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How to use test_up method in uiautomator
Best Python code snippet using uiautomator
analysis-old.py
Source:analysis-old.py 
1from __future__ import division2import pandas as pd3import numpy as np4import random, os5import matplotlib6matplotlib.use('Agg')7import matplotlib.pyplot as plt8from collections import defaultdict, OrderedDict, Counter9import logging10# original 250-test.dat size is 95,000,000 rows11# head -1000000 250-test.dat > test-data-set.dat12# tail -1000000 250-test.dat >> test-data-set.dat13# head -1000000 control1.dat > test-control-set.dat14# tail -1000000 control1.dat >> test-control-set.dat15# when running in /data/users/sarthak it takes less than a min to load full16DATASET = '250-test.dat' #'test-data-set.dat'17CONTROLSET = 'control1.dat' #'test-control-set.dat'18DATALABEL = DATASET.split('.')[0]19CONTROLLABEL = CONTROLSET.split('.')[0]20NDEVICES = 100021LIST_OF_CONTROLLABELS = ['control5']22#LIST_OF_CONTROLLABELS = ['control'+str(l) for l in range(1,9)]23#LIST_OF_CONTROLLABELS.remove('control3') #causes errors unslicable24DATAPATH = '../data/'25PROCESSEDPATH = '../processed/'26OUTPUTPATH = '../output/'27header_row = ['Device_number',28 'end_time',29 'date_service_created',30 'service_class_name',31 'cmts_inet',32 'service_direction',33 'port_name',34 'octets_passed',35 'device_key',36 'service_identifier']37logging.basicConfig(format='%(asctime)s %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p')38logger = logging.getLogger()39logger.setLevel('DEBUG')40#############################################################################41# load dataframes42#############################################################################43def load_csv(ds=DATASET, cs=CONTROLSET):44 df1 = pd.read_csv(DATAPATH + ds, delimiter='|', names=header_row)45 df2 = pd.read_csv(DATAPATH + cs, delimiter='|', names=header_row)46 return df1, df247def filter_csv(df, dfname):48 df_up = df[df['service_direction'] == 2][['Device_number', 'end_time',49 'octets_passed', 'service_class_name']]50 df_dw = df[df['service_direction'] == 1][['Device_number', 'end_time',51 'octets_passed', 'service_class_name']]52 df_up.to_pickle(PROCESSEDPATH+dfname+'_up.pkl')53 df_dw.to_pickle(PROCESSEDPATH+dfname+'_dw.pkl')54 return55def load_dataframe(dfname):56 df_up = pd.read_pickle(PROCESSEDPATH+dfname+'_up.pkl')57 df_dw = pd.read_pickle(PROCESSEDPATH+dfname+'_dw.pkl')58 return df_up, df_dw59def save_dfs():60 df1, df2 = load_csv()61 filter_csv(df1, DATALABEL)62 filter_csv(df2, CONTROLLABEL)63 return64#############################################################################65# Helper func66#############################################################################67def getSortedCDF(data):68 """69 return x,y for a cdf given one dimensional unsorted data x70 """71 sorted_data = np.sort( data )72 YSCALE = len(sorted_data)73 yvals = [y/YSCALE for y in range( len(sorted_data) )]74 return sorted_data, yvals75#############################################################################76# Analysis77#############################################################################78class DataProcessor:79 def __init__(self, DFNAME):80 self.dfname = DFNAME81 return82 def set_dfname(self, DFNAME):83 self.dfname = DFNAME84 return85 def load_csv(self):86 return pd.read_csv(self.dfname + '.dat', delimiter='|',87 names=header_row)88 def splitDateTime(self, df_temp):89 """90 dataframes are passed by reference by default.91 """92 df_temp['date'] = df_temp['end_time'].apply(lambda x:93 x.split(" ")[0])94 df_temp['time'] = df_temp['end_time'].apply(lambda