seleccionats!', QMessageBox.Discard) elif control == False: QMessageBox.warning(self, 'Warning!', 'El preu per a la referència %s i pel número de client %s no està guardat a la base de dades' % (ref_control, num_client)) else: #Calcular import total suma = 0 for i in range(len(base_imponible)): suma = suma + base_imponible[i] suma = round(suma, 2) iva = round(0.21 * suma, 2) total = round(suma + iva, 2) #Fer factura i pujar al drive NOMCOM = self.nomcom.text() NOMFIS = self.nomfis.text() DIR = self.direccio.text() NIF = self.nif.text() POBLACIO = self.poblacio.text() TEL = self.telf.text() forma_pago = self.formapago.text() dim = len(prod) factura(carpeta_albaranes, 'ALBARAN', num_client, NOMCOM, NOMFIS, DIR, NIF, TEL, POBLACIO, num_fact, data, forma_pago, dim, ref, prod, units, preu, base_imponible, suma, iva, total) #Plantilla de la factura per al client seleccionat #Factura a la base de dades (nom comercial, B.I., I.V.A., total) os.chdir(carpeta_data) #Factures emeses create_database_factures('factures_emeses') delete_from_database('factures_emeses', 'nom', str(num_fact).zfill(4)) fill_database_factures('factures_emeses', dia, mes_numero, ano, str(num_fact).zfill(4), suma, 21, total) #Albaranes delete_from_database_general('CompanyName', 'albaranes', 'num_albaran', str(num_fact).zfill(4)) fill_database_general('CompanyName', 'albaranes(num_client, num_albaran, data, base_imp, iva, total)', '(?,?,?,?,?,?)', [str(num_client).zfill(4), num_fact, '%s-%s-%s' % (ano, mes_numero, dia), suma, iva, total]) upload_to_drive_factura(carpeta_albaranes, 'Albaranes', 'ALBARAN', num_fact, data, NOMCOM, num_client, 'ventes.db', 'facturacio_clients.db', 'facturacio_total.db', 'factures_emeses.db') QMessageBox.information(self, 'Information', 'Albaran modificat correctament!') self.reinit_dialog() def reinit_dialog(self): self.numclient.setText('') self.nomcom.setText('') self.direccio.setText('') self.nomfis.setText('') self.poblacio.setText('') self.nif.setText('') self.telf.setText('') self.formapago.setText('') self.numfact.setText('') self.table.clearContents() self.numclient.setStyleSheet('') self.numfact.setStyleSheet('') def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() else: event.ignore() class Factura_albaranes(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('FacturaAlbaran.ui', self) current_date = QDate.currentDate() self.data_factura.setDate(current_date) self.numclient.textChanged.connect(self.validar_num_client) self.seleccionar.clicked.connect(self.search) self.facturar.clicked.connect(self.fer_factura) def search(self): control, num = self.validar_num_client() if control == True: os.chdir(carpeta_data) if os.path.exists('clients.db') and os.path.exists('preus.db'): dades = select_from_database_general('clients', 'data', num, 'num_client', 'num_client', 'ASC') if len(dades) == 0: QMessageBox.warning(self, 'Warning!', 'Client no registrat!', QMessageBox.Discard) return False, 0 else: self.nomcom.setText(dades[0][1]) self.nomfis.setText(dades[0][2]) self.direccio.setText(dades[0][3]) self.poblacio.setText(dades[0][4]) self.nif.setText(dades[0][5]) self.telf.setText(dades[0][6]) self.formapago.setText(dades[0][7]) return True, num else: QMessageBox.warning(self, 'Warning!', 'Encara no has registrat cap client no has registrat cap preu!') return False, 0 else: QMessageBox.warning(self, 'Warning!', 'Dades incorrectes!', QMessageBox.Discard) return False, 0 def validar_num_client(self): num_client = self.numclient.text() validar = re.match('^[0123456789]+$', num_client) if num_client == '': #Si esta buit bordes grocs self.numclient.setStyleSheet('border: 1px solid yellow;') return False, num_client elif not validar:#Si no es valid bordes vermells self.numclient.setStyleSheet('border: 1px solid red;') return False, num_client else: self.numclient.setStyleSheet('border: 1px solid green;') return True, num_client def fer_factura(self): control, num_client = self.search() if control == True: data_inicial = self.calendar.selectedDate().toString("yyyy-MM-dd") data_final = self.calendar_2.selectedDate().toString("yyyy-MM-dd") #eConnect to the database database = sqlite3.connect('CompanyName.db') cursor = database.cursor() #Get the albaranes done between the delected dates sentencia = "SELECT * FROM albaranes WHERE num_client LIKE '%s' AND data BETWEEN '%s' AND '%s' ORDER BY data ASC" % (num_client, data_inicial, data_final) cursor.execute(sentencia) lines = cursor.fetchall() if len(lines) == 0: QMessageBox.warning(self, 'Warning!', 'Cap albaran realitzat per aquest client entre aquestes dates!') else: array_num_albaran = [] array_data = [] array_bi = [] array_iva = [] array_total = [] for i in range(len(lines)): array_num_albaran.append(lines[i][1]) #Numero d'albaran array_data.append(change_date_format(lines[i][2])) #Data array_bi.append(lines[i][3]) #Base imponible array_iva.append(lines[i][4]) #IVA array_total.append(lines[i][5]) #Total suma_bi = np.sum(array_bi) suma_iva = np.sum(array_iva) suma_total = np.sum(array_total) self.data_inicial.setDate(self.calendar.selectedDate()) self.data_final.setDate(self.calendar_2.selectedDate()) self.num_albaranes.setValue(len(lines)) self.bi.setText(str(suma_bi)) self.iva.setText(str(suma_iva)) self.total.setText(str(suma_total)) NOMCOM = self.nomcom.text() NOMFIS = self.nomfis.text() DIR = self.direccio.text() NIF = self.nif.text() POBLACIO = self.poblacio.text() TEL = self.telf.text() forma_pago = self.formapago.text() data = self.data_factura.date().toString("dd/MM/yyyy") dia = str(data[0:2]).zfill(2) mes = str(data[3:5]).zfill(2) ano = str(data[6:]).zfill(4) num_fact = assignar_numero_factura('numero_factura', ano) factura_de_albaranes(carpeta_factures, 'FACTURA', num_client, NOMCOM, NOMFIS, DIR, NIF, TEL, POBLACIO, num_fact, data, forma_pago, array_num_albaran, array_data, array_bi, array_iva, array_total, suma_bi, suma_iva, suma_total) os.chdir(carpeta_data) #Factura a la base de dades (nom comercial, B.I., I.V.A., total) create_database_factures('factures_emeses') fill_database_factures('factures_emeses', dia, mes, ano, str(num_fact).zfill(4), suma_bi, 21, suma_total) #Facturació per client base de dades create_database_ventes('facturacio_clients', ano) fill_database_ventes('facturacio_clients', ano, int(num_client), int(data[3:5]), suma_bi, 4) #Facturació total base de dades create_database_ventes('facturacio_total', 'data') fill_database_ventes('facturacio_total', 'data', int(data[6:]), int(data[3:5]), suma_bi, 3) upload_to_drive_factura(carpeta_factures, 'Factures', 'FACTURA', num_fact, data, NOMCOM, num_client, 'ventes.db', 'facturacio_clients.db', 'facturacio_total.db', 'factures_emeses.db') QMessageBox.information(self, 'Information', 'Factura realitzada correctament!') def reinit_dialog(self): self.numclient.setText('') self.nomcom.setText('') self.direccio.setText('') self.nomfis.setText('') self.poblacio.setText('') self.nif.setText('') self.telf.setText('') self.formapago.setText('') self.numclient.setStyleSheet('') def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() else: event.ignore() class Introduir_factures_rebudes(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('IntroduirFacturesRebudes.ui', self) self.data.setDate(QDate.currentDate()) self.nom.textChanged.connect(self.validar_nom) self.introduir.clicked.connect(self.guardar) self.eliminar.clicked.connect(self.delete) self.pujar_drive.clicked.connect(self.upload_database) def validar_nom(self): nom = self.nom.text() validar = re.match('^[a-z\sáéíóúàèìòùäëïöüñç0123456789.-]+$', nom, re.I) #Permetre lletres a-z, espais, accents, numeros if nom == '': #Si esta buit bordes grocs #re.I ignora majuscules i minuscules self.nom.setStyleSheet('border: 1px solid yellow;') return False, nom elif not validar:#Si no es valid bordes vermells self.nom.setStyleSheet('border: 1px solid red;') return False, nom else: self.nom.setStyleSheet('border: 1px solid green;') return True, nom def guardar(self): control, nom = self.validar_nom() if control == True: dia = str(self.data.date().day()) mes = str(self.data.date().month()) ano = str(self.data.date().year()) total = self.importe.value() IVA = self.iva.value() if total != 0 and IVA != 0: base_imponible = round(total/(100+IVA) * 100, 2) os.chdir(carpeta_data) create_database_factures('factures_rebudes') fill_database_factures('factures_rebudes', dia, mes, ano, nom, base_imponible, IVA, total) if self.pujar_drive_check.isChecked(): upload_to_drive_database('factures_rebudes') QMessageBox.information(self, 'Information', 'Dades enregistrades correctament') self.reinit_dialog() else: QMessageBox.warning(self, 'Warning!', 'L\' import i l\'I.V.A. no poden ser 0') else: QMessageBox.warning(self, 'Warning!', 'Dades incorrectes') def delete(self): control, nom = self.validar_nom() if control == True: dia = str(self.data.date().day()) mes = str(self.data.date().month()) ano = str(self.data.date().year()) total = self.importe.value() IVA = self.iva.value() if total != 0 and IVA != 0: base_imponible = round(total/(100+IVA) * 100, 2) os.chdir(carpeta_data) create_database_factures('factures_rebudes') delete_database_factures('factures_rebudes', dia, mes, ano, base_imponible, IVA, total) if self.pujar_drive_check.isChecked(): upload_to_drive_database('factures_rebudes') QMessageBox.information(self, 'Information', 'Dades enregistrades correctament') self.reinit_dialog() else: QMessageBox.warning(self, 'Warning!', 'L\' import i l\'I.V.A. no poden ser 0') else: QMessageBox.warning(self, 'Warning!', 'Dades incorrectes') def upload_database(self): upload_to_drive_database('factures_rebudes') QMessageBox.information(self, 'Information', 'Dades pujades correctament') def reinit_dialog(self): self.data.setDate(QDate.currentDate()) self.nom.setText('') self.iva.setValue(0.) self.importe.setValue(0.) self.nom.setStyleSheet('') def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() self.pujar_drive_check.setChecked(True) else: event.ignore() class Factures_rebudes(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('FacturesRebudes.ui', self) current_date = QDate.currentDate() day = current_date.day() self.data_final.setDate(current_date) self.data_inicial.setDate(current_date.addDays(-day+1)) self.seleccionar.clicked.connect(self.show_table) def show_table(self): dia_inicial = int(self.data_inicial.date().day()) mes_inicial = int(self.data_inicial.date().month()) ano_inicial = int(self.data_inicial.date().year()) dia_final = int(self.data_final.date().day()) mes_final = int(self.data_final.date().month()) ano_final = int(self.data_final.date().year()) os.chdir(carpeta_data) if not os.path.exists('factures_rebudes.db'): QMessageBox.warning(self, 'Warning!', 'No existeix cap factura rebuda!') else: lines = read_database_factures('factures_rebudes', 'ASC') matches = [] for i in range(len(lines)): if ano_inicial < ano_final : if int(lines[i][2]) < ano_final and int(lines[i][2]) > ano_inicial: #Si esta en mig es veuran complets matches.append(lines[i]) elif int(lines[i][2]) == ano_inicial: #Si l'any es el mateix comprovar el mes if int(lines[i][1]) > mes_inicial : matches.append(lines[i]) elif int(lines[i][2]) == mes_inicial and int(lines[i][0]) >= dia_inicial: #Comprovar el dia matches.append(lines[i]) elif int(lines[i][2]) == ano_final: #Si l'any es el mateix comprovar el mes if int(lines[i][1]) < mes_final: matches.append(lines[i]) elif int(lines[i][1]) == mes_final and int(lines[i][0]) <= dia_final: #Comprovar el dia matches.append(lines[i]) elif ano_inicial == ano_final and mes_inicial != mes_final: if int(lines[i][1]) > mes_inicial and int(lines[i][1]) < mes_final: matches.append(lines[i]) elif int(lines[i][1]) == mes_inicial and int(lines[i][0]) >= dia_inicial: matches.append(lines[i]) elif int(lines[i][1]) == mes_final and int(lines[i][0]) <= dia_final: matches.append(lines[i]) elif ano_inicial == ano_final and mes_inicial == mes_final: if int(lines[i][1]) == mes_inicial and int(lines[i][0]) >= dia_inicial and int(lines[i][0]) <= dia_final: matches.append(lines[i]) self.table.setRowCount(len(matches)) self.table.setColumnCount(6) self.table.setHorizontalHeaderLabels(['DATA', 'ESTABLIMENT', 'BASE IMPONIBLE', 'IVA %', 'IVA \u20ac', 'TOTAL']) font = QFont() font.setFamily('Segoe UI Black') font.setPointSize(9) for i in range(6): self.table.horizontalHeaderItem(i).setFont(font) llista = [] suma_bi = 0 suma_iva = 0 suma_total = 0 #display in the table for i in range(len(matches)): llista.append('') suma_bi += matches[i][4] suma_total += matches[i][6] for j in range(6): if j == 0: data = str(matches[i][0]).zfill(2) + '/' + str(matches[i][1]).zfill(2) + '/' + str(matches[i][2]) item = QTableWidgetItem(data) item.setTextAlignment(Qt.AlignHCenter) self.table.setItem(i,j, item) elif j == 4: iva = matches[i][5] / 100 iva_euros = round(iva * matches[i][4], 2) suma_iva += iva_euros item = QTableWidgetItem(str(iva_euros)) item.setTextAlignment(Qt.AlignHCenter) self.table.setItem(i,j, item) elif j == 5: item = QTableWidgetItem(str(matches[i][6])) item.setTextAlignment(Qt.AlignHCenter) self.table.setItem(i,j, item) else: item = QTableWidgetItem(str(matches[i][j+2])) item.setTextAlignment(Qt.AlignHCenter) self.table.setItem(i,j, item) self.table.setVerticalHeaderLabels(llista) header = self.table.horizontalHeader() header.setSectionResizeMode(0, QHeaderView.ResizeToContents) header.setSectionResizeMode(1, QHeaderView.Stretch) header.setSectionResizeMode(2, QHeaderView.ResizeToContents) header.setSectionResizeMode(3, QHeaderView.ResizeToContents) header.setSectionResizeMode(4, QHeaderView.ResizeToContents) header.setSectionResizeMode(5, QHeaderView.ResizeToContents) self.bi_tot.setText(str(round(suma_bi, 2)) + ' \u20ac') self.iva_tot.setText(str(round(suma_iva, 2)) + ' \u20ac') self.total_tot.setText(str(round(suma_total, 2)) + ' \u20ac') self.bi_tot.setStyleSheet('border: 1px solid red;') self.iva_tot.setStyleSheet('border: 1px solid red;') self.total_tot.setStyleSheet('border: 1px solid red;') def reinit_dialog(self): self.table.clearContents() self.bi_tot.setText('') self.iva_tot.setText('') self.total_tot.setText('') self.bi_tot.setStyleSheet('') self.iva_tot.setStyleSheet('') self.total_tot.setStyleSheet('') def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() else: event.ignore() class Factures_emeses(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('FacturesRebudes.ui', self) self.setWindowTitle('Factures emeses') current_date = QDate.currentDate() day = current_date.day() self.data_final.setDate(current_date) self.data_inicial.setDate(current_date.addDays(-day+1)) self.seleccionar.clicked.connect(self.show_table) def show_table(self): dia_inicial = int(self.data_inicial.date().day()) mes_inicial = int(self.data_inicial.date().month()) ano_inicial = int(self.data_inicial.date().year()) dia_final = int(self.data_final.date().day()) mes_final = int(self.data_final.date().month()) ano_final = int(self.data_final.date().year()) os.chdir(carpeta_data) if not os.path.exists('factures_emeses.db'): QMessageBox.warning(self, 'Warning!', 'No existeix cap factura emesa') else: lines = read_database_factures('factures_emeses', 'ASC') matches = [] for i in range(len(lines)): if ano_inicial < ano_final : if int(lines[i][2]) < ano_final and int(lines[i][2]) > ano_inicial: #Si esta en mig es veuran complets matches.append(lines[i]) elif int(lines[i][2]) == ano_inicial: #Si l'any es el mateix comprovar el mes if int(lines[i][1]) > mes_inicial : matches.append(lines[i]) elif int(lines[i][2]) == mes_inicial and int(lines[i][0]) >= dia_inicial: #Comprovar el dia matches.append(lines[i]) elif int(lines[i][2]) == ano_final: #Si l'any es el mateix comprovar el mes if int(lines[i][1]) < mes_final: matches.append(lines[i]) elif int(lines[i][1]) == mes_final and int(lines[i][0]) <= dia_final: #Comprovar el dia matches.append(lines[i]) elif ano_inicial == ano_final and mes_inicial != mes_final: if int(lines[i][1]) > mes_inicial and int(lines[i][1]) < mes_final: matches.append(lines[i]) elif int(lines[i][1]) == mes_inicial and int(lines[i][0]) >= dia_inicial: matches.append(lines[i]) elif int(lines[i][1]) == mes_final and int(lines[i][0]) <= dia_final: matches.append(lines[i]) elif ano_inicial == ano_final and mes_inicial == mes_final: if int(lines[i][1]) == mes_inicial and int(lines[i][0]) >= dia_inicial and int(lines[i][0]) <= dia_final: matches.append(lines[i]) self.table.setRowCount(len(matches)) self.table.setColumnCount(6) self.table.setHorizontalHeaderLabels(['DATA', 'NUM FACTURA', 'BASE IMPONIBLE', 'IVA %', 'IVA \u20ac', 'TOTAL']) font = QFont() font.setFamily('Segoe UI Black') font.setPointSize(9) for i in range(6): self.table.horizontalHeaderItem(i).setFont(font) llista = [] suma_bi = 0 suma_iva = 0 suma_total = 0 #display in the table for i in range(len(matches)): llista.append('') suma_bi += matches[i][4] suma_total += matches[i][6] for j in range(6): if j == 0: data = str(matches[i][0]).zfill(2) + '/' + str(matches[i][1]).zfill(2) + '/' + str(matches[i][2]) item = QTableWidgetItem(data) item.setTextAlignment(Qt.AlignHCenter) self.table.setItem(i,j, item) j = 2 elif j == 4: iva = matches[i][5] / 100 iva_euros = round(iva * matches[i][4], 2) suma_iva += iva_euros item = QTableWidgetItem(str(iva_euros)) item.setTextAlignment(Qt.AlignHCenter) self.table.setItem(i,j, item) elif j == 5: item = QTableWidgetItem(str(matches[i][6])) item.setTextAlignment(Qt.AlignHCenter) self.table.setItem(i,j, item) else: item = QTableWidgetItem(str(matches[i][j+2])) item.setTextAlignment(Qt.AlignHCenter) self.table.setItem(i,j, item) self.table.setVerticalHeaderLabels(llista) header = self.table.horizontalHeader() header.setSectionResizeMode(0, QHeaderView.ResizeToContents) header.setSectionResizeMode(1, QHeaderView.Stretch) header.setSectionResizeMode(2, QHeaderView.ResizeToContents) header.setSectionResizeMode(3, QHeaderView.ResizeToContents) header.setSectionResizeMode(4, QHeaderView.ResizeToContents) header.setSectionResizeMode(5, QHeaderView.ResizeToContents) self.bi_tot.setText(str(round(suma_bi, 2)) + ' \u20ac') self.iva_tot.setText(str(round(suma_iva, 2)) + ' \u20ac') self.total_tot.setText(str(round(suma_total, 2)) + ' \u20ac') self.bi_tot.setStyleSheet('border: 1px solid green;') self.iva_tot.setStyleSheet('border: 1px solid green;') self.total_tot.setStyleSheet('border: 1px solid green;') def reinit_dialog(self): self.table.clearContents() self.bi_tot.setText('') self.iva_tot.setText('') self.total_tot.setText('') self.bi_tot.setStyleSheet('') self.iva_tot.setStyleSheet('') self.total_tot.setStyleSheet('') def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() else: event.ignore() class Marge(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('Marge.ui', self) current_date = QDate.currentDate() day = current_date.day() self.data_final.setDate(current_date) self.data_inicial.setDate(current_date.addDays(-day+1)) self.dif_bi.textChanged.connect(self.validar_diferencia_bi) self.dif_iva.textChanged.connect(self.validar_diferencia_iva) self.dif_tot.textChanged.connect(self.validar_diferencia_tot) self.beneficis_stock.textChanged.connect(self.validar_beneficis_stock) self.bi_tot_1.setStyleSheet('border: 1px solid red;') self.iva_tot_1.setStyleSheet('border: 1px solid red;') self.total_tot_1.setStyleSheet('border: 1px solid red;') self.bi_tot_2.setStyleSheet('border: 1px solid green;') self.iva_tot_2.setStyleSheet('border: 1px solid green;') self.total_tot_2.setStyleSheet('border: 1px solid green;') self.stock.setStyleSheet('border: 1px solid green') self.seleccionar.clicked.connect(self.show_table) def validar_beneficis_stock(self): x = self.beneficis_stock.text() if float(x[0:len(x)-2].replace(',', '.')) < 0: #Si es negatiu son perdues self.beneficis_stock.setStyleSheet('border: 1px solid red;') elif float(x[0:len(x)-2].replace(',', '.')) == 0: self.beneficis_stock.setStyleSheet('border: 1px solid yellow;') else: self.beneficis_stock.setStyleSheet('border: 1px solid green;') def validar_diferencia_bi(self): x = self.dif_bi.text() x = float(x[0:len(x)-2].replace(',', '.')) if x < 0: #Si es negatiu son perdues self.dif_bi.setStyleSheet('border: 1px solid red;') elif x == 0: self.dif_bi.setStyleSheet('border: 1px solid yellow;') else: self.dif_bi.setStyleSheet('border: 1px solid green;') def validar_diferencia_iva(self): x = self.dif_iva.text() if float(x[0:len(x)-2].replace(',', '.')) < 0: #Si es negatiu son