Reset23/the-stack-v2-python · Datasets at Hugging Face

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# -*- coding: utf-8 -*- """ Created on Wed Oct 16 17:24:12 2019 @author: Libra """ import numpy as np # import phylib.utils._misc as phyutil import pandas as pd import h5py import matplotlib.pyplot as plt import matplotlib FR_Th = 1.0 def trialAlign(trials, oneTS): oneTS = oneTS[ np.bitwise_and( oneTS >= trials[0, 0] - 30000 * 5, oneTS <= (trials[-1, 0] + 30000 * 10) ) ] # only the performing period TSidx = 0 trial_index = 0 trial_id = np.ones_like(oneTS) while TSidx < len(oneTS) and trial_index < len(trials): if oneTS[TSidx] < trials[trial_index, 0] + (trials[trial_index, 7] + 8) * 30000: oneTS[TSidx] -= trials[trial_index, 0] trial_id[TSidx] = trial_index TSidx += 1 else: trial_index += 1 return (oneTS, trial_id) def baselineVector(oneTS, trial_id, trials, SU_id): tIdices = range(trials.shape[0]) base = [] for trial_index in tIdices: for binCount in np.histogram( oneTS[trial_id == trial_index], bins=4, range=(-60000, -30000) )[0]: base.append(binCount) if len(base) > 0 and np.std(base): return (np.mean(base), np.std(base)) # breakpoint() print("Error calculating base vector unit#%d" % (SU_id,)) return (0, 32767) def toHist(trials, oneTS, trial_id, sample, delay): sel = np.nonzero(np.bitwise_and(trials[:, 4] == sample, trials[:, 7] == delay))[0] return ( np.histogram( oneTS[np.isin(trial_id, sel)], np.linspace(-60000, 30000 * (delay + 6), num=(delay + 8) * 4 + 1), )[0] ) / len(sel) def toHistByPair(trials, oneTS, trial_id, isPaired, delay): if isPaired: sel = np.nonzero( np.bitwise_and(trials[:, 4] != trials[:, 5], trials[:, 7] == delay) )[0] else: sel = np.nonzero( np.bitwise_and(trials[:, 4] == trials[:, 5], trials[:, 7] == delay) )[0] return ( ( np.histogram( oneTS[np.isin(trial_id, sel)], np.linspace((delay - 1) * 30000, (delay + 6) * 30000, num=7 * 4 + 1), )[0] ), len(sel), ) def alignHeatmap(spkTS, spkCluster, unitInfo, trials): # bySample43 = [] bySample46 = [] # bySample83 = [] bySample86 = [] paired = [] nonpaired = [] baseVecAll = [] depth = [] s1s = 30000 spkNThresh = spkTS[-1] / s1s * FR_Th for SU_id in unitInfo.index: # breakpoint() wf = unitInfo.loc[SU_id].get("group") == "good" or ( np.isnan(unitInfo.loc[SU_id]['group']) and unitInfo.loc[SU_id]["KSLabel"] == "good" ) spkCount = unitInfo.loc[SU_id]["n_spikes"] if spkCount > spkNThresh and wf: oneTSAll = (spkTS[spkCluster == SU_id]).astype( "int64" ) # oneTSAll, all time stamp of a SU (oneTS, trial_id) = trialAlign(trials, oneTSAll) baseVec = baselineVector(oneTS, trial_id, trials, SU_id) baseVecAll.append(baseVec) # bySample43.append(toHist(trials, oneTS, trial_id, 4, 3)) bySample46.append(toHist(trials, oneTS, trial_id, 4, 6)) # bySample83.append(toHist(trials, oneTS, trial_id, 8, 3)) bySample86.append(toHist(trials, oneTS, trial_id, 8, 6)) # (p3, t3) = toHistByPair(trials, oneTS, trial_id, True, 3) (p6, t6) = toHistByPair(trials, oneTS, trial_id, True, 6) paired.append(np.array(p6) / t6) # paired.append((np.array(p3) + np.array(p6)) / (t3 + t6)) # (n3, tn3) = toHistByPair(trials, oneTS, trial_id, False, 3) (n6, tn6) = toHistByPair(trials, oneTS, trial_id, False, 6) nonpaired.append( np.array(n6) / tn6) # nonpaired.append((np.array(n3) + np.array(n6)) / (tn3 + tn6)) depth.append(unitInfo.loc[SU_id]["depth"]) depth = np.array(depth) if depth.shape[0] > 0: baseVecAll = np.array(baseVecAll) dIdx = np.argsort(depth) # bySample43 = np.array(bySample43) bySample46 = np.array(bySample46) # by1Sample83 = np.array(bySample83) bySample86 = np.array(bySample86) paired = np.array(paired) nonpaired = np.array(nonpaired) return ( ( # bySample43[dIdx, :], bySample46[dIdx, :], # bySample83[dIdx, :], bySample86[dIdx, :], ), (paired[dIdx, :], nonpaired[dIdx, :]), baseVecAll[dIdx], depth[dIdx], ) else: return ([], [], [], []) def plotOne(data, delay, ax, ylbl): im = plt.imshow(data, cmap="jet", aspect="auto", vmin=-3, vmax=3) if delay == 6: [ plt.plot([x, x], ax.get_ylim(), "-w") for x in np.array([2, 3, 9, 10]) * 4 - 0.5 ] ax.set_xticks(np.array([2, 7, 12]) * 4 - 0.5) ax.set_xticklabels([0, 5, 10]) # ax.set_xlabel('Time (s)') elif delay == 3: [ plt.plot([x, x], ax.get_ylim(), "-w") for x in np.array([2, 3, 6, 7]) * 4 - 0.5 ] ax.set_xticks(np.array([2, 7]) * 4 - 0.5) ax.set_xticklabels([0, 5]) if ylbl: ax.set_ylabel("Unit #") return im def plotOneSel(A, B, delay, ax, ylbl): plt.imshow((B - A) / (B + A), cmap="jet", aspect="auto", vmin=-1, vmax=1) # if delay==6: # [plt.plot([x,x],ax.get_ylim(),'-w') for x in np.array([2,3,9,10])*4-0.5] # ax.set_xticks(np.array([2,7,12])*4-0.5) # ax.set_xticklabels([0,5,10]) # # # elif delay==3: # [plt.plot([x,x],ax.get_ylim(),'-w') for x in np.array([2,3,6,7])*4-0.5] # ax.set_xticks(np.array([2,7])*4-0.5) # ax.set_xticklabels([0,5]) [plt.plot([x, x], ax.get_ylim(), "-w") for x in np.array([2, 3]) * 4 - 0.5] ax.set_xticks(np.array([2, 6]) * 4 - 0.5) ax.set_xticklabels(["S+0", "S+4"]) if ylbl: ax.set_ylabel("Unit #") ax.set_xlabel("Time (s)") def plotOneSelByPair(A, B, ax): im = plt.imshow((B - A) / (B + A), cmap="jet", aspect="auto", vmin=-1, vmax=1) [plt.plot([x, x], ax.get_ylim(), "-w") for x in np.array([2, 3]) * 4 - 0.5] ax.set_xticks(np.array([2, 7]) * 4 - 0.5) ax.set_xticklabels(["T+0", "T+5"]) ax.set_xlabel("Time (s)") return im def plotBehavior(trials, ax): correct = np.logical_xor(trials[:, 4] == trials[:, 5], trials[:, 6] == 1) licks = trials[:, 6] == 1 perf = [] lickPct = [] for ubound in range(16, len(correct), 16): perf.append(np.mean(correct[ubound - 16 : ubound])) lickPct.append(np.mean(licks[ubound - 16 : ubound])) plt.plot(perf, "-k", label="correct rate") plt.plot(lickPct, "--r", label="lick rate") ax.legend() ax.set_ylim(0, 1.0) ax.set_ylabel("correct rate, lick rate") ax.set_xlabel("block of 16 trials") ax.set_title("behavior performance") def plotHeatmap(trials, raw, byPaired, base, depth): import os cwd = os.getcwd() leafPath = os.path.split(cwd)[1] fh = plt.figure(3, figsize=[8, 10]) # ax = plt.subplot(3, 3, 1) # plotOne(((raw[0].transpose() - base[:, 0]) / base[:, 1]).transpose(), 3, ax, True) # ax.set_title("S1 3s delay") # ax = plt.subplot(3, 3, 2) # im = plotOne( # ((raw[2].transpose() - base[:, 0]) / base[:, 1]).transpose(), 3, ax, False # ) # plt.colorbar(im, ticks=[-3, 0, 3], format="%d") # ax.set_title("S2 3s delay") ax = plt.subplot(3, 3, 1) plotOne(((raw[0].transpose() - base[:, 0]) / base[:, 1]).transpose(), 6, ax, True) ax.set_title("S1 6s delay") ax = plt.subplot(3, 3, 2) im = plotOne( ((raw[1].transpose() - base[:, 0]) / base[:, 1]).transpose(), 6, ax, False ) plt.colorbar(im, ticks=[-3, 0, 3], format="%d") ax.set_title("S2 6s delay") # depth plot ax = plt.subplot(3, 3, 6) plt.plot(depth) ax.set_ylabel("distance from tip (um)") ax.set_xlabel("unit #") plt.minorticks_on() plt.grid(b=True, which="both") ax = plt.subplot(3, 3, 4) im = plotOneSel( raw[0][:, 0:24], raw[1][:, 0:24], # raw[0][:, 0:24] + raw[1][:, 0:24], # raw[2][:, 0:24] + raw[3][:, 0:24], 6, ax, False, ) ax.set_title("sample selectivity") # plt.colorbar(im,ticks=[-1,0,1],format='%d') ax = plt.subplot(3, 3, 5) im = plotOneSelByPair(byPaired[0], byPaired[1], ax) ax.set_title("pair/non-pair selectivity") plt.colorbar(im, ticks=[-1, 0, 1], format="%d") ax = plt.subplot(3, 3, 3) plotBehavior(trials, ax) fh.suptitle(leafPath.replace("_cleaned", "")) plt.tight_layout(rect=[0, 0, 1, 0.95]) #plt.show() # breakpoint() fh.savefig(leafPath.replace("_cleaned", "") + ".png", dpi=300, bbox_inches="tight") # matplotlib.rcParams['pdf.fonttype'] = 42 # matplotlib.rcParams['ps.fonttype'] = 42 # fh.savefig(leafPath.replace("_cleaned", "") + ".pdf", dpi=300, bbox_inches="tight") plt.close("all") # return (fh,ax) def runParse(): # s1s=30000 spkTS = np.load("spike_times.npy") spkCluster = np.load("spike_clusters.npy") unitInfo = pd.read_csv("cluster_info.tsv",sep='\t',index_col='id') trials = np.empty([0]) with h5py.File("events.hdf5", "r") as fe: dset = fe["trials"] trials = np.array(dset, dtype="int32") events=fe['events'] (raw, byPaired, baseVec, depth) = alignHeatmap(spkTS, spkCluster, unitInfo, trials) if raw and byPaired: plotHeatmap(trials, raw, byPaired, baseVec, depth) else: print("empty SU list\n") if __name__ == "__main__": # import os # os.chdir('K:/neupix/191015-DPA-Learning2_29_g0_imec0_cleaned') # s1s = 30000 spkTS = np.load("spike_times.npy") spkCluster = np.load("spike_clusters.npy") unitInfo = pd.read_csv("cluster_info.tsv",sep='\s+',index_col='id') trials = np.empty([0]) with h5py.File("events.hdf5", "r") as fe: dset = fe["trials"] trials = np.array(dset, dtype="int64") (raw, byPaired, baseVec, depth) = alignHeatmap(spkTS, spkCluster, unitInfo, trials) if byPaired.size > 0: plotHeatmap(trials, raw, byPaired, baseVec, depth) else: print("empty SU list\n")

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# coding: utf-8 # In[1]: from collections import Counter coins = [2000, 500, 100, 50, 10, 1] stock={2000:6,500:2,100:20,50:15,10:100,1:200} def fun(): bill=int(input("your bill in rupees")) givenamount=0 while givenamount<bill: givenamount+=int(input("given amount in rupees")) if givenamount>=bill: change=givenamount-bill coinsReturned = [] for i in coins: if stock[i]>0: while (change>=i and stock[i]>0): change=change-i coinsReturned.append(i) stock[i]-=1 a=stock print(a) print(coinsReturned) stock.update(a) print((stock)) def update(): total=[] full_amount=[] d=int(input("enter two thousand notes:")) f=int(input("enter five hundred notes:")) l=int(input("enter hundred notes:")) k=int(input("enter fifty rupee notes:")) j=int(input("enter ten rupee:")) p=int(input("enter one rupee:")) new_collec={2000:d,500:f,100:l,50:k,10:j,1:p} for i,j in new_collec.items(): mul=i*j total.append(mul) print("Total deposit amount:",sum(total)) updated=dict(Counter(stock)+Counter(new_collec)) stock.update(updated) for u,l in stock.items(): apx=u*l full_amount.append(apx) print("Total updated amount:",sum(full_amount)) fun() # In[27]: update() # fun() # In[25]: fun() # In[19]: update() # In[20]: fun() # In[17]: posting={'Rs500': ['23'],'Rs10': ['4'], 'Rs200': ['3'], 'Rs1': ['6'], 'Rs2000': ['23'], 'Rs50': ['5'], 'Rs100': ['4'], 'Rs5': ['3'], 'Rs2': ['2']} for x,y in posting.items(): for i in range(0,len(y)): y[i]=int(y[i]) #print(x,y) for s in y: s=int(s) b={x:s} posting.update(b) print(posting) # In[14]: posting={'Rs500': ['23'],'Rs10': ['4'], 'Rs200': ['3'], 'Rs1': ['6'], 'Rs2000': ['23'], 'Rs50': ['5'], 'Rs100': ['4'], 'Rs5': ['3'], 'Rs2': ['2']} for key,values in posting.items(): val=values for x in val: y=int(x) d={key:y} posting.update(d) print(posting) # In[68]: d={'given_amount': ['50'], 'bill_amount': ['8']} li=[] x={} for key,values in d.items(): val=values for x in val: y=int(x) d={key:y} li.append(y) x.update l=li[0]-li[1] d['change']=l print(l) print(d) # In[3]: c={'Rs5': 14, 'Rs10': 16, 'Rs200': 32, 'Rs1': 71, 'Rs50': 12, 'Rs100': 18, 'Rs500': 28, 'Rs2': 18, 'Rs2000': 8, 'id': 1} l=[] a=c.keys() print((a)) b=c.values() print(b) import re d=[re.findall(r'(\w+?)(\d+)', key)[0] for key,values in c.items() if key!='id'] print(d) for w,q in d: l.append(int(q)) print("fu:",sorted(l,reverse=True)) print(d) # In[ ]: h=dict(d) print(h) for x,y in h.items(): j=y for u in j: v=int(u) l.append(v) print(v) # In[57]: c={'Rs5': 14, 'Rs10': 16, 'Rs200': 32, 'Rs1': 71, 'Rs50': 12, 'Rs100': 18, 'Rs500': 28, 'Rs2': 18, 'Rs2000': 8, 'id': 1} d=[re.findall(r'(\w+?)(\d+)', key)[0] for key,values in c.items() if key!='id'] print(d) l=[] p=[] z=c.values() print(z) for w,q in d: l.append(int(q)) p.append(w) print("rupees:",p) print("fu:",(l)) dic=dict(zip(l,z)) print("full:",dic) print("dic:",dic.items()) i={'Rs' + str(key):values for key,values in dic.items()} print("sdfhd:",i) f={ key:values for key,values in c.items() if key!='id' } print("list :",c.items()) print(f) # In[ ]:

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# Generated by Django 3.0.2 on 2020-01-16 07:02 import core.utils from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Product', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=200)), ('description', models.TextField()), ('price', models.DecimalField(decimal_places=2, max_digits=20)), ('image', models.ImageField(blank=True, null=True, upload_to=core.utils.upload_image_path)), ], ), ]

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n=int(raw_input()) x = ((n*(n+1))/2) print(x)

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import socket import sys client_list = {} buff=4096 try: server_sock=socket.socket(socket.AF_INET, socket.SOCK_DGRAM) host=socket.gethostname() host_ip=socket.gethostbyname(host) #getting server host_ip for cross machine usage except socket.error: print 'Something went wrong, please try again' sys.exit(1) def server_setup(host_addr): server_sock.bind(host_addr) def client_details(client_data): #passing all client data in the form username:message #print client_data client_port=client_data.split(',')[3] client_port=client_port[:-2] client_port=int(client_port.strip()) client_ip=client_data.split(',')[2] client_ip=client_ip[2:] client_ip=client_ip[1:-1] client_ip=str(client_ip) username=client_data.split(',')[0] username=username[2:] username=username.strip() if client_data.find('SIGN-IN')!=-1: if client_list.has_key(username): server_sock.sendto("+> User already Signed In", ((client_ip,client_port))) else: client_list[username]=str(client_ip)+':'+str(client_port) server_sock.sendto('success',((client_ip),client_port)) return elif client_data.find('list')!=-1: list_to_send = list(set(client_list.keys()) - set([username])) list_name='' for i in list_to_send[:]: list_name=list_name+' '+i server_sock.sendto(str(list_name),((client_ip),client_port)) #print str(type(client_ip)) + str(type(client_port)) return elif client_data.find('send')!=-1: try: user_info_to_send= client_list[username] server_sock.sendto("+>" + str(user_info_to_send), ((client_ip),client_port)) except: server_sock.sendto("+> Username not present",((client_ip),client_port)) return def main(): if len(sys.argv)==3 and sys.argv[1]== '-sp': print 'Server Initialized...' else: print 'Usage server.py -sp <port number>' sys.exit(1) try: port= int(sys.argv[2]) except: print 'Please enter a valid port, Usage ChatServer.py -sp <port number>' host_addr=host_ip,port server_setup(host_addr) while 1: client_data = str(server_sock.recvfrom(buff)) client_details(client_data) main()

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import sys IS_PY2 = sys.version_info.major == 2 IS_PY3 = sys.version_info.major == 3 if IS_PY2: from urllib2 import urlopen elif IS_PY3: from urllib.request import urlopen

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a = input() b = input() c = input() d = input() s = input() Nikky = (a-b)*(s/(a+b)) e = s - ((s/(a+b))*(a+b)) steps1 = tuple([1]*a + [-1]*b) for i in range(e): Nikky += steps1[i] Byron = (c-d)*(s/(c+d)) e = s - ((s/(c+d))*(c+d)) steps2 = tuple([1]*c + [-1]*d) for i in range(e): Byron += steps2[i] if abs(Nikky) > abs(Byron): print "Nikky" elif abs(Nikky) < abs(Byron): print "Byron" else: print "Tied"

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def printArray(r, n1): for i in range(0,n1): print ("%d"%( r[i]),end=" ") r = [243, 140, 240, 141, 142, 46, 74] n1 = len(r) pancakeSort(r, n1); print ("Sorted Array ") printArray(r,n1)

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/Insecticide Resistance Capston Project Files(Machine Learning, Python)/ResPro Package/paac-calculator.py

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papaniivanderpuye/Public-Projects

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refs/heads/master

2020-03-20T16:44:39.118989

2018-07-10T23:58:22

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import math import sys #calculates paac for every sequence in file def aacSet(sequence,counts): #returns list with frequency of each letter #counts number of times each letter appears for aa in sequence: counts[aa] += 1 #making list for recording numbers myList =[] #adding all numbers for frequency of each letter to the list for key in sorted(counts.keys()): myList.append(str( counts[key]) ) return myList #checks if letters are the same def deltaFunction(R1,R2): if (R1 == R2): return 1 else: return 0 def deltaSet(sequence): deltaSetList =[] L =len(sequence) myLambda = 10 for i in range(myLambda): total = 0.0 #i am getting the letters calcultations of the sequence based on the formula in #the pdf jing hu sent me for j in range(L - (i+1)): R1 = sequence[j] # I am adding one because i starts from 0 #so it will not make sense considering the actual mathematical formula R2 = sequence[j +(i + 1)] total += deltaFunction(R1,R2) deltaNumber = total/float(L-i) deltaSetList.append(str(deltaNumber)) return deltaSetList #getting the actual values from the hydroFile def getAAIndexValues(hydroInfoFile): alist=[] values = [] fin= open(hydroInfoFile, "r") firstLine = True for line in fin: if(firstLine): firstLine = False #moves on if it is the first line, otherwise, the values list will be empty before it calculates. elif ( 'A' not in line): newLine = line.rstrip('\n').rstrip('*').rstrip('\r') newList = newLine.split() for aa in newList: #adds all vallues to a list, order is as it was read values.append(float(aa)) else: #normalizes values mean = sum(values)/float(len(values)) otherList =[(x-mean)**2 for x in values] deviation = math.sqrt( sum(otherList)/float(len(otherList)) ) normalized = [(x-mean)/deviation for x in values] alist.append(normalized) values=[] #copying the last lines mean = sum(values)/len(values) otherList =[(x-mean)**2 for x in values] deviation = math.sqrt( sum(otherList)/len(otherList) ) normalized = [(x-mean)/deviation for x in values] alist.append(normalized) values=[] fin.close() return alist def hydroFunction(R1,R2,alist,indexDict): #uses the dictionary to get the index of each letter for the alist index1 = indexDict[R1] index2 = indexDict[R2] product = float(alist[index1]) * float(alist[index2]) return product def hydroSet(sequence,listOfAAindexes,indexDict): hydroSetList =[] L =len(sequence) myLambda = 10 for x in range(len(listOfAAindexes)): for i in range(myLambda): total = 0.0; #i am getting the letters calcultations of the sequence based on the formula in #the pdf jing hu sent me for j in range(L - (i+1)): R1 = sequence[j] # I am adding one because i starts from 0 #so it will not make sense considering the actual mathematical formula R2 = sequence[j +(i + 1)] total += hydroFunction(R1,R2,listOfAAindexes[x],indexDict) hydroNumber = total/float(L-i) hydroSetList.append(str(hydroNumber)) return hydroSetList def helper(inputFile, output,number,hydroInfoFile): firstLine = True sequence ="" listOfAAindexes = getAAIndexValues(hydroInfoFile) #get all 9 sets, list of dictionaries #this is for the hydro function indexDict = {'A':0,'R':1,'N':2,'D':3,'C':4,'Q':5,'E':6,'G':7,'H':8,'I':9,'L':10,'K':11,'M':12,'F':13,'P':14,'S':15,'T':16,'W':17,'Y':18,'V':19} x = 1 for line in inputFile: if (line.startswith('>') == False): newLine = line.rstrip('\n').rstrip('*').rstrip('\r') sequence = sequence + newLine else: # resitance number if firstLine: #do nothing and anticipate next line firstLine = False else: output.write(number) dim = 1 #creat dictionary for aac function counts = { 'R':0,'K':0,'D':0,'E':0 ,'Q':0,'N':0, 'H':0, 'S':0, 'T':0, 'Y':0, 'C':0,'W':0 ,'A':0, 'I':0,'L':0, 'M':0, 'F':0, 'V':0,'P':0, 'G':0 } aacNum = aacSet(sequence,counts) deltaSetList = deltaSet(sequence) hydroSetList = hydroSet(sequence,listOfAAindexes,indexDict) #this writes the first 20 aac numbers for value in aacNum: output.write( " " + str(dim) + ":" + value) dim += 1 #this writes the delta set for key in deltaSetList: output.write( " " + str(dim) + ":" + key) dim += 1 #this writes the hydro set for key in hydroSetList: output.write( " " + str(dim) + ":" + key) dim += 1 output.write('\n') #reset sequence sequence = "" #for last sequence output.write(number) dim = 1 #creat dictionary for aac function counts = { 'R':0,'K':0,'D':0,'E':0 ,'Q':0,'N':0, 'H':0, 'S':0, 'T':0, 'Y':0, 'C':0,'W':0 ,'A':0, 'I':0,'L':0, 'M':0, 'F':0, 'V':0,'P':0, 'G':0 } aacNum = aacSet(sequence,counts) deltaSetList = deltaSet(sequence) hydroSetList = hydroSet(sequence,listOfAAindexes,indexDict) #this writes the first 20 aac numbers for value in aacNum: output.write( " " + str(dim) + ":" + value) dim += 1 #this writes the delta set for key in deltaSetList: output.write( " " + str(dim) + ":" + key) dim += 1 #this writes the hydro set for key in hydroSetList: output.write( " " + str(dim) + ":" + key) dim += 1 output.write('\n') #reset sequence sequence = "" #I did not put a return at the end becuase it seems like it rewrites the whole output #file when it starts again for the -1 labels def main(): h = "Hydrophobicity/hydroInfoFile.txt" for i in range(0,100): print("generating paac...."+ str(i) + "%",end='\r',flush=True) i1 = open("Samples/resistant-sample" + str(i+1) + ".txt","r") i2 = open("Samples/nonresistant-sample" + str(i+1) + ".txt","r") o = open("PAAC/proteinsPAAC" +str(i+1) + ".data","w") helper(i1,o,"1", h) helper(i2,o,"-1", h) i1.close() i2.close() o.close() i1 = open("originalData/resist_protein.txt","r") i2 = open("originalData/notresist_protein.txt","r") o = open("independentTest/proteinsPAAC.data","w") helper(i1,o,"1", h) helper(i2,o,"-1", h) i1 = open("originalData/independent_protein_set.txt","r") o = open("independentTest/proteinsPAAC.test","w") helper(i1,o,"1", h) i1.close() i2.close() o.close() main()

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mr-tanta/password-manager

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from tkinter import * from tkinter import messagebox from random import choice, randint, shuffle import pyperclip BLUE = "#f4f9f9" # ---------------------------- PASSWORD GENERATOR ------------------------------- # def generate_password(): letters = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'] numbers = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'] symbols = ['!', '#', '$', '%', '&', '(', ')', '*', '+'] password_letters = [choice(letters) for _ in range(randint(8, 13))] password_symbols = [choice(symbols) for _ in range(randint(3, 5))] password_numbers = [choice(numbers) for _ in range(randint(4, 6))] password_list = password_letters + password_symbols + password_numbers shuffle(password_list) password = "".join(password_list) password_input.insert(0, password) pyperclip.copy(password) # ---------------------------- SAVE PASSWORD ------------------------------- # def save(): website = website_input.get() email = email_input.get() password = password_input.get() if len(website) == 0 or len(password) == 0: messagebox.showinfo(title="Oops!", message="Please make sure you haven't left any fields empty.") else: is_ok = messagebox.askokcancel(title=website, message=f"These are the details entered: \nEmail: {email} " f"\nPassword: {password} \nIs it ok to save?") if is_ok: with open("data.txt", "a") as data_file: data_file.write(f"url: {website}\nusername: {email}\npassword: {password}\n\n ----!o ---- o!----\n\n") website_input.delete(0, END) password_input.delete(0, END) # ---------------------------- UI SETUP ------------------------------- # window = Tk() window.title("Password Manager") window.config(padx=40, pady=40, bg=BLUE) canvas = Canvas(width=200, height=200, bg=BLUE, highlightthickness=0) logo_img = PhotoImage(file="logo.png") canvas.create_image(100, 100, image=logo_img) canvas.grid(column=2, row=1) website_label = Label(text="Website:", bg=BLUE) website_label.grid(column=1, row=2) website_input = Entry(width=35) website_input.grid(row=2, column=2, columnspan=2) website_input.get() website_input.focus() email_label = Label(text="Email/Username", bg=BLUE) email_label.grid(column=1, row=3) email_input = Entry(width=35) email_input.grid(column=2, row=3, columnspan=2) email_input.insert(0, "abraham@tantasecure.com") password_label = Label(text="Password", bg=BLUE) password_label.grid(column=1, row=4) password_input = Entry(width=21) password_input.grid(column=2, row=4) password_input.get() add_button = Button(text="Add", width=36, command=save) add_button.grid(column=2, row=5, columnspan=2) generate_password_button = Button(text="Generate Password", command=generate_password) generate_password_button.grid(column=3, row=4) window.mainloop()

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80099226+mr-tanta@users.noreply.github.com

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/thinClient_server/db.py

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lksmllr/39-Inf-NP-Abschlussprojekt

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import sqlite3 import click from flask import current_app, g from flask.cli import with_appcontext def get_db(): if 'db' not in g: g.db = sqlite3.connect( current_app.config['DATABASE'], detect_types=sqlite3.PARSE_DECLTYPES ) g.db.row_factory = sqlite3.Row return g.db def close_db(e=None): db = g.pop('db', None) if db is not None: db.close() def init_db(): db = get_db() with current_app.open_resource('schema.sql') as f: db.executescript(f.read().decode('utf8')) @click.command('init-db') @with_appcontext def init_db_command(): """Clear the existing data and create new tables.""" init_db() click.echo('Initialized the database.') def init_app(app): app.teardown_appcontext(close_db) app.cli.add_command(init_db_command)

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lmueller@techfak.uni-bielefeld.de

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ChanKamyung/Covert-transmission-based-on-ICMP

