def analysis(self): QMessageBox.about(self, '提示', "分析需要些时间") self.text.clear() analysis.analysis() self.log_browser.append('<font color="red">分析好了</font>') self.job_analysis_menu.setEnabled(True) self.company_analysis_menu.setEnabled(True)
def foreach_decompose(file_num): f_filename = flst[file_num] ffile = read_hdf(f_filename) if adjust_ops == 'subrange': ffile.data = adjust.subrange(ffile.data, **adjust_ops.keywords) if rebin: ffile.data = analysis.rebin(ffile.data, fac=rebin) # central difference for interior, forward and backward at the boundaries grad = np.gradient(ffile.data, axis=pdim) if dfdp: h5_output.data = grad else: pf = - paxis * ffile.data # there are zeros because: 1. the origin is included in the axis points; 2. the tail of distribution is zero pf[pf == 0.] = 999999.0 h5_output.data = np.divide(grad, pf) if mininp: h5_output.data = adjust.subrange_phys(h5_output.data, bound=mininp, axis=pdim, axesdata=tmp_axis, update_axis=False) tmp = h5_output.data.copy() h5_output.data = analysis.analysis(h5_output.data, [getmin]) h5_output.run_attributes['TIME'][0] = ffile.run_attributes['TIME'][0] h5_output.run_attributes['ITER'][0] = ffile.run_attributes['ITER'][0] newname = outdir + os.path.basename(f_filename) write_hdf(h5_output, newname) if mininp: h5_output.data = pax[analysis.analysis(tmp, [min_index])] write_hdf(h5_output, outdir + inddir + os.path.basename(f_filename)) return f_filename
def master(release, user, pword, host, port): ## Get all human protein coding genes from ensembl and a count of all uniqe domains. import os import pfamDomains import mapPfamDomains import pdbChembl import uniprotChembl import analysis #Set the threshold for ligand binding to 50 micromolar th = 50 # Get Uniprot identifiers for all human proteins. os.system("R CMD BATCH --vanilla queryBioMaRt.R") # Map Pfam domains and positions to all Uniprot identifiers. pfamDomains.pfamDomains(release, user, pword, host, port) # Map small molecule binding to PFam domains. mapPfamDomains.mapPDs(th, release, user, pword, host, port) # Get all ChEMBL interactions in PDB and binding site residues. pdbDict = pdbChembl.query(release, user, pword, host, port) # Get all ChEMBL interactions in Uniprot and binding site annotation. uniprotDict = uniprotChembl.query(release, user, pword, host, port) # Analyze the data. analysis.analysis(th, release, user, pword, host, port)
def analysis(self): df = readTemplate() if (dbOrExcel == db): res = query('select * from t_sale_info') for re in res: row = [re[1], re[2], re[3], re[4], re[6], re[5]] df.loc[len(df)] = row else: df = common_merge_excel() # 分析 analysis(df)
def analyze(filepath, filename): # TXT to CSV analysis(os.path.join(app.config["UPLOAD_FOLDER"], filename), filename) os.remove(os.path.join(app.config["UPLOAD_FOLDER"], filename)) # Read CSV df = pd.read_csv(os.path.join("csvs", filename)) # functions to get the facts/information about the data contacts = np.unique(df["Contacts"]).shape[0] pie(filename) most(filename) word(filename) week(filename) msgs = number_of_msgs(filename) member = number_of_unique_members(filename) sdate = start_date(filename) edate = end_date(filename) avg = average_length_msg(filename)[:4] maxl, name = max_length_msg(filename) month = month_busy(filename) day = weekday_busy(filename) # there is two types of dashboard is created on the basis of number of contacts in dataset if np.unique(df["Contacts"]).shape[0] > 5: least(filename) return render_template( "dash2.html", filename=filename, msgs=msgs, member=member, sdate=sdate, edate=edate, day=day, avg=avg, maxl=maxl, name=name, month=month, ) else: return render_template( "dash1.html", filename=filename, msgs=msgs, member=member, sdate=sdate, edate=edate, day=day, avg=avg, maxl=maxl, name=name, month=month, )