perdues self.dif_iva.setStyleSheet('border: 1px solid red;') elif float(x[0:len(x)-2].replace(',', '.')) == 0: self.dif_iva.setStyleSheet('border: 1px solid yellow;') else: self.dif_iva.setStyleSheet('border: 1px solid green;') def validar_diferencia_tot(self): x = self.dif_tot.text() if float(x[0:len(x)-2].replace(',', '.')) < 0: #Si es negatiu son perdues self.dif_tot.setStyleSheet('border: 1px solid red;') elif float(x[0:len(x)-2].replace(',', '.')) == 0: self.dif_tot.setStyleSheet('border: 1px solid yellow;') else: self.dif_tot.setStyleSheet('border: 1px solid green;') def factures_taula(self, nom, headerlabel_array, table, bi, y, total): dia_inicial = int(self.data_inicial.date().day()) mes_inicial = int(self.data_inicial.date().month()) ano_inicial = int(self.data_inicial.date().year()) dia_final = int(self.data_final.date().day()) mes_final = int(self.data_final.date().month()) ano_final = int(self.data_final.date().year()) os.chdir(carpeta_data) lines = read_database_factures('%s' % nom, 'ASC') matches = [] for i in range(len(lines)): if ano_inicial < ano_final : if int(lines[i][2]) < ano_final and int(lines[i][2]) > ano_inicial: #Si esta en mig es veuran complets matches.append(lines[i]) elif int(lines[i][2]) == ano_inicial: #Si l'any es el mateix comprovar el mes if int(lines[i][1]) > mes_inicial : matches.append(lines[i]) elif int(lines[i][2]) == mes_inicial and int(lines[i][0]) >= dia_inicial: #Comprovar el dia matches.append(lines[i]) elif int(lines[i][2]) == ano_final: #Si l'any es el mateix comprovar el mes if int(lines[i][1]) < mes_final: matches.append(lines[i]) elif int(lines[i][1]) == mes_final and int(lines[i][0]) <= dia_final: #Comprovar el dia matches.append(lines[i]) elif ano_inicial == ano_final and mes_inicial != mes_final: if int(lines[i][1]) > mes_inicial and int(lines[i][1]) < mes_final: matches.append(lines[i]) elif int(lines[i][1]) == mes_inicial and int(lines[i][0]) >= dia_inicial: matches.append(lines[i]) elif int(lines[i][1]) == mes_final and int(lines[i][0]) <= dia_final: matches.append(lines[i]) elif ano_inicial == ano_final and mes_inicial == mes_final: if int(lines[i][1]) == mes_inicial and int(lines[i][0]) >= dia_inicial and int(lines[i][0]) <= dia_final: matches.append(lines[i]) table.setRowCount(len(matches)) table.setColumnCount(6) table.setHorizontalHeaderLabels(headerlabel_array) font = QFont() font.setFamily('Segoe UI Black') font.setPointSize(9) for i in range(6): table.horizontalHeaderItem(i).setFont(font) llista = [] suma_bi_reb = 0 suma_iva_reb = 0 suma_total_reb = 0 #display in the table for i in range(len(matches)): llista.append('') suma_bi_reb += matches[i][4] suma_total_reb += matches[i][6] for j in range(6): if j == 0: data = str(matches[i][0]).zfill(2) + '/' + str(matches[i][1]).zfill(2) + '/' + str(matches[i][2]) item = QTableWidgetItem(data) item.setTextAlignment(Qt.AlignHCenter) table.setItem(i,j, item) j = 2 elif j == 4: iva = matches[i][5] / 100 iva_euros = round(iva * matches[i][4], 2) suma_iva_reb += iva_euros item = QTableWidgetItem(str(iva_euros)) item.setTextAlignment(Qt.AlignHCenter) table.setItem(i,j, item) elif j == 5: item = QTableWidgetItem(str(matches[i][6])) item.setTextAlignment(Qt.AlignHCenter) table.setItem(i,j, item) else: item = QTableWidgetItem(str(matches[i][j+2])) item.setTextAlignment(Qt.AlignHCenter) table.setItem(i,j, item) table.setVerticalHeaderLabels(llista) header = table.horizontalHeader() header.setSectionResizeMode(0, QHeaderView.ResizeToContents) header.setSectionResizeMode(1, QHeaderView.ResizeToContents) header.setSectionResizeMode(2, QHeaderView.ResizeToContents) header.setSectionResizeMode(3, QHeaderView.ResizeToContents) header.setSectionResizeMode(4, QHeaderView.ResizeToContents) header.setSectionResizeMode(5, QHeaderView.ResizeToContents) bi.setText(str(round(suma_bi_reb, 2)) + ' \u20ac') y.setText(str(round(suma_iva_reb, 2)) + ' \u20ac') total.setText(str(round(suma_total_reb, 2)) + ' \u20ac') return suma_bi_reb, suma_iva_reb, suma_total_reb def show_table(self): os.chdir(carpeta_data) if not os.path.exists('factures_rebudes.db') and not os.path.exists('factures_emeses.db'): QMessageBox.warning(self, 'Warning!', 'No existeix cap factura emesa o rebuda') elif os.path.exists('factures_rebudes.db') and not os.path.exists('factures_emeses.db'): QMessageBox.warning(self, 'Warning!', 'Només existeixen factures rebudes') self.factures_taula('factures_rebudes', ['DATA', 'ESTABLIMENT', 'BASE IMPONIBLE', 'IVA %', 'IVA \u20ac', 'TOTAL'], self.table_1, self.bi_tot_1, self.iva_tot_1, self.total_tot_1) elif os.path.exists('factures_emeses.db') and not os.path.exists('factures_rebudes.db'): QMessageBox.warning(self, 'Warning!', 'Només existeixen factures emeses') self.factures_taula('factures_emeses', ['DATA', 'NUM FACTURA', 'BASE IMPONIBLE', 'IVA %', 'IVA \u20ac', 'TOTAL'], self.table_2, self.bi_tot_2, self.iva_tot_2, self.total_tot_2) else: suma_bi_reb, suma_iva_reb, suma_total_reb = self.factures_taula('factures_rebudes', ['DATA', 'ESTABLIMENT', 'BASE IMPONIBLE', 'IVA %', 'IVA \u20ac', 'TOTAL'], self.table_1, self.bi_tot_1, self.iva_tot_1, self.total_tot_1) suma_bi_eme, suma_iva_eme, suma_total_eme = self.factures_taula('factures_emeses', ['DATA', 'NUM FACTURA', 'BASE IMPONIBLE', 'IVA %', 'IVA \u20ac', 'TOTAL'], self.table_2, self.bi_tot_2, self.iva_tot_2, self.total_tot_2) #Calcular diferencies i beneficis diferencia_bi = suma_bi_eme - suma_bi_reb diferencia_iva = suma_iva_eme - suma_iva_reb diferencia_tot = suma_total_eme - suma_total_reb self.dif_bi.setText(str(round(diferencia_bi, 2)) + ' \u20ac') self.dif_iva.setText(str(round(diferencia_iva, 2)) + ' \u20ac') self.dif_tot.setText(str(round(diferencia_tot, 2)) + ' \u20ac') tableExists = check_table_exists('CompanyName', 'stock') if os.path.exists('CompanyName.db') and tableExists == True: lines = read_database('CompanyName', 'stock', 'REF', 'ASC') total_stock_price = 0 for i in range(len(lines)): total_stock_price += lines[i][4] self.stock.setText(str(round(total_stock_price, 2)) + ' \u20ac') self.beneficis_stock.setText(str(round(diferencia_bi+total_stock_price, 2)) + ' \u20ac') else: self.beneficis_stock.setText(str(round(diferencia_bi, 2)) + ' \u20ac') def reinit_dialog(self): self.table_1.clearContents() self.table_2.clearContents() self.bi_tot_1.setText('0,0' + ' \u20ac') self.iva_tot_1.setText('0,0' + ' \u20ac') self.total_tot_1.setText('0,0' + ' \u20ac') self.bi_tot_2.setText('0,0' + ' \u20ac') self.iva_tot_2.setText('0,0' + ' \u20ac') self.total_tot_2.setText('0,0' + ' \u20ac') self.dif_bi.setText('0,0' + ' \u20ac') self.dif_iva.setText('0,0' + ' \u20ac') self.dif_tot.setText('0,0' + ' \u20ac') def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() else: event.ignore() #VENTES class Facturacio_clients(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('Facturacio_clients.ui', self) self.seleccionar.clicked.connect(self.show_table) self.numclient.textChanged.connect(self.validar_num_client) self.result.textChanged.connect(self.change_color_result) self.total.textChanged.connect(self.change_color_total) self.percentatge_variacio.textChanged.connect(self.change_color_estadistiques) self.veure.clicked.connect(self.facturacio_client) self.veure_total.clicked.connect(self.show_total) self.estadistica.clicked.connect(self.show_statistics) def change_color_total(self): if self.total.text() != '': self.total.setStyleSheet('border: 1px solid orange;') def change_color_result(self): if self.result.text() != '': self.result.setStyleSheet('border: 1px solid orange;') def change_color_estadistiques(self): x = self.percentatge_variacio.text() if x != '': if float(x[0:len(x)-1]) < 0: self.percentatge_variacio.setStyleSheet('border: 1px solid red;') self.percentatge_fact.setStyleSheet('border: 1px solid red;') self.posicio.setStyleSheet('border: 1px solid red;') elif float(x[0:len(x)-1]) > 0: self.percentatge_variacio.setStyleSheet('border: 1px solid green;') self.percentatge_fact.setStyleSheet('border: 1px solid green;') self.posicio.setStyleSheet('border: 1px solid green;') def show_table(self): ano = self.any.value() mess = self.mes.value() ordre = self.order.currentText() os.chdir(carpeta_data) control = check_table_exists('facturacio_clients', str(ano)) if control == True: if ordre == 'Número client ascendent': lines = read_database('facturacio_clients', str(ano), 'ref', 'ASC') elif ordre == 'Número client descendent': lines = read_database('facturacio_clients', str(ano), 'ref', 'DESC') elif ordre == 'Facturació mensual ascendent' : lines = read_database('facturacio_clients', str(ano), mesos_minus[mess-1], 'ASC') else: lines = read_database('facturacio_clients', str(ano), mesos_minus[mess-1], 'DESC') self.table.setRowCount(len(lines)) self.table.setColumnCount(13) self.table.setHorizontalHeaderLabels(['CLIENT', 'GENER', 'FEBRER', 'MARÇ', 'ABRIL', 'MAIG', 'JUNY', 'JULIOL', 'AGOST', 'SETEMBRE', 'OCTUBRE', 'NOVEMBRE', 'DESEMBRE']) font = QFont() font.setFamily('Segoe UI Black') font.setPointSize(9) for i in range(13): self.table.horizontalHeaderItem(i).setFont(font) llista = [] for i in range(len(lines)): llista.append(lines[i][0]) for j in range(13): fact = float(lines[i][j]) self.table.setItem(i,j, QTableWidgetItem(str(round(fact, 2)))) self.table.setVerticalHeaderLabels(llista) for i in range(len(lines)): self.table.verticalHeaderItem(i).setFont(font) else: QMessageBox.warning(self, 'Warning!', 'Cap venta realitzada per l\'any seleccionat') def validar_num_client(self): num_client = self.numclient.text() validar = re.match('^[0123456789]+$', num_client) if num_client == '': #Si esta buit bordes grocs self.numclient.setStyleSheet('border: 1px solid yellow;') return False, num_client elif not validar:#Si no es valid bordes vermells self.numclient.setStyleSheet('border: 1px solid red;') return False, num_client else: self.numclient.setStyleSheet('border: 1px solid green;') return True, num_client def facturacio_client(self): mes = self.mes.value() ano = self.any.value() os.chdir(carpeta_data) control = check_table_exists('facturacio_clients', ano) if control == True: control, num_client = self.validar_num_client() if control == True: lines = select_from_database_general('facturacio_clients', ano, num_client, 'ref', 'ref', 'ASC') if len(lines) != 0: facturacio = round(lines[0][mes], 2) self.result.setText(str(facturacio) + '\u20ac') return facturacio else: QMessageBox.warning(self, 'Warning', 'Aquest client encara no ha realitzat cap compra!') return False else: QMessageBox.warning(self, 'Warning!', 'Dades incorrectes!', QMessageBox.Discard) return False else: QMessageBox.warning(self, 'Warning!', 'Cap venta realitzada per l\'any seleccionat') return False def show_total(self): os.chdir(carpeta_data) if os.path.exists('facturacio_total.db'): mes = self.mes.value() ano = self.any.value() lines = select_from_database_general('facturacio_total', 'data', ano, 'ref', 'ref', 'ASC') fact_total = round(lines[0][mes], 2) if len(lines) != 0: self.total.setText(str(fact_total) + '\u20ac') return fact_total else: QMessageBox.warning(self, 'Warning', 'Cap venta realitzada l\'any seleccionat!') return False else: QMessageBox.warning(self, 'Warning!', 'Cap venta realitzada l\'any seleccionat') return False def show_statistics(self): mes = self.mes.value() ano = self.any.value() fact_client = self.facturacio_client() total = self.show_total() if fact_client == False or total == False: pass else: #Percentatge de facturació del client respecte el total percent = round((float(fact_client)/float(total))*100, 2) self.percentatge_fact.setText(str(percent) + '%') #Variació respecte el mes anterior num_client = self.numclient.text() lines = select_from_database_general('facturacio_clients', ano, num_client, 'ref', 'ref', 'ASC') if mes != 1: #Si es gener no ho podem comparar amb el mes anterior del mateix any anterior = float(lines[0][mes-1]) variacio = round((float(fact_client) - anterior)/float(fact_client) * 100, 2) self.percentatge_variacio.setText(str(variacio) + '%') else: self.percentatge_variacio.setText('NULL') #Posició ranking facturació lines = read_database('facturacio_clients', ano, mesos_minus[mes-1], 'DESC') position = 0 for i in range(len(lines)): if lines[i][0] == num_client: position = i+1 self.posicio.setText(str(position)) def reinit_dialog(self): self.numclient.setText('') self.result.setText('') self.total.setText('') self.percentatge_fact.setText('') self.percentatge_variacio.setText('') self.posicio.setText('') self.any.setValue(2018) self.mes.setValue(1) self.table.clearContents() self.percentatge_variacio.setStyleSheet('') self.percentatge_fact.setStyleSheet('') self.posicio.setStyleSheet('') self.result.setStyleSheet('') self.numclient.setStyleSheet('') self.total.setStyleSheet('') def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() else: event.ignore() class Ranking_facturacio(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('RankingFacturacio.ui', self) self.seleccionar.clicked.connect(self.show_table) def show_table(self): ano = self.any.value() mes = self.mes.value() os.chdir(carpeta_data) if os.path.exists('facturacio_clients.db'): tableExists = check_table_exists('facturacio_clients', ano) if tableExists == True: lines = read_database('facturacio_clients', ano, mesos_minus[mes-1], 'DESC') self.table.setRowCount(len(lines)) self.table.setColumnCount(10) self.table.setHorizontalHeaderLabels(['POSICIÓ', 'FACTURACIÓ', 'CLIENT', 'NOM COMERCIAL', 'NOM FISCAL', 'ADREÇA', 'POBLACIÓ', 'NIF', 'TEL', 'FORMA PAGO']) font = QFont() font.setFamily('Segoe UI Black') font.setPointSize(9) for i in range(10): self.table.horizontalHeaderItem(i).setFont(font) llista = [] for i in range(len(lines)): llista.append('') dades_client = select_from_database_general('clients', 'data', lines[i][0], 'num_client', 'num_client', 'ASC') for j in range(10): if j == 0: self.table.setItem(i,j, QTableWidgetItem(str(i+1))) elif j == 1: self.table.setItem(i,j, QTableWidgetItem(str(lines[i][mes]))) else: self.table.setItem(i,j, QTableWidgetItem(dades_client[0][j-2])) self.table.setVerticalHeaderLabels(llista) header = self.table.horizontalHeader() for i in range(10): header.setSectionResizeMode(i, QHeaderView.ResizeToContents) else: QMessageBox.warning(self, 'Warning', 'Cap venta realitzada l\'any seleccionat!') else: QMessageBox.warning(self, 'Warning', 'Cap venta realitzada!') def reinit_dialog(self): self.table.clearContents() def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() else: event.ignore() class Grafics(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('Grafics.ui', self) self.numclient.textChanged.connect(self.validar_num_client) self.seleccionar.clicked.connect(self.veure_grafic) def validar_num_client(self): num_client = self.numclient.text() validar = re.match('^[0123456789]+$', num_client) if num_client == '': #Si esta buit bordes grocs self.numclient.setStyleSheet('border: 1px solid yellow;') return False, 0 elif not validar:#Si no es valid bordes vermells self.numclient.setStyleSheet('border: 1px solid red;') return False, 0 else: self.numclient.setStyleSheet('border: 1px solid green;') return True, num_client def fer_grafic_facturacio_total(self): os.chdir(carpeta_data) if os.path.exists('facturacio_total.db'): x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] y = [] mesos = ['Gen', 'Feb', 'Mar', 'Abr', 'Mai', 'Jun', 'Jul', 'Ag', 'Set', 'Oct', 'Nov', 'Des'] if self.comboBox.currentText() == 'Tots els anys': lines = read_database('facturacio_total', 'data', 'ref', 'ASC') plt.figure() for i in range(len(lines)): y.append(lines[i][1:]) #Calcular mitja mitja = [] for i in range(len(y)): suma = 0 for j in range(len(y[i])): suma += y[i][j] mitja.append(suma/12) mitja_total = 0 for i in range(len(mitja)): mitja_total += mitja[i] mitja_total = mitja_total/len(mitja) mitja_arr = np.linspace(mitja_total, mitja_total, 12) for i in range(len(lines)): plt.plot(x, y[i], '-o', label = lines[i][0]) plt.plot(x, mitja_arr, '--', label = 'Mitjana total= %.2f \u20ac' % mitja_total) #Customize plot plt.title('Facturació total') plt.ylabel('Facturació \u20ac') plt.xticks(x, mesos) plt.legend() #plt.show() plt.savefig('facturacio_total.png') if self.refresh.isChecked(): plt.gcf().clear() else: year = self.ano.value() lines = select_from_database_general('facturacio_total', 'data', str(year), 'ref', 'ref', 'ASC') if len(lines) == 0: QMessageBox.warning(self, 'Warning!', 'No existeix facturació per l\'any seleccionat') else: suma = 0 zeros = 0 for i in range(12): suma += float(lines[0][i]) if float(lines[0][i]) == 0: zeros += 1 mitja = suma/(12-zeros) mitja_arr = [] for i in range(12): mitja_arr.append(mitja) plt.plot(x, lines[0][1:], '-o', label = lines[0][0]) plt.plot(x, mitja_arr, '--', label='Mitjana %s: %.2f \u20ac' % (year, mitja)) #Customize plot plt.title('Facturació total') plt.ylabel('Facturació \u20ac') plt.xticks(x, mesos) plt.legend() #plt.show() plt.savefig('facturacio_total.png') if self.refresh.isChecked(): plt.gcf().clear() else: QMessageBox.warning(self, 'Warning!', 'No existeix facturació!') def fer_grafic_facturacio_clients(self): os.chdir(carpeta_data) if os.path.exists('facturacio_clients.db'): x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] y = [] mesos = ['Gen', 'Feb', 'Mar', 'Abr', 'Mai', 'Jun', 'Jul', 'Ag', 'Set', 'Oct', 'Nov', 'Des'] if self.seleccio_clients.currentText() == 'Tots els clients': year = self.spinBox_any.value() lines = read_database('facturacio_clients', str(year), 'ref', 'ASC') plt.figure(figsize=(8, 6)) for i in range(len(lines)): y.append(lines[i][1:]) #Calcular mitja mitja = [] mitja_arr = [] for i in range(len(y)): suma = 0 for j in range(len(y[i])): suma += y[i][j] mitja.append(suma/12) mitja_total = 0 for i in range(len(mitja)): mitja_total += mitja[i] for i in range(len(x)): mitja_arr.append(mitja_total/len(mitja)) for i in range(len(lines)): plt.plot(x, y[i], '-o', label = lines[i][0]) plt.plot(x, mitja_arr, '--', label = 'Mitjana total') #Customize plot plt.title('Facturació total') plt.ylabel('Facturació \u20ac') plt.xticks(x, mesos) plt.legend(loc='upper left') plt.savefig('facturacio_clients.png') if self.refresh.isChecked(): plt.gcf().clear() else: year = self.spinBox_any.value() control, num_client = self.validar_num_client() control_2 = False if control == True: if check_table_exists('clients', 'data'): lines = select_from_database_general('clients', 'data', str(num_client).zfill(4), 'num_client', 'num_client', 'ASC') if len(lines) != 0: control_2 = True else: QMessageBox.warning(self, 'Warning!', 'Client no registrat!') control_2 = False else: QMessageBox.warning(self, 'Warning!', 'Encara no has registrat cap client!') else: QMessageBox.warning(self, 'Warning!', 'Número de client no vàlid!') if control_2 == True: lines = select_from_database_general('facturacio_clients', str(year), str(num_client).zfill(4), 'ref', 'ref', 'ASC') if len(lines) != 0: suma = 0 for i in range(len(x)): suma += float(lines[0][i]) mitja = suma/12 mitja_arr = [] for i in range(len(x)): mitja_arr.append(mitja) plt.plot(x, lines[0][1:], '-o', label = lines[0][0]) plt.plot(x, mitja_arr, '--', label='Mitjana %s' % num_client) #Customize plot plt.title('Facturació total') plt.ylabel('Facturació \u20ac') plt.xticks(x, mesos) plt.legend() plt.savefig('facturacio_clients.png') if self.refresh.isChecked(): plt.gcf().clear() else: QMessageBox.warning(self, 'Warning!', 'Aquest client no ha facturat res!') else: QMessageBox.warning(self, 'Warning!', 'No existeix facturació!') def veure_grafic(self): os.chdir(carpeta_data) self.reinit_dialog() if self.checkBox.isChecked() and self.check_clients.isChecked(): QMessageBox.warning(self, 'Warning!', 'Només pots seleccionar un dels dos gràfics!') elif self.checkBox.isChecked(): self.fer_grafic_facturacio_total() filename = 'facturacio_total.png' image = QImage(filename) self.imageLabel.setPixmap(QPixmap.fromImage(image)) elif self.check_clients.isChecked(): self.fer_grafic_facturacio_clients() filename = 'facturacio_clients.png' image = QImage(filename) self.imageLabel.setPixmap(QPixmap.fromImage(image)) else: QMessageBox.warning(self, 'Warning!', 'Has de marcar alguna de les dues opcions!') def reinit_dialog(self): os.chdir(carpeta_data) if os.path.exists('facturacio_total.png') and os.path.exists('facturacio_clients.png'): os.remove('facturacio_total.png') os.remove('facturacio_clients.png') elif