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2020-03-27T08:45:31

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#!/usr/bin/python3 # _*_ coding=utf-8 _*_ import sys sys.setrecursionlimit(1000000) #压缩大文件实时会出现超出递归深度，故修改限制 #定义哈夫曼树的节点类 class node(object): def __init__(self, value=None, left=None, right=None, father=None): self.value = value self.left = left self.right = right self.father = father def build_father(left, right): n = node(value= left.value + right.value, left= left, right= right) left.father = right.father = n return n def encode(n): if n.father == None: return b'' if n.father.left == n: return node.encode(n.father) + b'0' #左节点编号'0' else: return node.encode(n.father) + b'1' #右节点编号'1' #哈夫曼树构建 def build_tree(l): if len(l) == 1: return l sorts = sorted(l, key= lambda x: x.value, reverse= False) #升序排列 n = node.build_father(sorts[0], sorts[1]) sorts.pop(0) sorts.pop(0) sorts.append(n) return build_tree(sorts) def encode(node_dict, debug=False): ec_dict = {} for x in node_dict.keys(): ec_dict[x] = node.encode(node_dict[x]) if debug == True: #输出编码表（用于调试） print(x) print(ec_dict[x]) return ec_dict def encodefile(inputfile): #数据初始化 node_dict = {} #建立原始数据与编码节点的映射，便于稍后输出数据的编码 count_dict = {} print("Starting encode...") with open(inputfile,"rb") as f: bytes_width = 1 #每次读取的字节宽度 i = 0 f.seek(0,2) count = f.tell() / bytes_width print('length =', count) nodes = [] #结点列表，用于构建哈夫曼树 buff = [b''] * int(count) f.seek(0) #计算字符频率,并将单个字符构建成单一节点 while i < count: buff[i] = f.read(bytes_width) if count_dict.get(buff[i], -1) == -1: count_dict[buff[i]] = 0 count_dict[buff[i]] = count_dict[buff[i]] + 1 i = i + 1 print("Read OK") #print(count_dict) #输出权值字典,可注释掉 for x in count_dict.keys(): node_dict[x] = node(count_dict[x]) nodes.append(node_dict[x]) tree = build_tree(nodes) #哈夫曼树构建 ec_dict = encode(node_dict) #构建编码表 print("Encode OK") head = sorted(count_dict.items(), \ key= lambda x: x[1] ,reverse= True) #对所有根节点降序排序 bit_width = 1 print("head:",head[0][1]) #动态调整编码表的字节长度，优化文件头大小 if head[0][1] > 255: bit_width = 2 if head[0][1] > 65535: bit_width = 3 if head[0][1] > 16777215: bit_width = 4 print("bit_width:", bit_width) name = inputfile.split('.') with open(name[0]+'.hfm', 'wb') as o: name = inputfile.split('/') o.write((name[-1] + '\n').encode('utf-8')) #写出原文件名 o.write(int.to_bytes(len(ec_dict), 2, byteorder= 'big')) #写出结点数量 o.write(int.to_bytes(bit_width, 1, byteorder= 'big')) #写出编码表字节宽度 for x in ec_dict.keys(): #编码文件头 o.write(x) o.write(int.to_bytes(count_dict[x], bit_width, byteorder= 'big')) print('head OK') raw = 0b1 last = 0 for i in range(int(count)): #开始压缩数据 for x in ec_dict[buff[i]]: raw = raw << 1 if x == 49: raw = raw | 1 if raw.bit_length() == 9: raw = raw & (~(1 << 8)) o.write(int.to_bytes(raw ,1 , byteorder= 'big')) o.flush() raw = 0b1 tmp = int(i / len(buff) * 100) if tmp > last: print("encode:", tmp, '%') #输出压缩进度 last = tmp if raw.bit_length() > 1: #处理文件尾部不足一个字节的数据 offset = 8 - (raw.bit_length() - 1) raw = raw << offset raw = raw & (~(1 << raw.bit_length() - 1)) o.write(int.to_bytes(raw ,1 , byteorder = 'big')) o.write(int.to_bytes(offset, 1, byteorder = 'big')) ### print("File encode successful.") def decodefile(inputfile): print("Starting decode...") with open(inputfile, 'rb') as f: f.seek(0, 2) eof = f.tell() f.seek(-1, 1) offset = int.from_bytes(f.read(1), byteorder= 'big') f.seek(0) name = inputfile.split('/') outputfile = inputfile.replace(name[-1], \ f.readline().decode('utf-8')) #删除 NULL byte outputfile = list(outputfile) for i in range(len(outputfile) - 1): #最后一个'\n'不用管 if ord(outputfile[i]) < 32: outputfile[i] = '_' outputfile = ''.join(outputfile) with open(inputfile.replace('.hfm', '') + outputfile.replace('\n','') ,'wb') as o: count = int.from_bytes(f.read(2), \ byteorder= 'big') #取出结点数量 bit_width = int.from_bytes(f.read(1), \ byteorder= 'big') #取出编码表字宽 de_dict = {} for i in range(int(count)): #解析文件头 key = f.read(1) value = int.from_bytes(f.read(bit_width), byteorder= 'big') de_dict[key] = value node_dict = {} nodes = [] inverse_dict = {} for x in de_dict.keys(): node_dict[x] = node(de_dict[x]) nodes.append(node_dict[x]) tree = build_tree(nodes) #重建哈夫曼树 ec_dict = encode(node_dict) #建立编码表 for x in ec_dict.keys(): #反向字典构建 inverse_dict[ec_dict[x]] = x data = b'' raw = 0 last = 0 for i in range(f.tell(), eof - 1): #efo处记录的是offset，故此处-1 raw = int.from_bytes(f.read(1), byteorder= 'big') #print("raw:",raw) j = 8 while j > 0: if (raw >> (j - 1)) & 1 == 1: data = data + b'1' raw = raw & (~(1 << (j - 1))) else: data = data + b'0' raw = raw & (~(1 << (j - 1))) if inverse_dict.get(data, -1) != -1: o.write(inverse_dict[data]) o.flush() if i == eof - 2 and j - offset == 1: break #print("decode",data,":",inverse_dict[data]) data = b'' j = j - 1 tep = int(i / eof * 100) if tep > last: print("decode:", tep,'%') #输出解压进度 last = tep raw = 0 print("File decode successful.") if __name__ == '__main__': if input("1：压缩文件\t2：解压文件\n请输入你要执行的操作：") == '1': encodefile(input("请输入要压缩的文件：")) else: decodefile(input("请输入要解压的文件："))

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from models4rl.agents.base_agent import BaseAgent from models4rl.replay_buffers.replay_buffer import ReplayBuffer from models4rl.explorers.base_explorer import BaseExplorer import torch from torch.nn import Module from torch.optim.optimizer import Optimizer import copy from gym.spaces.discrete import Discrete class DQN(BaseAgent): def __init__( self, action_space:Discrete, q_network:Module, optimizer:Optimizer, criterion, # こいつをどうするか DDQNも explorer:BaseExplorer, replay_buffer:ReplayBuffer, batch_size:int=32, gamma:float=0.99, target_update_step_interval:int=0, target_update_episode_interval:int=5 ): """ コンストラクタ Args: action_space (Discrete): 行動空間 env.action_spaceをそのまま渡せばOK、一次元離散値 q_network (Module): 学習するネットワーク型はとりあえず親クラスを書いている optimizer (Optimizer): 最適化アルゴリズム criterion (Module): 損失関数型はとりあえず自作するときの親クラスを書いている explorer (BaseExplorer): 探索アルゴリズム replay_buffer (ReplayBuffer): 経験再生用のメモリ batch_size (int): 経験再生からリプレイを行う際にサンプリングする数, 初期値32 (replay bufferに含めてもいいかもしれない) gamma (float): 割引率, 初期値0.99 target_update_step_interval (int): 学習の目標となるQNetworkを何ステップ毎に更新するか. 0の場合は更新されない. 1ステップが終了するとカウントはリセットされる. 初期値0 target_update_episode_interval (int): 学習の目標となるQNetworkを何エピソード毎に更新するか. 初期値5 """ assert target_update_step_interval >= 0, 'target_update_step_interval must be positive or 0.' assert target_update_episode_interval > 0, 'target_update_episode_interval must be positive.' super(DQN, self).__init__(action_space, explorer, gamma) self.q_network = q_network self.optimizer = optimizer self.criterion = criterion self.target_network = copy.deepcopy(self.q_network) self.replay_buffer = replay_buffer self.batch_size = batch_size self.state = None self.action = None self.target_update_step_interval = target_update_step_interval self.target_update_episode_interval = target_update_episode_interval self.episode = 1 self.step = 1 # エピソードごとではなく総ステップ数 def act_and_train(self, observation, reward): """ 学習と行動を行う関数。引数として渡された状態と報酬は一度保存し、次のステップで使用される。 Args: observation (numpy.array): 前回行った行動による移り先の状態(現在の状態) reward (float): 前回行った行動による報酬 Returns: int: 移り先の状態にて起こす行動 Examples: # init obs = env.reset() reward = 0 done = False ... # run 必ずこの順番で呼ぶ action = agent.act_and_train(obs, reward) obs, reward, done, info = env.step(action) """ next_state = torch.tensor(observation).float() self.replay_buffer.append_and_update( self.state, self.action, next_state, reward, q_network=self.q_network, target_network=self.target_network, gamma=self.gamma ) self.replay() if self.target_update_step_interval and self.step % self.target_update_step_interval == 0: self._update_target_network() self.state = next_state self.action = self.explorer.explore(self.action_space.sample, lambda: self._choice_greedy_action(next_state)) self.step += 1 return self.action def replay(self): if len(self.replay_buffer) < self.batch_size: return state_batch, next_state_batch, action_batch, reward_batch = self.replay_buffer.get_batch(self.batch_size).values() self.q_network.eval() q_values = self.q_network(state_batch).gather(1, action_batch) self.target_network.eval() next_q_values = torch.zeros_like(q_values, dtype=float) next_q_values = self.target_network(next_state_batch).max(1)[0].detach() target_values = self.gamma * next_q_values + reward_batch self.q_network.train() loss = self.criterion(q_values, target_values.unsqueeze(1).float()) self.optimizer.zero_grad() loss.backward() for p in self.q_network.parameters(): p.grad.data.clamp_(-1, 1) self.optimizer.step() def _update_target_network(self): self.target_network.load_state_dict(self.q_network.state_dict()) def _choice_greedy_action(self, observation): self.q_network.eval() with torch.no_grad(): action = self.q_network(observation).max(dim=0)[1].item() # todo: この辺のテンソルの扱いが雑なので注意 return action def act_greedily(self, observation): next_state = torch.tensor(observation).float() return self._choice_greedy_action(next_state) def stop_episode_and_train(self, observation, reward): """ エピソード終了時の処理(学習も行う) Args: observation (ndarray): 観測した状態 reward (float): 即時報酬 """ next_state = torch.tensor(observation).float() self.replay_buffer.append_and_update( self.state, self.action, next_state, reward, q_network=self.q_network, target_network=self.target_network, gamma=self.gamma ) self.replay() self.explorer.end_episode() self.action = None self.state = None self.episode += 1 if self.episode % self.target_update_episode_interval == 0: self._update_target_network()

[ "dododosa14@gmail.com" ]

dododosa14@gmail.com

aa66289c1f185034ae18f11611dab6900cc1a9c4

329b910c4a74a7ebca9eeb1de43a82d4a3fc8d8a

/0x01-python-if_else_loops_functions/1-last_digit.py

b1c1ba7deaf4663fee52131892453894df17caee

[]

no_license

MarioEstebanSuaza/holbertonschool-higher_level_programming

58ff5a4c5b70a89c29881f9958531199a85a0327

de8fb6b03f9e680d9cb0f7798440ccc4b3260f48

refs/heads/master

2022-11-26T00:18:32.893077

2020-08-06T23:39:05

null

0

null

UTF-8

Python

false

401

py

#!/usr/bin/python3 import random number = random.randint(-10000, 10000) if number < 0: ln = (number * -1) % 10 * -1 else: ln = number % 10 if ln > 5: print("Last digit of", number, "is", ln, "and is greater than 5") elif ln == 0: print("Last digit of", number, "is", ln, "and is 0") elif ln < 6 and ln != 0: print("Last digit of", number, "is", ln, "and is less than 6 and not 0")

[ "andres_felipe0191@hotmail.com" ]

andres_felipe0191@hotmail.com

56d2bc727c3712ef9ae6a951625f0414fac943a8

41c0d29efcb3ac0e22237bd3fadc5cdf550698cd

/tests/testing_config/custom_components/test/alarm_control_panel.py

0e2842f869561a20b2f53b381cca7907aafd5e77

[ "Apache-2.0" ]

permissive

EthanW1215/home-assistant

7c19ce668821f3063b3d46f9e9a0ef5a6e102689

a48ac4d18fab253572780671f896b3a417322699

refs/heads/master

2020-09-05T09:02:59.513681

2019-11-05T18:57:08

220,050,094

2

0

Apache-2.0

2019-11-06T17:13:33

2019-11-06T17:13:32

null

UTF-8

Python

false

2,494

py

""" Provide a mock alarm_control_panel platform. Call init before using it in your tests to ensure clean test data. """ from homeassistant.components.alarm_control_panel import AlarmControlPanel from homeassistant.const import ( STATE_ALARM_ARMED_AWAY, STATE_ALARM_ARMED_HOME, STATE_ALARM_ARMED_NIGHT, STATE_ALARM_DISARMED, STATE_ALARM_TRIGGERED, ) from tests.common import MockEntity ENTITIES = {} def init(empty=False): """Initialize the platform with entities.""" global ENTITIES ENTITIES = ( {} if empty else { "arm_code": MockAlarm( name=f"Alarm arm code", code_arm_required=True, unique_id="unique_arm_code", ), "no_arm_code": MockAlarm( name=f"Alarm no arm code", code_arm_required=False, unique_id="unique_no_arm_code", ), } ) async def async_setup_platform( hass, config, async_add_entities_callback, discovery_info=None ): """Return mock entities.""" async_add_entities_callback(list(ENTITIES.values())) class MockAlarm(MockEntity, AlarmControlPanel): """Mock Alarm control panel class.""" def __init__(self, **values): """Init the Mock Alarm Control Panel.""" self._state = None MockEntity.__init__(self, **values) @property def code_arm_required(self): """Whether the code is required for arm actions.""" return self._handle("code_arm_required") @property def state(self): """Return the state of the device.""" return self._state def alarm_arm_away(self, code=None): """Send arm away command.""" self._state = STATE_ALARM_ARMED_AWAY self.async_write_ha_state() def alarm_arm_home(self, code=None): """Send arm home command.""" self._state = STATE_ALARM_ARMED_HOME self.async_write_ha_state() def alarm_arm_night(self, code=None): """Send arm night command.""" self._state = STATE_ALARM_ARMED_NIGHT self.async_write_ha_state() def alarm_disarm(self, code=None): """Send disarm command.""" if code == "1234": self._state = STATE_ALARM_DISARMED self.async_write_ha_state() def alarm_trigger(self, code=None): """Send alarm trigger command.""" self._state = STATE_ALARM_TRIGGERED self.async_write_ha_state()

[ "balloob@gmail.com" ]

balloob@gmail.com

4772ac0f8f8819b65a1babed8879a5bd6ca68e9a

e8a6f0d4fac4a185985595d002af84ad02d33556

/basicforms/settings.py

12c812d98007bf32ce07b1bb2088edabfd34351f

[]

no_license

Pratik2080/django-form

7ffa3941240e9db94c7488ab373dafc52714eafa

4d2d8218d32313afb2b3cc10ec4e6d7b7a8c9944

refs/heads/main

2023-02-24T14:42:21.416974

2021-02-01T10:39:48

334,905,248

0

null

UTF-8

Python

false

3,246

py

""" Django settings for basicforms project. Generated by 'django-admin startproject' using Django 3.1.4. For more information on this file, see https://docs.djangoproject.com/en/3.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.1/ref/settings/ """ from pathlib import Path # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'q717-6cx!4q4$=@4@fq&6i79*832^9*!vk%brkzdt-4*&x27pp' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = False ALLOWED_HOSTS = ['*'] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'basicapp' ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'basicforms.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'basicforms.wsgi.application' # Database # https://docs.djangoproject.com/en/3.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT = 'staticfiles'

[ "pratik.m@lendenclub.com" ]

pratik.m@lendenclub.com

452ceb9dcf5efcbd44cb38c204ccf4e5e73dd5b3

2c5ef7ecdf2c2588fd95ad04a563e7c89d95822a

/Lesson15_GetandReverse.py

ea825e755a58f04dd3604fba9d6a511131edb2f8

[]

no_license

edwardgorelik/practice

6cdd8d7f45bb09988e1fe22a7b79204932bb530a

5d3d5636bfa7b9b87e87a1bf1d6a579c75aec19a

refs/heads/master

2021-01-25T13:58:59.534701

2018-04-29T05:34:53

123,636,150

0

null

UTF-8

Python

false

399

py

def getLine(): while True: line = input("Give me a sentence: ") if not len(line.split()) > 1: print("Needs to be at least two words in the sentence") continue else: break return line def reverseLine(line): myList = line.split() newLine = " ".join(myList[::-1]) return newLine line = getLine() print(reverseLine(line))

[ "e.gorelik11@gmail.com" ]

e.gorelik11@gmail.com

f69b247266479c8b06f621afe813162dae323169

efce0eeb3dd40a7067108f6a38951fcac8d2ea57

/3_foglalkozás_marcius_21/3_nevsor.py

928240eb0ce748f76da331b82ffd316df3af4ff5

[]

no_license

mekispeter/Python-SAP-2019

f0fc1870f763d3777f708e2ae73f450e471410df

c046edc4026b6be93c8efac48c6ef3b57ac64ac4

refs/heads/master

2020-04-24T06:11:13.209206

2019-05-15T14:43:58

171,756,122

1

0

null

UTF-8

Python

false

271

py

csoport = ["Niki", "Tündi", "Nóri", "Rebeka", "Timi", "Prisca", "Virág", "Mira", "Kami", "Lilla", "Odett", "Livi", "Dóri", "Viki", "Tami"] nev = input("Kit keresel?") if nev in csoport: print("Velünk van!") else: print("Nem tagja a csoportunknak.")

[ "noreply@github.com" ]

mekispeter.noreply@github.com

93caa612177f6f54aab7869362be49eebf2b6f37

f7bd260a492362a8d06c3e70561cd61724eaa2b2

/terminal/PWMControlGui_gtk.py

6283df3bb0e2d97369d5f5eb6836bacb0150c1a6

[]

no_license

ersteller/pySerial2AVR

48f355119147006d93586c5e4bf608b0a87142ca

08bb3ea2b9345451e178d1e2f447af73dba5ca6d

refs/heads/master

2022-08-04T22:48:53.090657

2022-07-20T13:26:58

5,402,091

0

null

UTF-8

Python

false

3,230

py

''' Created on 25.11.2012 @author: Jan ''' """ helloworld with pygtk and glade This is not threadable """ import time import sys import os import threading from remoAVR import AVR try: import pygtk pygtk.require("2.0") except: pass try: import gtk import gtk.glade except: sys.exit(1) class HellowWorldGTK: """This is an Hello World GTK application""" fStopthread = False def __init__(self): self.baselen = 0xffff # initial base resolution #Set the Glade file (relative path) self.gladefile = os.path.split(__file__)[0] + "\\pwmgui.glade" self.wTree = gtk.glade.XML(self.gladefile) #Create our dictionay and connect it dic = { "on_btnInit_clicked" : self.btnInit_clicked, "on_window1_destroy" : self.quit, "on_scalePrescale_value_changed": self.on_prescal_change, "on_scaleOutA_value_changed": self.on_dutycycle_change_a, "on_scaleOutB_value_changed": self.on_dutycycle_change_b, "on_scaleOutC_value_changed": self.on_dutycycle_change_c, "on_scaleICR_value_changed" : self.on_dutycycle_change_i, } self.wTree.signal_autoconnect(dic) #gtk.gdk.threads_init() # unlocking thread lock in main process self.mc = AVR() #self.mc.reset() def btnInit_clicked(self, widget): print "PWM init" self.mc.pwm_init() def quit(self, widget): self.fStopthread = True gtk.main_quit() def on_prescal_change(self, widget): cs = int(widget.get_value()) self.mc.pwm_prescaler(cs) pass def on_dutycycle_change_a(self, widget): duty_cycle = widget.get_value() self.on_dutycycle_change("OCR3A", duty_cycle) def on_dutycycle_change_b(self, widget): duty_cycle = widget.get_value() self.on_dutycycle_change("OCR3B", duty_cycle) def on_dutycycle_change_c(self, widget): duty_cycle = widget.get_value() self.on_dutycycle_change("OCR3C", duty_cycle) def on_dutycycle_change_i(self, widget): duty_cycle = int(widget.get_value()) self.baselen = duty_cycle self.mc.pwm("ICR3", self.baselen) self.on_dutycycle_change("OCR3A", self.wTree.get_widget("scaleOutA").get_value()) self.on_dutycycle_change("OCR3B", self.wTree.get_widget("scaleOutB").get_value()) self.on_dutycycle_change("OCR3C", self.wTree.get_widget("scaleOutC").get_value()) def on_dutycycle_change(self, param, dc): print "scaling", dc self.mc.pwm(param, int(dc * self.baselen / 100)) def main(self): while not hwg.fStopthread: print time.time() time.sleep(5) if __name__ == "__main__": hwg = HellowWorldGTK() gtk.gdk.threads_init() #t= threading.Thread(target=hwg.main) #t.start() gtk.gdk.threads_enter() gtk.mainloop() gtk.gdk.threads_leave()

[ "Jan@Jans-QPC" ]

Jan@Jans-QPC

3fc39627ead959993c021edf1f2c93d6e75d77d7

b22588340d7925b614a735bbbde1b351ad657ffc

/athena/MuonSpectrometer/MuonCnv/MuonByteStream/share/ReadMuonRDO_jobOptions.py

88cedafcca34011aab6b2d80c4eeda885bc8aa15

[]

no_license

rushioda/PIXELVALID_athena

90befe12042c1249cbb3655dde1428bb9b9a42ce

22df23187ef85e9c3120122c8375ea0e7d8ea440

refs/heads/master

2020-12-14T22:01:15.365949

2020-01-19T03:59:35

234,836,993

1

0

null

UTF-8

Python

false

262

py

ByteStreamAddressProviderSvc = Service( "ByteStreamAddressProviderSvc" ) ByteStreamAddressProviderSvc.TypeNames += [ "RpcPadContainer/RPCPAD", "MdtCsmContainer/MDTCSM", "TgcRdoContainer/TGCRDO", "CscRawDataContainer/CSCRDO" ]

[ "rushioda@lxplus754.cern.ch" ]

rushioda@lxplus754.cern.ch

bb737fe68800e1577799e178e2a02eed7e399cfe

1178abea66ba9592a1c2821da036f3660b382abb

/manager/views/commissions.py

7bb9ba609bdce979d01aaf582108a4a1ef58cf70

[ "Apache-2.0" ]

permissive

jordancarlson08/MyStuff

fdcfc125637164a6369730b7cb275dfdbfcbedc6

4f4f6fdd298ce00e4a1f8a4621aaf94c0ccdb773

refs/heads/master

2021-01-23T01:00:55.764993

2014-11-04T21:25:22

16,844,357

0

null

UTF-8

Python

false

1,234

py

from django import forms from django.conf import settings from django.http import HttpResponse, HttpResponseRedirect, Http404 from manager.models import * from account.models import * from catalog.models import * from . import templater from datetime import * from django.core.mail import send_mail from base_app.user_util import * from django.contrib.auth.decorators import login_required, user_passes_test @login_required @user_passes_test(manager_check) def process_request(request): '''Shows the commissions for a user''' form = CommissionsForm() c_list = Commission.objects.all() tvars = { 'form':form, } return templater.render_to_response(request, 'commissions.html', tvars) @login_required @user_passes_test(manager_check) def process_request__get(request): '''Shows the commissions for a user''' c_list = Commission.objects.filter(transaction__employee__id=request.urlparams[0]) tvars = { 'c_list':c_list, } return templater.render_to_response(request, 'commissions_list.html', tvars) class CommissionsForm(forms.Form): '''A form for new stores''' emp = forms.ModelChoiceField(label='Employee' ,queryset=get_employees(), widget=forms.Select(attrs={'class': 'form-control', 'id':'emp'}))

[ "jordancarlson08@gmail.com" ]

jordancarlson08@gmail.com

b1be52483eed9dc9fe64b43eb9be826989148f64

eef1bcc789c4f6c72bedc1f00bf259a09ceb3bf3

/discordbot.py

a65e2f5f1d32419ca0561bb245f1a90d17be17cd

[]

no_license

kurosukee/discord_bot_text_to_speach

9dc522144710e84dba6389fe811fbd9b9f21e219

ceb6bc9dd40c7cccc6ff4b4f50f9a54ac63c5130

refs/heads/master

2023-08-02T20:45:58.952449

2021-09-26T03:44:32

396,017,966

0

null

UTF-8

Python

false

1,487

py

import discord import ffmpeg import re import os from discord.ext import commands from os import getenv from gtts import gTTS bot = commands.Bot(command_prefix='!') voice_client = None @bot.event async def on_ready(): print('ログインしました。') @bot.command() async def join(ctx): vc = ctx.author.voice if vc is None: await ctx.channel.send('ボイチャ繋がってないわよ') return else: await vc.channel.connect() return @bot.command() async def bye(ctx): vc = ctx.voice_client if vc is None: await ctx.channel.send('ボイチャにいないわよ') return else: await vc.disconnect() @bot.event async def on_message(message): if message.content.startswith('!'): await bot.process_commands(message) return if message.content == 'うー': await message.channel.send('にゃー') return if message.content.startswith('http'): return vc = message.guild.voice_client if vc == None: return else: content = re.sub('$.*?$',"顔文字", message.content) tmpfile = str(message.id) + '.mp3' tts = gTTS(text=content, lang='ja') tts.save(tmpfile) source = discord.FFmpegPCMAudio(tmpfile) vc.play(source) while vc.is_playing(): continue try: os.remove(tmpfile) except OSError as e: return bot.run(getenv('BOT'))

[ "ayaka.nishiyama.1117@gmail.com" ]

ayaka.nishiyama.1117@gmail.com

2a86ba9cb07e96cce5dc1c6921d5dd4a301d23a6

523f8f5febbbfeb6d42183f2bbeebc36f98eadb5

/139_best.py

e93df2a757060670d3f2938475f5496a4d88e1cb

[]

no_license

saleed/LeetCode

655f82fdfcc3000400f49388e97fc0560f356af0

48b43999fb7e2ed82d922e1f64ac76f8fabe4baa

refs/heads/master

2022-06-15T21:54:56.223204

2022-05-09T14:05:50

209,430,056

2

0

null

UTF-8

Python

false

414

py

class Solution(object): def wordBreak(self, s, wordDict): """ :type s: str :type wordDict: List[str] :rtype: List[str] """ dp=[False] *(len(s)+1) dp[0]=True for i in range(1,len(s)+1): for j in range(i): if dp[j] and s[j:i] in wordDict: dp[i]=True break return dp[-1]

[ "noelsun@mowennaierdeMacBook-Pro.local" ]

noelsun@mowennaierdeMacBook-Pro.local

1a4dc42e47e33f6a65cd5afb973b8281be750191

3732272dc6c937a150a97c4b3254b3270e482b85

/LibraryManagmentSystem/urls.py

14aecb7a60d2448b3627f64059858cf201a207f5

[]

no_license

harshakhmk/Library-Management-System

e4d023dbc371bfe5f2a90d4a7b97667ca579362c

c7af3fd05a9bd4a58156747df2b5112ab2cea6f9

refs/heads/master

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"""LibraryManagmentSystem URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path, include from django.conf import settings # add this from django.conf.urls.static import static urlpatterns = [ path("admin/", admin.site.urls), path("accounts/", include("authentication.urls")), path("", include("library.urls")), ] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

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# -*- coding: utf-8 -*- __author__ = "kartik" def area(a,b,c): return 2*(b*c + c*a) def q(u): return u/110 (x,y,z) = list(map(int,input("Enter length,breadth and height seperated by space :").split())) quantity = q(area(x,y,z)) if int(quantity) < quantity: quantity = int(quantity) + 1 print ("Quantity of paint = {} liters".format(quantity)) print ("Total cost = {}".format(quantity*1000)) print ("COST = ₹" + str(quantity*1000))

[ "kartikjodhpur@gmail.com" ]

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def main(): n = int(input()) v = list(map(int, input().split())) v_s = sorted(v) w = v_s[0] for i in range(1, n): w = (w + v_s[i])/2 print(w) if __name__ == '__main__': main()

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2020-04-03T06:30:12.881806

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def readinput(): n = int(input()) # Length for j in range(n): nextnum = int(input()) # Read each value insequence.append(nextnum) best.append(0) # Initialize best[k] for each position return def solve(): for j in range(len(insequence)): prev = [ best[k] for k in range(j) if insequence[j]%insequence[k] == 0 ] if prev: best[j] = 1 + max(prev) else: best[j] = 1 insequence = [] best = [] readinput() solve() print(max(best))

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@ops.RegisterGradient('BiasAddGrad') def _BiasAddGradGrad(op, received_grad): 'Gradient for the BiasAddGrad op.\n\n Args:\n op: BiasAddGrad op for which we are calculating gradients.\n received_grad: The gradients passed to the BiasAddGrad op.\n \n Returns:\n A single gradient Tensor for the input to BiasAddGrad (which\n is the gradient of the bias term in BiasAdd)\n ' try: data_format = op.get_attr('data_format') except ValueError: data_format = None shape = array_ops.shape(op.inputs[0]) rank = array_ops.rank(op.inputs[0]) bias_shape = array_ops.shape(received_grad) if (data_format == 'NCHW'): expanded_shape = array_ops.concat(0, [array_ops.ones_like(shape[:(- 3)]), bias_shape, array_ops.ones_like(shape[(- 2):])]) tile_mults = array_ops.concat(0, [shape[:(- 3)], [1], shape[(- 2):]]) else: expanded_shape = array_ops.concat(0, [array_ops.ones_like(shape[:(- 1)]), bias_shape]) tile_mults = array_ops.concat(0, [shape[:(- 1)], [1]]) expanded_grad = array_ops.reshape(received_grad, expanded_shape) return array_ops.tile(expanded_grad, tile_mults)

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venkateshchary/django_mysql_docker

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#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'DataWare.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

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from diagrams import Diagram from diagrams.onprem.compute import Server from diagrams.onprem.container import Docker from diagrams.onprem.database import Postgresql from diagrams.onprem.queue import Kafka from diagrams.programming.language import Python with Diagram(filename="docs/site_checker_architecture"): kafka = Kafka("Kafka") postgres = Postgresql("PostgreSQL") producer = Python("site_checker\nproducer") consumer = Python("site_checker\nconsumer") website = Server("www.website.com") website << producer producer >> kafka >> consumer >> postgres with Diagram(filename="docs/site_checker_config_ini"): kafka = Kafka("Kafka") postgres = Postgresql("PostgreSQL") producer = Python("site_checker\nproducer") consumer = Python("site_checker\nconsumer") websites = [Server(f"www.website_{n}.com\nTherad-{n}") for n in range(3)] websites << producer producer >> kafka >> consumer >> postgres with Diagram(filename="docs/site_checker_cli_parameters"): kafka = Kafka("Kafka") postgres = Postgresql("PostgreSQL") consumer = Python("site_checker\nconsumer") producer1 = Docker("site_checker\nproducer_1") producer2 = Docker("site_checker\nproducer_2") producer3 = Docker("site_checker\nproducer_3") website1 = Server("www.website_1.com") website2 = Server("www.website_2.com") website3 = Server("www.website_3.com") website1 << producer1 >> kafka website2 << producer2 >> kafka website3 << producer3 >> kafka kafka >> consumer >> postgres