def fetch(driver): print("----- Fetching -----") driver.get("https://roobet.com/crash") driver.set_window_size(1920, 1200) time.sleep(5) driver.find_element_by_tag_name('body').send_keys(Keys.TAB) driver.find_element_by_tag_name('body').send_keys(Keys.TAB) driver.find_element_by_tag_name('body').send_keys(Keys.TAB) driver.find_element_by_tag_name('body').send_keys(Keys.ENTER) actions = ActionChains(driver) #actions.move_by_offset( 795, 718).click().perform() a = driver.find_element(By.CSS_SELECTOR, ".tick_2dJyV:nth-child(1)").text new_csv() df = pd.DataFrame([[a, pd.datetime.now()]], columns=(["Crash Point"], ["datetime"])) df.to_csv("scraped.csv", mode='a', header=False) anal = analysis("scraped.csv") while True: b = driver.find_element(By.CSS_SELECTOR, ".tick_2dJyV:nth-child(1)").text if b != a: a = b print("---- Last Crash: " + str(b)) bdf = pd.DataFrame([[b, pd.datetime.now()]], columns=(["Crash Point"], ["datetime"])) bdf.to_csv("scraped.csv", mode='a', header=False) b = b[:-1] b = float(b) anal.main(b, 1.5)
def run_simulation(gas_type, pressure, num_grid, grid_distance, scint_distance): global counter file = open('output_raw.log', 'a+') make_json_file(gas_type, pressure, num_grid, grid_distance, scint_distance) cmd = '../build/mmSim config.json -t 6' os.system(cmd) silhouette_score, efficiency = analysis('sim.root', 'output.root') dir_name = 'output/' + str(counter) try: os.mkdir(dir_name) except: pass shutil.move('config.json', str(dir_name) + '/config.json') # shutil.move('sim.root', str(dir_name) + '/sim.root') shutil.move('output.root', str(dir_name) + '/output.root') try: os.remove('sim.root') except: pass file.write('%s %s %s %s %s %s %s\n' % (gas_type, pressure, num_grid, grid_distance, scint_distance, silhouette_score, efficiency)) file.close() counter += 1 return silhouette_score, efficiency
def plot_contour(self, file1, file2, file_num, ax, fig): try: data = self.get_data(file2, file_num + 1) except: data = read_hdf(file2.filename).data if ('operation' in self.general_dict.keys()): data = analysis(data, self.general_dict['operation']) axes = self.get_axes(file_num) axis1 = self.construct_axis(file1, axes[0], file_num) axis2 = self.construct_axis(file1, axes[1], file_num) if ('operation' in self.general_dict.keys()): axis2 = reflect(axis2, self.general_dict['operation']) grid_bounds = [axis1[0], axis1[-1], axis2[0], axis2[-1]] levels = np.linspace(1.5, np.max(data) - 0.5, int(np.max(data) + .001) - 1) rep = np.array( [len(axis1) / data.shape[1], len(axis2) / data.shape[0]]) imAx = ax.contour(np.kron(data, np.ones((rep[1], rep[0]))), levels=levels, linewidths=0.5, colors=self.get_colormap(file_num + 1), extent=grid_bounds)
def periodically_classify(calibration, filename='data.csv'): while True: data = read_delete_when_available(filename) result = analysis.analysis([data[:, 0]], [data[:, 1]]) calibration['new'] = [[result[0][0]], [result[1][0]]] prediction = analysis.KNN.predict_proba([[result[0][0], result[1][0]]]) label_classification(calibration, prediction)
def test_analysis(self): analysis_runner = analysis() # analysis_runner.crawl(u"Frankfurt am Main") # analysis_runner.save_to_pickle() analysis_runner.load_from_pickle() self.assertEqual(len(analysis_runner.venues), 50) self.assertGreater(len(analysis_runner.tips), 500)
def test_assert_equal_x64_simple_para_clang_O0(self): evaluateResult( analysis( C_PATH + 'x86_64/simple_c_para_type_test-clang-m64-O0_ex.cfg', arch64), program_arity(C_PATH + 'x86_64/simple_c_para_type_test-clang-m64-O0.o'))
def cmd(opt): ##変数の初期化 Filenames = [] fn = [] l = 1 a = {"sd": "00000000", "ed": "99999999", "ex": "export.csv", "l": 1} ##コマンドラインオプションをそれぞれ変数に代入 for o in opt[1:]: s = o.split("=") a[s[0]] = s[1] ##全てのファイルが選択されていた場合の処理 if a["f"] == "all": Filenames = os.listdir("targets") Filenames.remove("tmp") #カンマ区切りで与えられたファイル名を分割してリスト化 else: Filenames = a["f"].split(",") try: l = int(a["l"]) except ValueError: print("lの値は0~4で指定してください。") if not a["sd"].isdecimal() and a["ed"].isdecimal(): print("sd,edはyyyymmddの形式、半角英数字で入力してください。初期設定に戻します") a["sd"] = "00000000" a["ed"] = "99999999" fn = Filenames try: if l != 0: Filenames = divider.divider(Filenames, l) analysis.analysis(Filenames, a["sd"], a["ed"], a["ex"], fn) if l != 0: for f in Filenames: os.remove("targets/" + f) except FileNotFoundError: print("対象ファイルが存在しません。正しく exports/ 内に存在しているか確認してください。")