os.path.exists('facturacio_total.png'): os.remove('facturacio_total.png') elif os.path.exists('facturacio_clients.png'): os.remove('facturacio_clients.png') self.imageLabel.clear() if self.refresh.isChecked(): plt.gcf().clear() def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() self.numclient.setText('') self.numclient.setStyleSheet('') else: event.ignore() class Registre_ventes(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('RegistreVentes.ui', self) self.okbutton.clicked.connect(self.show_table) self.facturacio.textChanged.connect(self.canviar_color_fact) self.unitats.textChanged.connect(self.canviar_color_units) def canviar_color_fact(self): self.facturacio.setStyleSheet('border: 1px solid green;') def canviar_color_units(self): self.unitats.setStyleSheet('border: 1px solid green;') def show_table(self): month = self.mes.value() ano = self.any.value() os.chdir(carpeta_data) mes = mesos[int(month)-1] os.chdir(carpeta_data) control = check_table_exists('ventes', ano) control_2 = check_table_exists('facturacio_ref', ano) if control == False or control_2 == False: QMessageBox.warning(self, 'Warning!', 'No hi ha ventes realitzades aquest any!', QMessageBox.Discard) else: if self.comboBox_unitats.currentText() == 'Unitats ascendent': sales = read_database('ventes', str(ano), mesos_minus[month-1], 'ASC') elif self.comboBox_unitats.currentText() == 'Unitats descendent': sales = read_database('ventes', str(ano), mesos_minus[month-1], 'DESC') if self.comboBox_facturacio.currentText() == 'Facturació ascendent': lines = read_database('facturacio_ref', str(ano), mesos_minus[month-1], 'ASC') elif self.comboBox_facturacio.currentText() == 'Facturació descendent': lines = read_database('facturacio_ref', str(ano), mesos_minus[month-1], 'DESC') unitats_totals = 0 facturacio_total = 0 for i in range(len(sales)): unitats_totals += sales[i][month] for i in range(len(lines)): facturacio_total += lines[i][month] if len(sales) != 0 and len(lines) != 0: #Display the table self.table.setRowCount(len(sales)) self.table.setColumnCount(13) self.table.setHorizontalHeaderLabels(['REF', 'GENER', 'FEBRER', 'MARÇ', 'ABRIL', 'MAIG', 'JUNY', 'JULIOL', 'AGOST', 'SETEMBRE', 'OCTUBRE', 'NOVEMBRE', 'DESEMBRE']) llista = [] for i in range(len(sales)): llista.append(sales[i][0]) for j in range(13): self.table.setItem(i,j, QTableWidgetItem(str(sales[i][j]))) self.table.setVerticalHeaderLabels(llista) font = QFont() font.setFamily('Segoe UI Black') font.setPointSize(9) for i in range(13): self.table.horizontalHeaderItem(i).setFont(font) for i in range(len(sales)): self.table.verticalHeaderItem(i).setFont(font) #Display the table 2 self.table_2.setRowCount(len(lines)) self.table_2.setColumnCount(13) self.table_2.setHorizontalHeaderLabels(['REF', 'GENER', 'FEBRER', 'MARÇ', 'ABRIL', 'MAIG', 'JUNY', 'JULIOL', 'AGOST', 'SETEMBRE', 'OCTUBRE', 'NOVEMBRE', 'DESEMBRE']) llista = [] for i in range(len(lines)): llista.append(lines[i][0]) for j in range(13): self.table_2.setItem(i,j, QTableWidgetItem(str(lines[i][j]))) self.table_2.setVerticalHeaderLabels(llista) for i in range(13): self.table_2.horizontalHeaderItem(i).setFont(font) for i in range(len(lines)): self.table_2.verticalHeaderItem(i).setFont(font) #Display facturacio total and unitats totals self.facturacio.setText(str(facturacio_total) + ' \u20ac') self.unitats.setText(str(unitats_totals) + ' u.') else: QMessageBox.warning(self, 'Warning!', 'No hi ha ventes realitzades aquest mes!', QMessageBox.Discard) def reinit_dialog(self): self.table.clearContents() self.table_2.clearContents() self.facturacio.setText('') self.unitats.setText('') self.facturacio.setStyleSheet('') self.unitats.setStyleSheet('') def closeEvent(self, event): result = QMessageBox.question(self, 'Sortint...','Segur que vols sortir?', QMessageBox.Yes | QMessageBox.No) if result == QMessageBox.Yes: event.accept() self.reinit_dialog() else: event.ignore() #STOCK class Stock(QDialog): def __init__(self): QDialog.__init__(self) os.chdir(carpeta_data) uic.loadUi('Stock.ui', self) self.show_table() self.guardar.clicked.connect(self.save_stock) self.visualitzar.clicked.connect(self.show_table) def show_table(self): if os.path.exists('cataleg.db'): if self.order.currentText() == 'Referència ascendent': lines = read_database('cataleg', 'data', 'ref', 'ASC') #[ref, prod, preu] elif self.order.currentText() == 'Referència descendent': lines = read_database('cataleg', 'data', 'ref', 'DESC') elif self.order.currentText() == 'Alfabètic ascendent': lines = read_database('cataleg', 'data', 'prod', 'ASC') elif self.order.currentText() == 'Alfabètic descendent': lines = read_database('cataleg', 'data', 'prod', 'DESC') #Comprovar si hi ha o no stock existent tablas = [ """ CREATE TABLE IF NOT EXISTS stock( REF TEXT NOT NULL, NAME TEXT NOT NULL, QUANTITY REAL NOT NULL, UNIT_PRICE REAL NOT NULL, TOTAL_PRICE REAL NOT NULL ); """ ] create_database('CompanyName', tablas) stock_lines = read_database('CompanyName', 'stock', 'REF', 'ASC') self.table.setRowCount(len(lines)) self.table.setColumnCount(5) if len(stock_lines) == 0: #No previous stock llista = [] for i in range(len(lines)): llista.append('') for j in range(5): if j == 0: #REF self.table.setItem(i,j, QTableWidgetItem(lines[i][0])) elif j == 1: #NAME self.table.setItem(i,j, QTableWidgetItem(lines[i][1])) elif j == 2: #QUANTITY sp = QSpinBox() sp.setMaximum(9999) self.table.setCellWidget(i,j, sp) elif j == 3: #UNIT PRICE self.table.setItem(i,j, QTableWidgetItem(str(lines[i][2]))) elif j == 4: #TOTAL PRICE total_price = lines[i][2] * self.table.cellWidget(i,2).value() self.table.setItem(i,j, QTableWidgetItem(str(total_price))) else: llista = [] for i in range(len(lines)): llista.append('') for j in range(5): if j == 0: #REF self.table.setItem(i,j, QTableWidgetItem(lines[i][0])) elif j == 1: #NAME self.table.setItem(i,j, QTableWidgetItem(lines[i][1])) elif j == 2: #QUANTITY item = select_from_database_general('CompanyName', 'stock', lines[i][0], 'REF', 'REF', 'ASC') if len(item) != 0: quantity = item[0][2] else: quantity = 0 sp = QSpinBox() sp.setMaximum(9999) sp.setValue(quantity) self.table.setCellWidget(i,j, sp) elif j == 3: #UNIT PRICE self.table.setItem(i,j, QTableWidgetItem(str(lines[i][2]))) elif j == 4: #TOTAL PRICE total_price = lines[i][2] * self.table.cellWidget(i,2).value() self.table.setItem(i,j, QTableWidgetItem(str(total_price))) self.table.setHorizontalHeaderLabels(['REF', 'PRODUCTE', 'QUANTITAT', 'PREU UNITAT', 'PREU TOTAL']) self.table.setVerticalHeaderLabels(llista) font = QFont() font.setFamily('Segoe UI Black') font.setPointSize(9) header = self.table.horizontalHeader() for i in range(5): self.table.horizontalHeaderItem(i).setFont(font) header.setSectionResizeMode(i, QHeaderView.ResizeToContents) header.setSectionResizeMode(1, QHeaderView.Stretch) #Stock total value tableExists = check_table_exists('CompanyName', 'stock') lines = read_database('CompanyName', 'stock', 'REF', 'ASC') total_stock_price = 0 for i in range(len(lines)): total_stock_price += lines[i][4] self.stock.setText(str(round(total_stock_price, 2)) + ' \u20ac') else: QMessageBox.warning(self, 'Warning!', 'No existeix catàleg!') def save_stock(self): lines = self.table.rowCount() for i in range(lines): current_quantity = self.table.cellWidget(i,2).value() if current_quantity != 0: current_ref = self.table.item(i,0).text() current_name = self.table.item(i,1).text() current_unit_price = float(self.table.item(i,3).text()) current_total_price = float(self.table.item(i,4).text()) fill_table_stock('CompanyName', [current_ref, current_name, current_quantity, current_unit_price, current_total_price]) QMessageBox.information(self, 'Information', 'Dades guardades correctament!')
[ "PyQt5.QtWidgets.QSpinBox", "matplotlib.pyplot.ylabel", "PyQt5.uic.loadUi", "PyQt5.QtGui.QPixmap.fromImage", "PyQt5.QtGui.QImage", "numpy.array", "PyQt5.QtWidgets.QMessageBox.question", "os.remove", "os.path.exists", "os.listdir", "PyQt5.QtCore.QDate.currentDate", "matplotlib.pyplot.plot", "...
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import numpy as np import torch from mmhuman3d.core.filter.builder import build_filter # test different data type def test_data_type_torch(): noisy_input = torch.randn((100, 17, 3)) cfg = dict(type='OneEuroFilter', min_cutoff=0.004, beta=0.7) oneeuro = build_filter(cfg) out_g = oneeuro(noisy_input) cfg = dict(type='Gaus1dFilter', window_size=11, sigma=4) gaus1d = build_filter(cfg) out_s = gaus1d(noisy_input) cfg = dict(type='SGFilter', window_size=11, polyorder=2) savgol = build_filter(cfg) out_o = savgol(noisy_input) # verify the correctness accel_input = noisy_input[:-2] - 2 * noisy_input[1:-1] + noisy_input[2:] accel_out_g = out_g[:-2] - 2 * out_g[1:-1] + out_g[2:] accel_input_abs = torch.mean(torch.abs(accel_input)) assert accel_input_abs >= torch.mean(torch.abs(accel_out_g)) accel_out_s = out_s[:-2] - 2 * out_s[1:-1] + out_s[2:] assert accel_input_abs >= torch.mean(torch.abs(accel_out_s)) accel_out_o = out_o[:-2] - 2 * out_o[1:-1] + out_o[2:] assert accel_input_abs >= torch.mean(torch.abs(accel_out_o)) assert out_g.shape == noisy_input.shape == out_s.shape == out_o.shape def test_data_type_torch_zero(): noisy_input = torch.zeros((50, 20, 3)) cfg = dict(type='OneEuroFilter', min_cutoff=0.004, beta=0.7) oneeuro = build_filter(cfg) out_g = oneeuro(noisy_input) cfg = dict(type='Gaus1dFilter', window_size=11, sigma=4) gaus1d = build_filter(cfg) out_s = gaus1d(noisy_input) cfg = dict(type='SGFilter', window_size=11, polyorder=2) savgol = build_filter(cfg) out_o = savgol(noisy_input) # verify the correctness accel_input = noisy_input[:-2] - 2 * noisy_input[1:-1] + noisy_input[2:] accel_out_g = out_g[:-2] - 2 * out_g[1:-1] + out_g[2:] assert torch.mean(accel_input) >= torch.mean(accel_out_g) accel_out_s = out_s[:-2] - 2 * out_s[1:-1] + out_s[2:] assert torch.mean(accel_input) >= torch.mean(accel_out_s) accel_out_o = out_o[:-2] - 2 * out_o[1:-1] + out_o[2:] assert torch.mean(accel_input) >= torch.mean(accel_out_o) assert out_g.shape == noisy_input.shape == out_s.shape == out_o.shape def test_data_type_torch_cuda(): if not torch.cuda.is_available(): return noisy_input = torch.randn((3, 24, 4)).cuda() cfg = dict(type='OneEuroFilter', min_cutoff=0.0004, beta=0.7) oneeuro = build_filter(cfg) out_g = oneeuro(noisy_input) cfg = dict(type='Gaus1dFilter', window_size=6, sigma=1) gaus1d = build_filter(cfg) out_s = gaus1d(noisy_input) cfg = dict(type='SGFilter', window_size=7, polyorder=2) savgol = build_filter(cfg) out_o = savgol(noisy_input) assert out_g.shape == noisy_input.shape == out_s.shape == out_o.shape def test_data_type_np(): noisy_input = np.random.rand(100, 24, 6) cfg = dict(type='OneEuroFilter', min_cutoff=0.004, beta=0.1) oneeuro = build_filter(cfg) out_g = oneeuro(noisy_input) cfg = dict(type='Gaus1dFilter', window_size=5, sigma=2) gaus1d = build_filter(cfg) out_s = gaus1d(noisy_input) cfg = dict(type='SGFilter', window_size=5, polyorder=2) savgol = build_filter(cfg) out_o = savgol(noisy_input) assert out_g.shape == noisy_input.shape == out_s.shape == out_o.shape
[ "torch.abs", "numpy.random.rand", "torch.mean", "torch.cuda.is_available", "mmhuman3d.core.filter.builder.build_filter", "torch.zeros", "torch.randn" ]
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import numpy as np from matplotlib import pyplot as plt import pandas as pd df = pd.read_csv("C:/Users/Elton/Downloads/temperature.csv") print(df.head()) #Extração de X e Y x,y = df[['temperatura']].values, df[['classification']].values print('x:\n',x) print('y:\n',y) #Prós processamento from sklearn.preprocessing import LabelEncoder #Codifica uma entrada como um valor numerico #print((LabelEncoder)) #Conversao de y em valores numericos le = LabelEncoder() y = le.fit_transform(y.ravel()) print('y:\n',y) #Modelo from sklearn.linear_model import LogisticRegression #Classificador clf = LogisticRegression() clf.fit(x,y) #Gerando 100 valores de temperatura linearmente espaçados entre 0 e 45 x_test = np.linspace(start = 0,stop = 45, num = 100).reshape(-1,1) #Predição desses valores y_pred=clf.predict(x_test) #print(y_pred) #Conversao de y pred para valores originais y_pred = le.inverse_transform(y_pred) #print(y_pred) #print(x_test) #output output = {'new_temp': x_test.ravel(), 'new_class': y_pred.ravel()} output = pd.DataFrame(output) #print(output.head()) #print(output.tail()) #Estatisticas print(output.info) print(output.describe()) #Contagem de valores gerados output['new_class'].value_counts().plot.bar(figsize=(10,5), rot=0, title='#de novos valores gerados'); #Distribuição do output produzido #Conseguimos inferir a classificação novas temperaturas #a partir de um dataset com 6 exemplos output.boxplot(by='new_class', figsize=(10, 5)) print(80*'=') plt.show() #Sistema automatico def classify_temp(): '''Classificaça o input do usuario''' ask = True while ask: #input de temperatura temp = input("Insira a temparatura(graus celsius): ") #Transformar para numpy array temp = np.array(float(temp)).reshape(-1,1) #Realizar classificação class_temp = clf.predict(temp) #Transformação inversa para retorna string original class_temp = le.inverse_transform(class_temp) #Classificação print(f'A classificação da temperatura {temp.ravel()[0]} é:', class_temp[0]) #Perguntar ask = input('Nova classificação(y/n): ') == 'y' classify_temp()
[ "sklearn.preprocessing.LabelEncoder", "pandas.read_csv", "sklearn.linear_model.LogisticRegression", "numpy.linspace", "pandas.DataFrame", "matplotlib.pyplot.show" ]
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import cv2 import os import json import numpy as np import editdistance from spellchecker import SpellChecker from linesegm2.LineSegmentation import LineSegmentation from Model import Model from SamplePreprocessor import preprocess from DataLoader import Batch from WordSegmentation import wordSegmentation, prepareImg def infer(model, fnImg): "recognize text in image provided by file path" img = preprocess(cv2.imread(fnImg, cv2.IMREAD_GRAYSCALE), Model.imgSize) batch = Batch(None, [img]) (recognized, probability) = model.inferBatch(batch, True) spell = SpellChecker() # find those words that may be misspelled #misspelled = spell.unknown([recognized]) recognized[0] = spell.correction(recognized[0]) #print('Recognized:', '"' + recognized[0] + '"') #print('Probability:', probability[0]) return (recognized[0], probability[0]) def line_segment(filepath, filenames, model): out_dict={} out_path='../data/out/' truth_path='../data/true_text/' compare=False if os.path.exists(truth_path+'truth.json'): numCharErr = 0 numCharTotal = 0 numWordOK = 0 numWordTotal = 0 compare = True with open(truth_path+'truth.json', 'r') as truth: truth_file = json.load(truth) for filename in filenames: fullpath=os.path.join(filepath,filename) f=filename.split('.')[0] ext=filename.split('.')[1] if ext=='pdf': continue out_dict[f] = [] print('Reading image "' + filename + '"..') im = cv2.imread(fullpath) output_path = out_path+f if not os.path.exists(output_path): os.mkdir(output_path) line_segmentation = LineSegmentation(img=im, output_path=output_path) lines = line_segmentation.segment() if(len(lines)==0): im = cv2.imread(fullpath, 0) _ , imbw = cv2.threshold(im, 0, 255, cv2.THRESH_BINARY|cv2.THRESH_OTSU) lines = [imbw] n_line=1 n_word=0 for line in lines: img = prepareImg(line, 50) # execute segmentation with given parameters # -kernelSize: size of filter kernel (odd integer) # -sigma: standard deviation of Gaussian function used for filter kernel # -theta: approximated width/height ratio of words, filter function is distorted by this factor # - minArea: ignore word candidates smaller than specified area res = wordSegmentation(img, kernelSize=25, sigma=11, theta=7, minArea=100, increase_dim=10) # iterate over all segmented words #print('Segmented into %d words'%len(res)) for (j, w) in enumerate(res): (wordBox, wordImg) = w (x, y, w, h) = wordBox imgloc=output_path+'/%d.png'%j # increase contrast # preprocess so that it is similar to IAM dataset kernel = np.ones((2, 2), np.uint8) wordImg = cv2.erode(wordImg, kernel, iterations = 1) cv2.imwrite(imgloc, wordImg) # save word #FilePaths.fnInfer = 'out/%s/%d.png'%(f,j) #result, prob = infer(model, imgloc) try: result, prob = infer(model, imgloc) except: print("Couldn't infer: image%d"%j) result="" #compare with ground truth if compare: numWordOK += 1 if truth_file[f][n_word] == result else 0 numWordTotal += 1 dist = editdistance.eval(result, truth_file[f][n_word]) numCharErr += dist numCharTotal += len(truth_file[f][n_word]) print('[OK]' if dist==0 else '[ERR:%d]' % dist,'"' + truth_file[f][n_word] + '"', '->', '"' + result + '"') #updating output dictionary out_dict[f].append(result) n_word+=1 #deleting intermediate file os.remove(imgloc) cv2.rectangle(img,(x,y),(x+w,y+h),0,1) # draw bounding box in summary image # output summary image with bounding boxes around words cv2.imwrite(output_path+'/summary%d.png'%n_line, img) n_line+=1 if compare: charErrorRate = numCharErr / numCharTotal wordAccuracy = numWordOK / numWordTotal print('Character error rate: %f%%. Word accuracy: %f%%.' % (charErrorRate*100.0, wordAccuracy*100.0)) return out_dict
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import os import traceback import cv2 import numpy as np from keras.models import load_model from flask import flash, request, redirect, url_for, render_template from werkzeug.utils import secure_filename from app import app ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif', 'jfif'} model = load_model('model' + os.sep + 'inception_v3') def allowed_file(filename): return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS @app.route('/') def index(): return render_template('index.html') def predict(image_path): img = cv2.imread(image_path) resized_img = cv2.resize(img, (128, 128), interpolation=cv2.INTER_NEAREST) img = np.reshape(resized_img, (1, 128, 128, 3)) prediction = model.predict(img) max_index = np.argmax(prediction) print(prediction) if max_index == 1: return "No Disease" else: return "Has Disease" @app.route('/', methods=['POST']) def upload_image(): file = request.files['file'] try: if file and allowed_file(file.filename): filename = secure_filename(file.filename) image_path = os.path.join(app.config['UPLOAD_FOLDER'], filename) file.save(image_path) prediction = predict(image_path) return render_template('index.html', filename=filename, prediction=prediction) else: flash('Allowed image types are -> png, jpg, jpeg, gif') return redirect(request.url) except: traceback.print_exc() @app.route('/display/<filename>') def display_image(filename): # print('display_image filename: ' + filename) return redirect(url_for('static', filename='uploads/' + filename), code=301) if __name__ == "__main__": app.run()
[ "flask.render_template", "app.app.run", "keras.models.load_model", "numpy.reshape", "flask.flash", "os.path.join", "numpy.argmax", "flask.url_for", "flask.redirect", "app.app.route", "werkzeug.utils.secure_filename", "cv2.resize", "traceback.print_exc", "cv2.imread" ]
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import numpy as np import re from utils import get_input class Board: def __init__(self, grid): self.array = np.array(grid).astype('int') self.drawn = np.zeros((5,5)) self.bingo = False def update(self, draw): hits = self.array == draw self.drawn[hits] = 1 if (self.drawn.sum(axis=0) == 5).any() or (self.drawn.sum(axis=1) == 5).any(): self.bingo = True def score(self): return (self.array[self.drawn == 0]).sum().item() def reset(self): self.drawn == 0 def parse_input(input): lines = [line.lstrip() for line in input.splitlines()] nums = [int(o) for o in lines[0].split(',')] boards = [ [ re.split('\D+', row) for row in lines[i:i+5]] for i in range(2, len(lines), 6) ] boards = np.stack([np.array(board, dtype=np.uint8) for board in boards]) return nums, boards def win_bingo(nums, grids): boards = [Board(grid) for grid in grids] bingo = False for num in nums: for board in boards: board.update(num) if board.bingo: bingo = board.bingo break if bingo: break return num * board.score() def lose_bingo(nums, grids): boards = [Board(grid) for grid in grids] bingos = 0 game_over = False for num in nums: for board in boards: if not board.bingo: board.update(num) if board.bingo: bingos += 1 if bingos == len(boards): game_over = True break if game_over: break print(board.array, board.drawn, num) return num * board.score() if __name__ == '__main__': example = """7,4,9,5,11,17,23,2,0,14,21,24,10,16,13,6,15,25,12,22,18,20,8,19,3,26,1 22 13 17 11 0 8 2 23 4 24 21 9 14 16 7 6 10 3 18 5 1 12 20 15 19 3 15 0 2 22 9 18 13 17 5 19 8 7 25 23 20 11 10 24 4 14 21 16 12 6 14 21 17 24 4 10 16 15 9 19 18 8 23 26 20 22 11 13 6 5 2 0 12 3 7""" nums, grids = parse_input(example) print(win_bingo(nums, grids)) print(lose_bingo(nums, grids)) input = get_input('day04.txt') nums, grids = parse_input(input) print(win_bingo(nums, grids)) print(lose_bingo(nums, grids))