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import os import os.path as osp import pickle import numpy as np import os.path as osp import json annodir = '/grogu/user/jianrenw/data/OAK_TEST/Label' def voc_ap(rec, prec, use_07_metric=False): """Compute VOC AP given precision and recall. If use_07_metric is true, uses the VOC 07 11-point method (default:False). """ if use_07_metric: # 11 point metric ap = 0. for t in np.arange(0., 1.1, 0.1): if np.sum(rec >= t) == 0: p = 0 else: p = np.max(prec[rec >= t]) ap = ap + p / 11. else: # correct AP calculation # first append sentinel values at the end mrec = np.concatenate(([0.], rec, [1.])) mpre = np.concatenate(([0.], prec, [0.])) # compute the precision envelope for i in range(mpre.size - 1, 0, -1): mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) # to calculate area under PR curve, look for points # where X axis (recall) changes value i = np.where(mrec[1:] != mrec[:-1])[0] # and sum (\Delta recall) * prec ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) return ap def parse_rec(file_name,idk,classname,limitset,train_map): json_name = file_name f = osp.join(annodir,json_name) labels = json.load(open(f,'r')) bbox = [] for label in labels: obj_id = label['id'] category = label['category'] box2d = label['box2d'] abbox = [box2d['x1'], box2d['y1'], box2d['x2'], box2d['y2']] if idk == False and classname == category: bbox.append(abbox) elif idk == True: # this category belongs to limitset if category in limitset.keys(): # this category not belongs to the trainset if limitset[category] not in train_map.values(): bbox.append(abbox) return bbox def voc_eval(classname,class_id,superframe,train_map,outputs,limitset,idk =False,ovthresh=0.5,use_07_metric=False): #print(len(outputs)) # load annots recs = {} for frame in superframe: recs[frame] = parse_rec(frame,idk,classname,limitset,train_map) # extract gt objects for this class class_recs = {} npos = 0 for frame in superframe: bbox = np.array(recs[frame]) det = [False] * len(recs[frame]) npos = npos + len(recs[frame]) class_recs[frame] = {"bbox": bbox, "det": det} # no positive values if npos == 0: return -1,-1,-1 # read predictions BB = [] confidence = [] image_ids = [] for obj in outputs: image_ids.append(obj['image_id']) confidence.append(obj['score']) BB.append([obj['xmin'],obj['ymin'],obj['xmax'],obj['ymax']]) confidence = np.array(confidence) BB = np.array(BB) # no valid predictions if confidence.shape[0] == 0: return 0, 0, 0 # sort by confidence sorted_ind = np.argsort(-confidence) BB = BB[sorted_ind, :] image_ids = [image_ids[x] for x in sorted_ind] # go down dets and mark TPs and FPs nd = len(image_ids) tp = np.zeros(nd) fp = np.zeros(nd) for d in range(nd): R = class_recs[image_ids[d]] bb = BB[d, :].astype(float) ovmax = -np.inf BBGT = R["bbox"].astype(float) if BBGT.size > 0: # compute overlaps # intersection ixmin = np.maximum(BBGT[:, 0], bb[0]) iymin = np.maximum(BBGT[:, 1], bb[1]) ixmax = np.minimum(BBGT[:, 2], bb[2]) iymax = np.minimum(BBGT[:, 3], bb[3]) iw = np.maximum(ixmax - ixmin + 1.0, 0.0) ih = np.maximum(iymax - iymin + 1.0, 0.0) inters = iw * ih # union uni = ( (bb[2] - bb[0] + 1.0) * (bb[3] - bb[1] + 1.0) + (BBGT[:, 2] - BBGT[:, 0] + 1.0) * (BBGT[:, 3] - BBGT[:, 1] + 1.0) - inters ) overlaps = inters / uni ovmax = np.max(overlaps) jmax = np.argmax(overlaps) if ovmax > ovthresh: if not R["det"][jmax]: tp[d] = 1.0 R["det"][jmax] = 1 else: fp[d] = 1.0 else: fp[d] = 1.0 # compute precision recall fp = np.cumsum(fp) tp = np.cumsum(tp) rec = tp / float(npos) # avoid divide by zero in case the first detection matches a difficult # ground truth prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) ap = voc_ap(rec, prec, use_07_metric) return rec, prec, ap

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pipecat/my_blog

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from __future__ import unicode_literals from django.db import models # Create your models here. class Article(models.Model): title = models.CharField(max_length = 100) category = models.TextField(max_length = 50,blank = True) date_time = models.DateField(auto_now_add = True) content = models.TextField(blank = True,null = True) def __unicode__(self): return self.title class Meta: ordering = ['-date_time']

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"""OctreeSlice class. For viewing one slice of a multiscale image using an octree. """ import logging import math from typing import Callable, Optional import numpy as np from ....components.experimental.chunk import ChunkRequest, LayerRef from ....types import ArrayLike from .._image_view import ImageView from ._octree_loader import OctreeLoader from .octree import Octree from .octree_chunk import OctreeChunk, OctreeLocation from .octree_intersection import OctreeIntersection, OctreeView from .octree_level import OctreeLevel, OctreeLevelInfo from .octree_util import OctreeMetadata LOGGER = logging.getLogger("napari.octree.slice") class OctreeSlice: """A viewed slice of a multiscale image using an octree. Parameters ---------- data The multi-scale data. layer_ref : LayerRef Reference to the layer containing the slice. meta : OctreeMetadata The base shape and other info. image_converter : Callable[[ArrayLike], ArrayLike] For converting to displaying data. Attributes ---------- loader : OctreeLoader Uses the napari ChunkLoader to load OctreeChunks. """ def __init__( self, data, layer_ref: LayerRef, meta: OctreeMetadata, image_converter: Callable[[ArrayLike], ArrayLike], ): self.data = data self._meta = meta slice_id = id(self) self._octree = Octree(slice_id, data, meta) self.loader: OctreeLoader = OctreeLoader(self._octree, layer_ref) thumbnail_image = np.zeros( (64, 64, 3) ) # blank until we have a real one self.thumbnail: ImageView = ImageView(thumbnail_image, image_converter) @property def loaded(self) -> bool: """True if the data has been loaded. Because octree multiscale is async, we say we are loaded up front even though none of our chunks/tiles might be loaded yet. Returns ------- bool True if the data as been loaded. """ return self.data is not None @property def octree_level_info(self) -> Optional[OctreeLevelInfo]: """Information about the current octree level. Returns ------- Optional[OctreeLevelInfo] Information about current octree level, if there is one. """ if self._octree is None: return None try: return self._octree.levels[self.octree_level].info except IndexError as exc: index = self.octree_level num_levels = len(self._octree.levels) raise IndexError( f"Octree level {index} is not in range(0, {num_levels})" ) from exc def get_intersection(self, view: OctreeView) -> OctreeIntersection: """Return the given view's intersection with the octree. The OctreeIntersection primarily contains the set of tiles at some level that need to be drawn to depict view. The "ideal level" is generally chosen automatically based on the screen resolution described by the OctreeView. Parameters ---------- view : OctreeView Intersect this view with the octree. Returns ------- OctreeIntersection The given view's intersection with the octree. """ level = self._get_auto_level(view) return OctreeIntersection(level, view) def _get_auto_level(self, view: OctreeView) -> OctreeLevel: """Return the automatically selected octree level for this view. Parameters ---------- view : OctreeView Get the OctreeLevel for this view. Returns ------- OctreeLevel The automatically chosen OctreeLevel. """ index = self._get_auto_level_index(view) if index < 0 or index >= self._octree.num_levels: raise ValueError(f"Invalid octree level {index}") return self._octree.levels[index] def _get_auto_level_index(self, view: OctreeView) -> int: """Return the automatically selected octree level index for this view. Parameters ---------- view : OctreeView Get the octree level index for this view. Returns ------- int The automatically chosen octree level index. """ if not view.auto_level: # Return current level, do not update it. return self.octree_level # Find the right level automatically. Choose a level where the texels # in the octree tiles are around the same size as screen pixels. # We can do this smarter in the future, maybe have some hysteresis # so you don't "pop" to the next level as easily, so there is some # sort of dead zone between levels? ratio = view.data_width / view.canvas[0] if ratio <= 1: return 0 # Show the best we've got! # Choose the right level... max_level = self._octree.num_levels - 1 return min(math.floor(math.log2(ratio)), max_level) def _get_octree_chunk(self, location: OctreeLocation) -> OctreeChunk: """Return the OctreeChunk at his location. Do not create the chunk if it doesn't exist. Parameters ---------- location : OctreeLocation Return the chunk at this location. Returns ------- OctreeChunk The returned chunk. """ level = self._octree.levels[location.level_index] return level.get_chunk(location.row, location.col, create=False) def on_chunk_loaded(self, request: ChunkRequest) -> bool: """Called when an asynchronous ChunkRequest was loaded. This overrides Image.on_chunk_loaded() fully. Parameters ---------- request : ChunkRequest The request for the chunk that was loaded. Returns ------- bool True if the chunk's data was added to the octree. """ location = request.location if location.slice_id != id(self): # There was probably a load in progress when the slice was changed. # The original load finished, but we are now showing a new slice. # Don't consider it error, just ignore the chunk. LOGGER.debug( "on_chunk_loaded: wrong slice_id: %s", location, ) return False # Do not add the chunk. octree_chunk = self._get_octree_chunk(location) if octree_chunk is None: # This location in the octree does not contain an OctreeChunk. # That's unexpected, because locations are turned into # OctreeChunk's when a load is initiated. So this is an error, # but log it and keep going, maybe some transient weirdness? LOGGER.error( "on_chunk_loaded: missing OctreeChunk: %s", octree_chunk, ) return False # Did not add the chunk. LOGGER.debug("on_chunk_loaded: adding %s", octree_chunk) # Get the data from the request. incoming_data = request.chunks.get('data') # Loaded data should always be an ndarray. assert isinstance(incoming_data, np.ndarray) # Add that data to the octree's OctreeChunk. Now the chunk can be draw. octree_chunk.data = incoming_data # Setting data should mean: assert octree_chunk.in_memory assert not octree_chunk.needs_load return True # Chunk was added.

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import unittest def break_words(stuff): """This funciton will break up words for us.""" words = stuff.split(' ') return words class MyTest(unittest.TestCase): def setUp(self): print 'In setUp()' self.fixture = break_words() def tearDown(self): print 'In tearDown()' del self.fixture #def testmethod(self): # self.assertEqual(ex25.break_words(sentance), ['this', 'is', 'a', 'test', 'to', 'show', 'broken', 'words.'], 'test') def test_break(self): self.failUnlessEqual(['this', 'is', 'a', 'test', 'to', 'show', 'broken', 'words.'], self.fixture.break_words('This is a test.')) if __name__ == '__main__': unittest.main()

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s=input("輸入一字元為:") if(s==s[::-1]): print("YES") else: print("NO")

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# SPDX-FileCopyrightText: 2019-2022 REFITT Team # SPDX-License-Identifier: Apache-2.0 """Unit tests for data broker interfaces."""

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/src/genie/libs/parser/iosxe/tests/test_show_memory.py

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#!/bin/env python import unittest from unittest.mock import Mock from pyats.topology import Device from genie.metaparser.util.exceptions import SchemaEmptyParserError,\ SchemaMissingKeyError from genie.libs.parser.iosxe.show_memory import ShowMemoryStatistics class test_show_memory_statistics(unittest.TestCase): dev = Device(name='c3850') empty_output = {'execute.return_value': ''} golden_parsed_output = { "tracekey": "1#f8e3c2db7822c04e58ce2bd2fc7e476a", "name": { "processor": { "total": 856541768, "free": 501425640, "largest": 501041348, "head": "FF86F21010", "lowest": 499097976, "used": 355116128 }, "lsmpi_io": { "total": 6295128, "free": 824, "largest": 412, "head": "FF867C51A8", "lowest": 824, "used": 6294304 } } } golden_output = {'execute.return_value': '''\ Tracekey : 1#f8e3c2db7822c04e58ce2bd2fc7e476a Head Total(b) Used(b) Free(b) Lowest(b) Largest(b) Processor FF86F21010 856541768 355116128 501425640 499097976 501041348 lsmpi_io FF867C51A8 6295128 6294304 824 824 412''' } def test_empty(self): self.dev = Mock(**self.empty_output) obj = ShowMemoryStatistics(device=self.dev) with self.assertRaises(SchemaEmptyParserError): parsered_output = obj.parse() def test_golden(self): self.maxDiff = None self.dev = Mock(**self.golden_output) obj = ShowMemoryStatistics(device=self.dev) parsed_output = obj.parse() self.assertEqual(parsed_output, self.golden_parsed_output) if __name__ == '__main__': unittest.main()

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/Marmoset_Pipeline_2019/code/marmoset/maskMarmosetTargetBySlice.py

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import SimpleITK as sitk import numpy as np import scipy.ndimage import histFunctions import ndreg3D import sys targetfilename = sys.argv[1] outputfilename = sys.argv[2] outputtargetfilename = sys.argv[3] useWhiten = True maskBeforeSmooth = True smoothMask = False greyBackground = True smoothAtlasAgain = True blackVentricles = True multicolorMask = False target = sitk.ReadImage(targetfilename) refImg = sitk.ReadImage(targetfilename) origRefImg = sitk.ReadImage(targetfilename) inflectionpoint = histFunctions.getInflectionPoint(target) mymask = ndreg3D.imgMakeMask(target,threshold=inflectionpoint) mymaskarray = sitk.GetArrayFromImage(mymask) refImg = ndreg3D.imgMask(refImg,mymask) # now go slice by slice to remove the ventricles from the mask mymaskarray = sitk.GetArrayFromImage(mymask) structure = [[1,1,1],[1,1,1],[1,1,1]] #241 - might need to add a zero onto zc1trunc for the case where nothing needs to be cut labelarray = -1*np.ones(mymaskarray.shape) for i in range(0,origRefImg.GetSize()[1]): if np.unique(sitk.GetArrayFromImage(refImg[:,i,:])).shape[0] < 2: temparray = np.ones((labelarray.shape[0],labelarray.shape[2])) temparray[2:temparray.shape[0]-2,2:temparray.shape[1]-2] = np.zeros((temparray.shape[0]-4, temparray.shape[1]-4)) labelarray[:,i,:] = temparray continue else: inflectionpoint = histFunctions.getInflectionPointRANSAC(refImg[:,i,:]) myslicemask = ndreg3D.imgMakeSliceMask(refImg[:,i,:],threshold=inflectionpoint, openingRadiusMM = 0.04) # look for connected components, filter out anything that doesn't have enough pixels myslicemaskarray = sitk.GetArrayFromImage(myslicemask) myslicemaskarray = myslicemaskarray + 1 myslicemaskarray[np.where(myslicemaskarray==2)[0],np.where(myslicemaskarray==2)[1]] = 0 label, num_features = scipy.ndimage.measurements.label(myslicemaskarray,structure) for ii in np.add(range(num_features-1),2):#at this point, 1 = background, 0 = brain if len(np.where(label==ii)[0]) < 8: label[np.where(label==ii)[0], np.where(label==ii)[1]] = 0 else: label[np.where(label==ii)[0], np.where(label==ii)[1]] = 1 # swap back to background = 0, brain = 1 labelarray[:,i,:] = label # now search for connected components (other than background obviously) and accept the big ones labelarray[np.where(labelarray==-1)] = 0 labelarrayint = np.copy(labelarray.astype(np.int)) labelarrayimg = sitk.VotingBinaryHoleFilling(sitk.GetImageFromArray(labelarrayint), radius=(1,1,1), majorityThreshold=1, foregroundValue = 0., backgroundValue = 1.) labelarrayimgout = np.copy(sitk.GetArrayFromImage(labelarrayimg)) labelarrayimgout = labelarrayimgout + 1 labelarrayimgout[np.where(labelarrayimgout==2)] = 0 structure = [[[1,1,1],[1,1,1],[1,1,1]],[[1,1,1],[1,1,1],[1,1,1]],[[1,1,1],[1,1,1],[1,1,1]]] label, num_features = scipy.ndimage.measurements.label(sitk.GetArrayFromImage(labelarrayimg),structure) for i in range(num_features): print(str(i) + "," + str(np.where(label==i)[0].shape[0])) # now remove all large regions from the original mask (mymaskarray). Ignore regions 0 and 1 because they are brain and background for i in range(2,num_features): if np.where(label==i)[0].shape[0] > 250: mymaskarray[np.where(label==i)] = 0 # after this phase, try replacing the slices in mymaskarray with the slices in labelarrayimg if there are not too many pixels different pixelsperslice = mymaskarray[:,0,:].shape[0] * mymaskarray[:,0,:].shape[1] mymaskarraycurated = -1*np.ones(mymaskarray.shape) for i in range(mymaskarray.shape[1]): if np.unique(sitk.GetArrayFromImage(origRefImg[:,i,:])).shape[0] < 2: mymaskarraycurated[:,i,:] = np.copy(mymaskarray[:,i,:]) else: if np.where((mymaskarray[:,i,:] == labelarrayimgout[:,i,:])==False)[0].shape[0]/float(pixelsperslice) < 0.18: mymaskarraycurated[:,i,:] = np.copy(labelarrayimgout[:,i,:]) else: mymaskarraycurated[:,i,:] = np.copy(mymaskarray[:,i,:]) # if i want a multicolor mask then remove regions again if multicolorMask == True: for i in range(2,num_features): if np.where(label==i)[0].shape[0] > 250: mymaskarraycurated[np.where(label==i)] = -1 # set brain to 2, background to 1, ventricles to 0 mymaskarraycurated[np.where(mymaskarraycurated==1)] = 2 mymaskarraycurated[np.where(mymaskarraycurated==0)] = 1 mymaskarraycurated[np.where(mymaskarraycurated==-1)] = 0 if blackVentricles == True: mymcmask = np.copy(mymaskarraycurated) for i in range(2,num_features+1): if np.where(label==i)[0].shape[0] > 250: mymcmask[np.where(label==i)] = -1 # set brain to 2, background to 1, ventricles to 0 mymcmask[np.where(mymcmask==1)] = 2 mymcmask[np.where(mymcmask==0)] = 1 mymcmask[np.where(mymcmask==-1)] = 0 # add something here to do ventricle masking slice by slice. maybe in both coronal and sagittal planes structure = [[1,1,1],[1,1,1],[1,1,1]] for i in range(mymcmask.shape[1]): label, num_features = scipy.ndimage.measurements.label(np.squeeze(-1.0*(mymaskarraycurated[:,i,:]-1)),structure) # here 1 = background, 0 = brain, everything else = ventricles for ii in range(2,num_features+1): # check if the label touches the boundary of the image if np.max(np.where(label==ii)[0]) == label.shape[0]-1 or np.max(np.where(label==ii)[1]) == label.shape[1]-1 or np.min(np.where(label==ii)[0]) == 0 or np.min(np.where(label==ii)[1]) == 0: continue if len(np.where(label==ii)[0]) > 8: tempslice = np.squeeze(mymcmask[:,i,:]) tempslice[np.where(label==ii)] = 0 mymcmask[:,i,:] = tempslice for i in range(mymcmask.shape[0]): label, num_features = scipy.ndimage.measurements.label(np.squeeze(-1.0*(mymaskarraycurated[i,:,:]-1)),structure) # here 1 = background, 0 = brain, everything else = ventricles for ii in range(2,num_features+1): # check if the label touches the boundary of the image if np.max(np.where(label==ii)[0]) == label.shape[0]-1 or np.max(np.where(label==ii)[1]) == label.shape[1]-1 or np.min(np.where(label==ii)[0]) == 0 or np.min(np.where(label==ii)[1]) == 0: continue if len(np.where(label==ii)[0]) > 8: tempslice = np.squeeze(mymcmask[i,:,:]) tempslice[np.where(label==ii)] = 0 mymcmask[i,:,:] = tempslice # save for STS mymcmaskimg = sitk.GetImageFromArray(mymcmask) mymcmaskimg.SetDirection((1,0,0,0,1,0,0,0,1)) mymcmaskimg.SetOrigin((0,0,0)) mymcmaskimg.SetSpacing(origRefImg.GetSpacing()) #sitk.WriteImage(mymcmaskimg,outputdirectoryname + '/' + patientnumber + '_mymcmask.img') if smoothMask == True: mymaskarraycuratedimg = sitk.GetImageFromArray(mymaskarraycurated.astype(np.float32)) mymaskarraycuratedimg.SetDirection((1,0,0,0,1,0,0,0,1)) mymaskarraycuratedimg.SetOrigin((0,0,0)) mymaskarraycuratedimg.SetSpacing(origRefImg.GetSpacing()) ndreg3D.imgWrite(sitk.SmoothingRecursiveGaussian(mymaskarraycuratedimg,0.04),outputfilename) else: mymaskarraycuratedimg = sitk.GetImageFromArray(mymaskarraycurated.astype(np.int8)) mymaskarraycuratedimg.SetDirection((1,0,0,0,1,0,0,0,1)) mymaskarraycuratedimg.SetOrigin((0,0,0)) mymaskarraycuratedimg.SetSpacing(origRefImg.GetSpacing()) ndreg3D.imgWrite(mymaskarraycuratedimg,outputfilename) #mymaskarrayfilled = scipy.ndimage.morphology.binary_fill_holes(mymaskarray,np.ones((5,5,5))) #mymaskarrayfilledimg = sitk.GetImageFromArray(mymaskarrayfilled.astype('int8')) #mymaskarrayfilledimg.SetSpacing(target.GetSpacing()) #sitk.WriteImage(mymaskarrayfilledimg,outputfilename) targetmasked = ndreg3D.imgMask(target,mymaskarraycuratedimg) sitk.WriteImage(targetmasked,outputtargetfilename)

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/高频题目/myStack.py

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''' 两个队列实现栈 ''' import queue class MyStack: def __init__(self): self.list1 = [] self.list2 = [] def push(self, x: int) -> None: if not self.list1: self.list1.append(x) return while self.list1: t = self.list1[0] self.list2.append(t) del self.list1[0] self.list1.append(x) while self.list2: t = self.list2[0] self.list1.append(t) del self.list2[0] def pop(self) -> int: if not self.list1: return -1 t = self.list1[0] del self.list1[0] return t def top(self) -> int: return self.list1[0] def empty(self) -> bool: return True if len(self.list1) == 0 else False

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from __future__ import division from __future__ import print_function import time import numpy as np import scipy.sparse as sp import networkx as nx import tensorflow.compat.v1 as tf from sklearn.metrics import roc_auc_score from sklearn.metrics import average_precision_score import random # # flags = tf.app.flags # FLAGS = flags.FLAGS def construct_self_feed_dict(emb, train_drug_miRNA_matrix, positive_mask, negative_mask, placeholders): """Construct feed dictionary.""" feed_dict = dict() feed_dict.update({placeholders['emb']: emb}) feed_dict.update({placeholders['adj_label']: train_drug_miRNA_matrix}) feed_dict.update({placeholders['positive_mask']: positive_mask}) feed_dict.update({placeholders['negative_mask']: negative_mask}) return feed_dict def construct_attention_feed_dict(emb, train_drug_miRNA_matrix, positive_mask, negative_mask, placeholders): """Construct feed dictionary.""" feed_dict = dict() feed_dict.update({placeholders['emb'][i]: emb[i] for i in range(len(emb))}) feed_dict.update({placeholders['adj_label']: train_drug_miRNA_matrix}) feed_dict.update({placeholders['positive_mask']: positive_mask}) feed_dict.update({placeholders['negative_mask']: negative_mask}) return feed_dict def constructNet(miRNA_dis_matrix): miRNA_matrix = np.mat(np.zeros((miRNA_dis_matrix.shape[0], miRNA_dis_matrix.shape[0]), dtype=np.int8)) dis_matrix = np.mat(np.zeros((miRNA_dis_matrix.shape[1], miRNA_dis_matrix.shape[1]), dtype=np.int8)) mat1 = np.hstack((miRNA_matrix, miRNA_dis_matrix)) mat2 = np.hstack((miRNA_dis_matrix.T, dis_matrix)) return np.vstack((mat1, mat2)) def sigmoid(x): return 1 / (1 + np.exp(-x)) def normalize_features(feat): degree = np.asarray(feat.sum(1)).flatten() # set zeros to inf to avoid dividing by zero degree[degree == 0.] = np.inf degree_inv = 1. / degree degree_inv_mat = sp.diags([degree_inv], [0]) feat_norm = degree_inv_mat.dot(feat) return feat_norm def matrix_normalize(similarity_matrix): similarity_matrix[np.isnan(similarity_matrix)] = 0 if similarity_matrix.shape[0] == similarity_matrix.shape[1]: for i in range(similarity_matrix.shape[0]): similarity_matrix[i, i] = 0 for i in range(200): D = np.diag(np.array(np.sum(similarity_matrix, axis=1)).flatten()) # 求得每一行的sum，再使其对角化 D = np.linalg.pinv(np.sqrt(D)) # 开方，再取伪逆矩阵 similarity_matrix = D * similarity_matrix * D else: for i in range(similarity_matrix.shape[0]): if np.sum(similarity_matrix[i], axis=1) == 0: similarity_matrix[i] = similarity_matrix[i] else: similarity_matrix[i] = similarity_matrix[i] / np.sum(similarity_matrix[i], axis=1) return similarity_matrix def masked_bilinearsigmoid_cross_entropy(preds, labels, mask, negative_mask): """Softmax cross-entropy loss with masking.""" loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=preds, labels=labels) mask += negative_mask mask = tf.cast(mask, dtype=tf.float32) # mask /= tf.reduce_mean(mask) mask = tf.reshape(mask, shape=[79924]) loss *= mask return tf.reduce_mean(loss) def gcn_masked_softmax_cross_entropy(preds, labels, positive_mask, negative_mask, pos_weight): """Softmax cross-entropy loss with masking.""" loss = tf.nn.weighted_cross_entropy_with_logits(targets=labels, logits=preds, pos_weight=pos_weight) # preds = tf.cast(preds, tf.float32) # labels = tf.cast(labels, tf.float32) # loss = tf.square(preds - labels) positive_mask += negative_mask # print(mask) mask = tf.cast(positive_mask, dtype=tf.float32) # mask /= tf.reduce_mean(mask) mask = tf.reshape(mask, shape=[79924]) loss *= mask return tf.reduce_mean(loss) def generate_mask(train_drug_miRNA_matrix, N): num = 0 mask = np.zeros(train_drug_miRNA_matrix.shape) while (num < 1 * N): a = random.randint(0, 105) b = random.randint(0, 753) if train_drug_miRNA_matrix[a, b] != 1 and mask[a, b] != 1: mask[a, b] = 1 num += 1 mask = np.reshape(mask, [-1, 1]) return mask def load_data(train_arr, test_arr): """Load data.""" labels = np.loadtxt("drug-miRNA.txt") logits_test = sp.csr_matrix((labels[test_arr, 2], (labels[test_arr, 0] - 1, labels[test_arr, 1] - 1)), shape=(106, 754)).toarray() logits_test = logits_test.reshape([-1, 1]) # logits_test = np.hstack((logits_test,1-logits_test)) logits_train = sp.csr_matrix((labels[train_arr, 2], (labels[train_arr, 0] - 1, labels[train_arr, 1] - 1)), shape=(106, 754)).toarray() logits_train = logits_train.reshape([-1, 1]) # logits_temp_train = logits_train # # train_list = [] # train_list.append(logits_temp_train) train_mask = np.array(logits_train[:, 0], dtype=np.bool).reshape([-1, 1]) test_mask = np.array(logits_test[:, 0], dtype=np.bool).reshape([-1, 1]) # train_mask = np.array(logits_train[:, 0]).reshape([-1, 1]) # test_mask = np.array(logits_test[:, 0]).reshape([-1, 1]) M = sp.csr_matrix((labels[train_arr, 2], (labels[train_arr, 0] - 1, labels[train_arr, 1] - 1)), shape=(106, 754)).toarray() return logits_train, logits_test, train_mask, test_mask, labels def weight_variable_glorot(input_dim, output_dim, name=""): init_range = np.sqrt(6.0 / (input_dim + output_dim)) initial = tf.random_uniform( [input_dim, output_dim], minval=-init_range, maxval=init_range, dtype=tf.float32) return tf.Variable(initial, name=name) def dropout_sparse(x, keep_prob, num_nonzero_elems): noise_shape = [num_nonzero_elems] random_tensor = keep_prob random_tensor += tf.random_uniform(noise_shape) dropout_mask = tf.cast(tf.floor(random_tensor), dtype=tf.bool) pre_out = tf.sparse_retain(x, dropout_mask) return pre_out * (1. / keep_prob) def sparse_to_tuple(sparse_mx): if not sp.isspmatrix_coo(sparse_mx): sparse_mx = sparse_mx.tocoo() coords = np.vstack((sparse_mx.row, sparse_mx.col)).transpose() values = sparse_mx.data shape = sparse_mx.shape return coords, values, shape def preprocess_graph(adj): adj = sp.coo_matrix(adj) adj_ = adj + sp.eye(adj.shape[0]) rowsum = np.array(adj_.sum(1)) degree_mat_inv_sqrt = sp.diags(np.power(rowsum, -0.5).flatten()) adj_normalized = adj_.dot(degree_mat_inv_sqrt).transpose().dot(degree_mat_inv_sqrt).tocoo() return sparse_to_tuple(adj_normalized) def construct_feed_dict(adj_norm, adj_label, features, positive_mask, negative_mask, placeholders): feed_dict = dict() feed_dict.update({placeholders['features']: features}) feed_dict.update({placeholders['adj_norm']: adj_norm}) feed_dict.update({placeholders['adj_label']: adj_label}) feed_dict.update({placeholders['positive_mask']: positive_mask}) feed_dict.update({placeholders['negative_mask']: negative_mask}) return feed_dict def masked_cross_entropy(preds, labels, label_mask, test_mask): """Accuracy with masking.""" preds = tf.cast(preds, tf.float32) labels = tf.cast(labels, tf.float32) error = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=preds) # pos_weight = 1 # error = tf.nn.weighted_cross_entropy_with_logits(logits=preds, targets=labels, pos_weight=pos_weight) label_mask += test_mask mask = tf.cast(label_mask, dtype=tf.float32) mask = tf.reshape(mask, shape=[79924]) error *= mask return tf.sqrt(tf.reduce_mean(error)) def masked_accuracy(preds, labels, label_mask, test_mask): preds = tf.cast(preds, tf.float32) labels = tf.cast(labels, tf.float32) error = tf.square(preds - labels) label_mask += test_mask mask = tf.cast(test_mask, dtype=tf.float32) mask = tf.reshape(mask, shape=[79924]) error *= mask return tf.sqrt(tf.reduce_mean(error))