def index(): if request.method == 'POST': address = request.form['address'] result = analysis(address) if isinstance(result,str): return render_template('index.html',message=result) return redirect(url_for('score', address=address)) return render_template('index.html',message='')
def run(num_sets, num_tasks, utilization, percent, soft_contrib, duration, cpu_count): # Create taskset tset = create(num_sets, num_tasks, utilization) cbs_res = None edf_res = None for tasks in tset: config = default_config(duration, cpu_count) add(config, tasks, percent, soft_contrib) config_cbs(config) cbs_res = analysis.merge(analysis.analysis(run_model(config)), cbs_res) config_edf(config) edf_res = analysis.merge(analysis.analysis(run_model(config)), edf_res) return analysis.division(cbs_res, len(tset)), analysis.division(edf_res, len(tset))
def index(): if request.method == 'POST': address = request.form['address'] result = analysis(address) if isinstance(result, str): return render_template('index.html', message=result) return redirect(url_for('score', address=address)) return render_template('index.html', message='')
def main(): print "Place that you want to search : " # replace whitespace with '+' search_key = raw_input().replace(" ", "+") # Find place place_id and place_information from search_key place_id, place_information = get_place_id(search_key) print "searching person who checked-in the place" # send place_id users = get_images(place_id) system('clear') print "3 seconds later, usernames will be write to screen... " sleep(3) for i in users: print usernames[i] raw_input('Please enter a key to continue') system('clear') print "Successfully,users saved and analysis is starting..." analysis(place_information, users)
def plot_grid(self, file, file_num, ax, fig): try: data = self.get_data(file, file_num) except: data = read_hdf(file.filename).data if ('operation' in list(self.general_dict.keys())): data = analysis(data, self.general_dict['operation']) axes = self.get_axes(file_num) axis1 = self.construct_axis(file, axes[0], file_num) axis2 = self.construct_axis(file, axes[1], file_num) grid_bounds = [axis1[0], axis1[-1], axis2[0], axis2[-1]] maximum, minimum = self.get_min_max(file_num) if (self.is_log_plot(file_num)): if (maximum == 0): new_max = 0 else: new_max = maximum / np.abs(maximum) * 10**( int(np.log10(np.abs(maximum))) + 1) if (minimum == 0): new_min = 0 else: new_min = minimum / np.abs(minimum) * 10**( int(np.log10(np.abs(minimum))) + 1) threshold = self.general_dict['log_threshold'][file_num] imAx = ax.imshow(data, aspect='auto', origin='lower', \ interpolation='bilinear', vmin=new_min, vmax=new_max, \ norm=matplotlib.colors.SymLogNorm(threshold), extent=grid_bounds, cmap=self.get_colormap(file_num)) else: imAx = ax.imshow(data, aspect='auto', origin='lower', \ interpolation='bilinear', vmin=minimum, vmax=maximum, extent=grid_bounds, cmap=self.get_colormap(file_num)) if ('x1_zoom' in list(self.general_dict.keys())): plt.xlim(self.general_dict['x1_zoom']) if ('x2_zoom' in list(self.general_dict.keys())): plt.ylim(self.general_dict['x2_zoom']) indices = self.get_indices(file_num) selectors = indices[1:-1] if (indices[0].lower() == 'raw'): long_name = self.get_name(file, 'q') + self.append_legend( file_num) + r'$\/$' + self.get_units(file, 'q') else: long_name = self.get_name(file) + self.append_legend( file_num) + r'$\/$' + self.get_units(file) if (self.is_log_plot(file_num)): self.add_colorbar(imAx, long_name, self.mod_tickers(minimum, maximum, threshold), ax, fig) else: self.add_colorbar(imAx, long_name, None, ax, fig) self.set_labels(ax, file, axes, file_num)