[ "numpy.array", "re.split", "numpy.zeros", "utils.get_input" ]
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#!/usr/bin/env python3 # encoding: utf-8 from collections import defaultdict from copy import deepcopy from typing import Dict, List import numpy as np from mlagents_envs.environment import UnityEnvironment from mlagents_envs.side_channel.engine_configuration_channel import \ EngineConfigurationChannel from mlagents_envs.side_channel.environment_parameters_channel import \ EnvironmentParametersChannel from rls.common.data import Data from rls.common.specs import EnvAgentSpec, SensorSpec from rls.common.yaml_ops import load_config from rls.envs.unity.wrappers.core import ObservationWrapper from rls.utils.np_utils import get_discrete_action_list class BasicUnityEnvironment(object): def __init__(self, worker_id=0, file_name=None, port=5005, render=False, seed=42, timeout_wait=60, env_copies=12, env_name='3DBall', real_done=True, initialize_config={}, engine_config={ 'width': 84, 'height': 84, 'quality_level': 5, 'time_scale': 20, 'target_frame_rate': -1, 'capture_frame_rate': 60 }, **kwargs): self._n_copies = env_copies self._real_done = real_done self._side_channels = self.initialize_all_side_channels( initialize_config, engine_config) env_kwargs = dict(seed=seed, worker_id=worker_id, timeout_wait=timeout_wait, side_channels=list(self._side_channels.values())) # 注册所有初始化后的通讯频道 if file_name is not None: env_dict = load_config('rls/configs/unity/env_dict.yaml') env_kwargs.update(file_name=file_name, base_port=port, no_graphics=not render, additional_args=[ '--scene', str(env_dict.get(env_name, 'None')) ]) self.env = UnityEnvironment(**env_kwargs) self.env.reset() self.initialize_environment() def initialize_all_side_channels(self, initialize_config, engine_config): """ 初始化所有的通讯频道 """ engine_configuration_channel = EngineConfigurationChannel() engine_configuration_channel.set_configuration_parameters(**engine_config) float_properties_channel = EnvironmentParametersChannel() float_properties_channel.set_float_parameter('env_copies', self._n_copies) for k, v in initialize_config.items(): float_properties_channel.set_float_parameter(k, v) return dict(engine_configuration_channel=engine_configuration_channel, float_properties_channel=float_properties_channel) def initialize_environment(self): """ 初始化环境，获取必要的信息，如状态、动作维度等等 """ self.behavior_names = list(self.env.behavior_specs.keys()) self._vector_idxs = defaultdict(list) self._vector_dims = defaultdict(list) self._visual_idxs = defaultdict(list) self._visual_dims = defaultdict(list) self._a_dim = defaultdict(int) self._discrete_action_lists = {} self._is_continuous = {} self._actiontuples = {} self.env.reset() for bn, spec in self.env.behavior_specs.items(): for i, obs_spec in enumerate(spec.observation_specs): # TODO: optimize if len(obs_spec.shape) == 1: self._vector_idxs[bn].append(i) self._vector_dims[bn].append(obs_spec.shape[0]) elif len(obs_spec.shape) == 3: self._visual_idxs[bn].append(i) self._visual_dims[bn].append(list(obs_spec.shape)) else: raise ValueError( "shape of observation cannot be understood.") action_spec = spec.action_spec if action_spec.is_continuous(): self._a_dim[bn] = action_spec.continuous_size self._discrete_action_lists[bn] = None self._is_continuous[bn] = True elif action_spec.is_discrete(): self._a_dim[bn] = int(np.asarray( action_spec.discrete_branches).prod()) self._discrete_action_lists[bn] = get_discrete_action_list( action_spec.discrete_branches) self._is_continuous[bn] = False else: raise NotImplementedError( "doesn't support continuous and discrete actions simultaneously for now.") self._actiontuples[bn] = action_spec.empty_action( n_agents=self._n_copies) def reset(self, reset_config): for k, v in reset_config.items(): self._side_channels['float_properties_channel'].set_float_parameter( k, v) self.env.reset() return self.get_obs(only_obs=True) def step(self, actions, step_config): """ params: actions, type of dict or np.ndarray, if the type of actions is not dict, then set those actions for the first behavior controller. """ for k, v in step_config.items(): self._side_channels['float_properties_channel'].set_float_parameter( k, v) actions = deepcopy(actions) # TODO: fix this for bn in self.behavior_names: if self._is_continuous[bn]: self._actiontuples[bn].add_continuous(actions[bn]) else: self._actiontuples[bn].add_discrete( self._discrete_action_lists[bn][actions[bn]].reshape(self._n_copies, -1)) self.env.set_actions(bn, self._actiontuples[bn]) self.env.step() return self.get_obs() @property def AgentSpecs(self): ret = {} for bn in self.behavior_names: ret[bn] = EnvAgentSpec( obs_spec=SensorSpec( vector_dims=self._vector_dims[bn], visual_dims=self._visual_dims[bn]), a_dim=self._a_dim[bn], is_continuous=self._is_continuous[bn] ) return ret @property def StateSpec(self) -> SensorSpec: return SensorSpec() @property def agent_ids(self) -> List[str]: return self.behavior_names def get_obs(self, behavior_names=None, only_obs=False): """ 解析环境反馈的信息，将反馈信息分为四部分：向量、图像、奖励、done信号 """ behavior_names = behavior_names or self.behavior_names whole_done = np.full(self._n_copies, False) whole_info_max_step = np.full(self._n_copies, False) all_obs_fa, all_obs_fs = {}, {} all_reward = {} for bn in behavior_names: ps = [] # TODO: optimize while True: ds, ts = self.env.get_steps(bn) if len(ts): ps.append(ts) if len(ds) == self._n_copies: break elif len(ds) == 0: self.env.step() # some of environments done, but some of not else: raise ValueError( f'agents number error. Expected 0 or {self._n_copies}, received {len(ds)}') obs_fs, reward = ds.obs, ds.reward obs_fa = deepcopy(obs_fs) done = np.full(self._n_copies, False) begin_mask = np.full(self._n_copies, False) info_max_step = np.full(self._n_copies, False) info_real_done = np.full(self._n_copies, False) for ts in ps: # TODO: 有待优化 _ids = ts.agent_id reward[_ids] = ts.reward info_max_step[_ids] = ts.interrupted # 因为达到episode最大步数而终止的 # 去掉因为max_step而done的，只记录因为失败/成功而done的 info_real_done[_ids[~ts.interrupted]] = True done[_ids] = True begin_mask[_ids] = True # zip: vector, visual, ... for _obs, _tobs in zip(obs_fa, ts.obs): _obs[_ids] = _tobs if self._real_done: done = np.array(info_real_done) _obs_fa = Data() _obs_fs = Data() if len(self._vector_idxs[bn]) > 0: _obs_fa.update(vector={f'vector_{i}': obs_fa[vi] for i, vi in enumerate(self._vector_idxs[bn])}) _obs_fs.update(vector={f'vector_{i}': obs_fs[vi] for i, vi in enumerate(self._vector_idxs[bn])}) if len(self._visual_idxs[bn]) > 0: _obs_fa.update(visual={f'visual_{i}': obs_fa[vi] for i, vi in enumerate(self._visual_idxs[bn])}) _obs_fs.update(visual={f'visual_{i}': obs_fs[vi] for i, vi in enumerate(self._visual_idxs[bn])}) all_obs_fa[bn] = _obs_fa all_obs_fs[bn] = _obs_fs all_reward[bn] = reward whole_done = np.logical_or(whole_done, done) whole_info_max_step = np.logical_or(whole_info_max_step, info_max_step) if only_obs: all_obs_fa.update( {'global': Data(begin_mask=np.full((self._n_copies, 1), True))}) return all_obs_fa else: rets = {} for bn in self.behavior_names: rets[bn] = Data(obs_fa=all_obs_fa[bn], obs_fs=all_obs_fs[bn], reward=all_reward[bn], done=whole_done, info=dict(max_step=whole_info_max_step)) rets.update( {'global': Data(begin_mask=begin_mask[:, np.newaxis])}) # [B, 1] return rets def __getattr__(self, name): """ 不允许获取BasicUnityEnvironment中以'_'开头的属性 """ if name.startswith('_'): raise AttributeError( "attempted to get missing private attribute '{}'".format(name)) return getattr(self.env, name) class ScaleVisualWrapper(ObservationWrapper): def observation(self, observation: Dict[str, Data]): def func(x): return np.asarray(x * 255).astype(np.uint8) for k in observation.keys(): observation[k].obs.visual.convert_(func) observation[k].obs_.visual.convert_(func) return observation
[ "rls.utils.np_utils.get_discrete_action_list", "mlagents_envs.side_channel.engine_configuration_channel.EngineConfigurationChannel", "mlagents_envs.side_channel.environment_parameters_channel.EnvironmentParametersChannel", "numpy.asarray", "numpy.logical_or", "rls.common.specs.SensorSpec", "numpy.array"...
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""" `myplotlib.tests` commands to help preview the custom styles (function names are self-descriptive). available functions are: * testColormaps * testColors * testScatter * testPlot * testErrorbar * testPlot2d * testVectorPlot2d * testAll """ import myplotlib.plots as myplt import myplotlib import matplotlib import matplotlib.pyplot as plt import numpy as np def __getAx(ax): if ax is None: fig, ax = plt.subplots() return ax def testColormaps(ax=None): ax = __getAx(ax) zz = [np.linspace(0, 1, 256)] pad = 0.3 dy = (1 - pad) / (len(myplotlib.CUSTOM_CMAPS)) pady = pad / (len(myplotlib.CUSTOM_CMAPS)) for i, cm in enumerate(myplotlib.CUSTOM_CMAPS): y1, y2 = (pady * (i + 0.5) + dy * i, pady * (i + 0.5) + dy * (i + 1)) ax.imshow(zz, extent=(0, 1, y1, y2), cmap=cm); if matplotlib.rcParams['text.usetex']: name = r"$\texttt{{'{}'}}$".format(cm) else: name = f"\'{cm}\'" ax.text(1.04, 0.5 * (y1 + y2), name, va='center', ha='left') ax.set_ylim(0, 1); ax.axis('off'); ax.set_title("extra colormaps") def testColors(ax=None): ax = __getAx(ax) prop_cycle = plt.rcParams['axes.prop_cycle'] colors = prop_cycle.by_key()['color'] title = 'default color cycle' ax.set_title(title) for j, c in enumerate(colors): v_offset = -(j / len(colors)) th = np.linspace(0, 2*np.pi, 512) ax.plot(th, .1*np.sin(th) + v_offset, color=c, lw=2) if matplotlib.rcParams['text.usetex']: name = r"$\texttt{{'C{}'}}$".format(j) else: name = f"\'C{j}\'" ax.annotate(name, (0, v_offset), xytext=(-1.5, 0), ha='right', va='center', color=c, textcoords='offset points') if matplotlib.rcParams['text.usetex']: name = r"$\texttt{{{}}}$".format(c.replace('#', '\#')) else: name = f"{c}" ax.annotate(name, (2*np.pi, v_offset), xytext=(1.5, 0), ha='left', va='center', color=c, textcoords='offset points') ax.axis('off') def testScatter(ax=None): ax = __getAx(ax) myplt.scatter(ax, np.random.random(100), np.random.random(100), xlog=True, ylog=True, s=(0.1 + np.random.random(100)) * 20, c='C0', label='drunk points', marker='*') myplt.scatter(ax, 10**(np.random.random(100)*2 - 2), 10**(np.random.random(100)*2 - 2), xlog=True, ylog=True, s=(0.1 + np.random.random(100)) * 20, c='C1', label='sober points', pady=0.5, padx=0.2, ylim=(1e-2, None), xlim=(1e-2, None)) plt.legend(loc = 'lower left') ax.set_xlabel(r'some funny number $x^2/y$ [units]'); ax.set_ylabel(r'other number $z_{\nu}$ [units]'); def testPlot(ax=None): ax = __getAx(ax) myplt.plot(ax, np.arange(100), 20 * (2 + np.sin(np.linspace(0, 20, 100)) + np.random.random(100)**5), padx=0.2, pady=1, c='C4', ls=':'); myplt.plot(ax, np.arange(100), 20 * (2 + np.sin(np.linspace(0, 20, 100)) + np.random.random(100)**5), padx=0.2, pady=1, c='C2'); ax.set_ylabel(r'probability [$\%$]') ax.set_xlabel('age [yr]') def testErrorbar(ax=None): ax = __getAx(ax) x = np.linspace(0, 1000, 10) y = np.sin(x / 100) dy = np.random.random(len(x)) * (y + 1.5) / 3 myplt.dataPlot(ax.errorbar, ax, x, y, yerr=dy, padx=0.1, pady=1, marker='o', markeredgecolor=ax.get_facecolor(), markerfacecolor='C11', markeredgewidth=1.5) ax.set_xlabel('time [s]') ax.set_ylabel('my very accurately measured variable [ly]') def testPlot2d(ax=None): ax = __getAx(ax) x = np.linspace(-3, 3, 240) y = np.linspace(-3, 3, 200) xx, yy = np.meshgrid(x, y) zz = (xx**2 - np.sin(xx * yy**3)) + 6 * np.exp(-(xx**2 + yy**2) / 0.2) myplt.plot2d(ax, x, y, zz, cmap='bipolar', centering='edge', padx=0.1, pady=0.1) ax.set_xlabel('landscape in $x$') ax.set_ylabel('landscape in $y$') def testVectorPlot2d(ax=None): ax = __getAx(ax) vortex_spacing = 0.5 extra_factor = 2. a = np.array([1, 0]) * vortex_spacing b = np.array([np.cos(np.pi / 3),np.sin(np.pi / 3)]) * vortex_spacing rnv = int(2 * extra_factor / vortex_spacing) vortices = [n * a + m * b for n in range(-rnv, rnv) for m in range(-rnv, rnv)] vortices = [(x, y) for (x, y) in vortices if -extra_factor < x < extra_factor and -extra_factor < y < extra_factor] sx, sy = (1000, 1000) xs = np.linspace(-1, 1, sx).astype(np.float64)[None,:] ys = np.linspace(-1, 1, sy).astype(np.float64)[:,None] vectors = np.zeros((sx,sy,2),dtype=np.float64) for (x,y) in vortices: rsq = (xs-x)**2 + (ys-y)**2 vectors[...,0] += (ys-y)/rsq vectors[...,1] += -(xs-x)/rsq myplt.plotVectorField(ax, xs, ys, vectors[:,:,0], vectors[:,:,1], norm=matplotlib.colors.LogNorm(1, 1e2), cmap='turbo', lic_contrast=1) def testAll(): fig = plt.figure(figsize=(12, 16)) axshape = (4, 2) axi = 1 ax = plt.subplot(*axshape, axi) testColors(ax) axi += 1 ax = plt.subplot(*axshape, axi) testColormaps(ax) axi += 1 ax = plt.subplot(*axshape, axi) testScatter(ax) axi += 1 ax = plt.subplot(*axshape, axi) testPlot(ax) axi += 1 ax = plt.subplot(*axshape, axi) testErrorbar(ax) axi += 1 ax = plt.subplot(*axshape, axi) testPlot2d(ax) axi += 1 ax = plt.subplot(*axshape, axi) testVectorPlot2d(ax) axi += 1 # ax = plt.subplot(*axshape, axi) # axi += 1 plt.tight_layout()
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# Code adapted from https://github.com/google-research/google-research/tree/master/flax_models/cifar # Original copyright statement: # Copyright 2020 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Wide Resnet Model. Reference: Wide Residual Networks, <NAME>, <NAME> https://arxiv.org/abs/1605.07146 Initially forked from github.com/google/flax/blob/master/examples/cifar10/models/wideresnet.py This implementation mimics the one from github.com/tensorflow/models/blob/master/research/autoaugment/wrn.py that is widely used as a benchmark. It uses identity + zero padding skip connections, with kaiming normal initialization for convolutional kernels (mode = fan_out, gain=2.0). The final dense layer uses a uniform distribution U[-scale, scale] where scale = 1 / sqrt(num_classes) as per the autoaugment implementation. Using the default initialization instead gives error rates approximately 0.5% greater on cifar100, most likely because the parameters used in the literature were finetuned for this particular initialization. Finally, the autoaugment implementation adds more residual connections between the groups (instead of just between the blocks as per the original paper and most implementations). It is possible to safely remove those connections without degrading the performance, which we do by default to match the original wideresnet paper. Setting `use_additional_skip_connections` to True will add them back and then reproduces exactly the model used in autoaugment. """ import numpy as np import flax from flax import linen as nn import jax import jax.numpy as jnp from typing import Any, Tuple, Optional _BATCHNORM_MOMENTUM = 0.9 _BATCHNORM_EPSILON = 1e-5 # Kaiming initialization with fan out mode. Should be used to initialize # convolutional kernels. conv_kernel_init_fn = jax.nn.initializers.variance_scaling( 2.0, 'fan_out', 'normal') def dense_layer_init_fn(key, shape, dtype=jnp.float32): """Initializer for the final dense layer. Args: key: PRNG key to use to sample the weights. shape: Shape of the tensor to initialize. dtype: Data type of the tensor to initialize. Returns: The initialized tensor. """ num_units_out = shape[1] unif_init_range = 1.0 / (num_units_out) ** (0.5) return jax.random.uniform(key, shape, dtype, -1) * unif_init_range def shake_shake_train(xa, xb, rng=None): """Shake-shake regularization in training mode. Shake-shake regularization interpolates between inputs A and B with *different* random uniform (per-sample) interpolation factors for the forward and backward/gradient passes. Args: xa: Input, branch A. xb: Input, branch B. rng: PRNG key. Returns: Mix of input branches. """ if rng is None: rng = flax.nn.make_rng() gate_forward_key, gate_backward_key = jax.random.split(rng, num=2) gate_shape = (len(xa), 1, 1, 1) # Draw different interpolation factors (gate) for forward and backward pass. gate_forward = jax.random.uniform( gate_forward_key, gate_shape, dtype=jnp.float32, minval=0.0, maxval=1.0) gate_backward = jax.random.uniform( gate_backward_key, gate_shape, dtype=jnp.float32, minval=0.0, maxval=1.0) # Compute interpolated x for forward and backward. x_forward = xa * gate_forward + xb * (1.0 - gate_forward) x_backward = xa * gate_backward + xb * (1.0 - gate_backward) # Combine using stop_gradient. return x_backward + jax.lax.stop_gradient(x_forward - x_backward) def shake_shake_eval(xa, xb): """Shake-shake regularization in testing mode. Args: xa: Input, branch A. xb: Input, branch B. Returns: Mix of input branches. """ # Blend between inputs A and B 50%-50%. return (xa + xb) * 0.5 def shake_drop_train(x, mask_prob, alpha_min, alpha_max, beta_min, beta_max, rng=None): """ShakeDrop training pass. See https://arxiv.org/abs/1802.02375 Args: x: Input to apply ShakeDrop to. mask_prob: Mask probability. alpha_min: Alpha range lower. alpha_max: Alpha range upper. beta_min: Beta range lower. beta_max: Beta range upper. rng: PRNG key (if `None`, uses `flax.nn.make_rng`). Returns: The regularized tensor. """ if rng is None: rng = flax.nn.make_rng() bern_key, alpha_key, beta_key = jax.random.split(rng, num=3) rnd_shape = (len(x), 1, 1, 1) # Bernoulli variable b_l in Eqn 6, https://arxiv.org/abs/1802.02375. mask = jax.random.bernoulli(bern_key, mask_prob, rnd_shape) mask = mask.astype(jnp.float32) alpha_values = jax.random.uniform( alpha_key, rnd_shape, dtype=jnp.float32, minval=alpha_min, maxval=alpha_max) beta_values = jax.random.uniform( beta_key, rnd_shape, dtype=jnp.float32, minval=beta_min, maxval=beta_max) # See Eqn 6 in https://arxiv.org/abs/1802.02375. rand_forward = mask + alpha_values - mask * alpha_values rand_backward = mask + beta_values - mask * beta_values return x * rand_backward + jax.lax.stop_gradient( x * rand_forward - x * rand_backward) def shake_drop_eval(x, mask_prob, alpha_min, alpha_max): """ShakeDrop eval pass. See https://arxiv.org/abs/1802.02375 Args: x: Input to apply ShakeDrop to. mask_prob: Mask probability. alpha_min: Alpha range lower. alpha_max: Alpha range upper. Returns: The regularized tensor. """ expected_alpha = (alpha_max + alpha_min) / 2 # See Eqn 6 in https://arxiv.org/abs/1802.02375. return (mask_prob + expected_alpha - mask_prob * expected_alpha) * x def activation(x, train, apply_relu=True, name=''): x = nn.GroupNorm(name=name, epsilon=1e-5, num_groups=min(x.shape[-1] // 4, 32))(x) if apply_relu: x = jax.nn.relu(x) return x def _output_add(block_x, orig_x): """Add two tensors, padding them with zeros or pooling them if necessary. Args: block_x: Output of a resnet block. orig_x: Residual branch to add to the output of the resnet block. Returns: The sum of blocks_x and orig_x. If necessary, orig_x will be average pooled or zero padded so that its shape matches orig_x. """ stride = orig_x.shape[-2] // block_x.shape[-2] strides = (stride, stride) if block_x.shape[-1] != orig_x.shape[-1]: orig_x = nn.avg_pool(orig_x, strides, strides) channels_to_add = block_x.shape[-1] - orig_x.shape[-1] orig_x = jnp.pad(orig_x, [(0, 0), (0, 0), (0, 0), (0, channels_to_add)]) return block_x + orig_x class GaussianFourierProjection(nn.Module): """Gaussian Fourier embeddings for noise levels.""" embedding_size: int = 256 scale: float = 1.0 @nn.compact def __call__(self, x): W = self.param('W', jax.nn.initializers.normal(stddev=self.scale), (self.embedding_size,)) W = jax.lax.stop_gradient(W) x_proj = x[:, None] * W[None, :] * 2 * jnp.pi return jnp.concatenate([jnp.sin(x_proj), jnp.cos(x_proj)], axis=-1) class WideResnetBlock(nn.Module): """Defines a single WideResnetBlock.""" channels: int strides: Tuple[int] = (1, 1) activate_before_residual: bool = False @nn.compact def __call__(self, x, temb=None, train=True): if self.activate_before_residual: x = activation(x, train, name='init_bn') orig_x = x else: orig_x = x block_x = x if not self.activate_before_residual: block_x = activation(block_x, train, name='init_bn') block_x = nn.Conv( self.channels, (3, 3), self.strides, padding='SAME', use_bias=False, kernel_init=conv_kernel_init_fn, name='conv1')(block_x) if temb is not None: block_x += nn.Dense(self.channels)(nn.swish(temb))[:, None, None, :] block_x = activation(block_x, train=train, name='bn_2') block_x = nn.Conv( self.channels, (3, 3), padding='SAME', use_bias=False, kernel_init=conv_kernel_init_fn, name='conv2')(block_x) return _output_add(block_x, orig_x) class WideResnetGroup(nn.Module): """Defines a WideResnetGroup.""" blocks_per_group: int channels: int strides: Tuple[int] = (1, 1) activate_before_residual: bool = False @nn.compact def __call__(self, x, temb=None, train=True): for i in range(self.blocks_per_group): x = WideResnetBlock(self.channels, self.strides if i == 0 else (1, 1), activate_before_residual=self.activate_before_residual and not i, )(x, temb, train) return x class WideResnet(nn.Module): """Defines the WideResnet Model.""" blocks_per_group: int channel_multiplier: int num_outputs: int @nn.compact def __call__(self, x, sigmas, train=True): # per image standardization N = np.prod(x.shape[1:]) x = (x - jnp.mean(x, axis=(1, 2, 3), keepdims=True)) / jnp.maximum(jnp.std(x, axis=(1, 2, 3), keepdims=True), 1. / np.sqrt(N)) temb = GaussianFourierProjection(embedding_size=128, scale=16)(jnp.log(sigmas)) temb = nn.Dense(128 * 4)(temb) temb = nn.Dense(128 * 4)(nn.swish(temb)) x = nn.Conv(16, (3, 3), padding='SAME', name='init_conv', kernel_init=conv_kernel_init_fn, use_bias=False)(x) x = WideResnetGroup(self.blocks_per_group, 16 * self.channel_multiplier, activate_before_residual=True)(x, temb, train) x = WideResnetGroup(self.blocks_per_group, 32 * self.channel_multiplier, (2, 2))(x, temb, train) x = WideResnetGroup(self.blocks_per_group, 64 * self.channel_multiplier, (2, 2))(x, temb, train) x = activation(x, train=train, name='pre-pool-bn') x = nn.avg_pool(x, x.shape[1:3]) x = x.reshape((x.shape[0], -1)) x = nn.Dense(self.num_outputs, kernel_init=dense_layer_init_fn)(x) return x