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import jieba import wordcloud import re import pandas as pd import numpy as np from PIL import Image from pyecharts import Map, Bar # 根据文本生成词云图 def generatewordcloud(filename): # 文字字体 font = 'msyh.ttc' # 读取背景图片 img = Image.open('data/image.jpg') img_array = np.array(img) # 读取文本 f = open(filename, 'r', encoding='utf-8') content = f.read().replace(' ','').strip() # print(content) words = jieba.lcut(content) words = ' '.join(words) wc = wordcloud.WordCloud( background_color='white', mask=img_array, font_path=font ) wc.generate_from_text(words) wc.to_file('dazhong-五山.png') class AlittleAnalyse: def __init__(self): self.df = pd.read_csv('alittle.csv', encoding='gbk') self.df = self.df.dropna(axis=0, how='all') # print(self.df) self.address = list(self.df.address) # print(self.address) self.districts_list = [each[:3] for each in self.address] districts_counts = pd.value_counts(self.districts_list) self.areas = districts_counts.index self.value = list(districts_counts) def generatemap(self): # 制作地图，关于一点点在广州各个区的分布，先建立列表，根据每个区 map = Map('一点点在广州市的分布', width=1200, height=1000) map.add('一点点在广州市的分布', self.areas, self.value, maptype='广州', visual_range=[min(self.value), max(self.value)], is_map_symbol_show=False, is_label_show=True, is_visualmap=True, label_text_color='#509') # map.show_config() map.render('一点点在广州市的分布.html') def generatebar(self): # 每个区的一点点数量的柱形图 bar = Bar('广州各个区一点点的对比',width=1000,height=800) bar.add('广州各个区一点点数量的对比',self.areas, self.value, is_label_show=True, mark_line=['min','max'], mark_point=['average'], xaxis_interval=0, xaxis_rotate=30,yaxis_rotate=30, ) bar.render('广州各个区一点点数量的对比.html') def rank_bar(self): bar = Bar('评分前10的一点点', width=1000, height=900) pd_rank = self.df['rank'] pd_title = self.df['title'] # print(pd_rank) rank_series = pd.Series(data=list(pd_rank), index=pd_title) # print(rank_series.sort_values(ascending=False)) rank_ten = rank_series[:10] bar.add('评分前10的一点点', rank_ten.index, rank_ten, is_label_show=True, xaxis_interval=0, xaxis_rotate=20, yaxis_rotate=20, ) bar.render('评分前10的一点点.html') pass def comments_rank(self): bar = Bar('评论数最高前10的一点点', width=1000, height=900) pd_comments_num = self.df['comments_num'] pd_title = self.df['title'] comments_num_series = pd.Series(data=list(pd_comments_num), index=pd_title) comments_num_series = comments_num_series.sort_values(ascending=False) rank_ten = comments_num_series[:10] bar.add('评论数最高前10的一点点', rank_ten.index, rank_ten, is_label_show=True, xaxis_interval=0, xaxis_rotate=20, yaxis_rotate=20, ) bar.render('评论数最高前10的一点点.html') pass if __name__ == '__main__': # generatewordcloud('comment-五山.txt') alittle = AlittleAnalyse() alittle.generatemap() # alittle.generatebar() # alittle.rank_bar() # alittle.comments_rank() pass

[ "651998382@qq.com" ]

651998382@qq.com

4049908ad1eb290d1d43f4777d5dc90b9e7ded3f

7fbec69c220ea20aacf694f6c2025f265cd25d19

/Project/backend/api/migrations/0001_initial.py

3050c690240b46851c23d15a3659e34a8f83f064

[]

no_license

LeonJelsma/EEC-Public

7113e212fe1da6f0139e8f84fb6bc72994bfb062

e5b5d12a5ae295764a99062c6e40d79fd6a30299

refs/heads/master

2023-02-21T15:09:35.405803

2021-01-26T14:41:43

333,094,940

0

null

UTF-8

Python

false

2,490

py

# Generated by Django 3.1.3 on 2021-01-14 14:54 import api.models from django.db import migrations, models import django.db.models.deletion import django_enumfield.db.fields import uuid class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Room', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=50)), ], ), migrations.CreateModel( name='Sensor', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=50, unique=True)), ('type', django_enumfield.db.fields.EnumField(enum=api.models.SensorType)), ('activated', models.BooleanField()), ('key', models.UUIDField(default=uuid.uuid4, editable=False)), ('room', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.room')), ], ), migrations.CreateModel( name='TempMeasurements', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('temperature', models.FloatField()), ('sensor', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.sensor')), ], ), migrations.CreateModel( name='PowerMeasurements', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('wattage', models.FloatField()), ('sensor', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.sensor')), ], ), migrations.CreateModel( name='AmbientMeasurements', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('air_quality', models.FloatField()), ('temperature', models.FloatField()), ('humidity', models.FloatField()), ('sensor', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='api.sensor')), ], ), ]

[ "leonjelsma@gmail.com" ]

leonjelsma@gmail.com

47cda0955cd4e9f9f85353add7ebd1f3eca8fde1

1ac0eb03b9b6650b1841e8a308768bc3af01fa02

/bborobotfix.py

9d803408c9b60dcba846693314bd003a3cdf6c68

[]

no_license

criptik/bbotime

85b035c8084c9825bfffd7847b9cbc4e92752c50

bc84db6b577c73d5ec2f79175a196f265d17c0dd

refs/heads/master

2023-08-13T23:37:10.419642

2021-10-14T22:16:18

286,582,517

0

null

UTF-8

Python

false

7,776

py

import sys import time import os import itertools from pprint import pprint import bbobase gibName = 'GiB' vsstr = ' vs. ' robotData = {} class BboRobotFixer(object): def __init__(self, args, travTableData): self.args = args self.travTableData = travTableData self.initRobotData() # this routine is called if robotScores are supplied, # use those to try to differentiate between two or more robot pairs def robotFix(self): #first check that all supplied robot scores are unique, otherwise we can't deduce anything if len(set(self.args.robotScores)) != len(self.args.robotScores): print('Error: Robot Scores must all be unique') sys.exit(1) for bdnum in range (1, self.args.boards + 1): # place rows in big table indexed by boardnumber and North and East names for row in self.travTableData[bdnum]: self.buildRobotData(bdnum, row) if self.args.debug: print('----- snapshot of robotData ----') print(robotData) # this maps a robot score to a list of possible keysets # ideally this maps to 1 (0 or >1 would be an error) robScoreKeyLists = {} for robscore in self.args.robotScores: robScoreKeyLists[robscore] = [] allLegalKeylists = self.getAllLegalRobotKeylists() for keylist in allLegalKeylists: totScore = self.getScoreAllBoards(keylist) # now see if it matches any total robotScores # and put it in robScoreKeyLists if it does for robscore in self.args.robotScores: if totScore == robscore: robScoreKeyLists[robscore].append(keylist) # now check that each robscore does appear exactly once in the robScoreKeyLists # Note: on success need to eventually go back thru self.travTableData and change GiB names errorExit = False for robscore in self.args.robotScores: keylistArray = robScoreKeyLists[robscore] if len(keylistArray) == 0: print(f'Error: no keylists combos match for robot score {robscore}') errorExit = True elif len(keylistArray) > 1: # see if we have a special case where we can just pick one of two chosenKeylist = self.checkKeylistDiffs(keylistArray, robscore) if chosenKeylist is not None: self.fixRobotNamesInTravs(robscore, chosenKeylist) else: print(f'Error: multiple keylists combos match for robot score {robscore}') for keylist in keylistArray: pprint(keylist) errorExit = True else: # exactly one entry in the list # fix up the robotnames to be unique self.fixRobotNamesInTravs(robscore, keylistArray[0]) if errorExit: sys.exit(1) def initRobotData(self): for rndnum in range(1, int(self.args.boards/self.args.bpr) + 1): robotData[rndnum] = {} def addRobotScores(self, bdnum, row, dir): # robotData will be keyed by roundnum and oppName # and the direction which helps if robot is playing robot rndnum = int((bdnum-1)/self.args.bpr) + 1 oppdir = 'East' if dir == 'North' else 'North' key = f'{dir}{vsstr}{row[oppdir].lower()}' if robotData[rndnum].get(key) == None: robotData[rndnum][key] = [] # add the score fscore = float(row['Score'][:-1]) # strip % sign off end if dir == 'East': fscore = 100.0 - fscore robotData[rndnum][key].append(fscore) # print(bdnum, dir, robotData) def buildRobotData(self, bdnum, row): # only do this if one of the two pairs is a robot pair for dir in ['North', 'East']: if row[dir] == gibName and row[bbobase.partnerDir[dir]] == gibName: self.addRobotScores(bdnum, row, dir) def robKeyOppNamesUnique(self, keylist): oppMap = {} for key in keylist: oppname = key.split(vsstr)[1] if oppMap.get(oppname) is None: oppMap[oppname] = 1 else: return False # if we get this far, success return True def getAllLegalRobotKeylists(self): # use itertools to get all the combinations keysets = [] for rndnum in range(1, int(self.args.boards/self.args.bpr) + 1): keysets.append(list(robotData[rndnum].keys())) if self.args.debug: pprint(keysets) allCombos = list(itertools.product(*keysets)) allLegalKeylists = [] for keylist in allCombos: # first make sure all the opponent names are unique across rounds # and if so, combine all the scores for all rounds into one list so we can avg it if self.robKeyOppNamesUnique(keylist): allLegalKeylists.append(keylist) return allLegalKeylists def getScoreAllBoards(self, keylist): # for this keylist, combine all the scores for all rounds into one list so we can avg it rndnum = 1 scores = [] for key in keylist: scores.extend(robotData[rndnum][key]) rndnum += 1 avg = round(sum(scores) / len(scores), 2) return avg def fixRobotNamesInTravs(self, robscore, keylist): print('robscore=', robscore) pprint(keylist) rndnum = 1 for key in keylist: for bdnum in range(((rndnum-1) * self.args.bpr) + 1, (rndnum * self.args.bpr) + 1): table_data = self.travTableData[bdnum] # find the row that has robotName in expected direction # and playing expected opp (direction, oppname) = key.split(vsstr) oppdir = 'East' if direction == 'North' else 'North' rowsChanged = 0 if self.args.debug: print(f'bdnum {bdnum}, {robscore}, {key}') for row in table_data: # todo: make this more robust if players start with the same substring if row[direction].startswith(gibName) and row[oppdir].lower().startswith(oppname): row[direction] = f'{gibName}-{robscore}' parddir = bbobase.partnerDir[direction] row[parddir] = f'{gibName}-{robscore}-pard' rowsChanged += 1 if self.args.debug: print(f'after: {rowsChanged} ', end='') pprint(row) assert(rowsChanged == 1) rndnum += 1 def checkKeylistDiffs(self, keylistArray, robscore): if len(keylistArray) > 2: return None # see if only differ in one key (keylist1, keylist2) = keylistArray numDiffs = 0 for (key1, key2) in zip(keylist1, keylist2): if key1 != key2: numDiffs += 1 (dir1, opp1) = key1.split(vsstr) (dir2, opp2) = key2.split(vsstr) if opp1 == 'gib' and opp2 == 'gib': # difference is resolvable, return one based on our position # in the self.args.robotScores array scorepos = self.args.robotScores.index(robscore) candidate = keylistArray[scorepos] if numDiffs == 1: print('picked keylist which differed insignificantly') return candidate else: return None

[ "tom.deneau@gmail.com" ]

tom.deneau@gmail.com

c61144eb7f25628c20e8d28c3d8594103e3f8761

163bbb4e0920dedd5941e3edfb2d8706ba75627d

/Code/CodeRecords/2665/60700/255557.py

db2e4ffd1c9d52076916379745902972866bd4fa

[]

no_license

AdamZhouSE/pythonHomework

a25c120b03a158d60aaa9fdc5fb203b1bb377a19

ffc5606817a666aa6241cfab27364326f5c066ff

refs/heads/master

2022-11-24T08:05:22.122011

2020-07-28T16:21:24

259,576,640

2

1

null

UTF-8

Python

false

419

py

def score(n, z): c = 0 while z != 1: if n % z == 0: n -= 1 z -= 1 c += 1 else: z -= 1 return c tests = int(input()) nums = [] for i in range(tests): nums.append(input()) for i in nums: num = i.split(' ') X = int(num[0]) Y = int(num[1]) Z = int(num[2]) c = [score(X, Z), score(Y, Z)] print(str(c[0])+' '+str(c[1]))

[ "1069583789@qq.com" ]

1069583789@qq.com

c4023411ab5a91a0f7cf836536b90a6a4b121250

931db63982cb9404135410599631287e098152af

/preferences.py

70d1267f06d95a3a1b65bdd1726888ac0bc7c483

[]

no_license

Readdeo/Mega-Auto-Converter

f8fde9530cf1cb7d54ac0d62d9af5456b126ddae

600a2d7c8f7967c90826867bd23a3ab547bacc1a

refs/heads/master

2021-01-19T22:55:34.548658

2017-04-20T17:44:23

88,893,337

1

0

null

UTF-8

Python

false

915

py

#Facebook user's ID who receives the sent messages FB_UID = XXXXX #Facebook account credentials. This account sends the messages. fb_email = "XXXXX" fb_password = "XXXXX" #Mega.co.nz account credentials mega_email = "XXXXX" mega_password = "XXXXX" # if testMode is true, the script will use other folders in Mega to do the job test_mode = False # if this is true, the script will notify you with Facebook messages send_FB_message = False mega_folders_test = {'converted':'TEST_CONVERTED', 'raw': 'TEST', 'finished': 'TESTFINISHED', 'cwo': 'TEST_CWO', 'skipped': 'SKIPPED', 'downloaded': 'TEST_DOWNLOADED'} mega_folders = {'converted':'CONVERTED', 'raw': 'RAW', 'finished': 'FINISHED', 'cwo': 'CURRENTLY_WORKING_ON', 'skipped': 'SKIPPED', 'downloaded': 'DOWNLOADED'} free_space_percent = 50 # encoding variables for ffmpeg convert_width_ratio = 0.3 codec = "libx265"

[ "readdeo@citromail.hu" ]

readdeo@citromail.hu

17530b0db0edd9795091ae246ac48dcc1bd18834

965af75565d1b36de2962cad22902f44dcb80b7e

/products/models.py

8f6c6f7111b12e07cff154516c6432d1afd823af

[]

no_license

Yojanpardo/cost_center

675e6e6becdf6d59e76607dba5ced0487402ad97

346a52d917e070d244119e5ed08e93a99b701f51

refs/heads/master

2022-12-01T09:44:15.716651

2022-11-22T13:41:21

167,300,526

0

null

2022-11-22T13:41:22

2019-01-24T04:02:54

Python

UTF-8

Python

false

448

py

from django.db import models # Create your models here. class Product(models.Model): """docstring for Product""" name = models.CharField(max_length=30) description = models.TextField(max_length=255,blank=True,null=True) image = models.ImageField(blank=True,null=True) quantity = models.IntegerField() created = models.DateTimeField(auto_now_add=True) modified = models.DateTimeField(auto_now=True) def __str__(self): return self.name

[ "Yojan.Pardo@gmail.com" ]

Yojan.Pardo@gmail.com

fb3d2ef22cbf4cf78c3d2d9308d619b96cdbe5c6

fb454873b8789ef25c832d2aac64498bb10dc45c

/day12.py

4dcc5329ca60d20c2ff07a50e1459316c5347d8c

[]

no_license

purivikas/stepfunc-data-science-course-work

bf9d750591125525deb23d46298e0e3b913507e8

274994a4f73c5716d8311a24dae39ec27ad31cb0

refs/heads/master

2021-11-25T08:53:06.261518

2021-11-22T16:57:47

102,774,460

1

0

null

UTF-8

Python

false

1,719

py

# Functional Programming # Hadoop - FaaS my_nums = range(100) my_new_num = [] for i in my_nums: my_new_num.append( (i+1)*(i+1)) my_new_num = [ (i+1)*(i+1) for i in my_nums ] def weired_sqr(n): return (n+1)*(n+1) my_new_num = [weired_sqr(m) for m in my_nums if (weired_sqr(m) % 2 == 0) and (weired_sqr(m) %3 == 0)] student_jobs = [('ahmed','teacher') , ('ben','hacker') ,('tom','trader'),('vikas','trader'),('alex','trader')] concise_jobs = [(u[0],v[:3]) for (u,v) in student_jobs] my_string = 'quick brown fox jumps over the lazy dog.' ''' Return a list conatining for every''' new_string = [ ( i.upper() ,len(i),i[-1]) for i in my_string.split() if i[1] != 'r' and i[1] != 'o'] new_string = [ ( i.upper() ,len(i),i[-1]) for i in my_string.split() if ( 'r' not in i) and ('o' not in i)] # Map Reduce Filter - # Mapping value to function ... def weired_sqr(n): return (n+1)*(n+1) list( map(weired_sqr,range(10))) # Cons - first has to def function # annonmous list ( map( lambda x: (x+1)*(x+1), [1,2,3,4,5] )) list ( map( lambda x: (x.upper(),len(x),x[-1]), 'quick brown fox jumps over the lazy dog'.split() )) list ( map( lambda x: (x,x+1), [1,2,3,4,5] )) list ( map( lambda x: (x,x+1), range(10) )) def my_criteria(n): if ( n % 2 == 0 ) and ( n % 3 == 0): return True return False list(filter(my_criteria,range(0,100))) my_str = 'quick brown fox jumps over the lazy dog' list ( map( lambda x: (x.upper(),len(x),x[-1]), filter( lambda x: ((x[1] != 'r' ) and (x[1] != 'o')), 'quick brown fox jumps over the lazy dog'.split()) )) # Reduce Function from functools import reduce import functools reduce( lambda x,y: x*y ,range (1,20))

[ "purivikas@hotmail.com" ]

purivikas@hotmail.com

a6604a953db2a729462d86dae03ead7286d62692

9329bf73d6d547a868a57e451d22e7c162a8cbd3

/textacy/fileio/read.py

682262424ac8366e4fb0d088e5c8dce8ffe07ca3

[ "Apache-2.0" ]

permissive

honnibal/textacy

cf892536fd10478016fa2e7e820585269c44b102

ddf3eeb238ac69470f3675fe27d1f99869ef99f7

refs/heads/master

2021-01-22T10:48:03.415828

2017-02-15T09:31:04

82,043,411

3

1

null

2017-02-15T09:30:08

null

UTF-8

Python

false

7,225

py

""" Module with functions for reading content from disk in common formats. """ from __future__ import absolute_import, division, print_function, unicode_literals from functools import partial import json import ijson from numpy import load as np_load from scipy.sparse import csc_matrix, csr_matrix from spacy.tokens.doc import Doc as SpacyDoc from textacy.compat import csv from textacy.fileio import open_sesame JSON_DECODER = json.JSONDecoder() def read_file(filepath, mode='rt', encoding=None): """ Read the full contents of a file. Files compressed with gzip, bz2, or lzma are handled automatically. """ with open_sesame(filepath, mode=mode, encoding=encoding) as f: return f.read() def read_file_lines(filepath, mode='rt', encoding=None): """ Read the contents of a file, line by line. Files compressed with gzip, bz2, or lzma are handled automatically. """ with open_sesame(filepath, mode=mode, encoding=encoding) as f: for line in f: yield line def read_json(filepath, mode='rt', encoding=None, prefix=''): """ Iterate over JSON objects matching the field given by ``prefix``. Useful for reading a large JSON array one item (with ``prefix='item'``) or sub-item (``prefix='item.fieldname'``) at a time. Args: filepath (str): /path/to/file on disk from which json items will be streamed, such as items in a JSON array; for example:: [ {"title": "Harrison Bergeron", "text": "The year was 2081, and everybody was finally equal."}, {"title": "2BR02B", "text": "Everything was perfectly swell."} ] mode (str, optional) encoding (str, optional) prefix (str, optional): if '', the entire JSON object will be read in at once; if 'item', each item in a top-level array will be read in successively; if 'item.text', each array item's 'text' value will be read in successively Yields: next matching JSON object; could be a dict, list, int, float, str, depending on the value of ``prefix`` Notes: Refer to ``ijson`` at https://pypi.python.org/pypi/ijson/ for usage details. """ with open_sesame(filepath, mode=mode, encoding=encoding) as f: if prefix == '': yield json.load(f) else: for item in ijson.items(f, prefix): yield item def read_json_lines(filepath, mode='rt', encoding=None): """ Iterate over a stream of JSON objects, where each line of file ``filepath`` is a valid JSON object but no JSON object (e.g. array) exists at the top level. Args: filepath (str): /path/to/file on disk from which json objects will be streamed, where each line in the file must be its own json object; for example:: {"title": "Harrison Bergeron", "text": "The year was 2081, and everybody was finally equal."}\n {"title": "2BR02B", "text": "Everything was perfectly swell."} mode (str, optional) encoding (str, optional) Yields: dict: next valid JSON object, converted to native Python equivalent """ with open_sesame(filepath, mode=mode, encoding=encoding) as f: for line in f: yield json.loads(line) def read_json_mash(filepath, mode='rt', encoding=None, buffersize=2048): """ Iterate over a stream of JSON objects, all of them mashed together, end-to-end, on a single line of a file. Bad form, but still manageable. Args: filepath (str): /path/to/file on disk from which json objects will be streamed, where all json objects are mashed together, end-to-end, on a single line,; for example:: {"title": "Harrison Bergeron", "text": "The year was 2081, and everybody was finally equal."}{"title": "2BR02B", "text": "Everything was perfectly swell."} mode (str, optional) encoding (str, optional) buffersize (int, optional): number of bytes to read in as a chunk Yields: dict: next valid JSON object, converted to native Python equivalent """ with open_sesame(filepath, mode=mode, encoding=encoding) as f: buffer = '' for chunk in iter(partial(f.read, buffersize), ''): buffer += chunk while buffer: try: result, index = JSON_DECODER.raw_decode(buffer) yield result buffer = buffer[index:] # not enough data to decode => read another chunk except ValueError: break def read_csv(filepath, encoding=None, dialect='excel', delimiter=','): """ Iterate over a stream of rows, where each row is an iterable of strings and/or numbers with individual values separated by ``delimiter``. Args: filepath (str): /path/to/file on disk from which rows will be streamed encoding (str) dialect (str): a grouping of formatting parameters that determine how the tabular data is parsed when reading/writing; if 'infer', the first 1024 bytes of the file is analyzed, producing a best guess for the correct dialect delimiter (str): 1-character string used to separate fields in a row Yields: List[obj]: next row, whose elements are strings and/or numbers .. seealso:: https://docs.python.org/3/library/csv.html#csv.reader """ with open_sesame(filepath, mode='rt', encoding=encoding, newline='') as f: if dialect == 'infer': dialect = csv.Sniffer().sniff(f.read(1024)) f.seek(0) for row in csv.reader(f, dialect=dialect, delimiter=delimiter): yield row def read_spacy_docs(spacy_vocab, filepath): """ Stream ``spacy.Doc`` s from disk at ``filepath`` where they were serialized using Spacy's ``spacy.Doc.to_bytes()`` functionality. Args: spacy_vocab (``spacy.Vocab``): the spacy vocab object used to serialize the docs in ``filepath`` filepath (str): /path/to/file on disk from which spacy docs will be streamed Yields: the next deserialized ``spacy.Doc`` """ with open_sesame(filepath, mode='rb') as f: for bytes_string in SpacyDoc.read_bytes(f): yield SpacyDoc(spacy_vocab).from_bytes(bytes_string) def read_sparse_csr_matrix(filepath): """ Read the data, indices, indptr, and shape arrays from a ``.npz`` file on disk at ``filepath``, and return an instantiated ``scipy.sparse.csr_matrix``. """ npz_file = np_load(filepath) return csr_matrix((npz_file['data'], npz_file['indices'], npz_file['indptr']), shape=npz_file['shape']) def read_sparse_csc_matrix(filepath): """ Read the data, indices, indptr, and shape arrays from a ``.npz`` file on disk at ``filepath``, and return an instantiated ``scipy.sparse.csc_matrix``. """ npz_file = np_load(filepath) return csc_matrix((npz_file['data'], npz_file['indices'], npz_file['indptr']), shape=npz_file['shape'])

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ZariaHoward/zariahoward.github.io

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from __future__ import print_function import time import requests # import cv2 # import operator import numpy as np # Import library to display results # import matplotlib.pyplot as plt # get_ipython().magic(u'matplotlib inline') # Display images within Jupyter import pickle from pymongo import MongoClient import pprint import json as json_lib import os import Queue import threading client = MongoClient('localhost:27017') db = client.Teenie # broken_objects = list(db.Photos.find("path":"true")) count=0 with open("/Volumes/HueyFreeman/errorPics.txt","r+") as f: for subdir, dirs, files in os.walk('/Volumes/HueyFreeman/Teenie_Harris_PNG1024'): for file in files: #print os.path.join(subdir, file) filepath = subdir + os.sep + file if filepath.endswith(".png"): # print(db.Photos.find({"path":filepath})) # print(list(db.Photos.find({"path":filepath}))) if len(list(db.Photos.find({"path":filepath}))) == 0: f.write(filepath+ "\n") count += 1 else: print( len(list(db.Photos.find({"path":filepath})))) f.close() print(count)

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def info(): pass <ref>info()

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#!/usr/bin/env python # -*- coding:utf-8 -*- # Author:Huihuihui # Time:2018/10/15 class DFA: file_object = '' line_number = 0 state = 0 # 状态 ResWord = ['int', 'if', 'then', 'else', 'end', 'repeat', 'until', 'read', 'write'] # 保留字 error_message = [] annotate_message = [] char_message = [] def __init__(self, file_name): self.file_object = file_name self.state = 0 self.line_number = 0 self.error_message = [] self.annotate_message = [] self.char_message = [] def Start_convert(self): for line in self.file_object: line = line.strip('\n') self.line_number += 1 line_length = len(line) i = 0 string = '' while i < line_length: ch = line[i] i += 1 if self.state == 0: string = ch if ch.isalpha(): self.state = 1 elif ch.isdigit(): self.state = 3 elif ch == '+': self.state = 5 elif ch == '-': self.state = 9 elif ch == '*': self.state = 13 elif ch == '/': self.state = 16 elif ch == '=': self.state = 20 i -= 1 elif ch == '<': self.state = 21 i -= 1 elif ch == '{': self.state = 22 i -= 1 elif ch == '}': self.state = 23 i -= 1 elif ch == ';': i -= 1 self.state = 24 elif ch.isspace(): self.state = 25 else: self.state = 26 # 不可识别状态 i -= 1 elif self.state == 1: while ch.isalpha() or ch.isdigit(): string += ch if i < line_length: ch = line[i] i += 1 else: break self.state = 2 i -= 2 elif self.state == 2: if string in self.ResWord: content = '关键字,' + string else: content = '标识符,' + string # print content self.char_message.append(content) string = '' # 回到初始情况 self.state = 0 # 回到状态0 elif self.state == 3: while ch.isdigit(): string += ch if i < line_length: ch = line[i] i += 1 else: break self.state = 4 i -= 2 # 回退2个字符 elif self.state == 4: content = '数字,' + string self.char_message.append(content) # print string string = '' # 回到初始情况 self.state = 0 # 回到状态0 elif self.state == 5: if ch == '+': self.state = 6 i -= 1 elif ch == '=': self.state = 7 i -= 1 else: self.state = 8 i -= 2 elif self.state == 6: # 判断++ content = '特殊符号,' + string + ch self.char_message.append(content) # print string + ch string = '' # 回到初始情况 self.state = 0 # 回到状态0 elif self.state == 7: # 判断+= content = '特殊符号,' + string + ch self.char_message.append(content) # print string + ch string = '' # 回到初始情况 self.state = 0 # 回到状态0 elif self.state == 8: # 判断+ content = '特殊符号,' + ch self.char_message.append(content) # print ch string = '' # 回到初始情况 self.state = 0 # 回到状态0 elif self.state == 9: if ch == '-': self.state = 10 i -= 1 elif ch == '=': self.state = 11 i -= 1 else: self.state = 12 i -= 2 elif self.state == 10: content = '特殊符号,' + string + ch self.char_message.append(content) # print string + ch#判断-- string = '' # 回到初始情况 self.state = 0 # 回到状态0 elif self.state == 11: # 判断-= content = '特殊符号,' + string + ch self.char_message.append(content) # print string + ch string = '' # 回到初始情况 self.state = 0 # 回到状态0 elif self.state == 12: # 判断- content = '特殊符号,' + ch self.char_message.append(content) # print ch string = '' # 回到初始情况 self.state = 0 # 回到状态0 elif self.state == 13: if ch == '=': self.state = 14 i -= 1 else: self.state = 15 i -= 2 elif self.state == 14: # 判断*= content = '特殊符号,' + string + ch self.char_message.append(content) # print string + ch string = '' # 回到初始情况 self.state = 0 # 回到状态0 elif self.state == 15: # 判断* content = '特殊符号,' + ch self.char_message.append(content) # print ch string = '' # 回到初始情况 self.state = 0 # 回到状态0 elif self.state == 16: if ch == '/': self.state = 17 i -= 1 elif ch == '=': self.state = 18 i -= 1 else: self.state = 19 i -= 2 elif self.state == 17: # 判断// content = '特殊符号,' + string + ch self.char_message.append(content) content = '注释,' + line[i:] self.annotate_message.append(content) # print content string = '' # 回到初始情况 self.state = 0 # 回到状态0 elif self.state == 18: # 判断/= content = '特殊符号,' + string + ch self.char_message.append(content) # print string + ch string = '' # 回到初始情况 self.state = 0 # 回到状态0 elif self.state == 19: # 判断/ content = '特殊符号,' + ch self.char_message.append(content) # print ch string = '' # 回到初始情况 self.state = 0 # 回到状态0 elif self.state == 20: content = '特殊符号,=' self.char_message.append(content) # print '=' self.state = 0 string = '' elif self.state == 21: content = '特殊符号,<' self.char_message.append(content) # print '<' self.state = 0 string = '' elif self.state == 22: content = '特殊符号,{' self.char_message.append(content) # print '{' self.state = 0 string = '' elif self.state == 23: content = '特殊符号,}' self.char_message.append(content) # print '}' self.state = 0 string = '' elif self.state == 24: content = '特殊符号,;' self.char_message.append(content) # print ';' self.state = 0 string = '' elif self.state == 25: while ch.isspace(): if i < line_length: ch = line[i] i += 1 else: break self.state = 0 i -= 1 elif self.state == 26: content = '行号:' + str(self.line_number) + ',' + ch self.error_message.append(content) # print 'error:' + ch self.state = 0 string = '' # print self.state def Get_error(self): # 获取错误信息 return self.error_message def Get_annotate(self): # 获取注释信息 return self.annotate_message def Get_char(self): # 获取识别信息 return self.char_message try: file_object = open("F:\Source\Python\ProducerCustomer.py") dfa = DFA(file_object) dfa.Start_convert() content = dfa.Get_char() for item in content: print(item) content = dfa.Get_annotate() for item in content: print(item) content = dfa.Get_error() for item in content: print(item) finally: file_object.close()

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LonelyHobby.noreply@github.com

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nekonbu72/gevent_websoclet_sample

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import sys from abc import ABCMeta, abstractmethod class CustomOutBase(metaclass=ABCMeta): orgout = sys.stdout def __init__(self): # self.orgout = sys.stdout sys.stdout = self def __enter__(self): return self def __exit__(self, ex_type, ex_value, trace): sys.stdout = self.orgout @abstractmethod def write(self, s: str) -> int: raise NotImplementedError() def stdout_temp(newout: CustomOutBase): def outer(func): def inner(*args, **kwargs): with newout: func(*args, **kwargs) return inner return outer def end_filter(func): def wrapper(*args, **kwargs): s = args[1] if isinstance(s, str): if s.strip() == "": return func(*args, **kwargs) else: raise TypeError() return wrapper class __CustomOut(CustomOutBase): def write(self, s: str) -> int: if s.strip() == "": return 0 print(f"@@@{s}@@@", file=self.orgout) return len(s) if __name__ == "__main__": @stdout_temp(__CustomOut()) def __printer(): print("Hello, print!") def __printer2(): print("Bye, print!") __printer() # @@@Hello, print!@@@ __printer2() # Bye, print!

