def insertVacancy(cursor, vacancy): print("in insert: ", vacancy) try: cursor.execute("INSERT INTO vacancies(id, \ name, \ premium, \ has_test, \ letter_required, \ type, \ employer_id, \ created_at, \ published_at, \ requirement, \ responsibility, \ last_update, \ job, \ developer_experience) \ VALUES(" + str(vacancy['id']) + "," + "\'" + vacancy['name'] + "\'," + str(vacancy['premium']) + "," + str(vacancy['has_test']) + "," + str(vacancy['response_letter_required']) + "," + "\'" + str(vacancy['type']['id']) + "\'," + str(vacancy['employer']['id']) + "," + "TIMESTAMP \'" + str(vacancy['created_at']) + "\'," + "TIMESTAMP \'" + str(vacancy['published_at']) + "\'," + "\'" + str(vacancy['snippet']['requirement']) + "\'," + "\'" + str(vacancy['snippet']['responsibility']) + "\'," + "TIMESTAMP \'" + str(getTime().isoformat()) + "\'," + "\'" + str(category(jobs, vacancy)) + "\', " + "\'" + str(category(developer_experience, vacancy)) + "\');") cursor.execute("INSERT INTO employers(id, name) VALUES(" + str(vacancy['employer']['id']) + ",\'" + str(vacancy['employer']['name']) + "\') " + "ON CONFLICT ON CONSTRAINT id_constr DO NOTHING;") # check if there's the same first if vacancy['address'] != "null": insertAddress(cursor, vacancy) if vacancy['contacts'] != "null": insertContact(cursor, vacancy) if vacancy['salary'] != "null": cursor.execute("UPDATE vacancies SET \ salary_from = " + str(vacancy['salary']['from']) + "," + "salary_to = " + str(vacancy['salary']['to']) + "," + "currency = " + "\'" + vacancy['salary']['currency'] + "\'," + "gross = " + str(vacancy['salary']['gross']) + " WHERE id =" + vacancy['id'] + ";") except KeyError: print("Vacancy wasn't inserted") cursor.execute("SELECT * FROM vacancies;") print(cursor.fetchall())
def get_by_id(self, id): cur = self.conn.cursor() cur.execute( """SELECT id, code, name, created_by FROM categories WHERE id=%s""", (id, )) row = cur.fetchone() cur.close() return category.category(row[0], row[1], row[2], row[3])
def get_all(self): cur = self.conn.cursor() cur.execute("""SELECT id, code, name, created_by FROM categories""") rows = cur.fetchall() cur.close() categories = [] for row in rows: categories.append(category.category(row[0], row[1], row[2], row[3])) return categories
def do_atom_category(self): return category()
def do_category(self): self.metadata() from category import category return category()
def do_category(self): return category()
def do_atom_category(self): from category import category return category()
def do_atom_category(self): self.metadata() from category import category return category()
def do_category(self): self.metadata() return category()
def test(): #holds the results results = [] #defaults the percent value percent = 0.0 #gets dictionary from filterNames subCategories = getNameFiles() #get the global variable data global data #get json file containing the imageString from android app data = request.get_json(cache=True) #get photo from data photo = data["photo"] #open photo and decode base64 string with open("test.png", mode="wb") as f: f.write(base64.b64decode(photo)) #run modeil subCat = executeWithParameter("subcategoriesgraph.pp", "subcategorieslabels.txt", "test.png") #format subcategory to be upercase and get subcategory prediction subCat = subCat.get("prediction").title() #set empty variable to check if there is a valid solution predictionName = None #for each key and value in subcategories dictionary for key, value in subCategories.items(): #TEST PRINT print(key) #TEST PRINT print(key == subCat) #if subcategory contains more than one name if key == subCat: #run model modelPredictionName = executeWithParameter(value[0], value[1], "test.png") #get prediction value