def farmerinfo(): username=request.form['username'] print(username) result=farmer_info(username) result[0][4]=result[0][4].title() print(result) stat1,stat2,stat3,stat41,stat42=analysis(result) print(stat1,stat2,stat3,stat41,stat42) return render_template('farmerinfo.html',result=result,stat1=stat1,stat2=stat2,stat3=stat3,stat41=stat41,stat42=stat42)
def __init__(this): this.dbconnection = dbconnection.dbconnection() this.analysis = analysis.analysis() print "Content-type: text/html\n" try: cookies = Cookie.SimpleCookie(os.environ["HTTP_COOKIE"]) userid = cookies['userid'].value print this.update(userid) except Exception, e: print "error getdata: %s" %(e)
def main(): for i in range(300, 321): for j in range(20, 26): file_name = str('movie_2_nucleus_working_raw_t' + str(i) + "_z0" + str(j)) print(file_name) temp = pd.DataFrame(analysis(file_name + '.png')) temp.to_csv(cwd + export_path + file_name + "_3", index=False, header=True)
def trading(): # not secure input method if request.method == 'POST': # request.form['ticker'].isalpha() == True: # this method doesn't allow entry of international tickers (syntax of TIC.EX) if request.form.get('quantum'): import quantum return 'qc pred' # str(quantum.analysis(request.form['ticker'])) else: import analysis return str(analysis.analysis(request.form['ticker'])) # placeholder else: return 'Not a ticker!'
def __init__(self): super().__init__() self.an = analysis() self.radio = [] self.rank = [] self.c = Communicate() self.c.bar_on.connect(self.bar_unlimit_on) self.c.bar_off.connect(self.bar_unlimit_off) self.initUI()
def analyze(self): a = analysis(self.symbol) clean = analysis(self.symbol).clean(a.df) if len(a.df)>0: self.earnings_est = clean(0) self.revenue = clean(1) self.earnings_history = clean(2) self.eps_trend = clean(3) self.eps_revisions = clean(4) self.growth_estimates = clean(5) else: self.earnings_est = np.nan self.revenue = np.nan self.earnings_history = np.nan self.eps_trend = np.nan self.eps_revisions = np.nan self.growth_estimates = np.nan self.a_list.append(self.symbol) self.attributes.append(a.attributes)
def __init__(self): self.dbc = DBcontroller() self.mecab = analysis() # self.mecab.getnouns(comment) self.product_name = [ "구글홈", "아이폰 XS", "갤럭시 S9", "엘지 G7", "엘지 그램 15 2018", "삼성 노트북 9 always", "갤럭시탭 S4", "아이패드 6세대", "아이패드 프로 3세대" ] self.product_name_DB_version = [ "go_s_home", "ap_p_ipxs", "ss_p_s9", "lg_p_g7", "lg_n_gram15", "ss_n_alwy9", "ss_t_galtap4", "ap_t_ipd6", "ap_t_pro3" ]
def analyse(): entity = request.args.get('entity') source = request.args.get('source') analysis_by_year, magnitude_by_year = analysis.analysis(entity, source) return { 'sentiment': { source: analysis_by_year }, 'magnitude': { source: magnitude_by_year } }
def upload_file(): cleanup() if request.method == 'POST': # check if the post request has the file part if 'file' not in request.files: flash('No file part') return redirect(request.url) file = request.files['file'] # if user does not select file, browser also # submit a empty part without filename if file.filename == '': flash('No selected file') return redirect(request.url) if file and allowed_file(file.filename): filename = secure_filename(file.filename) file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename)) # Do analysis analysis("." + url_for('uploaded_file', filename=filename)) output_filename = "output.xlsx" return redirect(url_for('uploaded_file', filename=output_filename)) return render_template("index.html")