[ "nakatomo222@gmail.com" ]

nakatomo222@gmail.com

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/lesson9/zhangbaocheng/sqlmng/migrations/0001_initial.py

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[]

no_license

cnjllin/devops6

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# -*- coding: utf-8 -*- # Generated by Django 1.11 on 2018-03-02 16:22 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='dbconf', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=32, verbose_name='\u540d\u5b57')), ('createtime', models.DateTimeField(auto_now_add=True, verbose_name='\u521b\u5efa\u65f6\u95f4')), ('updatetime', models.DateTimeField(auto_now=True, verbose_name='\u4fee\u6539\u65f6\u95f4')), ('note', models.TextField(blank=True, default='', null=True, verbose_name='\u5907\u6ce8')), ('user', models.CharField(max_length=128)), ('password', models.CharField(max_length=128)), ('host', models.CharField(max_length=16)), ('port', models.CharField(max_length=5)), ('env', models.CharField(blank=True, choices=[('1', '\u751f\u4ea7'), ('2', '\u6d4b\u8bd5')], max_length=1, null=True)), ], options={ 'ordering': ['-id'], 'abstract': False, }, ), ]

[ "1032231418@qq.com" ]

1032231418@qq.com

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/Labs/Lab 1/problem1.py

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[]

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s4m15v0/CSE221

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# -*- coding: utf-8 -*- """problem1.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1DjXtkHr5utg807ms_nKKRaLWUnX-8hUi """ def isPalindrome(word): if(len(word)==0): return False N = len(word) for i in range(0,N//2): if(word[i] != word[N-1-i]): return False return True #parity check def parityCheck(number): if(number%2==0): return True else: return False # main _file = open("/content/input.txt",'r') _read = _file.readlines() no_lines = len(_read) # count palindrome,not palindrome, parity , not parity palindrome_count = 0 not_palindrome_count = 0 float_numbers_count = 0 even_parity = 0 odd_parity = 0 #empty list result result = [] # for loop to loop the files in the _read for index in _read: i = index.split() _str="" #using try catch try: if(isinstance(int(i[0]),float)): pass else: # calling function for parity check if(parityCheck(int(i[0]))): _str +=str(i[0])+" has even parity and " even_parity+=1 else: _str +=str(i[0])+" has odd parity and " odd_parity+=1 #catch any exception error except: _str +=str(i[0])+" cannot have parity and " float_numbers_count+=1 if(isPalindrome(i[1])): _str += str(i[1])+" is a palindrome" palindrome_count+=1 else: _str += str(i[1])+" is not a palindrome" not_palindrome_count+=1 result.append(_str) # output.txt in write mode output_file = open("output.txt",'w') for i in result: output_file.write(i) output_file.write("\n") # opent the file record.txt in write mode record_file = open("record.txt","w") # calculate and write the line to file record_file.write("Percentage of Odd parity {}%\n".format((odd_parity/no_lines)*100)) record_file.write("Percentage of even parity {}%\n".format((even_parity/no_lines)*100)) record_file.write("Percentage of no parity {}%\n".format((float_numbers_count/no_lines)*100)) record_file.write("Percentage of Palindrome {}%\n".format((palindrome_count/no_lines)*100)) record_file.write("Percentage of non-Palindrome {}%\n".format((not_palindrome_count/no_lines)*100)) # closing all the files record_file.close() output_file.close() _file.close()

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import smart_imports smart_imports.all() class CommonTests(utils_testcase.TestCase): def setUp(self): super(CommonTests, self).setUp() game_logic.create_test_map() self.account = self.accounts_factory.create_account() self.storage = game_logic_storage.LogicStorage() self.storage.load_account_data(self.account.id) self.hero = self.storage.accounts_to_heroes[self.account.id] def test_rarities_abilities(self): for rarity, rarity_abilities in helpers.RARITIES_ABILITIES.items(): companion = logic.create_random_companion_record('%s companion' % rarity, abilities=rarity_abilities) self.assertEqual(companion.rarity, rarity) @mock.patch('the_tale.game.companions.objects.Companion.max_coherence', 100) @mock.patch('the_tale.game.heroes.abilities.companions.THOUGHTFUL.MULTIPLIER', [1, 1, 1, 1, 1]) @mock.patch('the_tale.game.heroes.abilities.companions._CompanionHealBase.PROBABILITY', [0, 0, 0, 0, 0]) def _test_companion_death_speed(self): companion_record = logic.create_random_companion_record('test companion', state=relations.STATE.ENABLED, dedication=relations.DEDICATION.BRAVE) # ,#,; companion = logic.create_companion(companion_record) self.hero.set_companion(companion) self.hero.preferences.set_companion_dedication(heroes_relations.COMPANION_DEDICATION.NORMAL) old_health = self.hero.companion.health while self.hero.companion: self.hero.companion.coherence = 50 self.storage.process_turn() game_turn.increment() self.hero.randomized_level_up() if not self.hero.is_alive: if hasattr(self.hero.actions.current_action, 'fast_resurrect'): self.hero.actions.current_action.fast_resurrect() if self.hero.companion: old_health = self.hero.companion.health

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a.eletsky@gmail.com

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renjiezhu/Poisson-Spot-Simulator

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# # PHYS 129 final project # Poisson's Spot # GUI # # June 6, 2018 modified by Renjie Zhu # June 5, 2018 modified by Renjie Zhu # May 23, 2018 created by Renjie Zhu # import sys import matplotlib.pyplot as plt from PyQt5.QtWidgets import ( QApplication, QWidget, QLabel, QMainWindow, QPushButton, QLineEdit, QErrorMessage, QMenuBar, QMessageBox, ) from PyQt5.QtCore import ( pyqtSlot, ) from disk import Poisson class Gui(QWidget): def __init__(self, left=300, top=300, width=450, height=140, title='New Window'): super().__init__() self.title = title self.left = left self.top = top self.width = width self.height = height self.l_label = QLabel('Distance between disk and screen', self) self.d_label = QLabel('Diameter of disk', self) self.w_label = QLabel('Wavelength of incident light', self) self.l_unit = QLabel('mm', self) self.d_unit = QLabel('mm', self) self.w_unit = QLabel('nm', self) self.l_edit = QLineEdit('1000', self) self.d_edit = QLineEdit('2', self) self.w_edit = QLineEdit('600', self) self.l_value = 0 self.d_value = 0 self.w_value = 0 self.reset_button = QPushButton('Reset', self) self.compute_button = QPushButton('Compute', self) self.error_dialog = QErrorMessage() self.ERROR_MESSAGE = "Make sure you give positive numbers.\nPlease try again." self.l_MESSAGE = """Please make sure that the distance between the screen and the disk is a hundred times larger than the diameter of the disk.""" self.help_box = QMessageBox() self.FRESNEL_MESSAGE = """ For Fresnel diffraction (the diffraction that causes Poisson's Point to show), the parameters must satisfy. the following requirement. \t\td^2 \t\t----- >~ 1, \t\tl * λ where \td is the diameter of disk, \tl is the distance between disk and screen, and \tλ is the wavelength of incident light. Please make sure your input meet this requirement! """ self.INFO_TITLE = 'Information about Poisson\'s Spot:' self.POISSON_MESSAGE = """ This program is used to simulate the Poisson Spot with parameters given by the user. Parameters include 1. distance between the disk and screen, 2. diameter of the disk, 3. wavelength of incident light. The plot will be shown as a 400 by 400 grid image of the Poisson Spot on a screen of 16mm by 16mm. Please make sure that you give integers as input and follow the suggestions if you encounter any. """ self.warning_box = QMessageBox() self.WARNING_MESSAGE = """ Due to both hardware and software limitations, the computation may take a rather long time to run. Please be patient if the program is not responding, and expect it to run for about 10 minutes until you see the result. The result will be saved to the program directory as an eps file with parameters listed in the filename. """ self.plot_info = """ Parameters: Distance between disk and screen: %d m Diameter of disk: %d mm Wavelength of incident light: %d nm """ def main_window(self): self.setWindowTitle(self.title) self.setGeometry(self.left, self.top, self.width, self.height) self.reset_button.setToolTip('Reset the entries.') self.compute_button.setToolTip( 'Compute the Poisson\'s Spot using parameters above.') self.compute_button.setDefault(True) self.l_label.move(10, 7) self.l_edit.move(260, 5) self.l_unit.move(420, 7) self.d_label.move(10, 42) self.d_edit.move(260, 40) self.d_unit.move(420, 42) self.w_label.move(10, 77) self.w_edit.move(260, 75) self.w_unit.move(420, 77) self.reset_button.move(200, 110) self.compute_button.move(310, 110) self.reset_button.clicked.connect(self.reset_on_click) self.compute_button.clicked.connect(self.compute_on_click) self.show() def fresnel_help_page(self): self.help_box.setWindowTitle(self.INFO_TITLE) self.help_box.setIcon(QMessageBox.Information) self.help_box.setText(self.FRESNEL_MESSAGE) self.help_box.show() def info_page(self): self.info_box.setWindowTitle(self.INFO_TITLE) self.info_box.setIcon(QMessageBox.Information) self.info_box.setText(self.POISSON_MESSAGE) self.info_box.show() def warning_page(self): self.warning_box.setWindowTitle('Warning!') self.warning_box.setIcon(QMessageBox.Warning) self.warning_box.setText(self.WARNING_MESSAGE) self.warning_box.setStandardButtons(self.warning_box.Cancel | self.warning_box.Ok) if self.warning_box.exec_() == self.warning_box.Ok: self.proceed_on_click() def start(self): self.main_window() @pyqtSlot(name='compute_on_click') def compute_on_click(self): try: l_value_temp = float(self.l_edit.text()) if l_value_temp < 0: self.value_error_handling() self.l_edit.setText('1000') return except ValueError: self.value_error_handling() self.l_edit.setText('1000') return try: d_value_temp = float(self.d_edit.text()) if d_value_temp < 0: self.value_error_handling() self.d_edit.setText('2') return except ValueError: self.value_error_handling() self.d_edit.setText('2') return try: w_value_temp = float(self.w_edit.text()) if w_value_temp < 0: self.value_error_handling() self.w_edit.setText('600') return except ValueError: self.value_error_handling() self.w_edit.setText('600') return # Convert to SI Units # l_value_temp: input (mm) -> m l_value_temp = l_value_temp * 1e-3 # d_value_temp: input (mm) -> m d_value_temp = d_value_temp * 1e-3 # w_value_temp: input (nm) -> m w_value_temp = w_value_temp * 1e-9 if d_value_temp > 0.01 * l_value_temp: self.value_error_handling(is_l_small=True) self.reset_input() return # Fresnel number requirement # d^2/(l*w) >~ 1 # Ask for new inputs if this is requirement is not met # and inform them about this problem fresnel_num = (d_value_temp ** 2) / (l_value_temp * w_value_temp) if fresnel_num < 0.95: self.value_error_handling(is_fresnel=True) self.reset_input() return self.l_value = l_value_temp self.d_value = d_value_temp self.w_value = w_value_temp self.warning_page() @pyqtSlot(name='reset_on_click') def reset_on_click(self): self.reset_input() @pyqtSlot(name='proceed_on_click') def proceed_on_click(self): instance = Poisson(lamda=self.w_value, rad=(self.d_value / 2), L=self.l_value) matrix = instance.poiss() self.matrix_plotting(matrix) def reset_input(self): self.l_edit.setText('1000') self.d_edit.setText('2') self.w_edit.setText('600') def value_error_handling(self, is_fresnel=False, is_l_small=False): if is_fresnel: self.fresnel_help_page() elif is_l_small: self.error_dialog.setWindowTitle('ERROR!!!') self.error_dialog.showMessage(self.l_MESSAGE) else: # This part will be trigger if error is not specified. # Which means value errors in this case. self.error_dialog.setWindowTitle('ERROR!!!') self.error_dialog.showMessage(self.ERROR_MESSAGE) def matrix_plotting(self, matrix): f1, ax1 = plt.subplots() ax1.imshow(matrix, interpolation='none', cmap='gray') ax1.axis('off') ax1.set_title('The Simulation of Poisson Spot on the Screen of Side Length 16 mm') f1.text(0.5, 0, self.plot_info % (self.l_value, self.d_value * 1e3, self.w_value * 1e9), verticalalignment='baseline') f1.show() filename = 'poisson_spot_%d_%d_%d.eps' % (self.l_value, self.d_value * 1e3, self.w_value * 1e9) f1.savefig(filename) if __name__ == '__main__': app = QApplication(sys.argv) ex = Gui() sys.exit(app.exec_())

[ "renjie.j.zhu@gmail.com" ]

renjie.j.zhu@gmail.com

2b37f66501969b13276ac35249571598deb4ca26

5bd9a1e97c6d02cf3cf65fa35bcdfaa8c3df3c03

/Python_API/Illumina_NexteraXT/nexteraXT_dna_library_prep_part4.ot2.py

685e25958cfa0ff3f784f263eb3b00a12f7a2d4d

[]

no_license

YazBraimah/OT2_protocols

40bccabc0adf8b4133eaac7634388e1d8e6fb667

663cae57f590cff30a004f9f0ba5414a38e88e91

refs/heads/master

2020-12-13T15:33:20.201307

2020-09-10T02:29:29

234,459,699

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py

from opentrons import labware, instruments """ Pool Libraries """ # labware setup in_plate = labware.load('96-PCR-flat', '1') tuberack = labware.load('tube-rack-2ml', '2') def run_custom_protocol( number_of_samples: int=24, number_of_pools: int=1, pool_volume: int=5 ): total_tips = number_of_samples * number_of_pools total_tiprack = total_tips // 96 + (1 if total_tips % 96 > 0 else 0) tipracks10 = [labware.load('tiprack-10ul', slot) for slot in ['3', '4', '5', '6', '7', '8', '9', '10', '11'][ :total_tiprack]] p10 = instruments.P10_Single( mount='right', tip_racks=tipracks10) if number_of_samples <= 24: input = [well for col in in_plate.cols('1', to='6') for well in col.wells('A', to='D')][:number_of_samples] else: input = [well for well in in_plate.wells()][:number_of_samples] # Transfer each library to pooling tube(s) for tube in tuberack.wells(0, length=number_of_pools): p10.transfer(pool_volume, input, tube, new_tip='always') run_custom_protocol(**{'number_of_samples': 24, 'number_of_pools': 1, 'pool_volume': 5})

[ "yahmed@syr.edu" ]

yahmed@syr.edu

f9bca7acd955016867e38b15e083abc468e59751

71982e1f40d20a03e8de696e4600f0bc96134762

/1_interpolation/cmain.py

5a3160bfe43e759bf9a4a0f4ab0a6422fadc3982

[]

no_license

amaliestokholm/numeric

cba762618d3aedd28e742f64b881346d1ff49ca2

310ef38bd79badff37c27f9070dbb00441011394

refs/heads/master

2021-01-18T22:54:05.772793

2017-06-30T07:06:12

87,079,258

0

null

UTF-8

Python

false

803

py

import numpy as np from interp import interpolation as interp # Define start and end point and the number of points in the function (n) and in the interpolation (N) n = 50 N = 100 start = 0 end = 4 * np.pi # Make input -- as an example, the cosine function is used. x = np.linspace(start, end, n) y = np.cos(x) z = np.linspace(start, end, N) y_dev = - np.sin(x) y_int = np.sin(x) # Initialize the class i_cos = interp(x, y) # Interpolate s_cspl, s_csplint, s_cspldev = i_cos.cspline(z) # Print the values in order to save the stdout for i in range(N): if i < n: print('%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s' % (z[i], s_cspl[i], s_csplint[i], s_cspldev[i], x[i], y[i], y_int[i], y_dev[i])) else: print('%s\t%s\t%s\t%s' % (z[i], s_cspl[i], s_csplint[i], s_cspldev[i]))

[ "stokholm@post.au.dk" ]

stokholm@post.au.dk

f329904c13684e820a4b6424c8f5e21dfc8aa986

2d2f2165d33b75e7ed6b30f5b3c93b022b9021a2

/workspaces/migrations/0018_searchresult.py

358a093d3ae0186238ed0ea4fdf65bb055a6ba05

[ "MIT" ]

permissive

angelxehg/tomatoe-chat-api

5634d195601e1b5269e09e4d0ad8a11f8e60fe7b

d22a75d8a15d478491f46ef02e597d2a710107e1

refs/heads/main

2022-12-31T01:31:07.865367

2020-10-22T18:53:12

265,900,917

0

null

UTF-8

Python

false

574

py

# Generated by Django 3.0.7 on 2020-08-19 18:47 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('workspaces', '0017_message'), ] operations = [ migrations.CreateModel( name='SearchResult', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('type', models.CharField(max_length=20)), ('content', models.CharField(max_length=500)), ], ), ]

[ "50889225+angelxehg@users.noreply.github.com" ]

50889225+angelxehg@users.noreply.github.com

10e5fa2f5498659c925e203978736cee5c3b6290

12246c255155df4af076cc75b5085b187d847fb2

/algorithm/algorithm_ evaluator_temp.py

1e0ba18a58bb9824d4ac41837a52c446a35895df

[]

no_license

babakaskari/MachineLearning

e349748fa8fae499faf86ff1ae7e0624fea3d6ff

c948b4b6c889bddbb09d0876a9448324e80a9b06

refs/heads/master

2023-03-12T07:50:28.616330

2021-02-23T12:25:10

315,897,013

0

null

2020-11-25T11:02:10

2020-11-25T09:57:05

Python

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Python

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py

# importing required libraries import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import LabelEncoder, StandardScaler from sklearn.metrics import mean_squared_error from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV from sklearn import preprocessing, metrics from sklearn.neighbors import KNeighborsClassifier from sklearn.linear_model import LogisticRegression from sklearn.ensemble import RandomForestClassifier, VotingClassifier from sklearn.utils import shuffle from sklearn.base import BaseEstimator, RegressorMixin # from xgboost import XGBRegressor from sklearn.svm import SVC from sklearn.tree import DecisionTreeClassifier from sklearn.experimental import enable_hist_gradient_boosting from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.ensemble import StackingClassifier from sklearn.ensemble import GradientBoostingClassifier from sklearn.ensemble import AdaBoostClassifier from sklearn.ensemble import BaggingClassifier from sklearn.ensemble import ExtraTreesClassifier from sklearn.ensemble import RandomTreesEmbedding import warnings warnings.filterwarnings('ignore') # preprocessing # ## ########################### df = pd.read_csv("../dataset/Acoustic Logger Data.csv") df1 = df.loc[df["LvlSpr"] == "Lvl"] df3 = df.loc[df["LvlSpr"] == "Spr"] df2 = pd.melt(df1, id_vars=['LvlSpr', 'ID'], value_vars=df.loc[:0, '02-May':].columns.values.tolist(), var_name='Date') df4 = pd.melt(df3, id_vars=['LvlSpr', 'ID'], value_vars=df.loc[:0, '02-May':].columns.values.tolist(), var_name='Date') df5 = pd.merge(df2, df4, on= ['ID', 'Date'], suffixes=("_Lvl", "_Spr")) df6 = df5.drop(['LvlSpr_Lvl', 'LvlSpr_Spr'], axis=1).dropna() df6['Date'] = pd.to_datetime(df6['Date'], format='%d-%b') df6['Date'] = df6['Date'].dt.strftime('%d-%m') df7 = pd.read_csv("../dataset/Leak Alarm Results.csv") df7['Date Visited'] = pd.to_datetime(df7['Date Visited'], format='%d/%m/%Y') df7['Date Visited'] = df7['Date Visited'].dt.strftime('%d-%m') df7 = df7.rename(columns={'Date Visited': 'Date'}) df8 = pd.merge(df6, df7, on=['ID', 'Date'], how='left') df8 = df8.sort_values(['Leak Alarm', 'Leak Found']).reset_index(drop=True) # df8["Leak Alarm"] = df8["Leak Alarm"].fillna(-1) # df8["Leak Found"] = df8["Leak Found"].fillna(-1) dataset = df8 ###################################################### Delete these row indexes from dataFrame indexNames = dataset[dataset['Leak Found'] == 'N-PRV'].index dataset.drop(indexNames, index=None, inplace=True) dataset.reset_index(drop=True, inplace=True) ###################################################### DROPPING LEAK ALARM & LEAK FOUND dataset["Leak Found"].replace(["Y", "N"], [1, 0], inplace=True) # dataset["Leak Alarm"].replace(["Y", "N"], [1, 0], inplace=True) dataset = dataset.drop(['Leak Alarm'], axis=1) # ############################################################ Convert Date categorical to numerical # dataset['Date'] = dataset['Date'].str.replace('\D', '').astype(int) date_encoder = preprocessing.LabelEncoder() date_encoder.fit(dataset['Date']) # print(list(date_encoder.classes_)) dataset['Date'] = date_encoder.transform(dataset['Date']) # print(dataset.to_string(max_rows=200)) print("Number of null values in dataset :\n", dataset.isna().sum()) # ##################################################### CORRELATION MATRIX # print(dataset.columns.values) # dataset2 = dataset.drop(["Leak Found"], axis=1) # df = pd.DataFrame(dataset2, columns=['Date', 'ID', 'value_Lvl', 'value_Spr']) # corrMatrix = df.corr() # sns.heatmap(corrMatrix, annot=True, cmap="YlGnBu") # plt.show() # ######################################################################## def evaluate_preds(model, x_true, y_true, y_preds): accuracy = metrics.accuracy_score(y_true, y_preds) precision = metrics.precision_score(y_true, y_preds) recall = metrics.recall_score(y_true, y_preds) f1 = metrics.f1_score(y_true, y_preds) metric_dict = {"accuracy": round(accuracy, 2), "precision": round(precision, 2), "recall": round(recall, 2), "f1": round(f1, 2)} print("Model score is : ", model.score(x_true, y_true)) print(f"Accuracy : {accuracy * 100:.2f}%") print(f"Precision : {precision: .2f}") print(f"Recall : {recall: .2f}") print(f"F1 Score : {f1: .2f}") return metric_dict # ##################################################### SPLIT THE DATASET x_labeled_data = dataset.loc[dataset['Leak Found'].notna()] y_labeled_data = x_labeled_data["Leak Found"] x_labeled_data = x_labeled_data.drop(["Leak Found"], axis=1) # ############################################## standard scaler scaler = StandardScaler() data_scaled = scaler.fit_transform(x_labeled_data) x_train = pd.DataFrame(data_scaled) print("x_train after normalization : ", x_train.head()) print("x_train description after normalization: ", x_labeled_data.describe()) # ############################################################ # x_train = x_train.sample(frac=1) x_unlabeled_data = dataset.loc[dataset['Leak Found'].isna()] y_unlabeled_data = x_unlabeled_data.drop(["Leak Found"], axis=1) x_train, x_test, y_train, y_test = train_test_split(x_labeled_data, y_labeled_data, test_size=0.2, random_state=42) x_train, x_cv, y_train, y_cv = train_test_split(x_train, y_train, stratify=y_train, test_size=0.2) rfc = RandomForestClassifier() rfc.fit(x_train, y_train) rfc_pred = rfc.predict(x_test) # gs_rfc_matrices = evaluate_preds(y_test, gs_rfc_pred) rfc_new_matrices = evaluate_preds(rfc, x_test, y_test, rfc_pred) # ############################################################ """ model_factory = [ LogisticRegression(), KNeighborsClassifier(), BaggingClassifier(n_estimators=100), XGBRegressor(nthread=1), GradientBoostingRegressor(), RandomForestClassifier(), BayesianRidge(), ] """ estimators = [ ('rf', RandomForestClassifier(n_estimators=10, random_state=42)), ] model_factory = [ RandomForestClassifier(), AdaBoostClassifier(), BaggingClassifier(), ExtraTreesClassifier(), GradientBoostingClassifier(), # RandomTreesEmbedding(), StackingClassifier(estimators=estimators), VotingClassifier(estimators=estimators), HistGradientBoostingClassifier(), ] # ####################################### for model in model_factory: model.seed = 42 num_folds = 3 scores = cross_val_score(model, x_train, y_train, cv=num_folds, scoring='neg_mean_squared_error') # print("Score is : ", scores) score_description = " %0.2f (+/- %0.2f)" % (np.sqrt(scores.mean() * -1), scores.std() * 2) print('{model:25} CV-5 RMSE: {score}'.format( model=model.__class__.__name__, score=score_description )) # //////////////////////////////// gs_rfc_grid = { 'n_estimators': [10, 50, 100, 200, 500], # 'max_depth': ['None', 20, 30], 'max_features': ['auto', 'sqrt', 'log2'], 'min_samples_split': [6], 'class_weight': ['balanced', 'balanced_subsample'], # 'max_iter': np.arange(100, 2000, 100), 'min_samples_leaf': [1, 2] } rfc = RandomForestClassifier() print("RandomForestClassifier parameters : ", rfc.get_params()) gs_rfc = GridSearchCV(estimator=rfc, param_grid=gs_rfc_grid, cv=5, n_jobs=-1, verbose=2,) gs_rfc.fit(x_train, y_train) print("RandomForestClassifier best parameter is : ", gs_rfc.best_params_) # print("RandomForestClassifier best parameter is : ", gs_rfc.best_params_['class_weight']) # print("RandomForestClassifier score : ", gs_rfc.score(x_test)) gs_rfc_pred = gs_rfc.predict(x_test) print("RandomForestClassifier Prediction : ", gs_rfc_pred) # gs_rfc_matrices = evaluate_preds(y_test, gs_rfc_pred) gs_rfc_matrices = evaluate_preds(gs_rfc, x_test, y_test, gs_rfc_pred) compare_matrices = pd.DataFrame({ "rfc_new": rfc_new_matrices, "grid search": gs_rfc_matrices, }) compare_matrices.plot.bar(rot=0) plt.show() # ################################################ AdaBoostClassifier starts abc = AdaBoostClassifier(n_estimators=100, random_state=0) abc.fit(x_train, y_train) clf_pred = abc.predict(x_test) print("AdaBoostClassifier Prediction : ", clf_pred) # gs_rfc_matrices = evaluate_preds(y_test, gs_rfc_pred) clf_matrices = evaluate_preds(abc, x_test, y_test, clf_pred) print("RandomForestClassifier parameters : ", rfc.get_params()) # ################################################ AdaBoostClassifier ends

[ "Siamak" ]

Siamak

76fcee228ba1a24b432c4535e92d0ceb516585e1

b37105847106177c4860a80cde3dda92ac70070b

/auth/app/db/database.py

98d4a9235bf24659b9d2148c996b02f3acb14647

[]

no_license

webclinic017/fastapi_with_nats

8a2cdc5dc68c5d64d9d2fca29f3a6a568699bce7

5b59472049fe3b6db050f01232148987a862235a

refs/heads/main

2023-03-30T04:29:42.713553

2021-03-30T13:31:41

null

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null

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Python

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py

import os import configparser from sqlalchemy import create_engine from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker # path = os.path.join(os.path.dirname(__file__), '..', 'config', 'config.ini') # config_ini = configparser.ConfigParser() # config_ini.read(path) # POSTGRES_SERVER = config_ini['POSTGRES']['POSTGRES_SERVER'] # POSTGRES_DB = config_ini['POSTGRES']['POSTGRES_DB'] # POSTGRES_SERVER = os.getenv('POSTGRES_SERVER') # POSTGRES_PORT = config_ini['POSTGRES']['POSTGRES_PORT'] # POSTGRES_USER = config_ini['POSTGRES']['POSTGRES_USER'] # POSTGRES_PASSWORD = config_ini['POSTGRES']['POSTGRES_PASSWORD'] SQLALCHAMY_DATABASE_URL = os.getenv('POSTGRES_URI') # SQLALCHAMY_DATABASE_URL = f'postgresql://{POSTGRES_USER}:{POSTGRES_PASSWORD}@/{POSTGRES_DB}?host={POSTGRES_SERVER}&port={POSTGRES_PORT}' engine = create_engine(SQLALCHAMY_DATABASE_URL, echo=True) Base = declarative_base() SessionLocal = sessionmaker(bind=engine, autocommit=False, autoflush=False,) def get_db(): db = SessionLocal() try: yield db finally: db.close()

[ "i.norihi.472@gmail.com" ]

i.norihi.472@gmail.com

0ef076eaa70916b75c905f4b777a1659d48397c3

4f41e5842f031739f8153ecac202c4849f849d37

/API/api_run.py

e44913214424853e82fc523312cf4e86d90af657

[]

no_license

khirod-datascience/Rah-model-api

e40fd2dff4473d9aaacddbd5ade3534bfd0177a5

2de3e48c9c2119b475937f0180454190a7f9c29d

refs/heads/main

2023-08-31T04:15:58.984547

2021-10-29T14:12:43

null

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null

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py

# -*- coding: utf-8 -*- """ Created on Tue Sep 27 12:21:00 2021 @author: Devdarshan """ from flask import Flask, request from flask_restful import Resource, Api import search_by_definition import fuzzy_wuzzy_Search_by_definition import next_word_predict app = Flask(__name__) api = Api(app) class predict(Resource): def get(self, query): return {'data': search_by_definition.manual_predict(query)} class predict_fuzzywuzzy(Resource): def get(self, query): return {'data': fuzzy_wuzzy_Search_by_definition.search_by_definition_fuzzy_wuzzy(query)} class predict_nextword(Resource): def get(self, query): return {'data': next_word_predict.predict_next(query)} api.add_resource(predict, '/searchByDefinition/<query>') api.add_resource(predict_fuzzywuzzy, '/fuzzyWuzzySearchByDefinition/<query>') api.add_resource(predict_nextword, '/predictNextWord/<query>') if __name__ == '__main__': app.run()