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# -*- coding: utf-8 -*- """ Created on Mon Apr 30 10:05:09 2018 @author: Administrator """ from sklearn.datasets import load_files from keras.utils import np_utils import numpy as np from glob import glob from keras.preprocessing import image import keras from sklearn.model_selection import train_test_split from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D from keras.models import Sequential, load_model, Model from keras.layers import Input, BatchNormalization from keras.layers import Dense, LSTM, GlobalAveragePooling1D, GlobalAveragePooling2D from keras.layers import Activation, Flatten, Dropout, BatchNormalization from keras.layers import Conv2D, MaxPooling2D, GlobalMaxPooling2D from PIL import ImageFile ImageFile.LOAD_TRUNCATED_IMAGES = True def load_dataset(path): data = load_files(path) dog_files = np.array(data['filenames']) dog_targets = np_utils.to_categorical(np.array(data['target']), 133) return dog_files, dog_targets train_files, train_targets = load_dataset('data/training_images') valid_files, valid_targets = load_dataset('data/validation_images') test_files, test_targets = load_dataset('data/testing_images') def path_to_tensors(image_path): img = image.load_img(image_path, target_size= (331, 331)) x = image.img_to_array(img) return np.expand_dims(x, axis = 0) # pre-process the data for Keras train_tensors = paths_to_tensor(train_files).astype('float32')/255 valid_tensors = paths_to_tensor(valid_files).astype('float32')/255 test_tensors = paths_to_tensor(test_files).astype('float32')/255 #using NASNet pretrained model model = keras.applications.nasnet.NASNetLarge(input_shape=(331,331,3), include_top=True, weights='imagenet', input_tensor=None, pooling=None) def multi_model(): model_input = Input(shape=(331, 331, 3)) x = BatchNormalization()(model_input) # Define a model architecture x = Conv2D(32, (5, 5), activation='relu', padding='same')(model_input) x = MaxPooling2D(pool_size=(2, 2))(x) x = Dropout(0.25)(x) x = Conv2D(128, (5, 5), activation='relu', padding='same')(x) x = MaxPooling2D(pool_size=(2, 2))(x) x = Dropout(0.25)(x) x = GlobalMaxPooling2D()(x) x = Dense(512, activation='relu')(x) x = Dropout(0.25)(x) y1 = Dense(228, activation='softmax')(x) model = Model(inputs=model_input, outputs=y1) # Compile the model model.compile(loss='categorical_crossentropy', optimizer='nadam', metrics=['accuracy']) return model multi_model = multi_model()
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import os import re import numpy as np from parsefchk import parse_fchk LAST_UPDATE = '201706021601' def gen_fchk(mfn, data, ofn='', title=None, suffix='_rlo', overwrite=False): # text: info, energy, coeff, rest # data: energy, coeff, dim if not ofn: t, ext = os.path.splitext(mfn) ofn = t + suffix + ext if not overwrite and os.path.exists(ofn): overwriteflag = input('Overwrite file %s? Y/N ' % ofn) if overwriteflag.lower() != 'y': print(ofn, 'skipped.') return False title = title or os.path.splitext(ofn)[0] fchk = parse_fchk(mfn) text = fchk['text'] dim_mo, dim_bs = fchk['data']['dim'] dim_nmo, dim_nbs = data.get('dim', (dim_mo, dim_bs)) assert dim_bs == dim_nbs if dim_nmo == dim_mo: for e in data['energy']: assert e.shape == (dim_nmo,) for c in data['coeff']: assert c.shape == (dim_nmo, dim_bs) elif dim_nmo < dim_mo: # for spin, e in enumerate(data['energy']): # data['energy'][spin] = np.append(e, np.zeros(dim_mo - dim_nmo), axis = 0) # assert data['energy'][spin].shape == (dim_mo,) data['energy'] = [np.append(e, np.zeros(dim_mo - dim_nmo), axis=0) for spin, e in enumerate(data['energy'])] # for spin, c in enumerate(data['coeff']): # data['coeff'][spin] = np.append(c, np.zeros((dim_mo - dim_nmo, dim_bs)), axis = 0) # assert data['coeff'][spin].shape == (dim_mo, dim_bs) data['coeff'] = [np.append(c, np.zeros((dim_mo - dim_nmo, dim_bs)), axis=0) for spin, c in enumerate(data['coeff'])] dim_nmo = dim_mo else: # raise AssertionError('Number of new orbitals exceeds number of original orbitals') for lid, line in enumerate(text['info']): text['info'][lid] = line.replace(str(dim_mo), str(dim_nmo)) text['energy'][0] = text['energy'][0].replace(str(dim_mo), str(dim_nmo)) text['coeff'][0] = text['coeff'][0].replace(str(dim_mo*dim_bs), str(dim_nmo*dim_bs)) if len(data['energy']) != len(text['energy']): print('Warning: Spin symmetry not match!') amoeline = text['energy'][0] bmoeline = amoeline.replace('Alpha Orbital Energies', 'Beta Orbital Energies ') text['energy'] = [amoeline, bmoeline] amocline = text['coeff'][0] bmocline = amocline.replace('Alpha MO coefficients', 'Beta MO coefficients ') text['coeff'] = [amocline, bmocline] # print('%s generated.' % ofn) f = open(ofn, 'w') write = f.write writes = lambda s: write(s + '\n') writel = lambda l: write('\n'.join(l) + '\n') writes(title) writel(text['info'][1:]) for spin, energy in enumerate(data['energy']): writes(text['energy'][spin]) for i, e in enumerate(['%16.8E' % e for e in energy]): write(e) if (i + 1) % 5 == 0: write('\n') if (i + 1) % 5 != 0: write('\n') for spin, coeff in enumerate(data['coeff']): writes(text['coeff'][spin]) # print(coeff) for i, e in enumerate(['%16.8E' % e for e in coeff.reshape(1,-1)[0]]): write(e) if (i + 1) % 5 == 0: write('\n') if (i + 1) % 5 != 0: write('\n') writel(text['rest']) f.close() return ofn def quicksave(mfn, xoao, fmxo, suffix='_rlo', overwrite=False): if xoao.ndim == 2: if fmxo.ndim == 2: energies = np.diag(fmxo) elif fmxo.ndim == 1: energies = fmxo else: raise UserWarning('Invalid shape of fock matrix') assert energies.shape[0] == xoao.shape[0] data = {'energy': [energies], 'coeff': [xoao], 'dim': xoao.shape} elif xoao.ndim == 3: if fmxo.ndim == 3: energies = np.diagonal(fmxo, axis1=1, axis2=2) elif fmxo.ndim == 2: energies = fmxo else: raise UserWarning('Invalid shape of fock matrix') assert energies.shape[1] == xoao.shape[1] assert energies.shape[0] == xoao.shape[0] data = {'energy': energies, 'coeff': xoao, 'dim': xoao.shape[1:]} else: raise UserWarning('Unrecognized data shape: %s' % xoao.shape) return gen_fchk(mfn, data, suffix=suffix, overwrite=overwrite) if __name__ == '__main__': import sys mfn = sys.argv[1] gen_fchk(mfn, parse_fchk(mfn)['data'])
[ "os.path.exists", "numpy.diagonal", "parsefchk.parse_fchk", "os.path.splitext", "numpy.diag", "numpy.zeros" ]
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from pickle import load from numpy import array from numpy import argmax from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.models import load_model from nltk.translate.bleu_score import corpus_bleu import sys import pika import os import urllib.parse # Parse CLODUAMQP_URL (fallback to localhost) url_str = os.environ.get('CLOUDAMQP_URL', 'amqp://guest:guest@localhost//') url = urllib.parse.urlparse(url_str) params = pika.ConnectionParameters(host=url.hostname, virtual_host=url.path[1:], credentials=pika.PlainCredentials(url.username, url.password)) connection = pika.BlockingConnection(params) # Connect to CloudAMQP #connection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost')) #connection = pika.BlockingConnection(pika.ConnectionParameters('127.0.0.1')) channel = connection.channel() channel.queue_declare(queue='rpc_queue') # load a clean dataset def load_clean_sentences(filename): return load(open(filename, 'rb')) # fit a tokenizer def create_tokenizer(lines): tokenizer = Tokenizer(char_level=False) tokenizer.fit_on_texts(lines) return tokenizer # max sentence length def max_length(lines): return max(len(line.split()) for line in lines) # map an integer to a word def word_for_id(integer, tokenizer): for word, index in tokenizer.word_index.items(): if index == integer: return word return None # generate target given source sequence def predict_sequence(model, tokenizer, source): prediction = model.predict(source, verbose=0)[0] integers = [argmax(vector) for vector in prediction] target = list() for i in integers: word = word_for_id(i, tokenizer) if word is None: break target.append(word) return ' '.join(target) # translate def translate(model, tokenizer, sources): predicted = list() for i, source in enumerate(sources): # translate encoded source text source = source.reshape((1, source.shape[0])) translation = predict_sequence(model, all_tokenizer, source) return{'ANSWER':translation} #print('ANSWER: %s' % (translation)) predicted.append(translation.split()) # load datasets dataset = load_clean_sentences('both.pkl') dataset1=dataset.reshape(-1,1) # prepare tokenizer all_tokenizer = create_tokenizer(dataset1[:,0]) all_vocab_size = len(all_tokenizer.word_index) + 1 all_length = max_length(dataset1[:, 0]) # load model model = load_model('model1.h5') # Setting up the chat #question = str(sys.argv[1]) #print('arg: %s' % (q)) #question = question.strip().split('\n') #we tokenize #X = all_tokenizer.texts_to_sequences(question) #X = pad_sequences(X, maxlen=all_length, padding='post') # find reply and print it out #translate(model, all_tokenizer, X) def on_request(ch, method, props, body): question = body.decode("utf-8") print(" [.] question(%s)" % question) question = (question.strip().split('\n')) X = all_tokenizer.texts_to_sequences(question) X = pad_sequences(X, maxlen=all_length, padding='post') #response = fib(n) response = translate(model, all_tokenizer, X) ch.basic_publish(exchange='', routing_key=props.reply_to, properties=pika.BasicProperties(correlation_id = \ props.correlation_id), body=str(response)) ch.basic_ack(delivery_tag = method.delivery_tag) channel.basic_qos(prefetch_count=1) channel.basic_consume(on_request, queue='rpc_queue') print(" [x] Awaiting RPC requests") channel.start_consuming()
[ "keras.preprocessing.text.Tokenizer", "keras.models.load_model", "pika.BlockingConnection", "os.environ.get", "pika.PlainCredentials", "numpy.argmax", "pika.BasicProperties", "keras.preprocessing.sequence.pad_sequences" ]
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from selfdrive.mapd.lib.geo import DIRECTION, distance_and_bearing, absoule_delta_with_direction, bearing_delta, bearing from common.numpy_fast import interp from selfdrive.config import Conversions as CV import numpy as np import re _ACCEPTABLE_BEARING_DELTA_V = [40., 20., 10., 5.] _ACCEPTABLE_BEARING_DELTA_BP = [30., 100., 200., 300.] _COUNTRY_LIMITS_KPH = { 'DE': { 'urban': 50., 'rural': 100., 'motorway': 0., 'living_street': 7., 'bicycle_road': 30. } } class WayRelation(): """A class that represent the relationship of an OSM way and a given `location` and `bearing` of a driving vehicle. """ def __init__(self, way, location=None, bearing=None): self.way = way self.reset_location_variables() self.direction = DIRECTION.NONE self._speed_limit = None # Create a numpy array with nodes data to support calculations. self._nodes_np = np.radians(np.array([[nd.lat, nd.lon] for nd in way.nodes], dtype=float)) # Define bounding box to ease the process of locating a node in a way. # [[min_lat, min_lon], [max_lat, max_lon]] self.bbox = np.row_stack((np.amin(self._nodes_np, 0), np.amax(self._nodes_np, 0))) if location is not None and bearing is not None: self.update(location, bearing) def __repr__(self): return f'(id: {self.id}, name: {self.name}, ref: {self.ref}, ahead: {self.ahead_idx}, \ behind: {self.behind_idx}, {self.direction}, active: {self.active})' def reset_location_variables(self): self.location = None self.bearing = None self.active = False self.ahead_idx = None self.behind_idx = None self._active_way_bearing = None @property def id(self): return self.way.id def update(self, location, bearing): """Will update and validate the associated way with a given `location` and `bearing`. Specifically it will find the nodes behind and ahead of the current location and bearing. If no proper fit to the way geometry, the way relation is marked as invalid. """ self.reset_location_variables() # Ignore if location not in way boundingn box if not self.is_location_in_bbox(location): return # TODO: Do this with numpy. Calculate distance and bearing to all nodes and then process array to find # best match if any. for idx, node in enumerate(self.way.nodes): distance_to_node, bearing_to_node = distance_and_bearing(location, (node.lat, node.lon)) delta, direction = absoule_delta_with_direction(bearing_delta(bearing, bearing_to_node)) if abs(delta) > interp(distance_to_node, _ACCEPTABLE_BEARING_DELTA_BP, _ACCEPTABLE_BEARING_DELTA_V): continue if direction == DIRECTION.AHEAD: self.ahead_idx = idx self.distance_to_node_ahead = distance_to_node if self.behind_idx is not None: break elif direction == DIRECTION.BEHIND: self.behind_idx = idx if self.ahead_idx is not None: break # Validate if self.ahead_idx is None or self.behind_idx is None or abs(self.ahead_idx - self.behind_idx) > 1: self.reset_location_variables() return self.active = True self.location = location self.bearing = bearing self._speed_limit = None self.direction = DIRECTION.FORWARD if self.ahead_idx - self.behind_idx > 0 else DIRECTION.BACKWARD def update_direction_from_starting_node(self, start_node_id): self._speed_limit = None if self.way.nodes[0].id == start_node_id: self.direction = DIRECTION.FORWARD elif self.way.nodes[-1].id == start_node_id: self.direction = DIRECTION.BACKWARD else: self.direction = DIRECTION.NONE def is_location_in_bbox(self, location): """Indicates if a given location is contained in the bounding box surrounding the way. self.bbox = [[min_lat, min_lon], [max_lat, max_lon]] """ radians = np.radians(np.array(location, dtype=float)) is_g = np.greater_equal(radians, self.bbox[0, :]) is_l = np.less_equal(radians, self.bbox[1, :]) return np.all(np.concatenate((is_g, is_l))) @property def speed_limit(self): if self._speed_limit is not None: return self._speed_limit # Get string from corresponding tag limit_string = self.way.tags.get("maxspeed") if limit_string is None: if self.direction == DIRECTION.FORWARD: limit_string = self.way.tags.get("maxspeed:forward") elif self.direction == DIRECTION.BACKWARD: limit_string = self.way.tags.get("maxspeed:backward") # When limit is set to 0. is considered not existing. Use 0. as default value. limit = 0. # https://wiki.openstreetmap.org/wiki/Key:maxspeed if limit_string is not None: # Look for matches of speed by default in kph, or in mph when explicitly noted. v = re.match(r'^\s*([0-9]{1,3})\s*?(mph)?\s*$', limit_string) if v is not None: conv = CV.MPH_TO_MS if v[2] is not None and v[2] == "mph" else CV.KPH_TO_MS limit = conv * float(v[1]) else: # Look for matches of speed with country implicit values. v = re.match(r'^\s*([A-Z]{2}):([a-z_]+):?([0-9]{1,3})?(\s+)?(mph)?\s*', limit_string) if v is not None: if v[2] == "zone" and v[3] is not None: conv = CV.MPH_TO_MS if v[5] is not None and v[5] == "mph" else CV.KPH_TO_MS limit = conv * float(v[3]) elif v[1] in _COUNTRY_LIMITS_KPH and v[2] in _COUNTRY_LIMITS_KPH[v[1]]: limit = _COUNTRY_LIMITS_KPH[v[1]][v[2]] * CV.KPH_TO_MS self._speed_limit = limit return self._speed_limit @property def ref(self): return self.way.tags.get("ref", None) @property def name(self): return self.way.tags.get("name", None) @property def active_bearing(self): """Returns the exact bearing of the portion of way we are currentluy located at. """ if self._active_way_bearing is not None: return self._active_way_bearing if not self.active: return None ahead_node = self.way.nodes[self.ahead_idx] behind_node = self.way.nodes[self.behind_idx] self._active_way_bearing = bearing((behind_node.lat, behind_node.lon), (ahead_node.lat, ahead_node.lon)) return self._active_way_bearing def active_bearing_delta(self, bearing): """Returns the delta between the given bearing and the exact bearing of the portion of way we are currentluy located at. """ if self.active_bearing is None: return None return bearing_delta(bearing, self.active_bearing) @property def node_behind(self): return self.way.nodes[self.behind_idx] if self.behind_idx is not None else None @property def node_ahead(self): return self.way.nodes[self.ahead_idx] if self.ahead_idx is not None else None @property def last_node(self): """Returns the last node on the way considering the traveling direction """ if self.direction == DIRECTION.FORWARD: return self.way.nodes[-1] if self.direction == DIRECTION.BACKWARD: return self.way.nodes[0] return None def edge_on_node(self, node_id): """Indicates if the associated way starts or ends in the node with `node_id` """ return self.way.nodes[0].id == node_id or self.way.nodes[-1].id == node_id def next_wr(self, way_relations): """Returns a tuple with the next way relation (if any) based on `location` and `bearing` and the `way_relations` list excluding the found next way relation. (to help with recursion) """ if self.direction not in [DIRECTION.FORWARD, DIRECTION.BACKWARD]: return None, way_relations possible_next_wr = list(filter(lambda wr: wr.id != self.id and wr.edge_on_node(self.last_node.id), way_relations)) possibles = len(possible_next_wr) if possibles == 0: return None, way_relations if possibles == 1 or (self.ref is None and self.name is None): next_wr = possible_next_wr[0] else: next_wr = next((wr for wr in possible_next_wr if wr.has_name_or_ref(self.name, self.ref)), possible_next_wr[0]) next_wr.update_direction_from_starting_node(self.last_node.id) updated_way_relations = list(filter(lambda wr: wr.id != next_wr.id, way_relations)) return next_wr, updated_way_relations def has_name_or_ref(self, name, ref): if ref is not None and self.ref is not None and self.ref == ref: return True if name is not None and self.name is not None and self.name == name: return True return False
[ "selfdrive.mapd.lib.geo.distance_and_bearing", "numpy.amin", "selfdrive.mapd.lib.geo.bearing", "numpy.less_equal", "re.match", "common.numpy_fast.interp", "numpy.array", "selfdrive.mapd.lib.geo.bearing_delta", "numpy.concatenate", "numpy.amax", "numpy.greater_equal" ]