def index(): if request.method != 'POST': max_val = 100 min_val = 0 future_val = 100 return render_template('index.html', max_val=max_val, min_val=min_val, future_val=future_val) err_msg = '' max_val = request.form['max'] min_val = request.form['min'] future_val = request.form['future'] file = request.files['file'] if not max_val.isdecimal(): err_msg += '上限値が設定されていません。' flash("上限値が設定されていません。", 'danger') if not min_val.isdecimal(): err_msg += '下限値が設定されていません。' flash("下限値が設定されていません。", 'danger') if not future_val.isdecimal(): err_msg += '予測日数が設定されていません。' flash("予測日数が設定されていません。", 'danger') if file.filename == '': err_msg += 'ファイルがありません。' flash("ファイルがありません。", 'danger') if err_msg != '': return render_template('index.html', max_val=max_val, min_val=min_val, future_val=future_val) # サニタイズ処理 basename = secure_filename(file.filename) # フルパス filename = os.path.join(app.config['TEMP_DIR'], basename) file.save(filename) # 予測値取得 header, datas = analysis(filename, app.config['IMAGES_DIR'], int(max_val), int(min_val), int(future_val)) return render_template('chart.html', header=header, datas=datas)
def main(): args = parser.parse_args() modify_arguments(args) # Resetting the graph and setting seeds tf.reset_default_graph() tf.set_random_seed(args.seed) np.random.seed(args.seed) random.seed(args.seed) with open(args.config_file, 'r') as stream: config = yaml.load(stream) args.config = Munch(modify_config(args, config)) logger.info(args) if args.mode == 'train': train(args) elif args.mode == 'test': test(args) elif args.mode == 'analysis': analysis(args)
def get_bounds(self, file_name, num, file_num): file_name = file_name + str(str(1000000 + num)[1:]) + '.h5' try: file = h5py.File(file_name, 'r') data = self.get_data(file, file_num) except: data = read_hdf(file_name).data if ('operation' in self.general_dict.keys()): data = analysis(data, self.general_dict.get('operation')) minimum, maximum = np.min(data), np.max(data) file.close() del data return minimum, maximum
def test_main_csv(): testing_input_csv_path = Path().cwd() / 'testing' / 'testing_data.csv' testing_output_csv_path = Path().cwd( ) / 'testing' / 'testing_data_output.csv' testing_output_template_csv_path = Path().cwd( ) / 'testing' / 'testing_data_complete.csv' analysis(testing_input_csv_path, testing_output_csv_path) fields = ['image1', 'image2', 'similar'] analysis_output_df = pd.read_csv(testing_output_csv_path, names=fields, usecols=fields, header=0) analysis_template_df = pd.read_csv(testing_output_template_csv_path, names=fields, usecols=fields, header=0) # Run the analysis program assert analysis_output_df.equals( analysis_template_df ), "Output csv: <" + str( testing_output_csv_path) + "> does not match template csv: <" + str( testing_output_template_csv_path) + ">"
def main(): # argumento eh o link do aplicativo # exemplo: https://play.google.com/store/apps/details?id=com.github.android try: arg = sys.argv[1] URL = arg + URL_SUFIX except IndexError: sys.exit('NO ARGUMENT') # informacoes extraidas do app e comentarios app_info = next(get_app_info(URL)) comments = list(get_comments(URL)) # faz a analise dos comentarios analysis(app_info, comments) # insere no banco # noinspection PyUnresolvedReferences try: create(app_info, comments) except pymongo.errors.DuplicateKeyError: sys.exit('THAT APPLICATION ALREADY EXISTS IN THE DB')
def computeNow(): # startOfCurrentDay = datetime.datetime.now().replace(hour=8, minute=0, second=0, microsecond=0) # currentTimePlus = startOfCurrentDay currentTimePlus = datetime.datetime.now() data = ct.classifyTime(currentTimePlus, 'randomTimePrice.csv', 'compute') a, b, bias = analysis.analysis() predict = data["PreviousMean"].values[0] * a + data["WeekDayMean"].values[ 0] * b + bias print("PreviousMean value: {}".format(data["PreviousMean"].values[0])) print("WeekDayMean value: {}".format(data["WeekDayMean"].values[0])) print("predict value: {}".format(predict)) print("Actual value: {}".format(data["Actual"].values[0])) return predict
def test_tips_tagging(self): analysis_runner = analysis() tagger_de = POSTagger(self.path_to_treetagger, self.path_to_parameter_file_de) analysis_runner.load_from_pickle() result = [] i = 0 for tip in analysis_runner.tips: tip.pos = tagger_de.tag(tip.text) result.append(tip) print tip.text, tip.pos i += 1 if i == 10: break