[ "66660324+khirod-datascience@users.noreply.github.com" ]

66660324+khirod-datascience@users.noreply.github.com

272726f297893f2cbace10bfa1026de380ddc5d4

81724f74e7603476c887dbdc2bfe2e913ee50334

/Analysis/config/simple_analysis.py

84a821f80753410d643f46358e8e686ad5c2e51a

[]

no_license

michelif/diphotons

7fa6ccc826f212f7e9900f870cc85a7e13738893

6102de180e1c1743aca6df6b197aa5aa459dc68f

refs/heads/master

2020-06-16T18:36:39.747819

2017-09-05T15:42:15

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#!/usr/bin/env cmsRun import FWCore.ParameterSet.Config as cms import FWCore.Utilities.FileUtils as FileUtils process = cms.Process("Analysis") process.load("diphotons.Analysis.highMassDiPhotons_cfi") from flashgg.MicroAOD.flashggPreselectedDiPhotons_cfi import flashggPreselectedDiPhotons from flashgg.Taggers.diphotonDumper_cfi import diphotonDumper import flashgg.Taggers.dumperConfigTools as cfgTools diphotonDumper.dumpTrees = False diphotonDumper.dumpWorkspace = False diphotonDumper.quietRooFit = True diphotonDumper.src=cms.InputTag("hcic4DiPhotons") cfgTools.addCategories(diphotonDumper, [## cuts are applied in cascade ("EBHighR9","max(abs(leadingPhoton.superCluster.eta),abs(subLeadingPhoton.superCluster.eta))<1.4442" "&& min(leadingPhoton.r9,subLeadingPhoton.r9)>0.94",0), ("EBLowR9","max(abs(leadingPhoton.superCluster.eta),abs(subLeadingPhoton.superCluster.eta))<1.4442",0), ("EEHighR9","min(leadingPhoton.r9,subLeadingPhoton.r9)>0.94",0), ("EELowR9","1",0), ], variables=["mass", "leadPt :=leadingPhoton.pt", "subleadPt :=subLeadingPhoton.pt", "leadCShapeMVA :=leadingPhoton.userFloat('cShapeMVA')", "subleadCShapeMVA :=subLeadingPhoton.userFloat('cShapeMVA')", "minR9 :=min(leadingPhoton.r9,subLeadingPhoton.r9)", "maxEta :=max(abs(leadingPhoton.superCluster.eta),abs(subLeadingPhoton.superCluster.eta))", "leadBlockChIso := leadingPhotonView.pfChIso03WrtChosenVtx", "leadBlockPhoIso := leadingPhotonView.pfPhoIso03", "leadRndConeChIso := leadingPhotonView.extraChgIsoWrtChoosenVtx('rnd03')", "leadRndConePhoIso:= leadingPhoton.extraPhoIso('rnd03')", "leadChIso := leadingPhoton.egChargedHadronIso", "leadChIso := leadingPhoton.egChargedHadronIso", "leadPhoIso := leadingPhoton.egPhotonIso", "leadNeutIso := leadingPhoton.egNeutralHadronIso", "leadHoE := leadingPhoton.hadTowOverEm", "leadSigmaIeIe := (?leadingPhoton.r9>0.8||leadingPhoton.egChargedHadronIso<20||leadingPhoton.egChargedHadronIso/leadingPhoton.pt<0.3?leadingPhoton.full5x5_sigmaIetaIeta:leadingPhoton.sigmaIetaIeta)", "leadPixSeed := leadingPhoton.hasPixelSeed", "leadPassEleVeto := leadingPhoton.passElectronVeto", "subleadBlockChIso := subLeadingPhotonView.pfChIso03WrtChosenVtx", "subleadBlockPhoIso := subLeadingPhotonView.pfPhoIso03", "subleadRndConeChIso := subleadingPhotonView.extraChgIsoWrtChoosenVtx('rnd03')", "subleadRndConePhoIso:= subleadingPhoton.extraPhoIso('rnd03')", "subleadChIso := subLeadingPhoton.egChargedHadronIso", "subleadPhoIso := subLeadingPhoton.egPhotonIso", "subleadNeutIso := subLeadingPhoton.egNeutralHadronIso", "subleadHoE := subLeadingPhoton.hadTowOverEm", "subleadSigmaIeIe := (?subLeadingPhoton.r9>0.8||subLeadingPhoton.egChargedHadronIso<20||subLeadingPhoton.egChargedHadronIso/subLeadingPhoton.pt<0.3?subLeadingPhoton.full5x5_sigmaIetaIeta:subLeadingPhoton.sigmaIetaIeta)", "subleadPixSeed := subLeadingPhoton.hasPixelSeed", "subleadPassEleVeto := subLeadingPhoton.passElectronVeto", ], histograms=["mass>>mass(1500,0,15000)", "leadPt>>leadPt(145,100,3000)", "subleadPt>>subleadPt(145,100,3000)", "leadBlockChIso>>leadBlockChIso(60,-10,50)", "leadBlockPhoIso>>leadBlockPhoIso(60,-10,50)", "leadChIso>>leadChIso(60,-10,50)", "leadPhoIso>>leadPhoIso(60,-10,50)", "leadNeutIso>>leadNeutIso(60,-10,50)", "leadHoE>>leadHoE(40,0,0.2)", "leadSigmaIeIe>>leadSigmaIeIe(50,0,5.e-2)", "leadPixSeed>>leadPixSeed(2,-0.5,1.5)", "subleadPassEleVeto>>subleadPassEleVeto(2,-0.5,1.5)", "subleadBlockChIso>>subleadBlockChIso(60,-10,50)", "subleadBlockPhoIso>>subleadBlockPhoIso(60,-10,50)", "subleadChIso>>subleadChIso(60,-10,50)", "subleadPhoIso>>subleadPhoIso(60,-10,50)", "subleadNeutIso>>subleadNeutIso(60,-10,50)", "subleadHoE>>subleadHoE(40,0,0.2)", "subleadSigmaIeIe>>subleadSigmaIeIe(50,0,5.e-2)", "subleadPixSeed>>subleadPixSeed(2,-0.5,1.5)", "subleadPassEleVeto>>subleadPassEleVeto(2,-0.5,1.5)", "subleadPt:leadPt>>ptSubVsLead(145,100,3000:145,100,3000)", "minR9>>minR9(110,0,1.1)", "maxEta>>maxEta(250,0,2.5)" ] ) process.load("FWCore.MessageService.MessageLogger_cfi") process.load("Configuration.StandardSequences.GeometryDB_cff") process.load("Configuration.StandardSequences.MagneticField_cff") process.load("Configuration.StandardSequences.FrontierConditions_GlobalTag_cff") process.GlobalTag.globaltag = 'POSTLS170_V5::All' process.maxEvents = cms.untracked.PSet( input = cms.untracked.int32(-1) ) process.MessageLogger.cerr.FwkReport.reportEvery = cms.untracked.int32( 1000 ) process.source = cms.Source("PoolSource", fileNames=cms.untracked.vstring( ) ) process.TFileService = cms.Service("TFileService", fileName = cms.string("test.root") ) process.trigger=diphotonDumper.clone() process.id=diphotonDumper.clone() process.triggerMva=diphotonDumper.clone(src=cms.InputTag("hmvaDiPhotons")) process.mva=diphotonDumper.clone(src=cms.InputTag("hmvaDiPhotons")) process.egid=diphotonDumper.clone(src=cms.InputTag("egLooseDiPhotons")) process.kin=diphotonDumper.clone(src=cms.InputTag("kinDiPhotons")) process.kin.dumpTrees = True process.isoKinDiphotons = process.tmpKinDiPhotons.clone(src="kinDiPhotons", cut="leadingPhoton.userFloat('genIso') < 10. && subLeadingPhoton.userFloat('genIso') < 10.") process.isohCic4Diphotons = process.tmpKinDiPhotons.clone(src="hcic4DiPhotons", cut="leadingPhoton.userFloat('genIso') < 10. && subLeadingPhoton.userFloat('genIso') < 10.") process.isohmvaDiphotons = process.tmpKinDiPhotons.clone(src="hmvaDiPhotons", cut="leadingPhoton.userFloat('genIso') < 10. && subLeadingPhoton.userFloat('genIso') < 10.") process.isoKin=diphotonDumper.clone(src=cms.InputTag("isoKinDiphotons")) process.isoId=diphotonDumper.clone(src=cms.InputTag("isohCic4Diphotons")) process.isoMva=diphotonDumper.clone(src=cms.InputTag("isohmvaDiphotons")) process.nonIsoKinDiphotons = process.tmpKinDiPhotons.clone(src="kinDiPhotons", cut="leadingPhoton.userFloat('genIso') >= 10. || subLeadingPhoton.userFloat('gensIso') >= 10.") process.nonIsohCic4Diphotons = process.tmpKinDiPhotons.clone(src="hcic4DiPhotons", cut="leadingPhoton.userFloat('genIso') >= 10. || subLeadingPhoton.userFloat('gensIso') >= 10.") process.nonIsohmvaDiphotons = process.tmpKinDiPhotons.clone(src="hmvaDiPhotons", cut="leadingPhoton.userFloat('genIso') >= 10. || subLeadingPhoton.userFloat('gensIso') >= 10.") process.nonIsoKin=diphotonDumper.clone(src=cms.InputTag("nonIsoKinDiphotons")) process.nonIsoId=diphotonDumper.clone(src=cms.InputTag("nonIsohCic4Diphotons")) process.nonIsoMva=diphotonDumper.clone(src=cms.InputTag("nonIsohmvaDiphotons")) process.load('HLTrigger.HLTfilters.hltHighLevel_cfi') process.hltHighLevel.HLTPaths = ["HLT_DoublePhoton85*","HLT_Photon250_NoHE*"] process.watchDog = cms.EDAnalyzer("IdleWatchdog", minIdleFraction=cms.untracked.double(0.5), tolerance=cms.untracked.int32(10), checkEvery=cms.untracked.int32(100), ) process.p1 = cms.Path( ((process.tmpKinDiPhotons*process.kinDiPhotons) * (process.kin + (process.isoKinDiphotons+process.nonIsoKinDiphotons)*(process.isoKin+process.nonIsoKin) + process.egLooseDiPhotons*process.egid + process.hmvaDiPhotons * (process.mva + (process.isohmvaDiphotons+process.nonIsohmvaDiphotons)*(process.isoMva +process.nonIsoMva) + (process.hltHighLevel*process.triggerMva) ) + process.hcic4DiPhotons * (process.id + (process.isohCic4Diphotons+process.nonIsohCic4Diphotons)*(process.isoId +process.nonIsoId) + (process.hltHighLevel*process.trigger) ) )) * process.watchDog ) from diphotons.MetaData.JobConfig import customize customize.setDefault("maxEvents",100) customize.setDefault("targetLumi",1.e+3) customize(process)

[ "pasquale.musella@cern.ch" ]

pasquale.musella@cern.ch

53b08521815f7bd672a1d8354b51b3693af42b6a

3af4bc44120806de09e9e9542719d80964f0f7cd

/python/skripte_datensammlung/tweet_extractor.py

3c4eb28861605cbba5c3d78e2d2c15ae6a822fc5

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TimSchmittmann/R-vs-Python

4dfa80e8a97c5651b6675a2e32c5132571e8d19d

4bacfaf236839a912f5a6a323d9756a47ab2d880

refs/heads/master

2020-04-10T09:13:45.422939

2018-12-10T11:20:55

160,929,993

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# -*- coding: utf-8 -*- ''' @author: Tim Schmittmann ''' from __future__ import print_function import twitter import time from langdetect import detect import csv import config import math import sys import subprocess from enum import Enum Mode = Enum('Mode', 'PAST RECENT') # Create an Api instance. api = twitter.Api(consumer_key=config.CONSUMER_KEY, consumer_secret=config.CONSUMER_SECRET, access_token_key=config.ACCESS_TOKEN, access_token_secret=config.ACCESS_TOKEN_SECRET) emoji_cnt_csv_path = "data/manual_settings/emoji_cnt_used_for_twitter_api_requests.csv" tweets_write_csv_base_path = "data/emoji_tweets/tweets_q3_emojis_" tweets_write_csv_ext = ".csv" mode = Mode.RECENT def read_max_id(tweet_csv_filepath, mode=Mode.PAST): max_id = 9999999999999999999 if mode == Mode.PAST: # go into the past starting from oldest tweet in file or starting from most recent if there is no file try: with open(tweet_csv_filepath, 'r', encoding='utf-8', newline='') as csvfile: reader = csv.reader(csvfile, delimiter=';') for row in reader: try: max_id = min(int(row[0]), max_id) except: pass except Exception as e: print(str(e)) return max_id #return False # most recent tweets only def read_min_id(tweet_csv_filepath, mode=Mode.PAST): if mode == Mode.PAST: # go into the past as far as API allows return 0 # go into the past until you find already processed tweets # in that case min_id is the highest tweet id found in the csv min_id = 0 try: with open(tweet_csv_filepath, 'r', encoding='utf-8', newline='') as csvfile: reader = csv.reader(csvfile, delimiter=';') for row in reader: try: min_id = max(int(row[0]), min_id) except: pass except Exception as e: print(str(e)) return min_id def get_all_emojis(emoji_cnt_csv_path): emojis = [] with open(emoji_cnt_csv_path, 'r', encoding='utf-8', newline='') as csvfile: reader = csv.reader(csvfile, delimiter=';') for row in reader: emojis.append(row[0]) return emojis def init_and_exec_requests_and_writes(tweets_write_csv_path, query_emojis, mode=Mode.PAST): max_id = read_max_id(tweets_write_csv_path, mode) min_id = read_min_id(tweets_write_csv_path, mode) print("MaxID: "+str(max_id)) print("MinID: "+str(min_id)) fieldnames = [ 'tweet_id', 'tweet_full_text', 'tweet_created_at', 'tweet_is_quote_status', 'tweet_retweet_count', 'tweet_favorite_count', 'tweet_favorited', 'tweet_retweeted', 'tweet_possibly_sensitive', 'user_id', 'user_description', 'user_followers_count', 'user_friends_count', 'user_listed_count', 'user_favourites_count', 'user_statuses_count', ] with open(tweets_write_csv_path, 'a', encoding='utf-8', newline='', buffering=1) as csvfile: writer = csv.DictWriter(csvfile, delimiter=';', fieldnames=fieldnames, quoting=csv.QUOTE_MINIMAL) writer.writeheader() request_tweets(min_id, max_id, query_emojis, lambda search_result: write_tweet(writer, search_result)) def build_request_query(min_id, max_id, query_emojis): q = "%20OR%20".join(query_emojis) query = "lang=de&q="+q+"%20-filter%3Aretweets%20-filter%3Areplies&result_type=recent&count=100&tweet_mode=extended" #query = "lang=de&q=a%20OR%20b%20OR%20c%20OR%20d%20OR%20e%20OR%20f%20OR%20g%20OR%20h%20OR%20i%20OR%20j%20OR%20k%20OR%20l%20OR%20m%20OR%20n%20OR%20o%20OR%20p%20OR%20q%20OR%20r%20OR%20s%20OR%20t%20OR%20u%20OR%20v%20OR%20w%20OR%20x%20OR%20y%20OR%20z%20-filter%3Aretweets%20-filter%3Areplies&result_type=recent&count=100" if max_id is not False: query += "&max_id="+str(max_id) if min_id is not False: query += "&since_id="+str(min_id) return query def request_tweets(min_id, max_id, query_emojis, write_cb, nr_of_requests = 999999): for i in range(nr_of_requests): print("Request "+str(i)) query = build_request_query(min_id, max_id, query_emojis) print(query) try: search_result = api.GetSearch(raw_query=query) if max_id is not False: # Move further into the past with each request max_id = search_result[-1].id - 1 if(len(search_result) == 0): print("Reached already processed tweets") break; # elif min_id is not False: # Get the most up to date tweets per request # min_id = search_result[0].id + 1 #print(max_id) #print([s for s in search_result]) write_cb(search_result) time.sleep(5) except Exception as e: print(str(e)) if str(e) == 'list index out of range': break; time.sleep(30) def write_tweet(writer, search_result): for tweet_dict in search_result: tweet_dict = tweet_dict.AsDict() try: if detect(tweet_dict['full_text']) != 'de': continue except: print("This text throws an error:", tweet_dict['full_text']) continue tweet = {} if 'id' in tweet_dict: tweet['tweet_id'] = tweet_dict['id'] if 'full_text' in tweet_dict: tweet['tweet_full_text'] = tweet_dict['full_text'] if 'created_at' in tweet_dict: tweet['tweet_created_at'] = tweet_dict['created_at'] if 'is_quote_status' in tweet_dict: tweet['tweet_is_quote_status'] = tweet_dict['is_quote_status'] if 'retweet_count' in tweet_dict: tweet['tweet_retweet_count'] = tweet_dict['retweet_count'] if 'favorite_count' in tweet_dict: tweet['tweet_favorite_count'] = tweet_dict['favorite_count'] if 'favorited' in tweet_dict: tweet['tweet_favorited'] = tweet_dict['favorited'] if 'retweeted' in tweet_dict: tweet['tweet_retweeted'] = tweet_dict['retweeted'] if 'possibly_sensitive' in tweet_dict: tweet['tweet_possibly_sensitive'] = tweet_dict['possibly_sensitive'] if 'id' in tweet_dict['user']: tweet['user_id'] = tweet_dict['user']['id'] if 'description' in tweet_dict['user']: tweet['user_description'] = tweet_dict['user']['description'] if 'followers_count' in tweet_dict['user']: tweet['user_followers_count'] = tweet_dict['user']['followers_count'] if 'friends_count' in tweet_dict['user']: tweet['user_friends_count'] = tweet_dict['user']['friends_count'] if 'listed_count' in tweet_dict['user']: tweet['user_listed_count'] = tweet_dict['user']['listed_count'] if 'favourites_count' in tweet_dict['user']: tweet['user_favourites_count'] = tweet_dict['user']['favourites_count'] if 'statuses_count' in tweet_dict['user']: tweet['user_statuses_count'] = tweet_dict['user']['statuses_count'] #print(str(tweet['id'])+": "+tweet_dict['created_at']) writer.writerow(tweet) # fieldnames.update(tweet.keys()) # tweets.append(tweet) def extract_inner_tweet_fields(): ''' for key in tweetDict: if key == 'user': for innerKey in tweetDict[key]: tweet[key + "_" + innerKey] = tweetDict[key][innerKey] elif key == 'urls' or key == 'user_mentions': for j in range(len(tweetDict[key])): for innerKey in tweetDict[key][j]: tweet[key + "_" + str(j) + "_" + innerKey] = tweetDict[key][j][innerKey] else: tweet[key] = tweetDict[key] ''' def main(emoji_cnt_csv_path, tweets_write_csv_base_path, tweets_write_csv_ext, mode): emojis = get_all_emojis(emoji_cnt_csv_path) for j in range(0, math.ceil(len(emojis) / 45)): from_emoji = j*45 to_emoji = min((j+1)*45, len(emojis)) print("From emoji "+str(from_emoji)+" to emoji "+str(to_emoji)) query_emojis = emojis[from_emoji:to_emoji] if len(query_emojis) > 0: tweets_write_csv_path = tweets_write_csv_base_path+str(from_emoji)+"-"+str(to_emoji)+tweets_write_csv_ext init_and_exec_requests_and_writes(tweets_write_csv_path, query_emojis, mode) if __name__ == '__main__': while True: main(emoji_cnt_csv_path, tweets_write_csv_base_path, tweets_write_csv_ext, mode)

[ "tim.schmittmann@gmx.de" ]

tim.schmittmann@gmx.de

ae54176dcfa773ec0636d58d7dd0a27b87fb7233

e7a8ab3898ef331ca11c63808c4d9449794308c2

/Leetcode/637-average_levels_binary_tree.py

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permissive

EdwaRen/Competitve-Programming

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refs/heads/master

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# Definition for a binary tree node. # class TreeNode(object): # def __init__(self, val=0, left=None, right=None): # self.val = val # self.left = left # self.right = right import collections class Solution(object): def averageOfLevels(self, root): """ :type root: TreeNode :rtype: List[float] """ level = deque() level.append(root) res = [] while level: n = len(level) level_sum = 0 for i in range(n): cur = level.popleft() level_sum += cur.val if cur.right: level.append(cur.right) if cur.left: level.append(cur.left) res.append(level_sum / float(n)) return res

[ "eddie.ren.2013@gmail.com" ]

eddie.ren.2013@gmail.com

7cbbea8b7e51984f9d29d90a14d37aec9d670e29

51b0cc866c1e24e09f67d947600ce0b070df1b76

/problems/problem_005.py

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[]

no_license

zzggbb/euler-problems

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a7e9da92b0b40a00cf0ffeea4b85a69c3b3f66df

refs/heads/master

2020-03-26T10:30:24.163715

2013-10-27T07:06:33

null

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null

UTF-8

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py

def getpassed(min,max): passed = 0 number = max while passed != 20: for i in range(min,(max + 1)): if number % i == 0: passed = passed + 1 #elif number % i != 0: else: number = number + 1 passed = 0 print ("The number " + str(number) + " is the lowest number" "divisible by all numbers from " + str(min) + " to " + str(max))

[ "zbradley96@gmail.com" ]

zbradley96@gmail.com

94f35b705932d2f07602d05cfde485af977a3f1e

d18d057168b5f856d9f2b37310e58e6c41f1387b

/streamz_ext/graph.py

550910ce3089851126d03d11c19c065f638ad7f1

[]

no_license

Zatsepingroup/streamz_ext

9cdfb440fa1152db690de906697956f0d10a0356

ec604c4dd8486070613302414d318e6478e80005

refs/heads/master

2020-03-26T21:28:07.088748

2018-08-15T20:26:16

null

0

null

UTF-8

Python

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py

from weakref import ref import matplotlib.pyplot as plt import networkx as nx from grave import plot_network from streamz import combine_latest from streamz.graph import * from streamz.graph import _clean_text from streamz_ext import Stream def create_graph_nodes(node, graph, prior_node=None, pc=None): """Create graph from a single node, searching up and down the chain with weakrefs to nodes in the graph nodes Parameters ---------- node: Stream instance graph: networkx.DiGraph instance """ edge_kwargs = {} if node is None: return t = hash(node) graph.add_node( t, label=_clean_text(str(node)), shape=node._graphviz_shape, orientation=str(node._graphviz_orientation), style=node._graphviz_style, fillcolor=node._graphviz_fillcolor, node=ref(node), ) if prior_node: tt = hash(prior_node) # If we emit on something other than all the upstreams vis it if ( isinstance(node, combine_latest) and node.emit_on != node.upstreams and prior_node in node.emit_on ): edge_kwargs["style"] = "dashed" if graph.has_edge(t, tt): return if pc == "downstream": graph.add_edge(tt, t) else: graph.add_edge(t, tt) for nodes, pc in zip( [list(node.downstreams), list(node.upstreams)], ["downstream", "upstreams"], ): for node2 in nodes: if node2 is not None: create_graph_nodes(node2, graph, node, pc=pc) def readable_graph(node, source_node=False): """Create human readable version of this object's task graph. Parameters ---------- node: Stream instance A node in the task graph """ import networkx as nx g = nx.DiGraph() if source_node: create_edge_label_graph(node, g) else: create_graph(node, g) mapping = {k: "{}".format(g.node[k]["label"]) for k in g} idx_mapping = {} for k, v in mapping.items(): if v in idx_mapping.keys(): idx_mapping[v] += 1 mapping[k] += "-{}".format(idx_mapping[v]) else: idx_mapping[v] = 0 gg = {k: v for k, v in mapping.items()} rg = nx.relabel_nodes(g, gg, copy=True) return rg, gg class LiveGraphPlot(object): """Live plotting of the streamz graph status""" def __init__( self, graph, layout="spectral", node_style=None, edge_style=None, node_label_style=None, edge_label_style=None, ax=None, force_draw=False, ): """ Parameters ---------- graph : nx.Graph The graph to be plotted layout : string or callable, optional, default: "spectral" Specifies the type of layout to use for plotting. It must be one of "spring", "circular", "random", "kamada_kawai", "shell", "spectral", or a callable. node_style : dict or callable, optional The style parameters for nodes, if callable must return a dict edge_style : dict or callable, optional The style parameters for edges, if callable must return a dict node_label_style : dict or callable, optional The style parameters for node labels, if callable must return a dict edge_label_style : dict or callable, optional The style parameters for edge labels, if callable must return a dict ax : matplotlib axis object, optional The axis to plot on. If not provided produce fig and ax internally. force_draw : bool, optional If True force drawing every time graph is updated, else only draw when idle. Defaults to False """ self.force_draw = force_draw if edge_label_style is None: edge_label_style = {} if node_label_style is None: node_label_style = {} if edge_style is None: edge_style = {} if node_style is None: node_style = {} self.node_style = node_style self.edge_style = edge_style self.node_label_style = node_label_style self.edge_label_style = edge_label_style self.layout = layout self.graph = graph if not ax: fig, ax = plt.subplots() self.ax = ax self.art = plot_network( self.graph, node_style=self.node_style, edge_style=self.edge_style, node_label_style=self.node_label_style, edge_label_style=self.edge_label_style, layout=self.layout, ax=self.ax, ) self.update() def update(self): """Update the graph plot""" # TODO: reuse the current node positions (if no new nodes added) self.art._reprocess() if self.force_draw: plt.draw() else: self.ax.figure.canvas.draw_idle() def decorate_nodes(graph, update_decorator=None, emit_decorator=None): """Decorate node methods for nodes in a graph Parameters ---------- graph : nx.Graph instance The graph who's nodes are to be updated update_decorator : callable, optional The function to wrap the update method. If None no decorator is applied. emit_decorator : callable, optional The function to wrap the _emit method. If None no decorator is applied. Returns ------- """ for n, attrs in graph.nodes.items(): nn = attrs["node"]() if nn.__class__ != Stream: if update_decorator: nn.update = update_decorator(attrs["node"]().update) if emit_decorator: nn._emit = emit_decorator(attrs["node"]()._emit) status_color_map = {"running": "yellow", "waiting": "green", "error": "red"} def node_style(node_attrs): d = { "size": 2000, "color": status_color_map.get(node_attrs.get("status", "NA"), "k"), } return d def run_vis(node, source_node=False, **kwargs): """Start the visualization of a pipeline Parameters ---------- node : Stream instance A node in the pipeline source_node : bool If True the input node is the source node and numbers the graph edges accordingly, defaults to False kwargs : Any kwargs passed to LiveGraphPlot Returns ------- """ g, gg = readable_graph(node, source_node=source_node) fig, ax = plt.subplots() gv = LiveGraphPlot(g, ax=ax, **kwargs) def update_decorator(func): node = hash(func.__self__) node_name = gg[node] # @wraps def wrapps(*args, **kwargs): g.nodes[node_name]["status"] = "running" gv.update() try: ret = func(*args, **kwargs) except Exception as e: g.nodes[node_name]["status"] = "error" gv.update() raise e else: g.nodes[node_name]["status"] = "waiting" gv.update() return ret return wrapps def emit_decorator(func): node = hash(func.__self__) node_name = gg[node] def wrapps(*args, **kwargs): g.nodes[node_name]["status"] = "waiting" gv.update() try: ret = func(*args, **kwargs) except Exception as e: g.nodes[node_name]["status"] = "error" gv.update() raise e else: return ret return wrapps node_g = nx.DiGraph() create_graph_nodes(node, node_g) decorate_nodes(node_g, update_decorator, emit_decorator) return gv

[ "cjwright4242@gmail.com" ]

cjwright4242@gmail.com

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/movieproject/movieproject/urls.py

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[]

no_license

Aiswaryakpda/movie

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"""movieproject URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.conf import settings from django.contrib import admin from django.urls import path,include from django.conf.urls.static import static urlpatterns = [ path('admin/', admin.site.urls), path('',include("movieapp.urls")) ] if settings.DEBUG: urlpatterns += static(settings.STATIC_URL,document_root=settings.STATIC_ROOT) urlpatterns += static(settings.MEDIA_URL,document_root=settings.MEDIA_ROOT)

[ "aiswaryakpda@gmail.com" ]

aiswaryakpda@gmail.com

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/peek_plugin_diagram/_private/tuples/GroupDispsTuple.py

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[]

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Synerty/peek-plugin-diagram

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from typing import List, Any from vortex.Tuple import addTupleType, TupleField, Tuple from peek_plugin_diagram._private.PluginNames import diagramTuplePrefix @addTupleType class GroupDispsTuple(Tuple): """ Group Disps Tuple This tuple stores a list of DispGroups that are in the 'ID:dispgroup' grid key in that coord set. """ __tupleType__ = diagramTuplePrefix + "GroupDispsTuple" coordSetId: int = TupleField() # A GridTuple, already encoded and ready for storage in the clients cache encodedGridTuple: str = TupleField()

[ "jarrod.chesney@synerty.com" ]

jarrod.chesney@synerty.com

78d9aa0431e117f3a130b270ab269f30ffc85cbe

f4823aed72eb36944f8aebf1a2f40f1a16f5a27a

/setup.py

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refs/heads/main

2023-02-25T07:05:11.689669

2021-02-02T19:38:30

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# author: Drew Botwinick, Botwinick Innovations # license: 3-clause BSD import setuptools with open("README.md", 'r') as f: readme_txt = f.read() setuptools.setup( name="botwinick_gis", version="0.0.4", author="Drew Botwinick", author_email="foss@drewbotwinick.com", description="GIS support package", long_description=readme_txt, long_description_content_type="text/markdown", url="https://github.com/dbotwinick/python-botwinick-gis", packages=setuptools.find_packages(), install_requires=['numpy', 'pyshp', 'pyproj', 'botwinick_math'], classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'Topic :: Scientific/Engineering :: GIS', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 3', 'License :: OSI Approved :: BSD License', "Operating System :: OS Independent", ], python_requires='>=2.7, >=3.6', )