x:95 x.split(" ")[1])96 return97 def save_pkl(self):98 df = self.load_csv()99 df_up = df[df['service_direction'] == 2][['Device_number', 'end_time',100 'octets_passed']]101 df_dw = df[df['service_direction'] == 1][['Device_number', 'end_time',102 'octets_passed']]103 self.splitDateTime(df_up)104 self.splitDateTime(df_dw)105 df_up.to_pickle(PROCESSEDPATH + self.dfname + '_up.pkl')106 df_dw.to_pickle(PROCESSEDPATH + self.dfname + '_dw.pkl')107 return df_up, df_dw108##############################################################################109def meta_data():110 listDS = ['test-data-set', 'test-control-set', '250-test', 'control1',111 'control2', 'control3' ,'control4', 'control5', 'control6',112 'control7', 'control8']113 attrib = defaultdict(list)114 for dfname in listDS:115 for direction in ['up', 'dw']:116 logger.info('load dataframe ' + dfname + '_' + direction)117 df = pd.read_pickle(PROCESSEDPATH + dfname +'_'+ direction +'.pkl')118 attrib['name'].append(dfname)119 attrib['direction'].append(direction)120 attrib['len'].append(len(df))121 df.sort('end_time', ascending=True)122 attrib['date_start'].append(df['end_time'].iloc[0])123 attrib['date_end'].append(df['end_time'].iloc[-1])124 devices = np.sort(df['Device_number'].unique())125 attrib['num_devices'].append(len(devices))126 attrib['device_start'].append(devices[0])127 attrib['device_end'].append(devices[-1])128 attrib['max_bytes'].append(df['octets_passed'].max())129 attrib['mean_bytes'].append(df['octets_passed'].mean())130 attrib['median_bytes'].append(df['octets_passed'].median())131 attrib['perc90_bytes'].append( np.percentile( df['octets_passed'],132 90) )133 logger.debug(pd.DataFrame(attrib))134 df_attributes = pd.DataFrame(attrib)[['name', 'direction', 'len',135 'date_start', 'date_end',136 'num_devices', 'device_start',137 'device_end', 'max_bytes',138 'mean_bytes', 'median_bytes',139 'perc90_bytes']]140 df_attributes.to_pickle(OUTPUTPATH + 'data_attributes.pkl')141 return df_attributes142##############################################################################143# SLICE data144# 1000 devices, 9/30 -- 12/25145##############################################################################146def get_dates(df):147 """148 given df, sort dates and get date_start and date_end149 """150 df.sort('end_time', ascending=True)151 date_start = df['end_time'].iloc[0]152 date_end = df['end_time'].iloc[-1]153 return date_start, date_end154def slice_df(df, num_devices, date_start, date_end):155 """156 num_devices = 1000157 date_start = 2014-09-30 00:00:00158 date_end = 2014-12-25 20:45:00159 """160 df2 = df[ (df['end_time'] >= date_start) & (df['end_time'] <= date_end) ]161 devices = df2['Device_number'].unique()162 sliced_devices = random.sample(devices, num_devices)163 df3 = df2[ df2['Device_number'].isin(sliced_devices) ]164 return df3165def slicer(ControlSet, DATA_up, DATA_dw, ndev):166 CONTROL_up, CONTROL_dw = load_dataframe(ControlSet)167 # get date_start, date_end168 date_start, date_stop = get_dates(CONTROL_up)169 # slice170 logger.debug(ControlSet + " ndev=" + str(ndev) + " control set unique: "\171 + str(len( CONTROL_up['Device_number'].unique() )) + " data set unique: "\172 + str(len( DATA_up['Device_number'].unique() )) )173 CUP_sliced = slice_df(CONTROL_up, ndev, date_start, date_stop)174 DUP_sliced = slice_df(DATA_up, ndev, date_start, date_stop)175 #logger.debug("sliced UP sets ")176 # get date_start, date_end177 date_start, date_stop = get_dates(CONTROL_dw)178 # slice179 logger.debug(ControlSet + " ndev=" + str(ndev) + " control set unique: "\180 + str(len( CONTROL_dw['Device_number'].unique() )) + " data set unique: "\181 + str(len( DATA_dw['Device_number'].unique() )) )182 CDW_sliced = slice_df(CONTROL_dw, ndev, date_start, date_stop)183 DDW_sliced = slice_df(DATA_dw, ndev, date_start, date_stop)184 # return name of outputfolder185 outputlabel = ControlSet + '_' + date_start.replace(" ","_") \186 + '_' + date_stop.replace(" ","_")187 #logger.debug("sliced DW sets ")188 return DUP_sliced, DDW_sliced, CUP_sliced, CDW_sliced, outputlabel189##############################################################################190class Plotter:191 def __init__(self, dlabel=DATALABEL, clabel=CONTROLLABEL, XSCALE='log'):192 self.test_up = 0193 self.test_dw = 0194 self.control_up = 0195 self.control_dw = 0196 self.DLABEL = dlabel197 self.CLABEL = clabel198 self.DISPLAY_MEAN = 1199 self.OUTPUTPATH = OUTPUTPATH200 self.XSCALE = XSCALE201 return202 def load_df(self):203 self.test_up, self.test_dw = load_dataframe(self.DLABEL)204 self.control_up, self.control_dw = load_dataframe(self.CLABEL)205 return206 def import_df(self, test_up, test_dw, control_up, control_dw):207 self.test_up = test_up208 self.test_dw = test_dw209 self.control_up = control_up210 self.control_dw = control_dw211 self.ALL_DF = {'test_up': self.test_up,212 'test_dw': self.test_dw,213 'control_up': self.control_up,214 'control_dw': self.control_dw}215 self.grouped = defaultdict(int)216 return217 def allBytes(self, df):218 return df219 def nonZeroBytes(self, df):220 return df[df['octets_passed'] > 0]221 def thresholdBytes(self, df, threshold=0):222 """223 return df, xlabel, filename224 """225 dfr = df[df['octets_passed'] > threshold]226 return dfr, "Bytes > "+str(threshold),"CDF-ThresholdBytes-"+str(threshold)227 def getStringsFromGroupBy(self, GROUPBY):228 """229 eg: input = ["Device_number", "time"]230 returns:231 xlab = " Bytes per Device-number per Time"232 filename = "Bytes_Device-number_Time"233 """234 xlab = " Bytes "235 filename = "Bytes"236 for col in GROUPBY:237 col2 = col.capitalize().replace("_", "-")238 xlab += " per "+col2239 filename += "_"+col2240 logger.debug("xlab, filename = "+xlab+"; "+filename)241 return xlab, filename242 def grouper(self, GROUPBY):243 """244 save df_grouped['octets_passed'] by ['Device_number'],245 ['Device_number', 'date'], ['Device_number', 'time'], ['end_time']246 """247 logger.debug("group ALL_DF according to "+str(GROUPBY))248 for name, df in self.ALL_DF.items():249 self.grouped[name] = df.groupby(GROUPBY, as_index=False)['octets_passed']250 logger.debug( "Group "+name+" by "+str(GROUPBY)+" len: "+str( len(self.grouped[name].first()) ) )251 return252 def getByteSeries(self, method, **kwargs):253 """254 for each sliced set255 method = all: series = df['octets_passed']256 method = threshold: series = thresholdBytes['octets_passed']257 method = peak, group = device: series = getGroupedByteStats(grouped).max()258 """259 self.byte_series = defaultdict(int)260 logger.debug("enter getByteSeries; method = "+method)261 if method=='all':262 #self.byte_series = self.ALL_DF263 for name, df in self.ALL_DF.items():264 self.byte_series[name] = self.allBytes(df)['octets_passed']265 elif method=='nonZero':266 for name, df in self.ALL_DF.items():267 self.byte_series[name] = self.nonZeroBytes(df)['octets_passed']268 elif method=='threshold':269 for name, df in self.ALL_DF.items():270 self.byte_series[name] = self.thresholdBytes(df, kwargs['threshold'])['octets_passed']271 elif method=='Peak':272 for name, gr in self.grouped.items():273 self.byte_series[name] = gr.max()['octets_passed']274 #logger.debug("name = "+name+"; max series len = "+ str(len(self.byte_series[name])))275 elif method=='Median':276 for name, gr in self.grouped.items():277 self.byte_series[name] = gr.median()['octets_passed']278 elif method=='90perc':279 for name, gr in self.grouped.items():280 #self.byte_series[name] = gr.quantile(.90)['octets_passed']281 self.byte_series[name] = gr.apply(lambda x: np.percentile(x, 90))282 else:283 logger.error("Unknown method = "+method)284 return285 def CDFPlotter(self, xlabel, figname):286 """287 given self BYTES[ ]: test_up/dw control_up/dw288 just plot in CDF289 """290 fig1, ax1 = plt.subplots(2,1, figsize=(8,10))291 labels = iter([self.DLABEL+'_up',self.CLABEL+'_up',self.DLABEL+'_dw',292 self.CLABEL+'_dw'])293 colors = iter(['b', 'g', 'b', 'g'])294 axis_ctr = iter([0,0,1,1])295 markers = iter(['o','x','o','x'])296 #logger.debug("byte_series.items() " + str(self.byte_series.items()))297 for name, series in self.byte_series.items():298 x, y = getSortedCDF(list(series))299 lab = next(labels)300 if self.DISPLAY_MEAN:301 calcMean = '%.2E' % np.mean(list(series))302 lab += ': '+ calcMean303 ax1[next(axis_ctr)].plot(x,y, color=next(colors), label=lab,304 marker=markers.next(), markevery=len(y)/10)305 ax1[0].set_xscale(self.XSCALE)306 ax1[1].set_xscale(self.XSCALE)307 ax1[1].set_xlabel(xlabel)308 ax1[0].set_ylabel("Normalized CDF UPLINK")309 ax1[1].set_ylabel("Normalized CDF DOWNLINK")310 ax1[0].grid(1)311 ax1[1].grid(1)312 ax1[0].legend(loc='best')313 ax1[1].legend(loc='best')314 fig1.tight_layout()315 fig1.savefig(self.OUTPUTPATH+figname)316 return317 def setOutputPath(self, outputlabel):318 # new outputpath by control-set and date319 logger.info("Plots stored in " + outputlabel)320 self.OUTPUTPATH = 'output/' + outputlabel + '/'321 if not os.path.exists(self.OUTPUTPATH):322 logger.debug("MakeDir " + self.OUTPUTPATH)323 os.makedirs(self.OUTPUTPATH)324 return325 def plotAll(self):326 """327 main method328 - Has sliced self.test_up, self.test_dw, self.control_up, self.control_dw329 - Plot330 # allBytes and nonZero then CDF all stats331 # group by dev [dev, dev-day, dev-time] then CDF all stats332 # group by [datetime] then CDF all stats333 # group by [datetime] then timeseries plot (x, y)334 """335 # allBytes and nonZero then CDF all stats336 self.getByteSeries('all')337 xlabel = 'All Bytes Seen'338 figname = 'CDF-allBytes'339 self.CDFPlotter(xlabel, figname)340 logger.debug("draw all CDF")341 self.getByteSeries('nonZero')342 xlabel = 'Bytes > 0'343 figname = 'CDF-nonZeroBytes'344 self.CDFPlotter(xlabel, figname)345 logger.debug("draw nonZero CDF")346 # group by dev [dev, dev-day, dev-time] then CDF all stats347 # group by [datetime] then CDF all stats348 for group_by in [['Device_number'], ['Device_number', 'date'],349 ['Device_number', 'time'], ['end_time']]:350 # store each group in self.grouped351 self.grouper(group_by)352 xlab, filename = self.getStringsFromGroupBy(group_by)353 for stats in ['Peak', 'Median', '90perc']:354 self.getByteSeries(stats)355 xlabel = stats + xlab356 figname = 'CDF-'+filename+'-'+stats357 self.CDFPlotter(xlabel, figname)358 logger.debug("draw CDF - Bytes per "+str(group_by)+" "+stats)359 # for the last groupby ['end_time']360 # group by [datetime] then timeseries plot (x, y)361 GROUPBY = ['end_time']362 xlab, filename = self.getStringsFromGroupBy(GROUPBY)363 for name, df in self.ALL_DF.items():364 self.grouped[name] = df.groupby(GROUPBY)365 for stats in ['Peak', 'Median', '90perc']:366 #return self.grouped367 if stats=='Peak':368 for name, df in self.grouped.items():369 self.byte_series[name] = df['octets_passed'].apply(lambda x: max(x))370 elif stats=='Median':371 for