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from . import tools import os from datetime import datetime import logging import matplotlib.cm as mplcm import matplotlib.pyplot as plt import numpy as np from ipywidgets import Layout import ipywidgets as widgets from IPython.display import display import cv2 DEFAULT_EXTENSIONS = ['jpg', 'png', 'tif', 'iff', 'peg', 'ppm'] class OutputWidgetHandler(logging.Handler): """ Custom logging handler sending logs to an output widget """ def __init__(self, *args, **kwargs): super(OutputWidgetHandler, self).__init__(*args, **kwargs) layout = { 'width': '100%', 'height': '160px', 'border': '1px solid black' } self.out = widgets.Output(layout=layout) def emit(self, record): """ Overload of logging.Handler method """ formatted_record = self.format(record) new_output = { 'name': 'stdout', 'output_type': 'stream', 'text': formatted_record + '\n' } self.out.outputs = (new_output,) + self.out.outputs def show_logs(self): """ Show the logs """ display(self.out) def clear_logs(self): """ Clear the current logs """ self.out.clear_output() def create_logger(): logger = logging.getLogger(__name__) handler = OutputWidgetHandler() handler.setFormatter(logging.Formatter('%(asctime)s - [%(levelname)s] %(message)s')) logger.addHandler(handler) logger.setLevel(logging.INFO) return handler, logger # Global variables set in tools module: # tools.set_binary_thresholds() # global original_shape # global target_binary # global target_overlay # global target_grey # tools.adjust_contour_filters() # global filtered_contours def set_binary_thresholds(target_fn, cropx=None, cropy=None, thresholds=(100, 255), invert=False, gamma=1.0, brightness=0, contrast=0, clahe=False, clahe_window=50, figwidth=32, figheight=16, displayplot=True): # set global variables to be available to the widgets: global original_shape global target_binary global target_overlay global target_grey # print(target_fn, thresholds, figwidth, figheight) # get initial images: if cropx and cropy: x0, x1 = cropx y0, y1 = cropy crop = (y0, y1, x0, x1) else: crop = None target_original, target_overlay, target_grey = tools.get_base_images(target_fn, crop=crop) # invert if invert: target_grey = cv2.bitwise_not(target_grey) # apply contrast limited adaptive histogram equalization (CLAHE) if clahe: clahe_model = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(clahe_window, clahe_window)) target_grey = clahe_model.apply(target_grey) # apply brightness/contrast target_bc = tools.apply_contrast(target_grey, contrast, brightness) # apply gamma transformation target_gamma = tools.apply_gamma(target_bc, gamma) # convert to binary image target_binary = tools.get_binary(target_gamma, thresh=thresholds[0], maxval=thresholds[1]) # display output if displayplot: tools.display_three_plots(target_original, target_bc, target_binary, figsize=(figwidth, figheight,)) original_shape = target_original.shape # return target_binary, target_overlay def adjust_contour_filters(figwidth=32, figheight=16, target_fn=None, area=(20, 50000), contour_ratio=0.67, minwidth=20, ): global filtered_contours target_savedir = tools.get_savedir(target_fn) minarea = area[0] maxarea = area[1] # calculate contours of images (contours, hierarchy) = cv2.findContours(target_binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # annotate contours filtered_ids, filtered_contours = tools.collect_contours(contours, hierarchy, minarea=minarea, maxarea=maxarea, skip_first=False, contour_ratio=contour_ratio, minwidth=minwidth ) # draw contours target_contours = tools.draw_contours(target_overlay, filtered_contours, target_savedir=target_savedir, color=(255, 0, 0), figwidth=figwidth / 2, figheight=figheight, ) def widget_find_discontinuities(ksize=(3, 13), edge_thresholds=(15, 100), min_length=30, target_fn=None): min_edge_threshold, max_edge_threshold = edge_thresholds target_savedir = tools.get_savedir(target_fn) tools.find_discontinuities(target_grey, ksize=(3, 13), min_edge_threshold=15, max_edge_threshold=100, min_length=30, target_savedir=target_savedir) def widget_map_color(cmap, ): "Displays greyscale image and an LUT-converted image" if target_grey.shape[0] / target_grey.shape[1] < 1: tools.display_two_plots_v(target_grey, tools.apply_cmap(target_grey, cmap=cmap), figsize=(16,32)) else: tools.display_two_plots(target_grey, tools.apply_cmap(target_grey, cmap=cmap), figsize=(32, 16)) def widget_contour_similarity(target_fn=None, figsize=(30, 60), nrows=0, ncols=0, equalize=True, cmap=mplcm.gist_ncar): target_savedir = tools.get_savedir(target_fn) df_matchDist, Z, band_images, sorted_idx = tools.get_similar_bands(filtered_contours, target_savedir, target_grey, ) idx_filtered = tools.plot_colored_bands(sorted_idx, band_images, target_savedir, figsize=figsize, nrows=nrows, ncols=ncols, equalize=equalize, cmap=cmap ) def widget_similarity_listener(b): widget_contour_similarity(wfilepath.value) def widget_plot_dendrogram(): return None def widget_equalize(rows, columns, saveas, savetype, show_images): if show_images: splitsave = saveas else: splitsave = None splits = tools.split_image(target_grey, rows, columns, splitsave, savetype, show_images) equalized_cols = [np.vstack([cv2.equalizeHist(img) for img in col]) for col in splits if len(col) > 0] res = np.hstack(equalized_cols) # stacking images side-by-side plt.close() fig, ax = plt.subplots(figsize=(20, 10)) plt.imshow(res) plt.tight_layout() # plt.savefig(f"{saveas}_equalized.{savetype}") cv2.imwrite(f"{saveas}_equalized.{savetype}", res) def widget_noise_calculator(filepath, gaussian_k, median_k, bilateral_k, bilateral_r, figwidth, figheight): img = cv2.imread(filepath) tools.calculate_noise(target_grey, gaussian_k, median_k, bilateral_k, bilateral_r, show=True, figsize=(figwidth, figheight)) def load_binary_widgets(DIRECTORY, ext_list=DEFAULT_EXTENSIONS): """ Loads the widgets necessary for image cropping and exposure adjustment. Parameters ---------- DIRECTORY: str The location containing the image(s) to crop ext_list: list List of file extensions (as strings) to display Returns ------- wdirectory, wfilepath, wcropx, wcropy, winvert, wclahe, wbrange, wgamma, wbright, wcontrast, wfigwidth, wfigheight widget objects """ global wfilepath # globalize to make available to observe & update functions # define styling of widgets: items_layout = Layout(width='auto') # define all widgets for binary thresholding and output figsize wdirectory = widgets.Text(value=DIRECTORY, description="Directory of images:") wfilepath = widgets.Dropdown( options=[os.path.join(DIRECTORY, f) for f in os.listdir(DIRECTORY) if f[-3:].upper() in [ext.upper() for ext in ext_list]], description='File:', layout=items_layout) def update_image_options(change): wfilepath.options = [os.path.join(change.new, f) for f in os.listdir(change.new) if f[-3:].lower() in ['jpg', 'png', 'tif', 'iff', 'peg', 'ppm']] wdirectory.observe(update_image_options, 'value') wcropx = widgets.IntRangeSlider(value=[0, 1000], min=0, max=1000, step=10, description='Crop X axis:', continuous_update=False, layout=items_layout) wcropy = widgets.IntRangeSlider(value=[0, 1000], min=0, max=1000, step=10, description='Crop Y axis:', continuous_update=False, layout=items_layout) winvert = widgets.Checkbox(value=False, description="Invert image", layout=items_layout) wclahe = widgets.Checkbox(value=False, description="CLAH equalization:", layout=items_layout) wclahewin = widgets.IntSlider(value=50, min=1, max=200, step=1, description='CLAHE window:', layout=items_layout) wbrange = widgets.IntRangeSlider(value=[100, 255], min=0, max=255, step=1, description='Thresholds:', layout=items_layout) wgamma = widgets.FloatSlider(value=0.8, min=0, max=2.0, step=0.05, description="Gamma:", layout=items_layout) wbright = widgets.IntSlider(value=0.0, min=-100, max=100, step=1, description="Brightness:", layout=items_layout) wcontrast = widgets.FloatSlider(value=0.8, min=0, max=3.0, step=0.05, description="Contrast:", layout=items_layout) wfigwidth = widgets.IntSlider(value=32, min=1, max=32, step=1, description='Fig width:', layout=items_layout) wfigheight = widgets.IntSlider(value=16, min=1, max=48, step=1, description='Fig height:', layout=items_layout) return wdirectory, wfilepath, wcropx, wcropy, winvert, wclahe, wclahewin, wbrange, wgamma, wbright, wcontrast, wfigwidth, wfigheight def load_evaluation_widget(DIRECTORY, ext_list=DEFAULT_EXTENSIONS): """ Load the main widget for analyzing images from the specified directory Parameters ---------- DIRECTORY : str directory path of all images to concatenate ext_list : list list of all file extensions to include Returns ------- widget_tab widget object """ # define styling of widgets: items_layout = Layout(width='auto') # define all widgets for binary thresholding and output figsize wdirectory, wfilepath, wcropx, wcropy, winvert, wclahe, wclahewin, wbrange, wgamma, wbright, wcontrast, wfigwidth, wfigheight = load_binary_widgets( DIRECTORY, ext_list) # set widgets for contour extraction warange = widgets.IntRangeSlider(value=[20, 10000], min=10, max=10000, step=10, description='Area:', continuous_update=False, layout=items_layout) wratio = widgets.FloatSlider(value=0.67, min=0.1, max=2.0, step=0.02, description='ht/wdth ratio:', continuous_update=False, layout=items_layout) wminwidth = widgets.IntSlider(value=30, min=1, max=250, step=1, description='Min width:', continuous_update=False, layout=items_layout) # ### set widgets for edge discontinuity detection wksize = widgets.IntRangeSlider(value=[3, 13], min=1, max=21, step=2, description='k size:', continuous_update=False, layout=items_layout) wedgethresholds = widgets.IntRangeSlider(value=[15, 100], min=1, max=100, step=1, description='Edge thresholds:', continuous_update=False, layout=items_layout) wminedgelen = widgets.IntSlider(value=30, min=1, max=250, step=1, description='Min edge length:', continuous_update=False, layout=items_layout) ### set widgets for color mapping cmap_list = ['Spectral', 'coolwarm', 'gist_rainbow', 'viridis', 'jet', 'inferno', 'hsv', 'nipy_spectral', 'gist_ncar', 'gist_stern', 'RdYlGn', ] wcmaps = widgets.Dropdown(options=[(x, getattr(mplcm, x)) for x in cmap_list], description='CMAP:', layout=items_layout) wsavecmap = widgets.Button(description="FUTURE: Save Me") ### set widgets for band similarity detection wcalcsimilar = widgets.Button(description="Show similarities") wdummy = widgets.IntSlider(value=30, min=1, max=250, step=1, description='Dummy slider:', continuous_update=False, layout=items_layout) wsavebands = widgets.Button(description="FUTURE: Save Me") wbandfigsize = widgets.IntRangeSlider(value=[30, 30], min=5, max=120, step=1, description='Figsize (w,h):', continuous_update=False, layout=items_layout) wbandnrows = widgets.IntSlider(value=0, min=0, max=40, step=1, description='Num. rows:', continuous_update=False, layout=items_layout) wbandncols = widgets.IntSlider(value=0, min=0, max=40, step=1, description='Num. cols:', continuous_update=False, layout=items_layout) wequalize = widgets.Checkbox(value=True, description="Equalize bands", layout=items_layout) wcalcsimilar.on_click(widget_contour_similarity) ### set widgets for noise detection wgaussian = widgets.IntSlider(value=5, min=1, max=15, step=2, description='Gaussian kernal size:', continuous_update=False, layout=items_layout) wmedian = widgets.IntSlider(value=5, min=1, max=15, step=2, description='Median kernal size:', continuous_update=False, layout=items_layout) wbilateralk = widgets.IntSlider(value=9, min=1, max=15, step=2, description='Bilateral kernal size:', continuous_update=False, layout=items_layout) wbilateralr = widgets.IntSlider(value=25, min=1, max=95, step=2, description='Bilateral radiius:', continuous_update=False, layout=items_layout) wnfigwidth = widgets.BoundedIntText(value=20, min=1, max=100, step=1, description="Figure width:", layout=items_layout) wnfigheight = widgets.BoundedIntText(value=30, min=1, max=100, step=1, description="Figure height:", layout=items_layout) # set reporting of widget values widgetlist = [wdirectory, wfilepath, wcropx, wcropy, winvert, wclahe, wclahewin, wbrange, wgamma, wbright, wcontrast, wfigwidth, wfigheight, warange, wratio, wminwidth, wksize, wedgethresholds, wminedgelen, wcmaps, wsavecmap, wbandfigsize, wbandnrows, wbandncols, wequalize, wgaussian, wmedian, wbilateralk, wbilateralr, wnfigwidth, wnfigheight, ] widgetnames = ["wdirectory", "wfilepath", "wcropx", "wcropy", "winvert", "wclahe", "wclahewin", "wbrange", "wgamma", "wbright", "wcontrast", "wfigwidth", "wfigheight", "warange", "wratio", "wminwidth", "wksize", "wedgethresholds", "wminedgelen", "wcmaps", "wsavecmap", "wbandfigsize", "wbandnrows", "wbandncols", "wequalize", "wgaussian", "wmedian", "wbilateralk", "wbilateralr", "wnfigwidth", "wnfigheight", ] def get_widget_value_string(): valuelog = {"TIME": datetime.now()} for i, w in enumerate(widgetlist): try: valuelog[widgetnames[i]] = w.value except AttributeError: pass logstring = "\n".join([f"{w:<15s}: {v}" for w, v in valuelog.items()]) return logstring def get_log_file(): savedir = os.path.join(wdirectory.value, 'log_files') if os.path.exists(savedir): pass else: os.mkdir(savedir) analysis_file = os.path.basename(wfilepath.value) logfile = os.path.join(savedir, f"{analysis_file}.log") return logfile wviewvalues = widgets.Button(description="Show widget values") wsavelog = widgets.Button(description=f"Save to {get_log_file()}", layout={'width': 'auto'}) outlog = widgets.Output(layout={'border': '1px solid black'}) @outlog.capture(clear_output=True) def report_widget_values(click): logstring = get_widget_value_string() print(logstring) def save_value_log(click): logfile = get_log_file() logstring = get_widget_value_string() with open(logfile, 'a') as handle: handle.write(logstring) def update_save_button(change): wsavelog.description = f"Save to {get_log_file()}" wviewvalues.on_click(report_widget_values) wsavelog.on_click(save_value_log) wfilepath.observe(update_save_button, 'value') ########################## # customize binary display outbin = widgets.interactive_output(set_binary_thresholds, {'target_fn': wfilepath, 'cropx': wcropx, 'cropy': wcropy, 'thresholds': wbrange, 'invert': winvert, 'gamma': wgamma, 'brightness': wbright, 'contrast': wcontrast, 'clahe': wclahe, 'clahe_window':wclahewin, 'figwidth': wfigwidth, 'figheight': wfigheight }) # customize contour extraction display outcont = widgets.interactive_output(adjust_contour_filters, { 'figwidth': wfigwidth, 'figheight': wfigheight, 'target_fn': wfilepath, 'area': warange, 'contour_ratio': wratio, 'minwidth': wminwidth, }) # customize discontinuity finder display outedge = widgets.interactive_output(widget_find_discontinuities, {'ksize': wksize, 'edge_thresholds': wedgethresholds, 'min_length': wminedgelen, 'target_fn': wfilepath, }) # LUT color mapping display outcmap = widgets.interactive_output(widget_map_color, {'cmap': wcmaps}) # customize noise display outnoise = widgets.interactive_output(widget_noise_calculator, {'filepath': wfilepath, 'gaussian_k': wgaussian, 'median_k': wmedian, 'bilateral_k': wbilateralk, 'bilateral_r': wbilateralr, 'figwidth': wnfigwidth, 'figheight': wnfigheight, }, ) # customize band similarity display outsimilar = widgets.interactive_output(widget_contour_similarity, {'target_fn': wfilepath, 'figsize': wbandfigsize, 'nrows': wbandnrows, 'ncols': wbandncols, 'equalize': wequalize, 'cmap': wcmaps, }, ) # update crop sliders with dimensions of original image def update_xylim(change): wcropx.max = original_shape[1] wcropy.max = original_shape[0] outbin.observe(update_xylim, ) # create tab views box_layout = Layout(display='flex', flex_flow='column', align_items='stretch', # border='dashed', width='50%', margin='10px', padding='10px', ) binarytab = widgets.VBox([widgets.VBox([wdirectory, wfilepath, wcropx, wcropy, widgets.HBox([winvert, wclahe], ), wclahewin, wbrange, wgamma, wbright, wcontrast, wfigwidth, wfigheight], layout=box_layout), outbin], layout=Layout(border='solid', margin='3')) contourtab = widgets.VBox([widgets.VBox([warange, wratio, wminwidth], layout=box_layout), outcont], layout=Layout(border='solid')) edgetab = widgets.VBox([widgets.VBox([wksize, wedgethresholds, wminedgelen], layout=box_layout), outedge], layout=Layout(border='solid')) noisetab = widgets.VBox([widgets.VBox([wgaussian, wmedian, wbilateralk, wbilateralr, wnfigwidth, wnfigheight], layout=box_layout), outnoise, ], layout=Layout(border='solid')) cmaptab = widgets.VBox([widgets.VBox([wcmaps, wsavecmap], layout=box_layout), outcmap, ], layout=Layout(border='solid')) bandstab = widgets.VBox([widgets.VBox([wcalcsimilar, wbandfigsize, wbandnrows, wbandncols, wcmaps, wequalize], layout=box_layout), outsimilar, ], layout=Layout(border='solid')) reporttab = widgets.VBox([widgets.VBox([wviewvalues, wsavelog, ], layout=box_layout), outlog, ], layout=Layout(border='solid')) # add layouts to tabs for condensed viewing and handling: tab = widgets.Tab() tab.children = [binarytab, contourtab, edgetab, noisetab, cmaptab, bandstab, reporttab, ] tab.set_title(0, "Create Mask") tab.set_title(1, "Create Contours") tab.set_title(2, "Find Discontinuities") tab.set_title(3, "View Noise") tab.set_title(4, "View False Color") tab.set_title(5, "View similarities") tab.set_title(6, "View widget values") return tab def crop_and_equalize(DIRECTORY, ext_list=DEFAULT_EXTENSIONS): # This interactive widget is for dividing the image up into columns and performing histogram equalization on each one. # define styling of widgets: items_layout = Layout(width='auto') box_layout = Layout(display='flex', flex_flow='column', align_items='stretch', # border='dashed', width='75%', margin='10px', padding='10px', ) wdirectory, wfilepath, wcropx, wcropy, winvert, wclahe, wbrange, wgamma, wbright, wcontrast, wfigwidth, wfigheight = load_binary_widgets( DIRECTORY, ext_list) def update_xylim(change): wcropx.max = original_shape[1] wcropy.max = original_shape[0] # customize display outbin = widgets.interactive_output(set_binary_thresholds, {'target_fn': wfilepath, 'cropx': wcropx, 'cropy': wcropy, 'thresholds': wbrange, 'invert': winvert, 'gamma': wgamma, 'brightness': wbright, 'contrast': wcontrast, 'clahe': wclahe, 'figwidth': wfigwidth, 'figheight': wfigheight }) outbin.observe(update_xylim, ) # define all widgets for image splitting and output split images # wdirectory = widgets.Text(value=DIRECTORY, description="Directory of images:") # wfilepath = widgets.Dropdown(options=[os.path.join(DIRECTORY, f) for f in os.listdir(DIRECTORY) if f[-3:] in ['jpg', 'png', 'peg', 'ppm']],description='File:', layout=items_layout) # wdirectory.observe(update_image_options, 'value') wrowfloat = widgets.FloatSlider(value=2, min=1, max=15.0, step=0.05, description="# rows:", layout=Layout(width='80%'), continuous_update=False) wcolfloat = widgets.FloatSlider(value=2, min=1, max=15.0, step=0.05, description="# columns:", layout=Layout(width='80%'), continuous_update=False) wrowtext = widgets.FloatText(value=2, description='# rows:', disabled=False, layout=items_layout) wcoltext = widgets.FloatText(value=2, description='# columns:', disabled=False, layout=items_layout) wsavesplits = widgets.Text(value=f"{DIRECTORY}split_image", description="Save new images as:", continuous_update=False) wfiletype = widgets.Dropdown(options=['jpg', 'png', 'svg', 'tif'], description='File type:', layout=items_layout) wshowsplit = widgets.Checkbox(value=False, description="Show splits:", layout=items_layout) # customize display outsplit = widgets.interactive_output(widget_equalize, {'rows': wrowfloat, 'columns': wcolfloat, 'saveas': wsavesplits, 'savetype': wfiletype, 'show_images': wshowsplit, }) # In[157]: croppingtab = widgets.VBox([widgets.VBox([wdirectory, wfilepath, wcropx, wcropy, winvert, wbrange, wgamma, wbright, wcontrast, wsavesplits, wfiletype], layout=box_layout), outbin], layout=Layout(border='solid', margin='3')) splittingtab = widgets.VBox([widgets.VBox([widgets.HBox([wrowfloat, wrowtext]), widgets.HBox([wcolfloat, wcoltext]), wsavesplits, wfiletype, wshowsplit, ], layout=box_layout), outsplit], layout=Layout(border='solid', margin='3')) # synchronise the slider and text box values def update_col_val(*args): wcolfloat.value = wcoltext.value def update_row_val(*args): wrowfloat.value = wrowtext.value wcoltext.observe(update_col_val, 'value') wrowtext.observe(update_row_val, 'value') # add layouts to tabs for condensed viewing and handling: tab = widgets.Tab() tab.children = [croppingtab, splittingtab, ] tab.set_title(0, "Crop image") tab.set_title(1, "Split & Equalize") return tab
[ "logging.getLogger", "IPython.display.display", "ipywidgets.FloatText", "ipywidgets.VBox", "numpy.hstack", "ipywidgets.Tab", "ipywidgets.Dropdown", "ipywidgets.BoundedIntText", "ipywidgets.FloatSlider", "matplotlib.pyplot.imshow", "os.path.exists", "ipywidgets.HBox", "os.listdir", "ipywidg...
