def download_archive_stream(request, share, subdir=None):
# try:
selection = request.GET.get('selection').split(',')
for item in selection:
test_path(item)
try:
return share.create_archive_stream(items=selection,subdir=subdir)
except Exception, e:
return json_error([e.message])
def download_archive_stream(request, share, subdir=None):
# try:
selection = request.GET.get('selection', '').split(',')
for item in selection:
test_path(item)
try:
return share.create_archive_stream(items=selection, subdir=subdir)
except Exception, e:
return json_error([str(e)])
def move_paths(request, share, subdir=None, json={}):
response = {'moved': [], 'failed': []}
for item in json['selection']:
test_path(item)
item_subpath = item if subdir is None else os.path.join(subdir, item)
try:
if share.move_path(item_subpath, json['destination']):
response['moved'].append(item)
else:
response['failed'].append(item)
except Exception, e:
pass
def move_paths(request, share, subdir=None, json={}):
response={'moved':[],'failed':[]}
for item in json['selection']:
test_path(item)
item_subpath = item if subdir is None else os.path.join(subdir,item)
try:
if share.move_path(item_subpath,json['destination']):
response['moved'].append(item)
else:
response['failed'].append(item)
except Exception, e:
pass
def delete_paths(request, share, subdir=None, json={}):
response={'deleted':[],'failed':[]}
for item in json['selection']:
test_path(item)
item_path = item if subdir is None else os.path.join(subdir,item)
try:
if share.delete_path(item_path):
response['deleted'].append(item)
else:
response['failed'].append(item)
except:
response['failed'].append(item)
ShareLog.create(share=share,user=request.user,action=ShareLog.ACTION_DELETED,paths=json['selection'],subdir=subdir)
return json_response(response)
def delete_paths(request, share, subdir=None, json={}):
response={'deleted':[],'failed':[]}
for item in json['selection']:
test_path(item)
item_path = item if subdir is None else os.path.join(subdir,item)
try:
if share.delete_path(item_path):
response['deleted'].append(item)
else:
response['failed'].append(item)
except:
response['failed'].append(item)
ShareLog.create(share=share,user=request.user,action=ShareLog.ACTION_DELETED,paths=json['selection'],subdir=subdir)
return json_response(response)
def test_etrade_for_bogus_data(self):
importer = EtradeImporter()
resource = utils.test_path('test_data/etrade/2020-09-09/TSLA.json')
chain = importer.from_json(resource)
self.assertTrue(
math.isnan(
chain.get_option('put', datetime(2020, 9, 11), 20).theta))
def test_etrade_for_dates_with_missing_file(self):
importer = EtradeImporter(utils.test_path('test_data/etrade'))
daterange = pd.date_range(start='2020-09-09', end='2020-12-04')
chains = [i for i in importer.for_dates('TSLA', daterange) if i]
self.assertEqual(2, len(chains))
def test_etrade_for_dates(self):
importer = EtradeImporter(utils.test_path('test_data/etrade'))
daterange = pd.date_range(start='2020-09-09', end='2020-09-10')
chains = list(importer.for_dates('TSLA', daterange))
self.assertEqual(2, len(chains))
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Jun 1 15:30:20 2019
@author: pedro
"""
import pandas as pd
from utils import test_path, j
from feature_engineering import create_rolling_features_dataframe
import glob
import os
files = glob.glob(test_path() + "/seg*.csv")
chunksize = 1500
for file in files:
df_chunks = pd.read_csv(j(test_path(), file), chunksize=chunksize)
chunk_list = []
for chunk in df_chunks:
chunk = chunk.reset_index(drop=True)
feat_row = create_rolling_features_dataframe(
chunk.acoustic_data.values)
chunk_list.append(feat_row)
train_data = pd.concat(chunk_list)
train_data = train_data.reset_index(drop=True)
train_data.to_csv(j(test_path(), 'feats_1500_' + os.path.basename(file)))
Created on Sat Apr 13 16:18:54 2019 @author: pedro """ import os import numpy as np import pandas as pd import scipy.signal as signal import matplotlib.pyplot as plt from utils import j, train_path, test_path, train_resampled_path df_tr = pd.read_csv(j(train_path(), 'train.csv'), nrows=150000) # too much data crushes my home laptop #df_tr = utils.read_file(j(train_path(), 'train.csv')) df_te = pd.read_csv(j(test_path(), 'seg_0a0fbb.csv')) #df_tr.plot() t = df_tr.time_to_failure #t.plot() freq = np.abs(1. / np.mean(t[0:100].diff())) dt = df_tr.time_to_failure.diff() dt[0] = dt[1] #dt.plot() # weird jumps weirdjumps = t[dt < 10 * dt[0:100].mean()] freq_jumps = np.abs(1. / np.mean(weirdjumps.diff())) weirdjumps.plot() # use them to guess the frequencies