def foreach_decompose(file_num): e_filename = e1[file_num] e1file = read_hdf(e_filename) e2file = read_hdf(e2[file_num]) e1data = analysis.analysis(e1file.data, ['fft']) e2data = analysis.analysis(e2file.data, ['fft']) h5_output.run_attributes['TIME'][0] = e1file.run_attributes['TIME'][0] h5_output.run_attributes['ITER'][0] = e1file.run_attributes['ITER'][0] if blockx or blocky: e1data = arraymask.mask(e1data, axes=[ky, kx], region=brg) e2data = arraymask.mask(e2data, axes=[ky, kx], region=brg) dive = np.multiply(kxvec, e1data) + np.multiply(kyvec, e2data) h5_output.data = np.multiply(kxvec, dive) # transverse part, longitudinal part if ift: tr1, e1data = (analysis.analysis(e1data - h5_output.data, ['ifft ' + ift]), analysis.analysis(h5_output.data, ['ifft ' + ift])) else: tr1, e1data = e1data - h5_output.data, h5_output.data if 't1' in ocomp: h5_output.data = np.abs(tr1) h5_output.run_attributes['NAME'][0], h5_output.data_attributes[ 'LONG_NAME'] = 'Et1', 'E_{t1}' newname = outdir + 'Et1' + os.path.basename(e_filename)[2:] write_hdf(h5_output, newname) h5_output.data = np.multiply(kyvec, dive) if ift: tr2, e2data = (analysis.analysis(e2data - h5_output.data, ['ifft ' + ift]), analysis.analysis(h5_output.data, ['ifft ' + ift])) else: tr2, e2data = e2data - h5_output.data, h5_output.data if 't2' in ocomp: h5_output.data = np.abs(tr2) h5_output.run_attributes['NAME'][0], h5_output.data_attributes[ 'LONG_NAME'] = 'Et2', 'E_{t2}' newname = outdir + 'Et2' + os.path.basename(e_filename)[2:] write_hdf(h5_output, newname) if 't' in ocomp: h5_output.data = np.sqrt(np.abs(np.square(tr1) + np.square(tr2))) h5_output.run_attributes['NAME'][0], h5_output.data_attributes[ 'LONG_NAME'] = 'ET', 'E_T' newname = outdir + 'ET' + os.path.basename(e_filename)[2:] write_hdf(h5_output, newname) if 'l' in ocomp: h5_output.data = np.sqrt(np.abs(np.square(e1data) + np.square(e2data))) h5_output.run_attributes['NAME'][0], h5_output.data_attributes[ 'LONG_NAME'] = 'EL', 'E_L' newname = outdir + 'EL' + os.path.basename(e_filename)[2:] write_hdf(h5_output, newname) return e_filename
def plot_lineout(self, file, file_num, ax, fig): try: data = self.get_data(file, file_num) except: data = read_hdf(file.filename).data if ('operation' in list(self.general_dict.keys())): data = analysis(data, self.general_dict['operation']) axes = self.get_axes(file_num) xx = self.construct_axis(file, axes[0], file_num) maximum, minimum = self.get_min_max(file_num) indices = self.get_indices(file_num) selectors = indices[1:-1] if (indices[0].lower() == 'raw'): label = self.get_name(file, 'q') + self.append_legend(file_num) else: label = self.get_name(file) + self.append_legend(file_num) if (self.is_log_plot(file_num)): ax.plot(xx, data, self.get_marker(file_num), label=label, linewidth=self.get_linewidth()) side = self.general_dict['side'][file_num] if (side == 'left'): ind = 0 else: ind = 1 threshold = self.general_dict['log_threshold'][ind] plt.yscale('symlog', linthreshy=threshold, vmin=minimum, vmax=maximum) ax.set_ylim([minimum, maximum]) else: ax.plot(xx, data, self.get_marker(file_num), label=label, linewidth=self.get_linewidth()) ax.set_ylim([minimum, maximum]) ax.minorticks_on() plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0)) self.set_labels(ax, file, axes, file_num)
def calibrate(filename, measurements=20, sep=1): calibrate_results = {} for label in LABELS: calibrate_results[label] = [[], []] for j in range(sep): for label in LABELS: print('Please think {} for {} seconds'.format(label, measurements)) time.sleep(3) for i in range(measurements): data = read_delete_when_available(filename) c1 = data[:, 0] c2 = data[:, 1] result = analysis.analysis([c1], [c2]) calibrate_results[label][0].append(result[0][0]) calibrate_results[label][1].append(result[1][0]) return calibrate_results