predictionName = modelPredictionName.get("prediction") #get prediction accuracy percent = modelPredictionName.get("accuracy") #TEST PRINT print("*********", percent) #format unique name predictionName = predictionName.title() #TEST PRINT print(subCat) #if subcategory predicts a value with only one unique name if subCat == "Air Conditioning": predictionName = "HVAC Unit" percent = 100 elif subCat == "Fondant Warmer": predictionName = "Fondat Warmer" percent = 100 elif subCat == "Ice Cream Machine": predictionName = "Ice Cream Machine" percent = 100 elif subCat == "Ice Machine": predictionName = "Ice Machine" percent = 100 elif subCat == "Juice Dispenser": predictionName = "Juicer" percent = 100 elif subCat == "Lighting": predictionName = "Air Curtain" percent = 100 elif subCat == "Microwave": predictionName = "Microwave" percent = 100 elif subCat == "Reach In Cooler": predictionName = "Cooler (Reach In)" percent = 100 elif subCat == "Walk In Coolers": predictionName = "Cooler (Walk In)" percent = 100 elif subCat == "Walk In Freezers": predictionName = "Freezer (Walk In)" percent = 100 elif subCat == "Water Heaters": predictionName = "Hot Water Heater" percent = 100 #get top 5 predictions results = getResults() #reset results refreshResults() #TEST PRINT str(results) print(results) #if no prediction exists if predictionName == None: subCat = "Invalid" #get category name categoryName = category(subCat) # result = f'{"name":"{prediction}", "category":"Test", "subCat":"Sub_CAT", "brand":"", "model":"112131111", location:"On the left wall"}' #build json to be sent in response result = json.dumps({ "name": predictionName, "category": categoryName, "subCat": subCat, "results": results, "percent": str(percent) }) #result = json.dumps({"name": predictionName}) #result = json.dumps({"category": categoryName}) #close photo file f.close() #return JSON return result
from category import category #S_1 = [{'title': 'Trump Bombs Mexico', 'summary': 'Trump'}] #S_2 = [{'title': 'Trump Bombs Mexico', 'summary': 'Trump'}] S_1 = [{'title': 'Trump Bombs Mexico', 'summary': 'Trump'}, {'title': 'Putin', 'summary': 'Putin'}, {'title': 'Trump Bombs BBC', 'summary': 'Trump'}, {'title': 'Putin Loves Snakes', 'summary': 'Putin'}] S_2= [{'title': 'Putin', 'summary': 'Putin'}, {'title': 'Trump Bombs BBC', 'summary': 'Trump'}, {'title': 'Putin Loves Snakes', 'summary': 'Putin'}] #S_2 = [{'title': 'Trump Bombs Mexico', 'summary': 'Trump'}, {'title': 'Putin', 'summary': 'Putin'}] thresh = 30.0 new_var = category(S_1, S_2, thresh) for n_list in new_var: print '----------' print n_list print for story in new_var[n_list]: print story
import dps_meminfo import category import time import proc_meminfo from pandas import Series, DataFrame, ExcelWriter #p = sp.Popen("adb shell dumpsys -t 60 meminfo", shell=True, stdout=sp.PIPE, stderr=sp.STDOUT) #p = sp.Popen("cat ./dpmeminfo.txt", shell=True, stdout=sp.PIPE, stderr=sp.STDOUT) #dumpsys_meminfo = p.stdout.readlines() #print dumpsys_meminfo process = {} oom_adj = oom_adj.oom_adj() category = category.category() dps_meminfo_0 = dps_meminfo.dps_meminfo(process, oom_adj, category) #''' df_OOM_ADJ = DataFrame() df_OOM_ADJ_Persist = DataFrame() df_category = DataFrame() df_all = DataFrame() writer = ExcelWriter('output.xlsx') index = 0 Persist_len = 0 while index < 100: index = index + 1
