def get_result_path(): cur_dir = get_root_path() dir_path = 'datas/' + MyCode.get() + '/' cur_dir += '/' cur_dir += dir_path agl.createDir(cur_dir) return cur_dir
def run(): fpath = env.get_root_path() for code in stock.get_codes(): fname = '/datasources/%s.csv' % (code) fname = fpath + fname df = stock.getFiveHisdatDf(code) df.to_csv(fname)
def load_user_info(): """json [user,pwd]""" fname = os.path.abspath(get_root_path()+'/../notify/'+'info.json') f = open(fname, 'r') t = str(f.read()) f.close() t = t.replace('\t','') t = t.replace('\n', '') return json.loads(t)
def __init__(self, reinit=False): self.fname = os.path.join(env.get_root_path(), 'datas', self.fname) if os.path.exists(self.fname) and reinit == False: self.df = pd.read_csv(self.fname) #print(self.df) else: result = self._initLabeDescTable() self.df = pd.DataFrame(result) self.df.to_csv(self.fname) print(self.df) print('write label_desc_table')
def getLocal(code): import pandas as pd from autoxd.cnn_boll import env fname = '../datas/%s.csv' % (code) fname = env.get_root_path() + '/datas/%s.csv' % (code) import os print(os.path.abspath(fname)) df = pd.read_csv(fname) df.index = pd.DatetimeIndex(df[df.columns[0]]) df = stock.TDX_BOLL_df(df) return df
def genImgToFile(code): """产生图形到文件 """ datas = load_data() # indexs: list 数据偏移索引 df = pd.read_csv(get_result_csv_path(), index_col=0) indexs = df['datas_index'].values cur_dir = get_root_path() fname = cur_dir + '/img_labels/imgs' if not os.path.exists(fname): agl.createDir(fname) for i in indexs: i = int(i) fname1 =fname + '/%s_%d.png'%(code, i) print(fname1) pl.figure draw(datas[i]) pl.savefig(fname1) pl.close()
def load_data(num=-1, method='img'): """加载imgs method: str img/data return: (x_train, y_train), (x_test, y_test) x_train, np.dnarray (num, row, col) y_train, (num, [0,0,0,1,0,0]) 分类标签 """ img_path = os.path.join(env.get_root_path(), 'img_labels/imgs/') files = os.listdir(img_path) files = files[:num] datas = None pre_code = '' imgs = [] labels = [] n = 28 label_converter = Label2Id() for f in files: fname = img_path + f #label #这里和pearson_clust里的数据加载有区别, 这里是遍历 f = f.split('.')[0] code, datas_index = str(f).split('_') print(code) datas_index = int(datas_index) label_path = os.path.join(env.get_root_path(), ('datas/%s/%s') % (code, g_fname_csv)) #... 等待人工标签结果, 人工标签最后再进行归类 table_colmns = "id,datas_index,code,dt,tick_period,clust_id,label_id,label_desc".split( ',') df = pd.read_csv(label_path) label = df[df[table_colmns[1]] == int(datas_index)] label_id = np.nan if len(label) > 0: label = label[table_colmns[-1]].values[0] if isinstance(label, str) and label[-1] == ',': label = label[:-1] label_id = label_converter.label_desc_to_label_id(label) labels.append(label_id) #img if method == 'img': img = cv2.imread(fname, cv2.IMREAD_GRAYSCALE) img = cv2.resize(img, (64 * 3, 48 * 3)) img = np.array(img) img[img == 255] = 0 if method == 'data': if pre_code != code: datas = main_load_data(code) #归一化 bolls = datas[datas_index] img = BollsToImg(bolls) img = img.astype(np.uint8) #print(img) imgs.append(img) #for i in range(5): #imgs += imgs #labels += labels data = np.array(imgs) labels = np.array(labels).astype(np.uint8) len_data = len(data) len_labels = len(labels) assert (len_data == len_labels) split_len_pos = int(len_data * 0.8) return (data[:split_len_pos], labels[:split_len_pos]), (data[split_len_pos:], labels[split_len_pos:])
def _getPath(self): """数据源目录 """ sources_path = env.get_root_path() + '/cnn_boll/datasources/' return sources_path
def _get_datas_dir(self): #cur_dir = os.path.abspath(os.path.dirname(sys.argv[0]) + '/..') cur_dir = env.get_root_path() cur_dir += '/datas' return cur_dir
def _getPath(self): """数据源目录 """ sources_path = 'datasources/' data_path = env.get_root_path() return data_path + '/' + sources_path