def main(argv): global API_URL API_URL = argv[1] print "Server: " + API_URL while True: try: task = quest_task() if None == task: print "no more task now, waiting for 20 seconds......" time.sleep(20) continue url = task['url'] print "new task " + url def progress(val, url): print "%d%% checked - %s" % (val, url) def meta(metadata): print metadata['title'] print "task_id = %d, reporting metadata to server......" % (task['_id'], ) print "ok." if 0 == report_meta(task['_id'], metadata) else "failed." totalpart, match_set, details = analysis.analysis(url, progress_callback = progress, meta_callback = meta) result = 100 * len(match_set) / totalpart print "completed. result = %d%%" % (result, ) print "reporting to server......" report_ret = report_result(task['_id'], result, json.dumps(details)) if 0 == report_ret: print "ok." else: print "failed - %d" % (report_ret, ) except Exception, e: print e print "waiting for 30 seconds......" time.sleep(30) continue
def score(address): score_data = analysis(address) yelp_list = sorted(score_data['yelp_results'].values(), key=lambda k: k['distance']) yelp_markers = [[restaurant['name'], restaurant['lat'], restaurant['lon']] for restaurant in yelp_list] num_places = len(yelp_list) sum_reviews = sum([restaurant['review_count'] for restaurant in yelp_list]) violent_rank = nth(str(int(score_data['violent_rank'] * 100))) property_rank = nth(str(int(score_data['property_rank'] * 100))) return render_template('score.html', number=score_data['address']['street_number'], street=score_data['address']['route'], town=score_data['address']['locality'], state=score_data['address']['state'], zip=score_data['address']['postal_code'], lum=score_data['lum'], rep_agency=score_data['police_name'], rep_agency_dist=score_data['pol_distance'], pop_served=score_data['pop_served'], violent_crime_pc=score_data['violent_crime_pc'], violent_rank=violent_rank, property_crime_pc=score_data['property_crime_pc'], property_rank=property_rank, restaurants=yelp_list, address_lat=score_data['address']['lat'], address_lng=score_data['address']['lng'], yelp_markers=json.dumps(yelp_markers), num_places=num_places, sum_reviews=sum_reviews, avg_yelp_rating=score_data['avg_yelp_rating'], num_walkable=score_data['num_walkable'], avg_walk=score_data['avg_walk'], avg_good_dr=score_data['avg_good_dr'], avg_bad_dr=score_data['avg_bad_dr'], walk_grade=score_data['walk_grade'], drive_grade=score_data['drive_grade'], rest_grade=score_data['rest_grade'], walk_img=score_data['walk_img'], gd_img=score_data['gd_img'], bd_img=score_data['bd_img'] )
# -*- coding: cp1251 -*- # # ls-dyna nodout file reader # import reader import analysis analysis.analysis().run() """ r=reader.lsdyna_reader () r.read_nodout ('nodout') r.nodeout_print_test() """
def test_assert_equal_x64_simple_para_clang_O0(self): evaluateResult(analysis(C_PATH+'x86_64/simple_c_para_type_test-clang-m64-O0_ex.cfg',arch64), program_arity(C_PATH+'x86_64/simple_c_para_type_test-clang-m64-O0.o'))
def test_assert_equal_x64_simple_gcc_O0(self): evaluateResult(analysis(C_PATH+'x86_64/simple_c_func_test-gcc-m64-O0_ex.cfg',arch64), program_arity(C_PATH+'x86_64/simple_c_func_test-gcc-m64-O0.o'))
def test_assert_equal_x86_indirect_call_test_2_clang_O0(self): evaluateResult(analysis(C_PATH+'x86/indirect_call_test_2-clang-m32-O0_ex.cfg',arch86), program_arity(C_PATH+'x86/indirect_call_test_2-clang-m32-O0.o'))
def test_assert_equal_x86_simple_clang_O0(self): evaluateResult(analysis(C_PATH+'x86/simple_c_func_test-clang-m32-O0_ex.cfg',arch86), program_arity(C_PATH+'x86/simple_c_func_test-clang-m32-O0.o'))
#coding=utf-8 import login import Search import json import analysis if __name__ == '__main__': client = login.APILogin() login.browserLogin() print "=========================" print "1.Run the Search Model" print "2.Run the Analysis Model" choice = int(raw_input("Your choice:")) if choice == 1: Search.search(client) elif choice == 2: analysis.analysis(client) else: print "Invalid choice!"