nBlocks = data['exptdesign']['numSessions'][0,0][0] subjectName = data['exptdesign']['subName'][0,0][0] ############################################################################# #Calculations by Frequency Pair ############################################################################# FS = FrequencySpecific(stimuli=stimuli) mF, countTotal = FS.frequencyPair_parse(accuracy) catA = FS.category_parse(accuracy) ############################################################################# #Calculations by morph ############################################################################# catObj = category(stimuli = stimuli) RT, ACC = catObj.wrapper(accuracy, reactionTime) pos_acc = catObj.parseData_freq_block(accuracy, stimuli) ############################################################################# #Calculating mean acc and RT overall ############################################################################# #calculate the mean overall accuracy by block O_accuracy = [] iBlock = 0 for iBlock in range(accuracy.size): O_accuracy.append(np.mean([accuracy[0,iBlock]])) #calculate the mean RT overall by block O_reactionTime = [] iBlock = 0
def do_category(self): from category import category return category()
def CategoryTrainingFigure_Funnel(fileDirectory, filename, session): #Use when debugging or manually editing #filename = ('20160630_1430-MR1040_block6') #fileDirectory = '/Users/courtney/GoogleDrive/Riesenhuber/05_2015_scripts/Vibrotactile/01_CategoryTraining/data/1040/' #session = '2' #load matfile data = sio.loadmat(fileDirectory + filename, struct_as_record=True) #pull relevant data from structures reactionTime = data['trialOutput']['RT'] sResp = data['trialOutput']['sResp'] correctResponse = data['trialOutput']['correctResponse'] accuracy = data['trialOutput']['accuracy'] level = data['trialOutput']['level'] stimuli = data['trialOutput']['stimuli'] nTrials = data['exptdesign']['numTrialsPerSession'][0, 0][0] nBlocks = data['exptdesign']['numSessions'][0, 0][0] subjectName = data['exptdesign']['subName'][0, 0][0] ############################################################################# #Calculations by Frequency Pair ############################################################################# FS = FrequencySpecific(stimuli=stimuli) mF, countTotal = FS.frequencyPair_parse(accuracy) catA = FS.category_parse(accuracy) ############################################################################# #Calculations by morph ############################################################################# catObj = category(stimuli=stimuli) RT, ACC = catObj.wrapper(accuracy, reactionTime) pos_acc = catObj.parseData_freq_block(accuracy, stimuli) ############################################################################# #Calculating mean acc and RT overall ############################################################################# #calculate the mean overall accuracy by block O_accuracy = [] iBlock = 0 for iBlock in range(accuracy.size): O_accuracy.append(np.mean([accuracy[0, iBlock]])) #calculate the mean RT overall by block O_reactionTime = [] iBlock = 0 for iBlock in range(reactionTime.size): O_reactionTime.append(np.mean(reactionTime[0, iBlock])) #x-axis label x = [] i = 0 for i in range(accuracy.size): x.append("Block: " + str(i + 1) + ", Level: " + str(level[0, i][0, 0])), ############################################################################# #Generating figures ############################################################################# #make trace containing each frequency pair x2 = [ '[25,100]', '[27,91]', '[29,91]', '[31,83]', '[33,77]', '[36,71]', '[38,67]', '[40,62.5]', '[62.5, 40]', '[67, 38]', '[71, 36]', '[77, 33]', '[83, 31]', '[91, 29]', '[91, 27]', '[100, 25]' ] x3 = [ '100%', '95%', '90%', '85%', '80%', '75%', '70%', '65%', '35%', '30%', '25%', '20%', '15%', '10%', '5%', '0%' ] x4 = ['[27,91]', '[40,62.5]', '[62.5, 40]', '[91, 27]'] #trace_PG_ACC_7 = make_trace_bar(x4, [pos_acc[0][0], pos_acc[0][1], pos_acc[0][2], pos_acc[0][3]], 'pos3') trace_PG_ACC_7 = make_trace_bar( x4, [pos_acc[0], pos_acc[1], pos_acc[2], pos_acc[3]], 'pos3') trace_ACC_FP = make_trace_bar(x2, mF, '') #make trace containing acc and RT for morph prototypeAcc = [0] * (len(ACC) // 3) middleAcc = [0] * (len(ACC) // 3) boundaryAcc = [0] * (len(ACC) // 3) prototypeRT = [0] * (len(ACC) // 3) middleRT = [0] * (len(ACC) // 3) boundaryRT = [0] * (len(ACC) // 3) for i in range(len(ACC) // 3): prototypeAcc[i] = ACC[3 * i] middleAcc[i] = ACC[3 * i + 1] boundaryAcc[i] = ACC[3 * i + 2] prototypeRT[i] = RT[3 * i] middleRT[i] = RT[3 * i + 1] boundaryRT[i] = RT[3 * i + 2] trace1 = make_trace_bar(x, prototypeAcc, "Category Prototype Acc") trace2 = make_trace_bar(x, middleAcc, "Middle Morph Acc") trace3 = make_trace_bar(x, boundaryAcc, "Category Boundary Acc") trace4 = make_trace_line(x, prototypeRT, "Category Prototype RT", 'n') trace5 = make_trace_line(x, middleRT, "Middle Morph RT", 'n') trace6 = make_trace_line(x, boundaryRT, "Category Boundary RT", 'n') #make trace containing overall acc and rt trace7 = make_trace_line(x, O_accuracy, "Overall Accuracy", 'y') trace8 = make_trace_line(x, O_reactionTime, "Overall RT", 'n') # make categorization curve traceCatCurve = [] for index, obj in enumerate(catA): traceCatCurve.append(make_trace_line(x3, obj, '', 'n')) # Generate Figure object with 2 axes on 2 rows, print axis grid to stdout fig = tls.make_subplots(rows=1, cols=1, shared_xaxes=True) fig_FP = tls.make_subplots(rows=1, cols=1, shared_xaxes=True) fig_CatCurve = tls.make_subplots(rows=1, cols=1) fig_pos = tls.make_subplots(rows=1, cols=1, shared_xaxes=True) #set figure layout to hold mutlitple bars fig['layout'].update(barmode='group', bargroupgap=0, bargap=0.25, title=subjectName + " Accuracy and RT By Morph Session " + session, yaxis=dict(dtick=.1)) xZip = x2[:len(x2)] yZip = countTotal fig_FP['layout'].update(barmode='group', bargroupgap=0, bargap=0.25, title=subjectName + " Accuracy By Frequency Pair, Session " + session, yaxis=dict(dtick=.1), annotations=[ dict( x=xZip[i], y=mF[i], showarrow=False, text=yZip[i], xanchor='center', yanchor='bottom', ) for i in range(len(xZip)) ]) fig_CatCurve['layout'].update(barmode='group', bargroupgap=0, bargap=0.25, title=subjectName + " Categorization Curve " + session, xaxis=dict(autorange='reversed', dtick=5, range=[0, 100], showgrid=True), yaxis=dict(range=[0, 100], dtick=5)) fig_pos['layout'].update(barmode='group', bargroupgap=0, bargap=0.25, title=subjectName + " Pos Accuracy RA stimuli " + session, yaxis=dict(dtick=.1)) colorRA = [ 'black', 'blue', 'black', 'black', 'black', 'black', 'black', 'blue', 'blue', 'black', 'black', 'black', 'black', 'black', 'blue', 'black' ] fig['data'] = [ trace1, trace2, trace3, trace7, trace4, trace5, trace6, trace8 ] fig_FP['data'] = [go.Bar(x=x2, y=mF, marker=dict(color=colorRA))] fig_CatCurve['data'] = [ go.Scatter(x=x3, y=catA, name='SubjectData'), go.Scatter(x=[50, 50], y=[0, 100], name='Category Boundary', line=dict(color='red')) ] fig_pos['data'] = [trace_PG_ACC_7] #bread crumbs to make sure entered the correct information print("Your graph will be saved in this directory: " + fileDirectory + "\n") print("Your graph will be saved under: " + filename + "\n") print("The session number you have indicated is: " + session + "\n") #save images as png in case prefer compared to html py.image.save_as( fig, fileDirectory + filename + "_CategTrainingMorphAccSession" + session + ".jpeg") py.image.save_as( fig_FP, fileDirectory + filename + "_FP_AccSession" + session + ".jpeg") py.image.save_as( fig_CatCurve, fileDirectory + filename + "_CatCurve_Session" + session + ".jpeg") #py.image.save_as(fig_pos, fileDirectory + filename + "_PosRA_ACC_Session" + session + ".jpeg") print("Done!")