[ "drew@polymathcoder.com" ]

drew@polymathcoder.com

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/Pendulum-Damping/PendulumDamping.py

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[]

no_license

bshreddy/PhysicsSims

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2023-07-25T08:48:38.877187

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import math, time import pyglet from pyglet.window import FPSDisplay, key class PendulumDamping: def __init__(self, position=(0, 0), length=1, radius=20, theta = math.pi/8, angular_velocity=0, damping=0.1): self.x, self.y = position self.length = length self.radius = radius self.theta = theta self.res = 200 self.angular_velocity = angular_velocity self.damping = damping def draw(self, offset=(0, 0)): offset_x, offset_y = offset x1, y1 = self.x + offset_x, self.y + offset_y x2, y2 = self.x + math.sin(self.theta) * self.length * 4 + offset_x, self.y - math.cos( self.theta) * self.length * 4 + offset_y self.makeVertices(x2, y2) pyglet.gl.glLineWidth(4) pyglet.graphics.draw(2, pyglet.gl.GL_LINES, ('v2f', (x1, y1, x2, y2)), ) circle = pyglet.graphics.vertex_list(self.res, ('v2f', self.verts)) circle.draw(pyglet.gl.GL_POLYGON) def step(self, dt): angular_accleration = (-980*.35 / self.length) * math.sin(self.theta) - self.damping * self.angular_velocity self.angular_velocity += angular_accleration * dt self.theta += self.angular_velocity * dt def makeVertices(self, X, Y): self.verts = [] for i in range(self.res): angle = math.radians(i / self.res * 360.0) x = self.radius * math.cos(angle) + X y = self.radius * math.sin(angle) + Y self.verts += [x, y] class Simulation(pyglet.window.Window): def __init__(self, width=1152, height=720, fullscreen=False): super().__init__(width, height, vsync=False, fullscreen=fullscreen, caption="Simple Pendulum") self.fps = FPSDisplay(self) self.T = 0 self.pendulums = [PendulumDamping(length=100)] @property def center(self): return self.width // 2, self.height // 2 def update(self, dt): for pendulum in self.pendulums: pendulum.step(dt) self.T += dt def on_draw(self): self.clear() for pendulum in self.pendulums: pendulum.draw((self.width // 2, self.height)) label = pyglet.text.Label(f"T = {self.T:.3f}", font_name='Halvetica Nenu', font_size=16, x=self.width - 10, y=10, anchor_x="right", anchor_y="bottom") label.draw() self.fps.draw() def on_key_press(self, symbol, modifiers): if symbol == key.Q and modifiers == key.MOD_COMMAND: pyglet.app.exit() def on_key_release(self, symbol, modifiers): if symbol == key.SPACE: pyglet.clock.schedule_interval(self.update, 1 / 500.0) if __name__ == "__main__": sim = Simulation() # pyglet.clock.set_fps_limit(500) pyglet.app.run()

[ "sh.bheemreddy@icloud.com" ]

sh.bheemreddy@icloud.com

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/StructVBERT/lxrt/optimization.py

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RunxinXu/AliceMind

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refs/heads/main

2023-08-14T07:32:10.625425

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# coding=utf-8 # Copyright 2019 project LXRT # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch optimization for BERT model.""" import math import torch from torch.optim import Optimizer from torch.optim.optimizer import required import logging logger = logging.getLogger(__name__) def warmup_cosine(x, warmup=0.002): if x < warmup: return x/warmup return 0.5 * (1.0 + torch.cos(math.pi * x)) def warmup_constant(x, warmup=0.002): """ Linearly increases learning rate over `warmup`*`t_total` (as provided to BertAdam) training steps. Learning rate is 1. afterwards. """ if x < warmup: return x/warmup return 1.0 def warmup_linear(x, warmup=0.002): """ Specifies a triangular learning rate schedule where peak is reached at `warmup`*`t_total`-th (as provided to BertAdam) training step. After `t_total`-th training step, learning rate is zero. """ if x < warmup: return x/warmup return max((x-1.)/(warmup-1.), 0) SCHEDULES = { 'warmup_cosine': warmup_cosine, 'warmup_constant': warmup_constant, 'warmup_linear': warmup_linear, } class BertAdam(Optimizer): """Implements BERT version of Adam algorithm with weight decay fix. Params: lr: learning rate warmup: portion of t_total for the warmup, -1 means no warmup. Default: -1 t_total: total number of training steps for the learning rate schedule, -1 means constant learning rate. Default: -1 schedule: schedule to use for the warmup (see above). Default: 'warmup_linear' b1: Adams b1. Default: 0.9 b2: Adams b2. Default: 0.999 e: Adams epsilon. Default: 1e-6 weight_decay: Weight decay. Default: 0.01 max_grad_norm: Maximum norm for the gradients (-1 means no clipping). Default: 1.0 """ def __init__(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_linear', b1=0.9, b2=0.999, e=1e-6, weight_decay=0.01, max_grad_norm=1.0): if lr is not required and lr < 0.0: raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr)) if schedule not in SCHEDULES: raise ValueError("Invalid schedule parameter: {}".format(schedule)) if not 0.0 <= warmup < 1.0 and not warmup == -1: raise ValueError("Invalid warmup: {} - should be in [0.0, 1.0[ or -1".format(warmup)) if not 0.0 <= b1 < 1.0: raise ValueError("Invalid b1 parameter: {} - should be in [0.0, 1.0[".format(b1)) if not 0.0 <= b2 < 1.0: raise ValueError("Invalid b2 parameter: {} - should be in [0.0, 1.0[".format(b2)) if not e >= 0.0: raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(e)) defaults = dict(lr=lr, schedule=schedule, warmup=warmup, t_total=t_total, b1=b1, b2=b2, e=e, weight_decay=weight_decay, max_grad_norm=max_grad_norm) super(BertAdam, self).__init__(params, defaults) def get_lr(self): lr = [] for group in self.param_groups: for p in group['params']: state = self.state[p] if len(state) == 0: return [0] if group['t_total'] != -1: schedule_fct = SCHEDULES[group['schedule']] lr_scheduled = group['lr'] * schedule_fct(state['step']/group['t_total'], group['warmup']) else: lr_scheduled = group['lr'] lr.append(lr_scheduled) return lr def step(self, closure=None): """Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: loss = closure() warned_for_t_total = False for group in self.param_groups: for p in group['params']: if p.grad is None: continue grad = p.grad.data if grad.is_sparse: raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead') state = self.state[p] # State initialization if len(state) == 0: state['step'] = 0 # Exponential moving average of gradient values state['next_m'] = torch.zeros_like(p.data) # Exponential moving average of squared gradient values state['next_v'] = torch.zeros_like(p.data) next_m, next_v = state['next_m'], state['next_v'] beta1, beta2 = group['b1'], group['b2'] # LXRT: grad is clipped outside. # Add grad clipping # if group['max_grad_norm'] > 0: # clip_grad_norm_(p, group['max_grad_norm']) # Decay the first and second moment running average coefficient # In-place operations to update the averages at the same time next_m.mul_(beta1).add_(1 - beta1, grad) next_v.mul_(beta2).addcmul_(1 - beta2, grad, grad) update = next_m / (next_v.sqrt() + group['e']) # bugfix ref: https://arxiv.org/pdf/2006.05987.pdf, changed by ming # update = (next_m / (1 - beta1 ** (state['step'] + 1))) / ((next_v / (1 - beta2 ** (state['step'] + 1))).sqrt() + group['e']) # Just adding the square of the weights to the loss function is *not* # the correct way of using L2 regularization/weight decay with Adam, # since that will interact with the m and v parameters in strange ways. # # Instead we want to decay the weights in a manner that doesn't interact # with the m/v parameters. This is equivalent to adding the square # of the weights to the loss with plain (non-momentum) SGD. if group['weight_decay'] > 0.0: update += group['weight_decay'] * p.data if group['t_total'] != -1: schedule_fct = SCHEDULES[group['schedule']] progress = state['step']/group['t_total'] lr_scheduled = group['lr'] * schedule_fct(progress, group['warmup']) # warning for exceeding t_total (only active with warmup_linear if group['schedule'] == "warmup_linear" and progress > 1. and not warned_for_t_total: logger.warning( "Training beyond specified 't_total' steps with schedule '{}'. Learning rate set to {}. " "Please set 't_total' of {} correctly.".format(group['schedule'], lr_scheduled, self.__class__.__name__)) warned_for_t_total = True # end warning else: lr_scheduled = group['lr'] update_with_lr = lr_scheduled * update p.data.add_(-update_with_lr) state['step'] += 1 # step_size = lr_scheduled * math.sqrt(bias_correction2) / bias_correction1 # No bias correction # bias_correction1 = 1 - beta1 ** state['step'] # bias_correction2 = 1 - beta2 ** state['step'] return loss

[ "lfl259702@alibaba-inc.com" ]

lfl259702@alibaba-inc.com

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61f30ca19d9553ffb55dd2deccf895405a199719

/src/update_workflows.py

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[ "MIT" ]

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deanishe/alfred-packal-search

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88427bcbf127e3d15674c71566f253cf312f96d6

refs/heads/master

2021-06-10T09:02:04.105014

2017-09-17T12:01:34

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#!/usr/bin/env python # encoding: utf-8 # # Copyright © 2014 deanishe@deanishe.net # # MIT Licence. See http://opensource.org/licenses/MIT # # Created on 2014-04-07 # """update_workflows.py [--force-update] Usage: update_workflows.py [--force-update] """ from __future__ import print_function, unicode_literals import subprocess import sys import os from datetime import datetime from plistlib import readPlist, readPlistFromString try: from xml.etree import cElementTree as ET except ImportError: from xml.etree import ElementTree as ET from workflow import web, Workflow from common import (CACHE_MAXAGE, Version, STATUS_SPLITTER, STATUS_UNKNOWN, STATUS_UPDATE_AVAILABLE, STATUS_UP_TO_DATE, STATUS_NOT_INSTALLED) log = None MANIFEST_URL = 'https://raw.github.com/packal/repository/master/manifest.xml' # WORKFLOW_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) ALFRED_PREFS = os.path.expanduser( '~/Library/Preferences/com.runningwithcrayons.Alfred-Preferences-3.plist') class Constant(object): def __init__(self, value): self.value = value def __repr__(self): return 'Constant({0:r})'.format(self.value) def __str__(self): return str(self.value) NOT_INSTALLED = Constant('NOT INSTALLED') def read_plist(path): """Convert plist to XML and read its contents.""" cmd = [b'plutil', b'-convert', b'xml1', b'-o', b'-', path] xml = subprocess.check_output(cmd) return readPlistFromString(xml) def get_workflow_directory(): """Return path to Alfred's workflow directory.""" prefs = read_plist(ALFRED_PREFS) syncdir = prefs.get('syncfolder') if not syncdir: log.debug('Alfred sync folder not found') return None syncdir = os.path.expanduser(syncdir) wf_dir = os.path.join(syncdir, 'Alfred.alfredpreferences/workflows') log.debug('Workflow sync dir : %r', wf_dir) if os.path.exists(wf_dir): log.debug('Workflow directory retrieved from Alfred preferences') return wf_dir log.debug('Alfred.alfredpreferences/workflows not found') return None def packal_metadata(xmlpath): """Return ``dict`` of metadata in ``package.xml`` file created by Packal""" tree = ET.parse(xmlpath) root = tree.getroot() data = {} for elem in root: data[elem.tag] = elem.text data['version'] = Version(data['version']) return data def get_installed_workflows(): """Return ``dict`` of installed workflows ``{bundleid : version}`` ``version`` is ``None`` if workflow isn't from Packal.org """ workflows = {} workflow_dir = get_workflow_directory() log.debug('reading workflows installed in %r ...', workflow_dir) for name in os.listdir(workflow_dir): path = os.path.join(workflow_dir, name) if not os.path.isdir(path): continue info_plist = os.path.join(path, 'info.plist') packal_xml = os.path.join(path, 'packal', 'package.xml') if not os.path.exists(info_plist): continue try: bundleid = readPlist(info_plist)['bundleid'] if not bundleid: log.warning('no bundleid in info.plist : %s', path) continue except Exception as err: log.error('bad info.plist in workflow %r: %s', path, err) continue try: metadata = {'version': None, 'bundle': bundleid} if os.path.exists(packal_xml): metadata.update(packal_metadata(packal_xml)) except Exception as err: log.error('bad packal/package.xml in workflow %r: %s', path, err) continue workflows[metadata['bundle']] = metadata['version'] log.debug('%d workflows installed locally', len(workflows)) return workflows def get_packal_workflows(): """Return list of workflows available on Packal.org""" workflows = [] r = web.get(MANIFEST_URL) r.raise_for_status() manifest = ET.fromstring(r.content) # these elements contain multiple, |||-delimited items list_elements = ('categories', 'tags', 'osx') for workflow in manifest: d = {} for elem in workflow: if elem.tag in list_elements: if not elem.text: d[elem.tag] = [] else: d[elem.tag] = [s.strip() for s in elem.text.split('|||')] # text elements elif elem.text: d[elem.tag] = elem.text else: d[elem.tag] = '' # convert timestamp to datetime d['updated'] = datetime.fromtimestamp(float(d['updated'])) d['version'] = Version(d['version']) workflows.append(d) log.debug('{} workflows available on Packal.org'.format(len(workflows))) return workflows def get_workflows(): """Return list of workflows on on Packal.org with update status.""" local_workflows = get_installed_workflows() packal_workflows = get_packal_workflows() for packal_workflow in packal_workflows: # set version number bundle = packal_workflow.get('bundle') local_version = local_workflows.get(bundle, NOT_INSTALLED) packal_version = packal_workflow['version'] log.debug('workflow `{0}` packal : {1} local : {2}'.format( packal_workflow['bundle'], packal_workflow['version'], local_version)) # log.debug('local version : {0}'.format(local_version)) if local_version is NOT_INSTALLED: packal_workflow['status'] = STATUS_NOT_INSTALLED elif not local_version: packal_workflow['status'] = STATUS_SPLITTER elif packal_version > local_version: packal_workflow['status'] = STATUS_UPDATE_AVAILABLE elif packal_version == local_version: packal_workflow['status'] = STATUS_UP_TO_DATE else: packal_workflow['status'] = STATUS_UNKNOWN return packal_workflows def main(wf): from docopt import docopt args = docopt(__doc__, argv=wf.args) if args.get('--force-update'): max_age = 1 log.debug('Forcing update of Packal workflows') else: max_age = CACHE_MAXAGE wf.cached_data('workflows', get_workflows, max_age=max_age) if __name__ == '__main__': wf = Workflow() log = wf.logger sys.exit(wf.run(main))

[ "deanishe@deanishe.net" ]

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# type: ignore """ A one-variable linear model with no bias. The dataset emits only pairs of (data, label) = (1, 1), meaning that the one weight in the model should approach 1 as gradient descent continues. We will use the mean squared error as the loss. Since each record is the same, the "mean" part of mean squared error means we can analyze every batch as if were just one record. Now, we can calculate the mean squared error to ensure that we are getting the gradient we are expecting. let: R = learning rate (constant) l = loss w0 = the starting value of the one weight w' = the updated value of the one weight then calculate the loss: (1) l = (label - (data * w0)) ** 2 take derivative of loss WRT w (2) dl/dw = - 2 * data * (label - (data * w0)) gradient update: (3) update = -R * dl/dw = 2 * R * data * (label - (data * w0)) Finally, we can calculate the updated weight (w') in terms of w0: (4) w' = w0 + update = w0 + 2 * R * data * (label - (data * w0)) """ import logging from typing import Any, Dict, Iterable, List, Optional, Tuple, cast import numpy as np import torch from determined import experimental, pytorch from determined.common import yaml from determined.pytorch import samplers from tests.experiment.fixtures import pytorch_counter_callback try: import apex except ImportError: # pragma: no cover pass class OnesDataset(torch.utils.data.Dataset): def __len__(self) -> int: return 64 def __getitem__(self, index: int) -> Tuple: return torch.Tensor([float(1)]), torch.Tensor([float(1)]) class TriangleLabelSum(pytorch.MetricReducer): """Return a sum of (label_sum * batch_index) for every batch (labels are always 1 here).""" @staticmethod def expect(batch_size, idx_start, idx_end): """What to expect during testing.""" return sum(batch_size * idx for idx in range(idx_start, idx_end)) def __init__(self) -> None: self.reset() def reset(self) -> None: self.sum = 0 # We don't actually expose a batch_idx for evaluation, so we track the number of batches # since the last reset(), which is only accurate during evaluation workloads or the very # first training workload. self.count = 0 def update(self, label_sum: torch.Tensor, batch_idx: Optional[int]) -> None: self.sum += label_sum * (batch_idx if batch_idx is not None else self.count) self.count += 1 def per_slot_reduce(self) -> Any: return self.sum def cross_slot_reduce(self, per_slot_metrics) -> Any: return sum(per_slot_metrics) def triangle_label_sum(updates: List) -> Any: out = 0 for update_idx, (label_sum, batch_idx) in enumerate(updates): if batch_idx is not None: out += batch_idx * label_sum else: out += update_idx * label_sum return out class StepableLRScheduler(torch.optim.lr_scheduler._LRScheduler): def get_lr(self) -> List[float]: return [self._step_count for _ in self.base_lrs] def get_onevar_model(n=1) -> torch.nn.Module: model = torch.nn.Linear(n, n, False) # Manually initialize the weight(s) to 0. model.weight.data.fill_(0) return model class MetricsCallback(pytorch.PyTorchCallback): def __init__(self): self.validation_metrics = [] self.training_metrics = [] self.batch_metrics = [] def on_validation_end(self, metrics: Dict[str, Any]) -> None: self.validation_metrics.append(metrics) def on_training_workload_end( self, avg_metrics: Dict[str, Any], batch_metrics: Dict[str, Any] ) -> None: self.training_metrics.append(avg_metrics) self.batch_metrics += batch_metrics class CheckpointCallback(pytorch.PyTorchCallback): def __init__(self): self.uuids = [] def on_checkpoint_upload_end(self, uuid: str) -> None: self.uuids.append(uuid) class BaseOneVarTrial(pytorch.PyTorchTrial): def __init__(self, context: pytorch.PyTorchTrialContext) -> None: self.context = context # The "features" hparam is only for TestPyTorchTrial.test_restore_invalid_checkpoint self.model = context.wrap_model( get_onevar_model(n=self.context.get_hparams().get("features", 1)) ) self.lr = 0.001 opt = torch.optim.SGD(self.model.parameters(), self.lr) self.opt = context.wrap_optimizer(opt) self.loss_fn = torch.nn.MSELoss() self.cls_reducer = context.wrap_reducer(TriangleLabelSum(), name="cls_reducer") self.fn_reducer = context.wrap_reducer(triangle_label_sum, name="fn_reducer") self.hparams = self.context.get_hparams() self.metrics_callback = MetricsCallback() self.checkpoint_callback = CheckpointCallback() if self.hparams.get("disable_dataset_reproducibility_checks"): self.context.experimental.disable_dataset_reproducibility_checks() def train_batch( self, batch: pytorch.TorchData, epoch_idx: int, batch_idx: int ) -> Dict[str, torch.Tensor]: data, label = batch self.cls_reducer.update(sum(label), batch_idx) self.fn_reducer.update((sum(label), batch_idx)) # Measure the weight right now. w_before = self.model.weight.data.item() # Calculate expected values for loss (eq 1) and weight (eq 4). loss_exp = (label[0] - data[0] * w_before) ** 2 w_exp = w_before + 2 * self.lr * data[0] * (label[0] - (data[0] * w_before)) output = self.model(data) loss = self.loss_fn(output, label) self.context.backward(loss) self.context.step_optimizer(self.opt) # Measure the weight after the update. w_after = self.model.weight.data.item() # Return values that we can compare as part of the tests. return { "loss": loss, "loss_exp": loss_exp, "w_before": w_before, "w_after": w_after, "w_exp": w_exp, "output": output, } @staticmethod def check_batch_metrics( metrics: Dict[str, Any], batch_idx: int, metric_keyname_pairs: Iterable[Tuple[str, str]], atol=1e-6, ) -> None: """Check that given metrics are equal or close enough to each other.""" for k_a, k_b in metric_keyname_pairs: m_a, m_b = metrics[k_a], metrics[k_b] try: assert torch.isclose( m_a, m_b, atol=atol ), f"Metrics {k_a}={m_a} and {k_b}={m_b} do not match at batch {batch_idx}" except TypeError: assert np.allclose( m_a, m_b, atol=atol ), f"Metrics {k_a}={m_a} and {k_b}={m_b} do not match at batch {batch_idx}" def build_training_data_loader(self) -> torch.utils.data.DataLoader: if self.hparams["dataloader_type"] == "determined": return pytorch.DataLoader( OnesDataset(), batch_size=self.context.get_per_slot_batch_size() ) elif self.hparams["dataloader_type"] == "torch": dataset = OnesDataset() seed = self.context.get_trial_seed() num_workers = self.context.distributed.get_size() rank = self.context.distributed.get_rank() batch_size = self.context.get_per_slot_batch_size() skip_batches = self.context.get_initial_batch() sampler = torch.utils.data.SequentialSampler(dataset) sampler = samplers.ReproducibleShuffleSampler(sampler, seed) sampler = samplers.RepeatSampler(sampler) sampler = samplers.DistributedSampler(sampler, num_workers=num_workers, rank=rank) batch_sampler = torch.utils.data.BatchSampler(sampler, batch_size, drop_last=False) batch_sampler = samplers.SkipBatchSampler(batch_sampler, skip_batches) return torch.utils.data.DataLoader(dataset, batch_sampler=batch_sampler) else: raise ValueError(f"unknown dataloader_type: {self.hparams['dataloader_type']}") def build_validation_data_loader(self) -> torch.utils.data.DataLoader: if self.hparams["dataloader_type"] == "determined": return pytorch.DataLoader( OnesDataset(), batch_size=self.context.get_per_slot_batch_size() ) elif self.hparams["dataloader_type"] == "torch": dataset = OnesDataset() num_workers = self.context.distributed.get_size() rank = self.context.distributed.get_rank() batch_size = self.context.get_per_slot_batch_size() sampler = torch.utils.data.SequentialSampler(dataset) sampler = samplers.DistributedSampler(sampler, num_workers=num_workers, rank=rank) batch_sampler = torch.utils.data.BatchSampler(sampler, batch_size, drop_last=False) return torch.utils.data.DataLoader(dataset, batch_sampler=batch_sampler) else: raise ValueError(f"unknown dataloader_type: {self.hparams['dataloader_type']}") def build_callbacks(self) -> Dict[str, pytorch.PyTorchCallback]: return {"metrics": self.metrics_callback, "checkpoint": self.checkpoint_callback} class OneVarTrial(BaseOneVarTrial): _searcher_metric = "val_loss" def evaluate_batch(self, batch: pytorch.TorchData, batch_idx: int) -> Dict[str, Any]: data, label = batch self.cls_reducer.update(sum(label), None) self.fn_reducer.update((sum(label), None)) loss = self.loss_fn(self.model(data), label) return {"val_loss": loss} class OneVarTrialWithMultiValidation(OneVarTrial): def evaluate_batch(self, batch: pytorch.TorchData, batch_idx: int) -> Dict[str, Any]: data, labels = batch output = self.model(data) val_loss = self.loss_fn(output, labels) mse = torch.mean(torch.square(output - labels)) return {"val_loss": val_loss, "mse": mse} class OneVarTrialPerMetricReducers(OneVarTrialWithMultiValidation): def evaluation_reducer(self) -> Dict[str, pytorch.Reducer]: return {"val_loss": pytorch.Reducer.AVG, "mse": pytorch.Reducer.AVG} class OneVarTrialWithTrainingMetrics(OneVarTrial): def train_batch( self, batch: pytorch.TorchData, epoch_idx: int, batch_idx: int ) -> Dict[str, torch.Tensor]: data, labels = batch output = self.model(data) labels = cast(torch.Tensor, labels) loss = self.loss_fn(output, labels) mse = torch.mean(torch.square(output - labels)) self.context.backward(loss) self.context.step_optimizer(self.opt) return {"loss": loss, "mse": mse} class AMPTestDataset(OnesDataset): STAGE_DATUM = { "one": 1.0, "zero": 0.0, "small": 2e-14, "large": 2e4, } def __init__(self, stages: List[str], aggregation_freq: int = 1) -> None: self.stages = stages self._agg_freq = aggregation_freq def __len__(self) -> int: return len(self.stages) * self._agg_freq def __getitem__(self, index: int) -> Tuple: x = self.STAGE_DATUM[self.stages[index // self._agg_freq]] return torch.Tensor([float(x)]), torch.Tensor([float(x)]) class OneVarAMPBaseTrial(OneVarTrial): _init_scale = None _growth_interval = None _stages = ( 5 * ["one"] + 1 * ["large"] + 4 * ["one"] + 1 * ["small"] + 4 * ["one"] + 1 * ["zero"] + 4 * ["one"] + [] ) def __init__(self, context: pytorch.PyTorchTrialContext): super().__init__(context) self._agg_freq = self.context._aggregation_frequency def build_training_data_loader(self) -> torch.utils.data.DataLoader: return pytorch.DataLoader( AMPTestDataset(self._stages, self._agg_freq), batch_size=self.context.get_per_slot_batch_size(), ) class OneVarApexAMPTrial(OneVarAMPBaseTrial): _growth_interval = 2000 def __init__(self, context: pytorch.PyTorchTrialContext) -> None: super().__init__(context) self.model, self.optimizer = self.context.configure_apex_amp( models=self.model, optimizers=self.opt, opt_level="O2", ) def train_batch( self, batch: pytorch.TorchData, epoch_idx: int, batch_idx: int ) -> Dict[str, torch.Tensor]: scale_before = apex.amp.state_dict()["loss_scaler0"]["loss_scale"] metrics = super().train_batch(batch, epoch_idx, batch_idx) metrics["scale_before"] = scale_before metrics["scale"] = apex.amp.state_dict()["loss_scaler0"]["loss_scale"] metrics["stage"] = self._stages[batch_idx // self._agg_freq] return metrics class OneVarAutoAMPTrial(OneVarAMPBaseTrial): _init_scale = 65536 _growth_interval = 4 def __init__(self, context: pytorch.PyTorchTrialContext) -> None: context.experimental.use_amp() # HACK: overwrite the scaler with a manually configured one, which # is not something we don't actually allow with the use_amp() API. context._scaler = torch.cuda.amp.GradScaler( init_scale=self._init_scale, growth_interval=self._growth_interval, ) super().__init__(context) self.scaler = self.context._scaler def train_batch( self, batch: pytorch.TorchData, epoch_idx: int, batch_idx: int ) -> Dict[str, torch.Tensor]: scale_before = self.scaler.get_scale() metrics = super().train_batch(batch, epoch_idx, batch_idx) metrics["scale_before"] = scale_before # self.scaler.update() gets called after this method returns metrics["stage"] = self._stages[batch_idx // self._agg_freq] return metrics class OneVarManualAMPTrial(OneVarAMPBaseTrial): _init_scale = 65536 _growth_interval = 4 def __init__(self, context: pytorch.PyTorchTrialContext) -> None: self.scaler = context.wrap_scaler( torch.cuda.amp.GradScaler( init_scale=self._init_scale, growth_interval=self._growth_interval ) ) super().__init__(context) def train_batch( self, batch: pytorch.TorchData, epoch_idx: int, batch_idx: int ) -> Dict[str, torch.Tensor]: data, label = batch scale_before = self.scaler.get_scale() # Measure the weight right now. w_before = self.model.weight.data.item() # Calculate expected values for loss (eq 1) and weight (eq 4). loss_exp = (label[0] - data[0] * w_before) ** 2 w_exp = w_before + 2 * self.lr * data[0] * (label[0] - (data[0] * w_before)) with torch.cuda.amp.autocast(): output = self.model(data) loss = self.loss_fn(output, label) scaled_loss = self.scaler.scale(loss) self.context.backward(scaled_loss) self.context.step_optimizer(self.opt, scaler=self.scaler) if (batch_idx + 1) % self._agg_freq == 0: self.scaler.update() # Measure the weight after the update. w_after = self.model.weight.data.item() # Return values that we can compare as part of the tests. return { "stage": self._stages[batch_idx // self._agg_freq], "scale_before": scale_before, "scale": self.scaler.get_scale(), "loss": loss, "loss_exp": loss_exp, "w_before": w_before, "w_after": w_after, "w_exp": w_exp, "output": output, } def evaluate_batch(self, batch: pytorch.TorchData, batch_idx: int) -> Dict[str, Any]: data, label = batch with torch.cuda.amp.autocast(): output = self.model(data) loss = self.loss_fn(output, label) return {"val_loss": loss} class OneVarApexAMPWithNoopScalerTrial(OneVarApexAMPTrial): def __init__(self, context: pytorch.PyTorchTrialContext) -> None: self.scaler = context.wrap_scaler( torch.cuda.amp.GradScaler( init_scale=self._init_scale, growth_interval=self._growth_interval, enabled=False, ) ) super().__init__(context) class OneVarManualAMPWithNoopApexTrial(OneVarManualAMPTrial): def __init__(self, context: pytorch.PyTorchTrialContext) -> None: super().__init__(context) self.model, self.optimizer = self.context.configure_apex_amp( models=self.model, optimizers=self.opt, opt_level="O2", enabled=False, ) class OneVarTrialCustomEval(BaseOneVarTrial): _searcher_metric = "val_loss" def evaluate_full_dataset(self, data_loader: torch.utils.data.DataLoader) -> Dict[str, Any]: loss_sum = 0.0 for data, labels in iter(data_loader): if torch.cuda.is_available(): data, labels = data.cuda(), labels.cuda() output = self.model(data) loss_sum += self.loss_fn(output, labels) loss = loss_sum / len(data_loader) return {"val_loss": loss} class OneVarTrialAccessContext(BaseOneVarTrial): _searcher_metric = "val_loss" def __init__(self, context: pytorch.PyTorchTrialContext) -> None: super().__init__(context) self.model_a = self.context.wrap_model(get_onevar_model()) self.model_b = self.context.wrap_model(get_onevar_model()) self.opt_a = self.context.wrap_optimizer( torch.optim.SGD(self.model_a.parameters(), self.context.get_hparam("learning_rate")) ) self.opt_b = self.context.wrap_optimizer( torch.optim.SGD(self.model_b.parameters(), self.context.get_hparam("learning_rate")) ) self.lrs_a = self.context.wrap_lr_scheduler( StepableLRScheduler(self.opt_a), step_mode=pytorch.LRScheduler.StepMode( self.context.get_hparam("lr_scheduler_step_mode") ), ) self.lrs_b = self.context.wrap_lr_scheduler( StepableLRScheduler(self.opt_b), step_mode=pytorch.LRScheduler.StepMode( self.context.get_hparam("lr_scheduler_step_mode") ), ) def train_batch( self, batch: pytorch.TorchData, epoch_idx: int, batch_idx: int ) -> Dict[str, torch.Tensor]: assert self.context.models assert self.context.optimizers assert self.context.lr_schedulers data, labels = batch output = self.model_a(data) loss = torch.nn.functional.binary_cross_entropy(output, labels.contiguous().view(-1, 1)) self.context.backward(loss) self.context.step_optimizer(self.opt_a) return {"loss": loss} def evaluate_batch(self, batch: pytorch.TorchData, batch_idx: int) -> Dict[str, Any]: assert self.context.models assert self.context.optimizers assert self.context.lr_schedulers data, labels = batch output = self.model_a(data) loss = self.loss_fn(output, labels) return {"val_loss": loss} class OneVarTrialGradClipping(OneVarTrial): def train_batch( self, batch: pytorch.TorchData, epoch_idx: int, batch_idx: int ) -> Dict[str, torch.Tensor]: data, labels = batch output = self.model(data) loss = self.loss_fn(output, labels) self.context.backward(loss) if "gradient_clipping_l2_norm" in self.context.get_hparams(): self.context.step_optimizer( self.opt, clip_grads=lambda params: torch.nn.utils.clip_grad_norm_( params, self.context.get_hparam("gradient_clipping_l2_norm") ), ) elif "gradient_clipping_value" in self.context.get_hparams(): self.context.step_optimizer( self.opt, clip_grads=lambda params: torch.nn.utils.clip_grad_value_( params, self.context.get_hparam("gradient_clipping_value") ), ) else: self.context.step_optimizer(self.opt) return {"loss": loss} class OneVarTrialWithNonScalarValidation(BaseOneVarTrial): _searcher_metric = "mse" def __init__(self, context: pytorch.PyTorchTrialContext) -> None: super().__init__(context) self.model = self.context.wrap_model(get_onevar_model()) self.opt = self.context.wrap_optimizer( torch.optim.SGD(self.model.parameters(), self.context.get_hparam("learning_rate")) ) def train_batch( self, batch: pytorch.TorchData, epoch_idx: int, batch_idx: int ) -> Dict[str, torch.Tensor]: data, labels = batch output = self.model(data) loss = self.loss_fn(output, labels) self.context.backward(loss) self.context.step_optimizer(self.opt) return {"loss": loss} def evaluate_full_dataset(self, data_loader: torch.utils.data.DataLoader) -> Dict[str, Any]: predictions = [] mse_sum = 0.0 for data, labels in iter(data_loader): if torch.cuda.is_available(): data, labels = data.cuda(), labels.cuda() output = self.model(data) predictions.append(output) mse_sum += torch.mean(torch.square(output - labels)) mse = mse_sum / len(data_loader) return {"predictions": predictions, "mse": mse} class OneVarTrialWithLRScheduler(OneVarTrial): def __init__(self, context: pytorch.PyTorchTrialContext) -> None: super().__init__(context) self.model = self.context.wrap_model(get_onevar_model()) self.opt = self.context.wrap_optimizer( torch.optim.SGD(self.model.parameters(), self.context.get_hparam("learning_rate")) ) self.lr_scheduler = self.context.wrap_lr_scheduler( StepableLRScheduler(self.opt), step_mode=pytorch.LRScheduler.StepMode( self.context.get_hparam("lr_scheduler_step_mode") ), ) def train_batch( self, batch: pytorch.TorchData, epoch_idx: int, batch_idx: int ) -> Dict[str, torch.Tensor]: metrics = super().train_batch(batch, epoch_idx, batch_idx) lr = self.lr_scheduler.get_last_lr()[0] metrics["lr"] = lr if ( self.context.get_hparam("lr_scheduler_step_mode") == pytorch.LRScheduler.StepMode.MANUAL_STEP ): self.lr_scheduler.step() return metrics class EphemeralLegacyCallbackCounter(pytorch.PyTorchCallback): """ Callback with legacy signature for on_training_epoch_start that takes no arguments. It is ephemeral: it does not implement state_dict and load_state_dict. """ def __init__(self) -> None: self.legacy_on_training_epochs_start_calls = 0 def on_training_epoch_start(self) -> None: # noqa # This is to test for a deprecation warning. logging.debug(f"calling {__name__} without arguments") self.legacy_on_training_epochs_start_calls += 1 class OneVarTrialCallbacks(OneVarTrial): def __init__(self, context: pytorch.PyTorchTrialContext) -> None: super().__init__(context) self.counter = pytorch_counter_callback.Counter() self.legacy_counter = EphemeralLegacyCallbackCounter() def build_callbacks(self) -> Dict[str, pytorch.PyTorchCallback]: return {"counter": self.counter, "legacyCounter": self.legacy_counter} if __name__ == "__main__": conf = yaml.safe_load( """ description: test-native-api-local-test-mode hyperparameters: global_batch_size: 32 dataloader_type: determined scheduling_unit: 1 searcher: name: single metric: val_loss max_length: batches: 1 smaller_is_better: true max_restarts: 0 """ ) experimental.create(OneVarTrial, conf, context_dir=".", local=True, test=True)

