def main(settings_file: str):
"""
The function calls the function of downloading and checking all files
then the data analysis function
Parameters
----------
settings_file : str
The name of the settings file
which is obtained from the command line
Returns
-------
None.
"""
class_error.student_info()
class_error.condition_info()
class_error.working_out()
try:
print('ini ' + settings_file + ' : ', end="")
ini = load_data.load_ini(settings_file)
except OSError:
raise class_error.SettingsFileError()
except ValueError:
raise class_error.SettingsFileError()
data = class_information.Information()
load_data.load_file(data, ini[0]['csv'], ini[0]['json'],
ini[0]['encoding'])
data.data_analysis(ini[1]['fname'], ini[1]['encoding'])
def update_plot_raw_signal():
new_file_name = file_menu.value
data = load_data.load_file(new_file_name, _WINDOW_SCALING)
data_raw_signal.data = dict(x=np.arange(0,
_SPARSNESS * len(data),
step=_SPARSNESS),
y=data)
def main(method_flag):
# load data
source_df, target_df = ld.load_file()
predicts, corrects = [], []
random.seed(123)
np.random.seed(123)
kf = KFold(shuffle=False,random_state=1,n_splits=mc._FOLD_NUM)
fold_num = 1
cnt = 0
for train, test in kf.split(target_df):
print('{0}/{1}'.format(fold_num, mc._FOLD_NUM))
target_train = target_df.iloc[train]
target_test = target_df.iloc[test]
idx, labels = transfer_model(source_df, target_train, target_test, method_flag, fold_num)
predicts.extend(idx.tolist())
corrects.extend(labels[0].tolist())
fold_num = fold_num+1
# save results
predicts = np.array(predicts)
corrects = np.array(corrects)
err = []
for i in range(len(predicts)):
if predicts[i] == corrects[i]:
err.append(0)
else:
err.append(1)
test = np.concatenate((np.reshape(predicts,[len(predicts),1]),np.reshape(corrects,[len(corrects),1]),\
np.reshape(err,[len(err),1])), axis=1)
save_data = pd.DataFrame(test)
save_data.to_csv('%s'%(mc._RESULT_FILE),index=False,header=False)
#save_data.to_csv('../results/results.csv',index=False,header=False)
fp = open('%s'%(mc._RESULT_FILE),'a')
#fp = open('../results/results.csv','a')
fp.write('%f\n'%((1.0-np.mean(err))*100.0))
fp.close()
def load_file(self, filename): ''' load file to network, currently supporting only tsv ''' import load_data load_data.load_file(self, filename)
import load_data as ld
h, w, names, coordinates, distances = ld.get_data_to_dict(ld.load_file("./metro.gr"))
ld.draw_subway(h, w, coordinates, distances)
p, d = ld.dijkstra(distances, 0, 350)
print(p)
print(list(map(lambda x: names[x], p)))
ld.draw_subway(h, w, coordinates, distances, path=p, file_name="trajet.svg")
def load_file(self, filename):
import load_data
load_data.load_file(self, filename)
def load_file(self, filename):
import load_data
load_data.load_file(self, filename)
spectrum_source.data = dict(f=f, y=spectrum)
spectrum_plot.x_range.end = freq.value * 0.001
alphas = []
for x in bins:
a = np.zeros_like(eq_range)
n = int(ceil(x))
a[:n] = (1 - eq_range[:n] * 0.05)
alphas.append(a)
eq_source.data['alpha'] = np.hstack(alphas)
_SPARSNESS = 3
_WINDOW_SCALING = 2
_BASE_FILE_NAME = load_data.get_file_names()[0]
_BASE_FILE = load_data.load_file(_BASE_FILE_NAME, _WINDOW_SCALING)
_BASE_FILE_RAW = load_data.load_raw_file(_BASE_FILE_NAME)
MAX_FREQ_KHZ = MAX_FREQ * 0.001
NUM_GRAMS = 800
GRAM_LENGTH = 512
TILE_WIDTH = 200
EQ_CLAMP = 20
PALETTE = [
'#081d58', '#253494', '#225ea8', '#1d91c0', '#41b6c4', '#7fcdbb',
'#c7e9b4', '#edf8b1', '#ffffd9'
]
PLOTARGS = dict(tools="", toolbar_location=None, outline_line_color='#595959')
data_raw_signal = ColumnDataSource(
data=dict(x=np.arange(0, _SPARSNESS * len(_BASE_FILE), step=_SPARSNESS),
y=_BASE_FILE))
from flask import Flask
from flask import request, jsonify
import load_data
app = Flask(__name__)
l = load_data.load_file() #获取数据，储存到l中
def find(l, d):
'从数据集中寻找，返回房间号，找不到则返回-1'
for i in l:
if i['id'] == d['id'] and i['data'] == d['data']:
return i['num']
return -1
@app.route('/found', methods=['POST'])
def index():
print('接收到数据:', request.json)
rec_l = request.json
return_list = [int(find(l, i)) for i in rec_l]
return jsonify(return_list)
app.run()
import load_data
import gale_shapley
team_file = "teams.tsv"
app_file = "applicants.tsv"
out_file = "out.csv"
if __name__ == "__main__":
print("Starting...")
teams = load_data.load_file(team_file, load_data.team)
print("Loaded teams...")
apps = load_data.load_file(app_file, load_data.app)
print("Loaded applicants...")
teams, rejects, itterations = gale_shapley.run(teams, apps)
print("Process done in " + str(itterations) + " iterations...")
with open(out_file, 'w') as f:
sep_char = ","
for team in teams:
f.write("'==" + sep_char + team.serialize(sep_char) + '\n')
for member in team.members:
f.write(team.name + sep_char + member.serialize(sep_char) +
'\n')
f.write("'==" + sep_char + "Teamless\n")
for reject in rejects:
f.write(reject.Data_read_from)
print("File written")
def load_file(self, filename):
'''
load file to network, currently supporting only tsv
'''
import load_data
load_data.load_file(self, filename)