name, df in self.grouped.items():372 self.byte_series[name] = df['octets_passed'].apply(lambda x: np.median(x))373 elif stats=='90perc':374 for name, df in self.grouped.items():375 self.byte_series[name] = df['octets_passed'].apply(lambda x: np.percentile(x, 90))376 else:377 logging.error("invalid stat, self.byte_series may be corrupted")378 #self.getByteSeries(stats)379 ylabel = stats + xlab380 figname = 'TimeSeries-'+filename+'-'+stats381 self.SeriesPlotter(ylabel, figname)382 return383 def SeriesPlotter(self, ylabel, figname):384 """385 given self BYTES[ ]: test_up/dw control_up/dw386 just plot in CDF387 """388 logger.debug("enter seriesplotter")389 fig1, ax1 = plt.subplots(2,1, figsize=(18,10))390 labels = iter([self.DLABEL+'_up',self.CLABEL+'_up',self.DLABEL+'_dw',391 self.CLABEL+'_dw'])392 colors = iter(['b', 'g', 'b', 'g'])393 axis_ctr = iter([0,0,1,1])394 markers = iter(['o','d','o','d'])395 #logger.debug("byte_series.items() " + str(self.byte_series.items()))396 for name, series in self.byte_series.items():397 x = pd.to_datetime(series.index)398 y = series.values399 lab = next(labels)400 if self.DISPLAY_MEAN:401 calcMean = '%.2E' % np.mean(list(series))402 lab += ': '+ calcMean403 ax1[next(axis_ctr)].plot_date(x,y, color=next(colors), label=lab,404 alpha=0.5, marker=markers.next())#,405 #marker=markers.next(), markevery=len(y)/10)406 #ax1[0].set_xscale(self.XSCALE)407 #ax1[1].set_xscale(self.XSCALE)408 ax1[1].set_xlabel("DateTime")409 ax1[0].set_ylabel(ylabel + " Bytes UPLINK")410 ax1[1].set_ylabel(ylabel + " Byes DOWNLINK")411 ax1[0].grid(1)412 ax1[1].grid(1)413 ax1[0].legend(loc='best')414 ax1[1].legend(loc='best')415 fig1.tight_layout()416 fig1.savefig(self.OUTPUTPATH+figname)417 return418##############################################################################419# MAIN methods420##############################################################################421#DATASET = 'test-data-set.dat'422#CONTROLSET = 'test-control-set.dat'423#DATALABEL = 'test-data-set'424#LIST_OF_CONTROLLABELS = ['test-control-set']425NDEVICES = 1000426def main2(ndev=NDEVICES, DataSet=DATALABEL, ControlSets=LIST_OF_CONTROLLABELS, method='sum'):427 logger.info("NDEVICES "+str(ndev)+" DateSet "+DataSet+" ControlSets "+str(ControlSets))428 # Plotter Class Object429 p = Plotter()430 # load test set up and down431 DATA_up, DATA_dw = load_dataframe(DataSet)432 # for control set in [control1 - control8]433 for ControlSet in ControlSets:434 # load test and control up and dw sets435 test_up, test_dw, control_up, control_dw, outputlabel = slicer(ControlSet, DATA_up,436 DATA_dw, ndev)437 test_up = process_df(test_up, method)438 test_dw = process_df(test_up, method)439 control_up = process_df(control_up, method)440 control_dw = process_df(control_up, method)441 outputlabel = method.upper() + '/' + outputlabel442 p.setOutputPath(outputlabel)443 p.import_df(test_up, test_dw, control_up, control_dw)444 p.DLABEL = DataSet445 p.CLABEL = ControlSet446 x = p.plotAll()447 logger.info("Done "+ControlSet)448 return p, x449def process_df(df, method='sum'):450 g = df.groupby(['Device_number', 'end_time', 'date', 'time'], as_index=False)451 if method=='sum':452 return g.sum()453 elif method=='max':454 return g.max()455 elif method=='wifi':456 return df[df['service_class_name'] == 'xwifi_dn' | df['service_class_name'] == 'xwifi_up']457def plotByDateRange(ndev=NDEVICES, DataSet=DATALABEL, ControlSets=LIST_OF_CONTROLLABELS, method='sum'):458 """459 load test set up and down460 for control set in [control1 - control8]461 load control set up and down and get date_start, date_end462 slice control set