from sklearn.ensemble import AdaBoostClassifier if __name__ == '__main__': # load raw txt data parser = parsing.parsing() extracted_features_path = './Data/Cornell/features/office_data_nodefeats.txt' raw_data = parser.parse_txt_data(extracted_features_path) # load ply data, here just test for office scene 1 pic_path_set = ['./Data/Cornell/office_data_ply/scene5.ply'] for pic_path in pic_path_set: office_data = parser.parse_ply_data(pic_path) print(office_data) # create analysis utils analyser = analysis.analysis() #========================================================= # K-NN #========================================================= # classify # kSet = [3] # experiment_number = 1 # # for k in kSet: # print('This is for k = ', k, '\n') # #k_classifier = nn.nearestneighbors(k) # # for i in range(0, experiment_number): # k_classifier = nn.nearestneighbors(k) # np.random.shuffle(raw_data)
def experiment(): if request.method == 'POST': # get the current value of i i = session['i'] j = session['j'] # for debugging: #print "i: ", i, " j: ", j # end experiment and show results if aborted if request.form.get('end') or j >= shuffle.blocks: #<<< return analysis.analysis() if request.form.get('quit'): #print "experiment stopped. exit to DOS..." return analysis.analysis() if request.form.get('abort'): #print "experiment aborted. exit to DOS..." return analysis.analysis() if request.form.get('resume'): #print "here we go!" # a new array is generated (not nice: the old one is overwritten) shuffle.save_array( shuffle.makeShuffle() ) session['tracker'] = [] session['timing'] = [] # count a new block session['j'] +=1 # reset i session['i'] = 0 ### i = session['i'] j = session['j'] #print "this is the resume status: i: ", i, " j: ", j # end experiment and show results if aborted j = session['j'] if j >= shuffle.blocks: #<<< return analysis.analysis() # get the array generated for this participant array_name = files.get_array() this_array = files.load_this(array_name) # timing logging session['timing'].append( time.time() ) real_time = str( datetime.datetime.now() ) real_time = real_time[:real_time.find('.')] # if the experiment has not started yet (i=-1), we set it up if i < 0: # count up -- this will make i == 0 and the next trial will be a real trial session['i'] +=1 # empty the tracker for the new participant session['tracker'] = [] session['timing'] = [] #print "--- ES GEHT LOS! ---" # for debugging # show the starting screen return render_template('experiment.html', i=i, length=shuffle.trials_per_block ) # if the experiment has started if i >= 0 and i < shuffle.trials_per_block: # get the data for the next trial (which will be rendered subsequently) express = this_array['expressions'][i] ident = this_array['identities'][i] mask = this_array['masks'][i] # since we will be popping from the mask list in js, we invert it mask = mask[::-1] # get the stimulus for the next trial img = stimuli.stim[ident][express] # the array tracking the experiment progression gets a new entry # which is a dict containing all properties session['tracker'].append(arrays.make_entry()) # the data of the next trial are appended to the end of the tracker; # they can be accessed later session['tracker'][-1]['express'] = express session['tracker'][-1]['ident'] = ident session['tracker'][-1]['filenames'] = img session['tracker'][-1]['time'] = real_time # as long as the counter does not exceed the number of trials, # run the experiment ! if i >= 1 and i < shuffle.trials_per_block: logwrite.track_response(-2) # prevent session tracker from becoming too large (this is an issue because cookies cant exceed 4kb...) if len(session['tracker']) > 3: del session['tracker'][0] # if the run is over if i == shuffle.trials_per_block: logwrite.track_response(-1) # make a plot pltname = feedback.plotThis(feedback.feedbackDir,feedback.feedbackFile) # the page gets returned, but without stimuli return render_template('experiment.html', i = shuffle.trials_per_block, j = j, blocks = (shuffle.blocks-1), length=shuffle.trials_per_block, pltname = 'static/feedback/'+pltname ) # we increase the counter by one session['i'] +=1 # the page with stimuli and everything gets returned # if the experiment is ongoing (i>0 and <length) return render_template('experiment.html', # trial tracking i = i, length=shuffle.trials_per_block, # presentation properties squareSize = stimuli.squareSize, waitTime = stimuli.waitTime, numRow = stimuli.numRow, numCol = stimuli.numCol, isi = stimuli.isi, # trial properties express = express, mask = mask, img = img )