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import os import caffe import numpy as np import skimage import tensorflow as tf from Preprocessor import Preprocessor from datasets.ImageNet import ImageNet from models.AlexNetConverter import AlexNetConverter from models.SDNet import SDNet from train.SDNetTrainer import SDNetTrainer im_s = 227 def preprocess(img): out = np.copy(img) out = out[:, :, [2, 1, 0]] # swap channel from RGB to BGR out = out.transpose((2, 0, 1)) # h, w, c -> c, h, w return out def load_image(path): # load image img = np.float32(skimage.io.imread(path)) img /= 127.5 img -= 1.0 # we crop image from center short_edge = min(img.shape[:2]) yy = int((img.shape[0] - short_edge) / 2) xx = int((img.shape[1] - short_edge) / 2) crop_img = img[yy: yy + short_edge, xx: xx + short_edge] # resize to 224, 224 resized_img = skimage.transform.resize(crop_img, (im_s, im_s)) return resized_img model = SDNet(num_layers=5, target_shape=[im_s, im_s, 3], batch_size=16, disc_pad='VALID') data = ImageNet() preprocessor = Preprocessor(target_shape=[im_s, im_s, 3]) trainer = SDNetTrainer(model=model, dataset=data, pre_processor=preprocessor, num_epochs=80, tag='refactored', lr_policy='const', optimizer='adam') model_dir = '../test_converter' proto_path = 'deploy.prototxt' ckpt = '../test_converter/model.ckpt-800722' save_path = os.path.join(model_dir, 'alexnet_v2.caffemodel') np.random.seed(42) img = load_image('cat.jpg') converter = AlexNetConverter(model_dir, model, trainer.sess, ckpt=ckpt, remove_bn=True, scale=1.0, bgr=True, im_size=(im_s, im_s), with_fc=False, use_classifier=False) with converter.sess: converter.extract_and_store() net, encoded = model.discriminator.encode(tf.constant(img, shape=[1, im_s, im_s, 3], dtype=tf.float32), with_fc=converter.with_fc, reuse=True, training=False) result_tf = encoded.eval() converter.load_and_set_caffe_weights(proto_path=proto_path, save_path=save_path) # Testing net_caffe = caffe.Net(proto_path, save_path, caffe.TEST) net_caffe.blobs['data'].data[0] = preprocess(img) assert net_caffe.blobs['data'].data[0].shape == (3, im_s, im_s) net_caffe.forward() result_caffe = net_caffe.blobs['Convolution5'].data[0] result_caffe = result_caffe.transpose((1, 2, 0)) # h, w, c -> c, h, w print(np.linalg.norm(result_tf - result_caffe))
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''' 间接平差 main.py ''' import pandas as pd import numpy as np import sys np.set_printoptions(threshold=sys.maxsize) #print显示完整array from gnssnet import * import math def Save2Excel(mats,name): data = pd.DataFrame(mats) writer = pd.ExcelWriter("C:\\Users\\sheld\\Desktop\\111\\"+ "间接平差的" +name + ".xlsx") data.to_excel(writer, "page_1", float_format = '%.6f')#浮点数，精确到6位小数 writer.save() writer.close() def init_GNSSNet(G): #读入所有的基线坐标信息 with open("data.csv",'r') as f: for line in f.readlines(): b= Baseline() b.init_baseline(line.split(',')) G.insert_Baseline(b) f.close() #已经手算过近似坐标了 with open("zhandian.csv",'r',encoding='UTF-8-sig') as f: i = 1 for line in f.readlines(): p = station() p.init_station2(line) if not p.cat_match(): p.beizhu(i) i = i + 1 G.insert_Station(p) f.close() n = 63 t = 24 r = n - t global G G = GNSSNet() init_GNSSNet(G) P = np.zeros([n,n], dtype = float) L = np.zeros([n,1], dtype = float) l = np.zeros([n,1], dtype = float) ''' L^ = BX^ + d l = L - (BX0 + d) V = Bx^ - l ''' B = np.zeros([n,t], dtype = float) d = np.zeros([n,1], dtype = float) X0 = np.zeros([t,1], dtype = float) x = np.zeros([t,1], dtype = float) #写入B,d矩阵 for baseline in G.BaselineSet: bl_inf = baseline.baseline_inf() num = bl_inf[0] origin = bl_inf[1] target = bl_inf[2] #起点 found,ifknow,stat_inf = G.Station_Cat_Match(origin) if ifknow: d[3 * num - 3][0] = d[3 * num - 3][0] - stat_inf[2] d[3 * num - 2][0] = d[3 * num - 2][0] - stat_inf[3] d[3 * num - 1][0] = d[3 * num - 1][0] - stat_inf[4] ## print(num,bl_inf,"起点是已知点",stat_inf) elif not ifknow: B[3 * num - 3][3 * stat_inf[6] - 3] = -1 B[3 * num - 2][3 * stat_inf[6] - 2] = -1 B[3 * num - 1][3 * stat_inf[6] - 1] = -1 ## print(num,bl_inf,"起点是未知点",stat_inf) else: print("有错误的站点名") #末尾 found,ifknow,stat_inf = G.Station_Cat_Match(target) if ifknow: d[3 * num - 3][0] = d[3 * num - 3][0] + stat_inf[2] d[3 * num - 2][0] = d[3 * num - 2][0] + stat_inf[3] d[3 * num - 1][0] = d[3 * num - 1][0] + stat_inf[4] ## print(num,bl_inf,"终点是已知点",stat_inf) elif not ifknow: B[3 * num - 3][3 * stat_inf[6] - 3] = 1 B[3 * num - 2][3 * stat_inf[6] - 2] = 1 B[3 * num - 1][3 * stat_inf[6] - 1] = 1 ## print(num,bl_inf,"终点是未知点",stat_inf) else: print("有错误的站点名") ## print(B[3 * num - 3]) ## print(B[3 * num - 2]) ## print(B[3 * num - 1]) ## print(d[3 * num - 3]) ## print(d[3 * num - 2]) ## print(d[3 * num - 1]) #定权 P = G.init_P(P) L = G.init_L(L) x0 = G.init_X0(X0) P = np.matrix(P) L = np.matrix(L) X0 = np.matrix(X0) d = np.matrix(d) B = np.matrix(B) Q = P.I print("X0:\n",X0) print("L:\n",L) print("d:\n",d) print("B:\n",B) l = L - np.dot(B,X0) - d print("l:\n",l) #化成毫米单位 l = np.matrix(np.dot(l, 1000)) #Nbbx^ - W = 0 Nbb = np.dot(np.dot(B.T, P), B) W = np.dot(np.dot(B.T, P), l) #矩阵的秩 print(np.linalg.matrix_rank(B, tol=None, hermitian=False)) print(np.linalg.matrix_rank(l, tol=None, hermitian=False)) #x^ x = np.dot(Nbb.I,W) V = np.dot(B,x) - l print('x',x,'\n') print('V',V,'\n') time = 1 V_total = V x_total = x ## ##while abs(V.max()) > 0.00001 and time < 10000: ## L = L + V/1000 ## X0 = X0 + x/1000 ## ## l = L - np.dot(B,X0) - d ## ## l = np.matrix(np.dot(l, 1000)) ## x = np.dot(Nbb.I,W) ## V = np.dot(B,x) - l ## ## V_total = V_total + V ## x_total = x_total + x ## time = time + 1 ## ## L = L + V/1000 X0 = X0 + x/1000 print('L^',L) print("time:",time,'\n') print('V_total',V_total,'\n') print('x_total',x_total,'\n') sigema02 = np.dot(np.dot(V_total.T,P),V_total)/r sigema0 = math.sqrt(sigema02) print(sigema0) Save2Excel(B,"B") Save2Excel(d,"d") Save2Excel(L,"L^") Save2Excel(l,"l") Save2Excel(X0,"X^") Save2Excel(V_total,"V_total") Save2Excel(x_total,"x_total") Save2Excel(Nbb,"Nbb")
[ "numpy.linalg.matrix_rank", "pandas.ExcelWriter", "math.sqrt", "numpy.dot", "numpy.zeros", "pandas.DataFrame", "numpy.matrix", "numpy.set_printoptions" ]
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import argparse import json import time import sys,os, copy import tensorflow as tf import numpy as np # sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) from competitor.common.paths import RESULTS_DIR from competitor.models.loader import load_model, load_env from competitor.heuristics.loader import load_heuristics from competitor.recourse.utils import get_instance_info from competitor.recourse.search import SequenceSearch, ParamsSearch from competitor.recourse.utils import relu_cost_fn from competitor.recourse.config import base_config class ModelConfig(object): def __init__(self, name, ckpt, data): self.name = name self.ckpt = ckpt self.data = data def parse_args(): parser = argparse.ArgumentParser() parser.add_argument('--target-model', dest='model_name', type=str) parser.add_argument('--ckpt', default='model.h5',dest='model_ckpt', type=str) parser.add_argument('--target-data', default='data.npy',dest='data_filename', type=str) parser.add_argument('--mode', dest='mode', default='vanilla', type=str) parser.add_argument('--l', dest='length', default=4, type=int) parser.add_argument('--actions', dest='action_names', type=str, nargs='+') parser.add_argument('--exp-name', default='test', type=str) parser.add_argument('--instance-id', default=0, type=int) options = parser.parse_args() if options.model_name == 'quickdraw' and options.data_filename == 'data.npy': options.data_filename = 'data.npz' return options def pick_instance(model, target_env, idx=None, false_only=True): dataset, actions, features, desired_label = target_env if idx is not None: if false_only and not is_false(model, dataset, desired_label, idx): return None, None, None else: return dataset.data[idx], dataset.labels[idx], idx else: idx = -1 labels = dataset.labels instances = dataset.data target_instance = target_label = None if false_only: found = False while not found: idx = np.random.randint(0, labels.shape[0]) if is_false(model, dataset, desired_label, idx): found = True target_label = labels[idx] target_instance = instances[idx] print(idx, target_label, target_instance, model.predict(instances[idx:idx + 1])) return target_instance, target_label, idx def is_false(model, dataset, desired_label, idx): labels = dataset.labels instances = dataset.data labeled_false = np.argmax(labels[idx]) != np.argmax(desired_label) predicted_false = np.argmax(desired_label) != np.argmax( model.predict(instances[idx:idx + 1])[0]) return labeled_false and predicted_false def run_on_instance(options, instance_id, sav_dir=None): print(options) sav_dir = os.path.join(sav_dir, str(instance_id)) if not os.path.exists(sav_dir): os.makedirs(sav_dir) with tf.Session() as session: env = load_env(options.model_name, options.data_filename, used_actions=options.action_names) model = load_model(options.model_name, options.model_ckpt) instance, label, instance_id = pick_instance(model, env, idx=instance_id) if instance_id is None: print('Target label satisfied by original instance, stopping...') run_info = start_recording(instance_id, options) data, actions, features, target_label = env for name, feature in features.items(): feature.initialize_tf_variables() if options.model_name == 'quickdraw': actions = [action.set_p_selector(i, len(actions)) for i, action in enumerate(actions)] heuristics = load_heuristics(options.mode, actions, model, options.length) search = SequenceSearch(model, actions, heuristics, sav_dir=sav_dir, config=base_config) if options.model_name == 'quickdraw': result = search.find_correction(instance.reshape((1, instance.shape[0], instance.shape[1])), np.array([target_label]), session) else: result = search.find_correction(instance.reshape((1, instance.shape[0])), np.array([target_label]), session) out = dict(info=get_instance_info(instance, features), output=result.summary() if result.best_result is not None else 'Not Found') return end_recording(run_info, out) # return dict(instance=instance) def run_on_instance(options, instance_id, sav_dir=None): if sav_dir is not None: sav_dir = os.path.join(sav_dir, str(instance_id)) if not os.path.exists(sav_dir): os.makedirs(sav_dir) with tf.Session() as session: env = load_env(options.model_name, options.data_filename, used_actions=options.action_names) model = load_model(options.model_name, options.model_ckpt) instance, label, instance_id = pick_instance(model, env, idx=instance_id) run_info = start_recording(instance_id, options) data, actions, features, target_label = env for name, feature in features.items(): feature.initialize_tf_variables() if options.model_name == 'quickdraw': actions = [action.set_p_selector(i, len(actions)) for i, action in enumerate(actions)] heuristics = load_heuristics(options.mode, actions, model, options.length) search = SequenceSearch(model, actions, heuristics, sav_dir=sav_dir, config=base_config) if options.model_name == 'quickdraw': result = search.find_correction(instance.reshape((1, instance.shape[0], instance.shape[1])), np.array([target_label]), session) else: result = search.find_correction(instance.reshape((1, instance.shape[0])), np.array([target_label]), session) out = dict(info=get_instance_info(instance, features), output=result.summary() if result.best_result is not None else 'Not Found') return end_recording(run_info, out) def start_recording(target_idx, options): info = create_run_info(target_idx, options) print( "\n\n______________________________________________________________________________________________________") print(target_idx, info['start']) print('Starting %s run on item %d at %d' % (options.mode, target_idx, info['start'])) return info def create_run_info(target_idx, options): return dict(idx=target_idx, mode=options.mode, model=options.model_name, start=time.time(), end=0, time_taken=0, success=False) def end_recording(record, result): record['end'] = time.time() record['time_taken'] = record['end'] - record['start'] print('Finished %s run on item %d at %d, time taken: %d' % ( record['mode'], record['idx'], record['end'], record['time_taken'])) if result['output'] != 'Not Found': result['success'] = True print('Run Successful for item %d' % (record['idx'])) else: print('No solution found for item %d' % (record['idx'])) sys.stdout.flush() return {**record, **result} if __name__ == '__main__': FLAGS = parse_args() sav_dir = os.path.join('results',FLAGS.exp_name,FLAGS.model_name) if not os.path.exists(sav_dir): os.makedirs(sav_dir) json.dump(vars(base_config), open(os.path.join(sav_dir, 'config.json'), 'w'), indent=4) for i in range(100): # Pass a sav_dir below to save separate output files for each sequence searched # This is useful for running it in a distributed manner a = time.time() output = run_on_instance(FLAGS, i, sav_dir=None) tf.reset_default_graph() print('Time taken for instance', time.time()-a) if FLAGS.model_name != 'quickdraw' or output['success']: save_filename = os.path.join(sav_dir, ('run_%s.json' % (output['idx']))) json.dump(output, open(save_filename, 'w+'), indent=4)
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# Copyright 2019 United Kingdom Research and Innovation # Author: <NAME> (<EMAIL>) """Architecture-aware wrap for a dense matrix. """ import numpy class AMatrix: def __init__(self, a, arch='cpu', copy_data=False): self.__arch = arch self.__gpu = None if arch[:3] == 'gpu': try: from . import cuda_wrap as cuda from .dense_cublas import Matrix, Vectors self.__op = Matrix(a) self.__gpu = cuda except: if len(arch) > 3 and arch[3] == '!': raise RuntimeError('cannot use GPU') if self.__gpu is None: from .dense_cpu import Matrix, Vectors if copy_data: self.__op = Matrix(a.copy()) else: self.__op = Matrix(a) self.__gpu = None self.__Vectors = Vectors self.__vectors = None vmin = numpy.amin(a) vmax = numpy.amax(a) self.__scale = max(abs(vmin), abs(vmax)) def as_operator(self): return self.__op def as_vectors(self): if self.__vectors is None: self.__vectors = self.__Vectors(self.__op, shallow=True) return self.__vectors def arch(self): return self.__arch def gpu(self): return self.__gpu def dots(self): return self.__op.dots() def data_type(self): return self.__op.data_type() def shape(self): return self.__op.shape() def order(self): return self.__op.order() def scale(self): return self.__scale
[ "numpy.amax", "numpy.amin" ]
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import numpy as np import tensorflow as tf from keras.preprocessing.image import load_img import matplotlib.pyplot as plt IMG_SIZE = 64 SAVE_PATH = "C:\\Users\\schaefer\\Desktop\\ML\\vehicle-detection\\save\\model" IMG_PATH = "data/vehicles/1.png" img = load_img(IMG_PATH, target_size=(IMG_SIZE, IMG_SIZE)) img = np.asarray(img) plt.imshow(img) plt.show() print("\n\n", img.shape, "\n\n") model = tf.keras.models.load_model(SAVE_PATH) output = model.predict(img) print(f"Predicted: {output}")
[ "matplotlib.pyplot.imshow", "numpy.asarray", "keras.preprocessing.image.load_img", "tensorflow.keras.models.load_model", "matplotlib.pyplot.show" ]
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import cv2 as cv import numpy as np import time from timeit import repeat from multiprocessing import Pool import threading backSub = cv.createBackgroundSubtractorMOG2() # list to store clicked coordinates coords = [] # cut the given frame and rect with np array of coords def cut_image(frame, rect, pts): x,y,w,h = rect croped = frame[y:y+h, x:x+w].copy() ## (2) make mask pts = pts - pts.min(axis=0) mask = np.zeros(croped.shape[:2], np.uint8) cv.drawContours(mask, [pts], -1, (255, 255, 255), -1, cv.LINE_AA) ## (3) do bit-op dst = cv.bitwise_and(croped, croped, mask=mask) return dst def process(frames): filee = open(r"labels.txt", "a") for frame in frames: blurred = cv.GaussianBlur(frame, (5, 5), 0) fg = backSub.apply(blurred) output = cv.connectedComponentsWithStats(fg, 4, cv.CV_32S) (numLabels, labels, stats, centroids) = output for i in range(0, numLabels): x = stats[i, cv.CC_STAT_LEFT] y = stats[i, cv.CC_STAT_TOP] w = stats[i, cv.CC_STAT_WIDTH] h = stats[i, cv.CC_STAT_HEIGHT] area = stats[i, cv.CC_STAT_AREA] label_text = "person" ' ' + str(x) + ' ' + str(y) + ' ' + str(w) + ' ' + str(h) + '\n' if area > 400 and area < 1000: filee.write(label_text) if __name__=="__main__": capture = cv.VideoCapture(cv.samples.findFileOrKeep("right_sample2.mov")) coords = [(931,318),( 0,366), (223,974), (1905,577)] points = np.asarray(coords) shape = cv.boundingRect(points) if not capture.isOpened(): print('Unable to open: ') exit(0) # store all frames in a list frames = [] print('reading frames...') start_read = time.time() while True: ret, frame = capture.read() if frame is None: break image = cut_image(frame, shape, points) frames.append(image) end_read = time.time() print('processing frames...') # make 5 chunks chunks = [frames[i::5] for i in range(5)] tasks = [] start_process = time.time() for chunk in chunks: tasks.append(threading.Thread(target=process, args=(chunk,))) tasks[-1].start() for task in tasks: task.join() end_process = time.time() print('read time: ', end_read-start_read) print('process time: ', end_process-start_process)
[ "cv2.createBackgroundSubtractorMOG2", "cv2.drawContours", "cv2.bitwise_and", "numpy.asarray", "numpy.zeros", "cv2.connectedComponentsWithStats", "threading.Thread", "cv2.samples.findFileOrKeep", "cv2.GaussianBlur", "time.time", "cv2.boundingRect" ]
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import numpy as np import random from BoxProposing_utils import * import scipy.spatial.distance as ssd class BoxProposalModule(object): def __init__(self, *args, **kwargs): self.MinimapRatio = kwargs.get('MinimapRatio', 2) # we down-sample the normal boundary map to save computation cost self.min_box_size = kwargs.get('min_box_size', 64) // self.MinimapRatio self.min_aspect_ratio = kwargs.get('min_aspect_ratio', 0.3) self.max_aspect_ratio = kwargs.get('max_aspect_ratio', 10.) self.anchor_x_step = kwargs.get('anchor_x_step', 12) // self.MinimapRatio self.anchor_y_step = kwargs.get('anchor_y_step', 12) // self.MinimapRatio self.SafeGap = kwargs.get('SafeGap', 2) // self.MinimapRatio # shrink the proposals so that they wont get too close to the boundaries self.overlap_threshold = kwargs.get('overlap_threshold', 1) # threshold for overlap with boundaries in normal maps def BoxFitting(self, normal_boundary_map, box_x, box_y): """ Given the center point and normal boundary map :param normal_boundary_map: ndarray, HxW, 0/1 :param box_x: int :param box_y: int :return: UL_x, UL_y, BR_x, BR_y """ h, w = normal_boundary_map.shape x_left_min = 0 x_left_max = box_x - self.min_box_size // 2 x_right_min = box_x + self.min_box_size // 2 x_right_max = w y_down_min = 0 y_down_max = box_y - self.min_box_size // 4 y_up_min = box_y + self.min_box_size // 4 y_up_max = h # if already crossing boundary or outside if (x_left_max < 0) or \ (x_right_min >= w) or \ (y_down_max < 0) or \ (y_up_min >= h) or \ (np.sum(normal_boundary_map[y_down_max:y_up_min, x_left_max:x_right_min]) > self.overlap_threshold): return -1 enlargeable_flag = True enlargement_count = 0 direction = [1, 2, 3, 4] # right, left, down, up while enlargeable_flag: random.shuffle(direction) enlargeable_flag = False for d in direction: if d == 4: # up if y_up_max-y_up_min < 2: continue mid = (y_up_min+y_up_max) // 2 if np.sum(normal_boundary_map[y_down_max:mid, x_left_max:x_right_min]) > self.overlap_threshold: y_up_max = mid else: y_up_min = mid enlargeable_flag = True break elif d == 3: # down if y_down_max-y_down_min < 2: continue mid = (y_down_max+y_down_min) // 2 if np.sum(normal_boundary_map[mid:y_up_min, x_left_max:x_right_min]) > self.overlap_threshold: y_down_min = mid else: y_down_max = mid enlargeable_flag = True break elif d == 2: # left if x_left_max-x_left_min < 2: continue mid = (x_left_max+x_left_min) // 2 if np.sum(normal_boundary_map[y_down_max:y_up_min, mid:x_right_min]) > self.overlap_threshold: x_left_min = mid else: x_left_max = mid enlargeable_flag = True break elif d == 1: # right if x_right_max-x_right_min < 2: continue mid = (x_right_max+x_right_min) // 2 if np.sum(normal_boundary_map[y_down_max:y_up_min, x_left_max:mid]) > self.overlap_threshold: x_right_max = mid else: x_right_min = mid enlargeable_flag = True break if enlargeable_flag: enlargement_count += 1 if enlargement_count >= 15: if random.random() < 0.2: break return x_left_max+self.SafeGap, y_down_max+self.SafeGap, x_right_min-self.SafeGap, y_up_min-self.SafeGap def BoxProposing(self, normal_boundary_map, proposalnumber): """ normal_boundary_map: ndarray, HxW 0/1, binary maps proposalnumber: how many proposals to return """ H, W = normal_boundary_map.shape Proposals = [] hs = [h for h in range(random.randint(0, self.anchor_y_step // self.MinimapRatio), H, self.anchor_y_step // self.MinimapRatio)] random.shuffle(hs) ws = [w for w in range(random.randint(0, self.anchor_x_step // self.MinimapRatio), W, self.anchor_x_step // self.MinimapRatio)] random.shuffle(ws) # random visits for h in hs[:]: for w in ws[:]: result = self.BoxFitting(normal_boundary_map, w, h) # filter out failed proposals if result == -1: continue UL_x, UL_y, BR_x, BR_y = result normal_boundary_map[UL_y:BR_y, UL_x: BR_x] = 1 center_x = (UL_x + BR_x) / 2 center_y = (UL_y + BR_y) / 2 result = [center_x - self.min_box_size / 2, center_y - self.min_box_size / 2, center_x + self.min_box_size / 2, center_y + self.min_box_size / 2] Proposals.append(np.array(result, dtype=np.int)*self.MinimapRatio) #if len(Proposals) >= proposalnumber * 4: #random.shuffle(Proposals) #return Proposals[:proposalnumber] random.shuffle(Proposals) return Proposals#[:proposalnumber] def BoxRefining(self, depth_map, proposalnumber, Proposals): idxes = len(Proposals) labels = np.zeros_like(Proposals, dtype=bool) if(len(labels.shape) == 2): labels = labels[:,0] xyz = depth2xyz(depth_map) for idx in range(idxes): UL_x, UL_y, BR_x, BR_y = Proposals[idx] masks = np.zeros((depth_map.shape[0],depth_map.shape[1]), dtype='uint8') masks[UL_y:BR_y,UL_x:BR_x] += 1 pt = xyz[masks==1] if pt.shape[0] == 0: continue pt_sample = sample_grid_neighbours(masks,100) labels[idx] = isplanar(pt,pt_sample,0.01,0.80,0.10) print(labels) Proposals = np.array(Proposals)[labels] return Proposals[:proposalnumber] def ValidBox(self, depth_map, mask): xyz = depth2xyz(depth_map) pt = xyz[mask==1] if pt.shape[0] == 0: return False pt_sample = sample_grid_neighbours(mask,100) if pt_sample is None: return False return isplanar(pt,pt_sample,0.01,0.95,0.10) #print(labels) #Proposals = np.array(Proposals)[labels] #return Proposals[:proposalnumber] def SynthTextBox(self,light_map_, depth_map_,seg,Proposals,proposalnumber): itext = [] ibb = [] res = [] idict = {'img':[], 'charBB':None, 'wordBB':None, 'txt':None} min_char_height = 15 min_asp_ratio = 0.4 text_renderer = RenderFont('data') for i in range(len(Proposals)): try: UL_x, UL_y, BR_x, BR_y = Proposals[i] depth_map = depth_map_#[UL_y:BR_y,UL_x:BR_x] light_map = light_map_#[UL_y:BR_y,UL_x:BR_x] xyz = depth2xyz(depth_map) seg = np.ones(depth_map.shape) masks = seg == 1 pt_sample = sample_grid_neighbours(masks,1000) try: coeffs, inliers = isplanar(xyz[masks],pt_sample,0.10,999,0.1, returnCoeffs= True) except: continue coeffs = np.array([coeffs]) inliers = np.array([inliers]) labels = np.array([1]) try: place_masks, Hs, Hinvs = filter_for_placement(xyz,seg,coeffs,labels) except: continue n_regions = len(place_masks) if n_regions < 1: return [] m = get_num_text_regions(n_regions) reg_idx = np.arange(min(2*m,n_regions)) np.random.shuffle(reg_idx) reg_idx = reg_idx[:m] placed = False img = light_map.copy() # process regions: num_txt_regions = len(reg_idx) NUM_REP = 5 # re-use each region three times: reg_range = np.arange(NUM_REP * num_txt_regions) % num_txt_regions for idx in reg_range: ireg = reg_idx[idx] txt_render_res = place_text(img,place_masks[ireg], Hs[ireg], Hinvs[ireg]) if txt_render_res is not None: placed = True img,text,bb,collision_mask = txt_render_res # update the region collision mask: place_masks[ireg] = collision_mask # store the result: itext.append(text) print("Placed") bw = char2wordBB(bb.copy(), ' '.join(itext)) #import matplotlib.pyplot as plt #plt.imshow(light_map_) #plt.scatter(bw[0,:,:]+UL_x,bw[1,:,:]+UL_y) #plt.show() print(bw) res.append(bw.copy()) break except: continue try: res = np.concatenate(res, axis=2) except: pass random.shuffle(res) return res[:proposalnumber]
