'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.10/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.10/howto/static-files/ STATIC_URL = '/static/' MEDIA_URL = '/media/' STATICFILES_DIRS = [ os.path.join(BASE_DIR, "HTMLTemplates/assets"), #'/var/www/static/', ] print "Base dir:", BASE_DIR STATIC_ROOT = os.path.join(os.path.join(dirname(BASE_DIR)), "static_cdn") MEDIA_ROOT = os.path.join(os.path.join(dirname(BASE_DIR)), "media")
import pandas as pd import numpy as np import matplotlib.pyplot as plt from macpath import dirname, join import sklearn from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split path = join(dirname(__file__), "california_housing_train.csv") data = pd.read_csv(path) data = data.values X = np.c_[data[:, 1], data[:, 2], data[:, 3], data[:, 4]] Y = np.c_[data[:, 0]] plt.scatter(X[:, 0], Y, s=10, color='g', marker='x') plt.title(" housing_meidan_age and median_house_value", fontsize=14) plt.xlabel('housing_median_age', fontsize=14) plt.ylabel('median_house_value', fontsize=10) plt.grid(True) plt.show() plt.scatter(X[:, 1], Y, s=10, color='g', marker='x') plt.title(" total_rooms and median_house_value", fontsize=14) plt.xlabel('total_rooms', fontsize=14) plt.ylabel('median_house_value', fontsize=10) plt.grid(True) plt.show()
def get_tens(iter): global cargo while 1: print "\nTENS State: ", while jump_to(cargo) == 'TENS': print "#%2.1f " % cargo, cargo = iter.next() yield (jump_to(cargo), cargo) def exit(iter): jump = raw_input('\n\n[co-routine for jump?] ').upper() print "...Jumping into middle of", jump yield (jump, iter.next()) print "\nExiting from exit()..." sys.exit() def toJSON(input): reader = Reader(input) parser = Parser(reader, asJson=True) return json.dumps(parser.runtime) # num_stream = math_gen(1) # cargo = num_stream.next() # gendct = {'ONES': get_ones(num_stream),'TENS': get_tens(num_stream), 'OUT_OF_RANGE': exit(num_stream)} # scheduler(gendct, jump_to(cargo)) if __name__ == "__main__": DIR = dirname(abspath(__file__)) TOMLFiles = glob(join(DIR, '*.toml')) if __name__ == '__main__': for filename in TOMLFiles: with open(filename) as file: print toJSON(file)
''' macpath 模块 macpath 模块( 参见 Example 13-2 )提供了 Macintosh 平台下的 os.path 功能. 你也可以使用它在其他平台处理 Macintosh 路径. ''' import macpath file = 'my:little:pony' print("isabs", "=>", macpath.isabs(file)) print("dirname", "=>", macpath.dirname(file)) print("basename", "=>", macpath.basename(file)) print("normpath", "=>", macpath.normpath(file)) print("split", "=>", macpath.split(file)) print("join", "=>", macpath.join(file, "zorba")) ''' isabs => True dirname => my:little basename => pony normpath => my:little:pony split => ('my:little', 'pony') join => my:little:pony:zorba '''