to 1000 devices,463 slice test set to 1000 devices, date_start, date_end464 plot CDFs465 """466 # Plotter Class Object467 p = Plotter()468 # load test set up and down469 DATA_up, DATA_dw = load_dataframe(DataSet)470 # for control set in [control1 - control8]471 for ControlSet in ControlSets:472 # load test and control up and dw sets473 test_up, test_dw, control_up, control_dw = slicer(ControlSet, DATA_up,474 DATA_dw)475 test_up = process_df(test_up)476 test_dw = process_df(test_up)477 control_up = process_df(test_up)478 control_dw = process_df(test_up)479 p.import_df(test_up, test_dw, control_up, control_dw)480 p.DLABEL = DataSet481 p.CLABEL = ControlSet482 logger.debug("loaded sliced sets " + DataSet + " " + ControlSet )483 # new outputpath by control-set and date484 outputlabel = method.upper() + '/' + ControlSet + '_' + date_start.replace(" ","_") \485 + '_' + date_stop.replace(" ","_")486 p.setOutputPath(outputlabel)487 # allBytes and nonZeroBytes488 p.CDFPlotter(p.allBytes, '')489 p.CDFPlotter(p.thresholdBytes, 0)490 # max, median, 90 percentile491 for method in ['Peak', 'Median', '90perc']:492 p.CDFPlotter(p.getBytesPerDevice, method)493 logger.debug("Bytes per Device method = " + method)494 p.CDFPlotter(p.getBytesPerDevicePerDay, method)495 logger.debug("Bytes per Device per Day method = " + method)496 p.CDFPlotter(p.getBytesPerDevicePerTimeSlot, method)497 logger.debug("Bytes per Device per Time method = " + method)498 logger.debug("DONE control: " + ControlSet + "; test: " + DataSet)499 return #DUP_sliced, DDW_sliced, CUP_sliced, CDW_sliced500def detailedTimeSeries(ds, cs):501 df1, df2 = load_csv(ds, cs)502 test_up, test_dw = filter_csv(df1, ds.split('.')[0])503 control_up, control_dw = filter_csv(df2, cs.split('.')[0])504 return test_up, test_dw, control_up, control_dw505def main():506 p = Plotter()507 p.load_df()508 p.splitDateTime()509 p.CDFPlotter(p.allBytes)510 p.CDFPlotter(p.nonZeroBytes)511 p.CDFPlotter(p.peakBytesPerDevice)512 p.CDFPlotter(p.peakBytesPerDevicePerDay)513 p.CDFPlotter(p.peakBytesPerDevicePerTimeSlot)...
app.py
Source:app.py
1from flask import Flask2import config3import models4from database import init_database, db_insert, db_delete5def create_app():6 # Flask7 app = Flask(__name__)8 # config9 app.config.update(config.cf)10 # DBåæå11 init_database(app)12 return app13app = create_app()14#--------------------------------------#15# ãã¼ã¿ãã¼ã¹åç
§ï¼viewï¼16#--------------------------------------#17@app.route('/')18def index():19 html_list = ""20 list_Tests = models.Test.query.all()21 html_list += "<table border><tr><th>Id</th><th>Data</th></tr>\n"22 for i in list_Tests:23 html_list += "<tr><th>%s</th><td>%s</td></tr>\n" % (str(i.id), i.data)24 html_list += "</table>\n"25 return "Hello Flask<br>"+html_list26#--------------------------------------#27# ä¸è¡è¿½å ï¼insertï¼28#--------------------------------------#29@app.route('/insert/<param>')30def insert(param):31 test_new = models.Test(data=param)32 db_insert(test_new)33 return "insert: %s" % (param)34#--------------------------------------#35# ä¸è¡å¤æ´ï¼updateï¼36#--------------------------------------#37@app.route('/update/<id>/<param>')38def update(id, param):39 test_up = models.Test.query.filter_by(id=id).first()40 test_up.data=param41 db_insert(test_up)42 return "update(id:%s): to %s" % (id, param)43#--------------------------------------#44# ä¸è¡åé¤ï¼deleteï¼45#--------------------------------------#46@app.route('/delete/<id>')47def delete(id):48 test_del = models.Test.query.filter_by(id=id).first()49 db_delete(test_del)...
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