[ "random.shuffle", "numpy.ones", "numpy.array", "numpy.zeros", "numpy.sum", "numpy.concatenate", "random.random", "numpy.zeros_like", "random.randint", "numpy.arange", "numpy.random.shuffle" ]
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# -*- coding: utf-8 -*- # Copyright 2018 The Blueoil Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================= """Test file for GraphRunner.""" import unittest from core.data_types import Float32, Uint32, Int32, QUANTIZED_NOT_PACKED from core.graph import Graph, GraphRunner from core.optimizer import Optimizer from core.operators import Add, AveragePool, BatchNormalization, Constant, Conv, Identity, Input, \ MaxPool, Operator, Output, Transpose, QTZ_binary_mean_scaling, QTZ_linear_mid_tread_half, Reshape, Softmax import numpy as np from typing import Any, Dict, List, Tuple class TestOptimizer(unittest.TestCase): """Test class for GraphRunner.""" def test_precompute1(self) -> None: """Test code for precompute optimizer.""" data1 = np.random.rand(3, 2, 2, 3) data2 = np.random.rand(3, 2, 2, 3) data3 = np.random.rand(3, 2, 2, 3) graph1 = self.create_sample_graph(data1, data2, data3) graph2 = self.create_precompute_graph(data1, data2, data3) optim = Optimizer() optim.precompute(graph1) # for debug # from frontend import TensorFlowIO # from core.model import Model # import os # io = TensorFlowIO() # tmp_dir = os.path.join('tmp') # if not os.path.exists(tmp_dir): # os.mkdir(tmp_dir) # path = os.path.join('tmp', 'test_precompute.pb') # model = Model() # model.graph = graph1 # io.write(model, path) self.assertEqual(graph1, graph2, 'precompute failed.') print("Precompute test #1 passed!") def test_precompute2(self) -> None: """Test code for precompute optimizer.""" data1 = np.random.rand(3, 2, 2, 3) data2 = np.random.rand(3, 2, 2, 3) data3 = np.random.rand(3, 2, 2, 3) graph1 = self.create_sample_graph(data1, data2, data3) graph2, scaling1, scaling2 = self.create_quantized_graph(data1, data2, data3) optim = Optimizer() optim.precompute(graph1, hard_quantized=True) self.assertEqual(graph1, graph2, 'precompute failed.') self.assertAlmostEqual(graph1.get_op('conv2').quantizer.scaling_factor, scaling2) # type: ignore print("Precompute test #2 passed!") def test_precompute3(self) -> None: """Test code for precompute optimizer.""" data1 = np.random.rand(3, 2, 2, 3) data2 = np.random.rand(3, 2, 2, 3) data3 = np.random.rand(3, 2, 2, 3) graph1 = self.create_sample_graph3(data1, data2, data3) graph2, scaling2, scaling3 = self.create_quantized_graph2(data1, data2, data3) optim = Optimizer() optim.precompute(graph1, hard_quantized=True) self.assertEqual(graph1, graph2, 'precompute failed.') self.assertAlmostEqual(graph1.get_op('conv2').quantizer.scaling_factor, scaling2) # type: ignore self.assertAlmostEqual(graph1.get_op('conv3').quantizer.scaling_factor, scaling3) # type: ignore print("Precompute test #3 passed!") def test_transpose_NHWC(self) -> None: """Test code for transpose_NHWC optimizer.""" data = np.random.rand(3, 2, 2, 1) graph1 = self.create_sample_graph2(data) graph2 = self.create_transposed_graph(data) optim = Optimizer() optim.transpose_NHWC(graph1) self.assertEqual(graph1, graph2, 'transpose to NHWC failed.') print("Transpose_NHWC test #1 passed!") def create_sample_graph(self, data1: np.ndarray, data2: np.ndarray, data3: np.ndarray) -> Graph: graph = Graph() # input x = Input( 'placeholder', [1, 5, 5, 3], Float32(), ) # constant and internal nodes w = Constant( 'weight', Float32(), data1 ) i = Identity( 'identity1', [3, 2, 2, 3], Float32(), {'input': w} ) t = Transpose( 'transpose1', [3, 2, 2, 3], Float32(), {'data': i}, perm=[3, 2, 1, 0] ) q = QTZ_binary_mean_scaling( 'qtz1', [3, 2, 2, 3], Float32(), {'input': t} ) # Conv conv1 = Conv( 'conv1', [1, 4, 4, 3], Float32(), {'X': x, 'W': q}, kernel_shape=[2, 2] ) i2 = Identity( 'identity2', [1, 4, 4, 3], Float32(), {'input': conv1} ) s1 = Constant( 'aq_const1', Float32(), np.array(1) ) s2 = Constant( 'aq_const2', Float32(), np.array(2) ) aq = QTZ_linear_mid_tread_half( 'aqtz1', [1, 4, 4, 3], Float32(), {'X': i2, 'Y': s1, 'Z': s2} ) dummy = Transpose( 'dummy', [1, 4, 4, 3], Float32(), {'data': aq}, perm=[0, 1, 2, 3] ) w2 = Constant( 'weight2', Float32(), data2 ) q2 = QTZ_binary_mean_scaling( 'qtz2', [3, 2, 2, 3], Float32(), {'input': w2} ) conv2 = Conv( 'conv2', [1, 3, 3, 3], Float32(), {'X': dummy, 'W': q2}, kernel_shape=[2, 2] ) s3 = Constant( 'aq_const1', Float32(), np.array(1) ) s4 = Constant( 'aq_const2', Float32(), np.array(2) ) aq2 = QTZ_linear_mid_tread_half( 'aqtz2', [1, 3, 3, 3], Float32(), {'X': conv2, 'Y': s3, 'Z': s4} ) w3 = Constant( 'weight3', Float32(), data3 ) i3 = Identity( 'identity3', [1, 3, 3, 3], Float32(), {'input': aq2} ) conv3 = Conv( 'conv3', [1, 2, 2, 3], Float32(), {'X': i3, 'W': w3}, kernel_shape=[2, 2] ) # One output y = Output( 'output', [1, 2, 2, 3], Float32(), {'input': conv3} ) # add ops to the graph graph.add_op_and_inputs(y) return graph def binary_mean_scaling(self, data: np.ndarray) -> Tuple[np.float32, np.ndarray]: return np.mean(np.abs(data)), np.sign(data).astype(np.float32) def create_precompute_graph(self, data1: np.ndarray, data2: np.ndarray, data3: np.ndarray) -> Graph: graph = Graph() # two inputs x = Input( 'placeholder', [1, 5, 5, 3], Float32(), ) scaling1, qdata = self.binary_mean_scaling(data1.transpose([3, 2, 1, 0])) w = Constant( 'weight', Float32(), qdata * scaling1 ) # Conv conv1 = Conv( 'conv1', [1, 4, 4, 3], Float32(), {'X': x, 'W': w}, kernel_shape=[2, 2] ) s1 = Constant( 'aq_const1', Float32(), np.array(1) ) s2 = Constant( 'aq_const2', Float32(), np.array(2) ) aq = QTZ_linear_mid_tread_half( 'aqtz1', [1, 4, 4, 3], Float32(), {'X': conv1, 'Y': s1, 'Z': s2} ) dummy = Transpose( 'dummy', [1, 4, 4, 3], Float32(), {'data': aq}, perm=[0, 1, 2, 3] ) scaling2, qdata2 = self.binary_mean_scaling(data2) w2 = Constant( 'weight2', Float32(), qdata2 * scaling2 ) conv2 = Conv( 'conv2', [1, 3, 3, 3], Float32(), {'X': dummy, 'W': w2}, kernel_shape=[2, 2] ) s3 = Constant( 'aq_const1', Float32(), np.array(1) ) s4 = Constant( 'aq_const2', Float32(), np.array(2) ) aq2 = QTZ_linear_mid_tread_half( 'aqtz2', [1, 3, 3, 3], Float32(), {'X': conv2, 'Y': s3, 'Z': s4} ) w3 = Constant( 'weight3', Float32(), data3 ) conv3 = Conv( 'conv3', [1, 2, 2, 3], Float32(), {'X': aq2, 'W': w3}, kernel_shape=[2, 2] ) # One output y = Output( 'output', [1, 2, 2, 3], Float32(), {'input': conv3} ) # add ops to the graph graph.add_op_and_inputs(y) return graph def create_quantized_graph(self, data: np.ndarray, data2: np.ndarray, data3: np.ndarray) \ -> Tuple[Graph, np.float32, np.float32]: graph = Graph() # two inputs x = Input( 'placeholder', [1, 5, 5, 3], Float32(), ) from modules.packer import Packer packer = Packer(1, 32) data = data.transpose([3, 2, 1, 0]) scaling, qdata = self.binary_mean_scaling(data) shape = list(data.shape) w = Constant( 'weight', Float32(), qdata * scaling, ) q = QTZ_binary_mean_scaling( 'qtz1', shape, Float32(), {'input': w} ) q.scaling_factor = scaling # Conv conv1 = Conv( 'conv1', [1, 4, 4, 3], Float32(), {'X': x, 'W': w}, kernel_shape=[2, 2], ) s1 = Constant( 'aq_const1', Float32(), np.array(1) ) s2 = Constant( 'aq_const2', Float32(), np.array(2) ) aq = QTZ_linear_mid_tread_half( 'aqtz1', [1, 4, 4, 3], QUANTIZED_NOT_PACKED(), {'X': conv1, 'Y': s1, 'Z': s2} ) dummy = Transpose( 'dummy', [1, 4, 4, 3], QUANTIZED_NOT_PACKED(), {'data': aq}, perm=[0, 1, 2, 3] ) scaling2, qdata2 = self.binary_mean_scaling(data2) w2 = Constant( 'weight2', Uint32(), packer.run(qdata2), packed=True, actual_shape=[3, 2, 2, 3] ) # quantizer connected to conv2 as 'conv2.quantizer' q2 = QTZ_binary_mean_scaling( 'qtz2', [3, 2, 2, 3], Uint32(), {'input': w2} ) q2.scaling_factor = scaling2 conv2 = Conv( 'conv2', [1, 3, 3, 3], Float32(), {'X': dummy, 'W': w2}, kernel_shape=[2, 2], quantized=True ) conv2.quantizer = q2 s3 = Constant( 'aq_const1', Float32(), np.array(1) ) s4 = Constant( 'aq_const2', Float32(), np.array(2) ) aq2 = QTZ_linear_mid_tread_half( 'aqtz2', [1, 3, 3, 3], Float32(), {'X': conv2, 'Y': s3, 'Z': s4} ) w3 = Constant( 'weight3', Float32(), data3 ) conv3 = Conv( 'conv3', [1, 2, 2, 3], Float32(), {'X': aq2, 'W': w3}, kernel_shape=[2, 2] ) # One output y = Output( 'output', [1, 2, 2, 3], Float32(), {'input': conv3} ) # add ops to the graph graph.add_op_and_inputs(y) return graph, scaling, scaling2 def create_sample_graph2(self, data: np.ndarray) -> Graph: graph = Graph() # input x = Input( 'placeholder', [3, 5, 5, 1], Float32(), dimension_format='CWHN' ) # constant and internal nodes w = Constant( 'weight', Float32(), data, dimension_format='CWHN' ) i = Identity( 'identity1', [3, 2, 2, 1], Float32(), {'input': w}, dimension_format='CWHN' ) q = QTZ_binary_mean_scaling( 'qtz1', [3, 2, 2, 1], Float32(), {'input': i}, dimension_format='CWHN' ) # Conv conv = Conv( 'conv', [3, 4, 4, 1], Float32(), {'X': x, 'W': q}, kernel_shape=[2, 2], dimension_format='CWHN' ) rs = Reshape( 'reshape', [1, 48], Float32(), {'data': conv} ) # One output y = Output( 'output', [1, 48], Float32(), {'input': rs}, ) # add ops to the graph graph.add_op_and_inputs(y) return graph def create_transposed_graph(self, data: np.ndarray) -> Graph: graph = Graph() data = data.transpose([3, 2, 1, 0]) # input x = Input( 'placeholder', [1, 5, 5, 3], Float32(), dimension_format='NHWC' ) # constant and internal nodes w = Constant( 'weight', Float32(), data, dimension_format='NHWC' ) i = Identity( 'identity1', [1, 2, 2, 3], Float32(), {'input': w}, dimension_format='NHWC' ) q = QTZ_binary_mean_scaling( 'qtz1', [1, 2, 2, 3], Float32(), {'input': i}, dimension_format='NHWC' ) # Conv conv = Conv( 'conv', [1, 4, 4, 3], Float32(), {'X': x, 'W': q}, kernel_shape=[2, 2], dimension_format='NHWC' ) rs = Reshape( 'reshape', [1, 48], Float32(), {'data': conv} ) # One output y = Output( 'output', [1, 48], Float32(), {'input': rs}, ) # add ops to the graph graph.add_op_and_inputs(y) return graph def create_sample_graph3(self, data1: np.ndarray, data2: np.ndarray, data3: np.ndarray) -> Graph: graph = Graph() # input x = Input( 'placeholder', [1, 5, 5, 3], Float32(), ) # constant and internal nodes w = Constant( 'weight', Float32(), data1 ) q = QTZ_binary_mean_scaling( 'qtz1', [3, 2, 2, 3], Float32(), {'input': w} ) # Conv conv1 = Conv( 'conv1', [1, 4, 4, 3], Float32(), {'X': x, 'W': q}, kernel_shape=[2, 2] ) i2 = Identity( 'identity2', [1, 4, 4, 3], Float32(), {'input': conv1} ) s1 = Constant( 'aq_const1', Float32(), np.array(1) ) s2 = Constant( 'aq_const2', Float32(), np.array(2) ) aq = QTZ_linear_mid_tread_half( 'aqtz1', [1, 4, 4, 3], Float32(), {'X': i2, 'Y': s1, 'Z': s2} ) w2 = Constant( 'weight2', Float32(), data2 ) q2 = QTZ_binary_mean_scaling( 'qtz2', [3, 2, 2, 3], Float32(), {'input': w2} ) conv2 = Conv( 'conv2', [1, 3, 3, 3], Float32(), {'X': aq, 'W': q2}, kernel_shape=[2, 2] ) w3 = Constant( 'weight3', Float32(), data3 ) q3 = QTZ_binary_mean_scaling( 'qtz3', [3, 2, 2, 3], Float32(), {'input': w3} ) conv3 = Conv( 'conv3', [1, 3, 3, 3], Float32(), {'X': aq, 'W': q3}, kernel_shape=[2, 2] ) y1 = Output( 'output1', [1, 3, 3, 3], Float32(), {'input': conv2} ) y2 = Output( 'output2', [1, 3, 3, 3], Float32(), {'input': conv3} ) # add ops to the graph graph.add_op_and_inputs(y1) graph.add_op_and_inputs(y2) return graph def create_quantized_graph2(self, data1: np.ndarray, data2: np.ndarray, data3: np.ndarray) -> Graph: graph = Graph() # input x = Input( 'placeholder', [1, 5, 5, 3], Float32(), ) # constant and internal nodes scaling1, qdata1 = self.binary_mean_scaling(data1) w = Constant( 'weight', Float32(), qdata1 * scaling1 ) q = QTZ_binary_mean_scaling( 'qtz1', [3, 2, 2, 3], Float32(), {'input': w} ) # Conv conv1 = Conv( 'conv1', [1, 4, 4, 3], Float32(), {'X': x, 'W': w}, kernel_shape=[2, 2] ) s1 = Constant( 'aq_const1', Float32(), np.array(1) ) s2 = Constant( 'aq_const2', Float32(), np.array(2) ) aq = QTZ_linear_mid_tread_half( 'aqtz1', [1, 4, 4, 3], QUANTIZED_NOT_PACKED(), {'X': conv1, 'Y': s1, 'Z': s2} ) from modules.packer import Packer packer = Packer(1, 32) scaling2, qdata2 = self.binary_mean_scaling(data2) w2 = Constant( 'weight2', Uint32(), packer.run(qdata2), packed=True, actual_shape=[3, 2, 2, 3] ) q2 = QTZ_binary_mean_scaling( 'qtz2', [3, 2, 2, 3], Float32(), {'input': w2} ) q2.scaling_factor = scaling2 conv2 = Conv( 'conv2', [1, 3, 3, 3], Float32(), {'X': aq, 'W': w2}, kernel_shape=[2, 2], quantized=True, ) conv2.quantizer = q2 scaling3, qdata3 = self.binary_mean_scaling(data3) w3 = Constant( 'weight2', Uint32(), packer.run(qdata3), packed=True, actual_shape=[3, 2, 2, 3] ) q3 = QTZ_binary_mean_scaling( 'qtz3', [3, 2, 2, 3], Float32(), {'input': w3} ) q3.scaling_factor = scaling3 conv3 = Conv( 'conv3', [1, 3, 3, 3], Float32(), {'X': aq, 'W': w3}, kernel_shape=[2, 2], quantized=True ) conv3.quantizer = q3 y1 = Output( 'output1', [1, 3, 3, 3], Float32(), {'input': conv2} ) y2 = Output( 'output2', [1, 3, 3, 3], Float32(), {'input': conv3} ) # add ops to the graph graph.add_op_and_inputs(y1) graph.add_op_and_inputs(y2) return graph, scaling2, scaling3 if __name__ == '__main__': unittest.main()
[ "core.optimizer.Optimizer", "numpy.abs", "modules.packer.Packer", "numpy.random.rand", "core.data_types.Uint32", "core.data_types.QUANTIZED_NOT_PACKED", "core.data_types.Float32", "numpy.array", "core.graph.Graph", "numpy.sign", "unittest.main" ]
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import chainer import chainer.functions as F import chainer.links as L from chainer import optimizers, cuda, serializers, Variable, initializers, Chain from chainer.functions.connection import convolution_2d from chainer.links.connection.convolution_2d import Convolution2D from chainer.functions.connection import deconvolution_2d from chainer.links.connection.deconvolution_2d import Deconvolution2D from chainer.functions.connection import linear from chainer.links.connection.linear import Linear import numpy as np xp = cuda.cupy def _l2normalize(v, eps=1e-12): return v / (((v**2).sum())**0.5 + eps) def max_singular_value(W, u=None, Ip=1): if u is None: u = xp.random.normal(size=(1, W.shape[0])).astype(xp.float32) _u = u for _ in range(Ip): _v = _l2normalize(xp.dot(_u, W.data), eps=1e-12) _u = _l2normalize(xp.dot(_v, W.data.transpose()), eps=1e-12) sigma = F.math.sum.sum(F.connection.linear.linear(_u, F.array.transpose.transpose(W))* _v) return sigma, _u, _v class SNConvolution2D(Convolution2D): def __init__(self, in_channels, out_channels, ksize, stride=1, pad=0, nobias=True, initialW=None, initial_bias=None, use_gamma=False, Ip=1): self.Ip = Ip self.u = None self.use_gamma = use_gamma super(SNConvolution2D, self).__init__(in_channels, out_channels, ksize, stride, pad, nobias, initialW, initial_bias) @property def W_bar(self): W_mat = self.W.reshape(self.W.shape[0], -1) sigma, _u, _ = max_singular_value(W_mat, self.u, self.Ip) sigma = F.array.broadcast.broadcast_to(sigma.reshape((1, 1, 1, 1)), self.W.shape) self.u = _u return self.W / sigma def _initialize_params(self, in_size): super(SNConvolution2D, self)._initialize_params(in_size) if self.use_gamma: W_mat = self.W.data.reshape(self.W.shape[0], -1) _, s, _ = np.linalg.svd(W_mat) with self.init_scope(): self.gamma = chainer.Parameter(s[0], (1, 1, 1, 1)) def __call__(self, x): if self.W.data is None: self._initialize_params(x.shape[1]) return convolution_2d.convolution_2d(x, self.W_bar, self.b, self.stride, self.pad) class SNDeconvolution2D(Deconvolution2D): def __init__(self, in_channels, out_channels, ksize, stride=1, pad=0, nobias=True, initialW=None, initial_bias=None, use_gamma=False, Ip=1): self.Ip = Ip self.u = None self.use_gamma = use_gamma super(SNDeconvolution2D, self).__init__(in_channels, out_channels, ksize, stride, pad, nobias, initialW, initial_bias) @property def W_bar(self): W_mat = self.W.reshape(self.W.shape[0], -1) sigma, _u, _ = max_singular_value(W_mat, self.u, self.Ip) sigma = F.array.broadcast.broadcast_to(sigma.reshape((1, 1, 1, 1)), self.W.shape) self.u = _u return self.W / sigma def _initialize_params(self, in_size): super(SNDeconvolution2D, self)._initialize_params(in_size) if self.use_gamma: W_mat = self.W.data.reshape(self.W.shape[0], -1) _, s, _ = np.linalg.svd(W_mat) with self.init_scope(): self.gamma = chainer.Parameter(s[0], (1, 1, 1, 1)) def __call__(self, x): if self.W.data is None: self._initialize_params(x.shape[1]) return deconvolution_2d.deconvolution_2d(x, self.W_bar, self.b, self.stride, self.pad) class SNLinear(Linear): def __init__(self, in_size, out_size, use_gamma=False, nobias=False, initialW=None, initial_bias=None, Ip=1): self.Ip = Ip self.u = None self.use_gamma = use_gamma super(SNLinear, self).__init__(in_size, out_size, nobias, initialW, initial_bias) @property def W_bar(self): sigma, _u, _ = max_singular_value(self.W, self.u, self.Ip) sigma = F.array.broadcast.broadcast_to(sigma.reshape((1, 1)), self.W.shape) self.u = _u return self.W / sigma def _initialize_params(self, in_size): super(SNLinear, self)._initialize_params(in_size) if self.use_gamma: _, s, _ = np.linalg.svd(self.W.data) with self.init_scope(): self.gamma = chainer.Parameter(s[0], (1, 1)) def __call__(self, x): if self.W.data is None: self._initialize_params(x.size // x.shape[0]) return linear.linear(x, self.W_bar, self.b)
[ "chainer.functions.connection.linear.linear", "chainer.functions.connection.deconvolution_2d.deconvolution_2d", "numpy.linalg.svd", "chainer.functions.connection.convolution_2d.convolution_2d", "chainer.Parameter", "chainer.functions.array.transpose.transpose" ]
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from ppadb.client import Client from PIL import Image from algo import minimax import math import numpy as np from time import sleep from tabulate import tabulate # red is 1 # yellow is 2 YELLOW = (255, 243, 0) RED = (222, 0, 0) X0, Y0 = (84, 633) DELTA = 94 ROWS, COLS = (6, 7) EMPTY = 0 PLAYER_PIECE = 1 AI_PIECE = 2 class AI: def __init__(self): self.device = None self.board = None self.image = None def set_device(self, dev): """ Set android device """ self.device = dev def show_board(self): """ Prints board to stdout, meant for debugging""" sleep(1) self.update_board() board = self.board.copy() board: list = np.flip(board, 0).tolist() for r in range(ROWS): for c in range(COLS): if board[r][c] == 1: board[r][c] = "R" elif board[r][c] == 2: board[r][c] = "Y" else: board[r][c] = " " print(tabulate(board, tablefmt="grid")) def put_piece(self, col_index): """ Tap on a column given index """ self.device.shell(f"input tap {X0 + col_index * DELTA} {Y0}") def is_my_turn(self): """If rgb value of (165, 216) is white, not our turn""" # left 216, 165 # right 211, 554 return not all(map(lambda x: x == 255, self.image[216][165][:3])) def update_image(self): """ Take screenshot and save """ scn_image = self.device.screencap() with open("screenshot.png", "wb") as fp: fp.write(scn_image) img = Image.open("screenshot.png") self.image = np.array(img, dtype=np.uint8) def update_board(self): """ Figure out board configuration from screenshot """ self.update_image() # Initialise board to 0 self.board = [[0 for _ in range(COLS)] for __ in range(ROWS)] for i in range(ROWS): for j in range(COLS): # find pieces from red component of pixel r = self.image[Y0 + i * DELTA][X0 + j * DELTA][0] if r == 255: self.board[i][j] = 2 elif r == 222: self.board[i][j] = 1 self.board = np.flip(self.board, 0) def play_one_move(self): """ Wait till turn and play best move """ print("checking if my turn...", end=" ") while True: self.update_image() if self.is_my_turn(): print("yes") break else: sleep(2) print("thinking...") self.update_board() # load minimax here col, minimax_score = minimax(self.board, 6, -math.inf, math.inf, True) print("selected column ", col) self.put_piece(col_index=col) self.show_board() def start(self): while True: self.play_one_move() def main(): adb = Client(host='127.0.0.1', port=5037) devices = adb.devices() if len(devices) == 0: print("No devices found.") quit(0) device = devices[0] player = AI() player.set_device(device) player.start() if __name__ == "__main__": try: main() except KeyboardInterrupt: print("\n------Exiting-------")
[ "numpy.flip", "tabulate.tabulate", "PIL.Image.open", "time.sleep", "algo.minimax", "numpy.array", "ppadb.client.Client" ]
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