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N,M = map(int,input().split()) arr = list(map(int,input().split())) ans = [] for i in range (N): for j in range (i+1,N): for k in range (j+1,N): ans.append(arr[i]+arr[j]+arr[k]) a = 0 for i in range(len(ans)): if ans[i] <= M : if ans[i]>a: a= ans[i] print(a)

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#!/usr/bin/env python import sys def pluralise(s): a = ["ch", "sh", "x", "s", "z"] b = ["a", "e", "i", "o", "u"] i = 0 while i < len(s): if s[len(s) - 2:] in a: s = s + "es" elif s[len(s) - 1:] in a: s = s + "es" elif s[len(s) - 1:] == "y" and s[len(s) - 2:len(s) - 1] not in b: s = s[:len(s) - 1] + "ies" elif s[len(s) - 1:] == "f": s = s[:len(s) - 1] + "ves" elif s[len(s) - 1:] == "o": s = s + "es" elif s[len(s) - 2:] == "fe": s = s[:len(s) - 2] + "ves" else: s = s + "s" return s def main(): lines = sys.stdin.readlines() i = 0 while i < len(lines): line = lines[i].strip() print(pluralise(line)) i = i + 1 if __name__ == '__main__': main()

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#!/usr/bin/env python3 # This file is part of Tryton. The COPYRIGHT file at the top level of # this repository contains the full copyright notices and license terms. import io import os import re from configparser import ConfigParser from setuptools import setup, find_packages def read(fname): return io.open( os.path.join(os.path.dirname(__file__), fname), 'r', encoding='utf-8').read() def get_require_version(name): if minor_version % 2: require = '%s >= %s.%s.dev0, < %s.%s' else: require = '%s >= %s.%s, < %s.%s' require %= (name, major_version, minor_version, major_version, minor_version + 1) return require config = ConfigParser() config.read_file(open(os.path.join(os.path.dirname(__file__), 'tryton.cfg'))) info = dict(config.items('tryton')) for key in ('depends', 'extras_depend', 'xml'): if key in info: info[key] = info[key].strip().splitlines() version = info.get('version', '0.0.1') major_version, minor_version, _ = version.split('.', 2) major_version = int(major_version) minor_version = int(minor_version) name = 'trytond_analytic_sale' download_url = 'http://downloads.tryton.org/%s.%s/' % ( major_version, minor_version) if minor_version % 2: version = '%s.%s.dev0' % (major_version, minor_version) download_url = ( 'hg+http://hg.tryton.org/modules/%s#egg=%s-%s' % ( name[8:], name, version)) local_version = [] if os.environ.get('CI_JOB_ID'): local_version.append(os.environ['CI_JOB_ID']) else: for build in ['CI_BUILD_NUMBER', 'CI_JOB_NUMBER']: if os.environ.get(build): local_version.append(os.environ[build]) else: local_version = [] break if local_version: version += '+' + '.'.join(local_version) requires = [] for dep in info.get('depends', []): if not re.match(r'(ir|res)(\W|$)', dep): requires.append(get_require_version('trytond_%s' % dep)) requires.append(get_require_version('trytond')) tests_require = [get_require_version('proteus')] dependency_links = [] if minor_version % 2: dependency_links.append( 'https://trydevpi.tryton.org/?local_version=' + '.'.join(local_version)) setup(name=name, version=version, description='Tryton module to add analytic accounting on sale', long_description=read('README.rst'), author='Tryton', author_email='bugs@tryton.org', url='http://www.tryton.org/', download_url=download_url, project_urls={ "Bug Tracker": 'https://bugs.tryton.org/', "Documentation": 'https://docs.tryton.org/', "Forum": 'https://www.tryton.org/forum', "Source Code": 'https://hg.tryton.org/modules/analytic_sale', }, keywords='tryton analytic account sale', package_dir={'trytond.modules.analytic_sale': '.'}, packages=( ['trytond.modules.analytic_sale'] + ['trytond.modules.analytic_sale.%s' % p for p in find_packages()] ), package_data={ 'trytond.modules.analytic_sale': (info.get('xml', []) + ['tryton.cfg', 'view/*.xml', 'locale/*.po', 'tests/*.rst']), }, classifiers=[ 'Development Status :: 5 - Production/Stable', 'Environment :: Plugins', 'Framework :: Tryton', 'Intended Audience :: Developers', 'Intended Audience :: Financial and Insurance Industry', 'Intended Audience :: Legal Industry', 'License :: OSI Approved :: ' 'GNU General Public License v3 or later (GPLv3+)', 'Natural Language :: Bulgarian', 'Natural Language :: Catalan', 'Natural Language :: Chinese (Simplified)', 'Natural Language :: Czech', 'Natural Language :: Dutch', 'Natural Language :: English', 'Natural Language :: Finnish', 'Natural Language :: French', 'Natural Language :: German', 'Natural Language :: Hungarian', 'Natural Language :: Indonesian', 'Natural Language :: Italian', 'Natural Language :: Persian', 'Natural Language :: Polish', 'Natural Language :: Portuguese (Brazilian)', 'Natural Language :: Russian', 'Natural Language :: Slovenian', 'Natural Language :: Spanish', 'Natural Language :: Turkish', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: Implementation :: CPython', 'Programming Language :: Python :: Implementation :: PyPy', 'Topic :: Office/Business', 'Topic :: Office/Business :: Financial :: Accounting', ], license='GPL-3', python_requires='>=3.6', install_requires=requires, dependency_links=dependency_links, zip_safe=False, entry_points=""" [trytond.modules] analytic_sale = trytond.modules.analytic_sale """, test_suite='tests', test_loader='trytond.test_loader:Loader', tests_require=tests_require, )

[ "saif.kazi76@gmail.com" ]

saif.kazi76@gmail.com

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/books/model/NotesAndTerms.py

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permissive

deepubansal/books-python-wrappers

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2022-12-05T11:25:01.694021

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MIT

2020-08-29T07:35:24

2020-08-19T13:26:04

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#$Id$ class NotesAndTerms: """This class is used to create object for notes and terms.""" def __init__(self): """Initialize parameters for notes and terms.""" self.notes = '' self.terms = '' def set_notes(self, notes): """Set notes. Args: notes(str): Notes. """ self.notes = notes def get_notes(self): """Get notes. Returns: str: Notes. """ return self.notes def set_terms(self, terms): """Set terms. Args: terms(str): Terms. """ self.terms = terms def get_terms(self): """Get terms. Returns: str: Terms. """ return self.terms def to_json(self): """This method is used to convert notes and terms object to json form. Returns: dict: Dictionary containing json object for notes and terms. """ data = {} if self.notes != '': data['notes'] = self.notes if self.terms != '': data['terms'] = self.terms return data

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sahaya.ramesh@zohocorp.com

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/parsifal/reviews/templatetags/get_user_article_evaluation.py

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thiagok2/parsifal-mec

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2021-08-18T08:21:49.044031

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from django import template from parsifal.reviews.models import Article, ArticleEvaluation register = template.Library() @register.assignment_tag def get_user_article_evaluation(article, user_id): status = Article.objects \ .get(id=article.id) \ .get_user_evaluation(user_id=user_id) return status

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tyagogoulartn@gmail.com

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/JogoVelha.py

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[]

no_license

Lucas-CSa/minimax-ai

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refs/heads/master

2023-05-04T15:00:18.363160

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import numpy as np import datetime from LuskinhaPlayer import BigBrainMoves from RandomPlayerJV import RandomPlayerJV from termcolor import colored class JogoVelha: def __init__(self, player1, player2): self.board = np.zeros( (9) ) self.players = [player1, player2] def printSymbol(number): if number==1: return colored('●', 'red') elif number==2: return colored('△', 'blue') else: return ' ' def printBoard(self): print('\n-----------') for i in range(0,3): print(JogoVelha.printSymbol(self.board[i])+" | ", end='') print('\n-----------') for i in range(3,6): print(JogoVelha.printSymbol(self.board[i])+" | ", end='') print('\n-----------') for i in range(6,9): print(JogoVelha.printSymbol(self.board[i])+" | ", end='') print('\n-----------') def movement(self, player, space): # # Only accepts player equal 1 or 2 # and position between 0 and 8 where: # # | 0 | 1 | 2 | # | 3 | 4 | 5 | # | 6 | 7 | 8 | # try: if(player not in (1,2)): raise Exception('Only players 1 or 2') if (self.board[space] == 0): self.board[space] = player else: raise Exception('Player '+str(player)+', you only can play in an empty space') except IndexError: raise Exception('Player '+str(player)+', you only can choose a number between 0 and 8') def hasWinner(self): if((self.board[0] == self.board[1] == self.board[2]) & (self.board[0] != 0)): return True elif((self.board[3] == self.board[4] == self.board[5]) & (self.board[3] != 0)): return True elif((self.board[6] == self.board[7] == self.board[8]) & (self.board[6] != 0)): return True elif((self.board[0] == self.board[3] == self.board[6]) & (self.board[0] != 0)): return True elif((self.board[1] == self.board[4] == self.board[7]) & (self.board[1] != 0)): return True elif((self.board[2] == self.board[5] == self.board[8]) & (self.board[2] != 0)): return True elif((self.board[0] == self.board[4] == self.board[8]) & (self.board[0] != 0)): return True elif((self.board[2] == self.board[4] == self.board[6]) & (self.board[2] != 0)): return True else: return False def isDraw(self): for i in range(0,9): if self.board[i] == 0: return False if self.hasWinner(): return False else: return True def game(self): k=1 while (not self.hasWinner()) and (not self.isDraw()): k = (int)(not k) inicio = datetime.datetime.now() self.movement(k+1, self.players[k].move(k+1, self.board)) dur = (datetime.datetime.now() -inicio).total_seconds() if(dur > 10): print('Player '+ self.players[k].name() + ' duration (seconds): '+ str(dur)) self.printBoard() if self.isDraw(): print('Draw!') # returning the winner name only for Tournament purpose return 'draw' else: print('Player '+ JogoVelha.printSymbol(k+1)+ ": " + self.players[k].name() + ' is the winner!') # returning the winner name only for Tournament purpose return self.players[k].name() def main(): JogoVelha(BigBrainMoves(), RandomPlayerJV()).game() JogoVelha(BigBrainMoves(), RandomPlayerJV()).game() if __name__ == '__main__': main()

[ "lucas.cisa19@gmail" ]

lucas.cisa19@gmail

a293d517bedd10d6570037e13c9af25a60b78578

0db4f248a6f8c036ff47a90e5f85e1e0b8e9b8a4

/label_visualize/audio/test.py

75110e91446d7606a03d851456e723cdb588f83c

[]

no_license

vunguyen1408/no-more-weekend

9c6afec60d92c7788d0c8b03472b200840d89d11

2b5025798e232ffc23cb1b498d2adb89078f602a

refs/heads/master

2021-08-08T11:54:38.244834

2017-11-10T07:22:00

null

0

null

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py

import os, os.path import pandas as pd import json import subprocess # def getLength(filename): # result = subprocess.Popen("ffprobe " + filename) # return result filename = '/u01/oracle/oradata/APEX/MARKETING_TOOL_02_JSON/2016-10-01/videos/2016-10-01_9.mp4' def getLength(input_video): result = subprocess.check_output(['ffprobe', '-i', input_video, '-show_entries', 'format=duration', '-v', 'quiet', '-of', 'csv=%s' % ("p=0")]) print (result) return float(result) print (getLength(filename))

[ "ltduong1994@gmail.com" ]

ltduong1994@gmail.com

b62b7c93a6b16398a156ceaca267898e51a6cae8

b3350357cf9f44a2357032460274113258db4949

/JavaAPIScraper/java_parser_2.py

38df83bdb59111e8edcf6646969f5e5b043860c3

[]

no_license

heenasurve/JAVA-API-Visualization

3f7be37362357adcc2a267ded08aec492b50d290

5a4527b3e928278582d9fe399d8466e407cbce16

refs/heads/master

2021-09-13T06:49:24.385831

2018-04-26T05:22:35

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from py2neo import Graph,Node,Relationship, NodeSelector import re import sys import plyj.parser import plyj.model as m files = ['rmi_server.java', 'rmi_client.java', 'rmi_server_interface.java'] descriptions = [ 'implements a remote object and registers it with the RMI registry', 'looks up and invokes a remote object.', 'provides the connection between the client and the server' ] graph = Graph(password="test") selector = NodeSelector(graph) p = plyj.parser.Parser() roles = [] for file in files: with open(file) as f: content = f.read().splitlines() roles.append(file.split(".")[0]) print("Roles : ") for role in roles: print(role) print("\n\n") for file in files: with open(file) as f: content = f.read().splitlines() roles.append(file.split(".")[0]) save_content = content imports = [] classes = [] interfaces = [] extends = [] implements =[] types = [] methods_in_sequence = [] for line in content: pattern = "java.rmi[.]?\w*" prog = re.compile(pattern) terms = line.split(" ") for term in terms: if prog.match(term): imports.append(term) if len(imports)>0: print("Imports : ") for package in imports: print(package) tree = p.parse_file(file) for type_decl in tree.type_declarations: types.append(type_decl) if type(type_decl) is plyj.model.ClassDeclaration: classes.append(type_decl) if type(type_decl) is plyj.model.InterfaceDeclaration: interfaces.append(type_decl) if len(interfaces) > 0: print("Interfaces : ") for interface in interfaces: print(interface.name) if len(classes) > 0: print("Classes : ") for class_type in classes: print(class_type.name) for type_decl in tree.type_declarations: if type_decl.extends is not None: if type(type_decl.extends) is plyj.model.Type: extends.append(type_decl.extends.name.value) else: if type(type_decl.extends) is list: extends.append(type_decl.extends[0].name.value) if hasattr(type_decl, "implements") and len(type_decl.implements) is not 0: for interface_impl in type_decl.implements: implements.append(interface_impl.name.value) for extended_class in extends: print(' -> extends ' + extended_class) for impl_interface in implements: print(' -> implements ' + impl_interface) methods = [] inner_statements = [] for type_decl in tree.type_declarations: no_of_method_decls = 0 index = 0 for declaratn in type_decl.body: if type(declaratn) is m.MethodDeclaration: no_of_method_decls += 1 for method_decl in [decl for decl in type_decl.body if type(decl) is m.MethodDeclaration]: param_strings = [] for param in method_decl.parameters: if type(param.type) is str: param_strings.append(param.type + ' ' + param.variable.name) else: param_strings.append(param.type.name.value + ' ' + param.variable.name) methods.append(method_decl.name + '(' + ', '.join(param_strings) + ')') #print(' ' + method_decl.name + '(' + ', '.join(param_strings) + ')') if method_decl.body is not None: for statement in method_decl.body: constructed_statement = "" constructed_statement = "\n" # assuming statements contained inside a block if hasattr(statement, "block") and statement.block.statements is not None: for stat in statement.block.statements: if hasattr(stat, "result"): constructed_statement += "return " + stat.result.value # statment that declares a variable that holds the result of a function call if type(stat) is plyj.model.VariableDeclaration: variable_decl = stat.variable_declarators[0] # variable declaration Type constructed_statement += stat.type.name.value # variable declaration - variable name constructed_statement += " " + variable_decl.variable.name + " = " if hasattr(variable_decl, "initializer"): # standard variable declaration if type(variable_decl.initializer) is plyj.model.Literal: constructed_statement += variable_decl.initializer.value + "\n" # an array access declaration if type(variable_decl.initializer) is plyj.model.ArrayAccess: initializer = variable_decl.initializer constructed_statement += initializer.target.value + "(" + initializer.index.value + ")" + "\n" # object creation statement if type(variable_decl.initializer) is plyj.model.InstanceCreation: initializer = variable_decl.initializer.type.name.value constructed_statement += "new " + initializer + "()" + "\n" # method_invocation_statement if type(variable_decl.initializer) is plyj.model.MethodInvocation: arguments = variable_decl.initializer.arguments constructed_statement += variable_decl.initializer.target.value + "." + variable_decl.initializer.name constructed_statement += "(" for arg in arguments: constructed_statement += arg.value + ", " constructed_statement += ")" + "\n" if hasattr(variable_decl.initializer, "expression") and \ type( variable_decl.initializer.expression) is plyj.model.MethodInvocation: expression = variable_decl.initializer.expression arguments = expression.arguments constructed_statement += expression.target.value + "." + expression.name constructed_statement += "(" for arg in arguments: constructed_statement += arg.value + ", " constructed_statement += ")" + "\n" # no variable holds the result of the method invocation if type(stat) is plyj.model.MethodInvocation: arguments = stat.type_arguments constructed_statement += stat.target.value + "." + stat.name constructed_statement += "(" for arg in arguments: constructed_statement += arg.value + ", " constructed_statement += ")" + "\n" # statement not contained in a block else: # plain return statement stat = statement if hasattr(stat, "result"): constructed_statement += "return " + stat.result.value + "\n" if type(stat) is plyj.model.VariableDeclaration: variable_decl = stat.variable_declarators[0] # variable declaration Type constructed_statement += stat.type.name.value # variable declaration - variable name constructed_statement += " " + variable_decl.variable.name + " = " if hasattr(variable_decl, "initializer"): # standard variable declaration if type(variable_decl.initializer) is plyj.model.Literal: constructed_statement += variable_decl.initializer.value + "\n" # an array access declaration if type(variable_decl.initializer) is plyj.model.ArrayAccess: initializer = variable_decl.initializer constructed_statement += initializer.target.value + "(" +initializer.index.value+ ")" + "\n" # object creation statement if type(variable_decl.initializer) is plyj.model.InstanceCreation: initializer = variable_decl.initializer.type.name.value constructed_statement += "new " + initializer + "()" + "\n" # method_invocation_statement if type(variable_decl.initializer) is plyj.model.MethodInvocation: arguments = variable_decl.initializer.arguments constructed_statement += variable_decl.initializer.target.value + "." + variable_decl.initializer.name constructed_statement += "(" for arg in arguments: constructed_statement += arg.value + ", " constructed_statement += ")" + "\n" if hasattr(variable_decl.initializer, "expression") and \ type( variable_decl.initializer.expression) is plyj.model.MethodInvocation: expression = variable_decl.initializer.expression arguments = expression.arguments constructed_statement += expression.target.value + "." + expression.name constructed_statement += "(" for arg in arguments: constructed_statement += arg.value + ", " constructed_statement += ")" + "\n" # # no variable holds the result of the method invocation # if type(stat) is plyj.model.MethodInvocation: # # arguments = variable_decl.arguments # constructed_statement += stat.target.value + "." + stat.name # # constructed_statement += '[' + ', '.join(arguments) + ']' # # # variable decaration statement # if type(statement) is plyj.model.VariableDeclaration: # variable_decl = statement.variable_declarators[0] # constructed_statement += statement.type.name.value + " " # constructed_statement += variable_decl.variable.name + " " + variable_decl.initializer # # # no variable holds the result of the method invocation # if type(statement) is plyj.model.MethodInvocation: # expression = variable_decl.initializer.expression # arguments = expression.arguments # constructed_statement += expression.target.value + "." + expression.name # constructed_statement += '[' + ', '.join(arguments) + ']' # inner_method = Node("contained_statement", # name=stat.target.value + "." + stat.name, # arguments=expression.arguments # ) # graph.create(inner_method) # inner_statements.append(inner_method) inner_statements.append(constructed_statement) for idx,method in enumerate(methods): print(' ' + "-> " + method) if len(inner_statements) > 0: print(inner_statements[idx] + "\n")

[ "hfsurve@hotmail.com" ]

hfsurve@hotmail.com

90745e3748852df86cbde84effdd8c05856a95bb

07e4bfaa0a9e36becef7f4163c42535810a8bc07

/application.py

a34959d4e8d9a1977bed57524ce6e6c08ffaa0ae

[]

no_license

uorocketry/UIgnite

8bef44668f4e1f46b9b8e904fc5f961948179eda

93cbe9f908c21c56501d4dab286aeabf4b8d78be

refs/heads/master

2021-09-24T11:59:03.593599

2018-10-09T16:19:09

108,095,504

2

1

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#!/usr/bin/python import argparse from Tkinter import * from Utility import * import serial import sys import time parser = argparse.ArgumentParser(description='establish serial communication with Serial port based on platform and port no.') parser.add_argument('platform', help="w -> windows | u-> unix | uw -> bash on windows (build 16299 and later)") parser.add_argument('port_number', help="if using unix, it is X in ttyUSBX | if using windows, it is X in COMX") args = parser.parse_args() def add_text(words, box): box.insert(CURRENT, words) box.pack() def ping(serial,message, box): add_text("\nPinging Igniter", box) serial.flushInput() serial.write(message) add_text("\nwaiting for reply", box) try: serial.readline() add_text("\n IGNITER: %s" % serial.readline(), box) except Exception as e: add_text( "\nIGNITER: could not read line", box) return def fire(serial, message, box): add_text("\nStarting Ignition!",box) serial.write(message) add_text("\nwaiting for confirmation",box) try: add_text("\n IGNITER: %s" % serial.readline(), box) except Exception as e: add_text( "\nIGNITER: could not read line", box) return def stop(serial,message1, message2, box): add_text("\nSTOPPING!",box) serial.write(message1) add_text("\nwaiting for confirmation",box) try: add_text("\n IGNITER: %s" % serial.readline(), box) except Exception as e: add_text( "\nIGNITER: could not read line", box) time.sleep(0.05) add_text("\nCLOSING!", box) serial.write(message2) add_text("\nwaiting for confirmation",box) try: add_text("\n IGNITER: %s" % serial.readline(), box) except Exception as e: add_text( "\nIGNITER: could not read line", box) return def main(argv): print (argv) temp = (configure_usb(argv[0], argv[1])) ser = serial.Serial(temp,timeout=2) try: ser.close() ser.open() except Exception as e: print("port error") root = Tk() root.title("UIgnite") frame = Frame(root) frame.pack() topframe = Frame(root) bottomframe = Frame(root) bottomframe.pack( side = BOTTOM ) topframe.pack(side = TOP) text = Text(bottomframe) text.insert(INSERT, "Hello.....") text.pack() redbutton = Button(topframe, text="STOP", fg="red",command=lambda:stop(ser,"1","5",text)) redbutton.pack( side = LEFT) greenbutton = Button(topframe, text="START", fg="green", command=lambda: fire(ser,"2",text)) greenbutton.pack( side = LEFT ) bluebutton = Button(topframe, text="PING", fg="blue", command=lambda: ping(ser,"6",text)) bluebutton.pack( side = RIGHT ) root.mainloop() if __name__ == "__main__": #print(sys.argv[1],sys.argv[2]) main(sys.argv[1:])

[ "pbuzuloiu@yahoo.ca" ]

pbuzuloiu@yahoo.ca

4be1b8d89abbdf4762c8e8d0166a630f7f34f665

b194de597ed9f327859d697f6f1980ff686fb50d

/תרגול4.py

030ff223cfbbff1f2511047700cf9cdc86809a17

[]

no_license

barjakobovitz/limodim

a43cb64fb488bada356caa7534c3ba777ee97f74

32ba6c74fc4bfe17505b0827bc4f6cac27e7a9d7

refs/heads/main

2023-01-24T02:55:08.158186

2020-11-24T15:27:49

315,673,554

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null

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py

#ex1 lst=["what","is","The","matrix","neo"] a=1 z=3 low= min(a,z) high= max(a,z) new_list= sorted(lst[low:high+1], key=str.lower) print (lst) print (new_list) #ex2 phone_book=[["bar","0545900961"],["zvi","0545680300"]] name="zvi" found= False phone= "" for contact in phone_book: if contact[0]==name: found=True phone=contact[1] if found==True: print ("name: ", contact[0], "phone: ", contact[1]) break if found==False: print ("didnt found")

[ "noreply@github.com" ]

barjakobovitz.noreply@github.com

c1f4ea1630b5cb9c568febf67aadb059bc3f6f4d

214a1f5f650729e25858aea46ce1be6359e760cb

/ex3.py

8206702c9758994d74109ec43f02a488e6bfd91d

[]

no_license

deekshithas/pycode

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refs/heads/master

2020-03-27T10:11:47.283796

2018-08-28T06:38:44

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null

UTF-8

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false

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py

import cv2 #scaling,resizing and interpolation img=cv2.imread("flower.jpg") cv2.imshow("original image",img) cv2.waitKey(0) img_scaled=cv2.resize(img,None,fx=0.75,fy=0.75) cv2.imshow("scaling linear interpolation",img_scaled) cv2.waitKey() #lets double the size of our image img_scaled1=cv2.resize(img,None,fx=2,fy=2,interpolation=cv2.INTER_CUBIC) cv2.imshow("scaling-cubic interpolation",img_scaled1) cv2.waitKey() #lets skew the resized by setting exact dimensions img_scaled2=cv2.resize(img,(900,400),interpolation=cv2.INTER_AREA) cv2.imshow("scaling-skewed size",img_scaled2) cv2.waitKey() cv2.destroyAllWindows()

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Jubayer247/mixed

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2023-07-21T23:23:12.462707

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from gtts import gTTS tts = gTTS('hello', lang='en') tts.save('hello.mp3')

[ "mdjubayer247@gmail.com" ]

mdjubayer247@gmail.com

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/asking/forms.py

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GeniailBonehead/asking

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2023-08-28T03:13:16.033814

2021-11-10T20:23:59

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from django import forms from django.contrib.auth.forms import AuthenticationForm class UserLoginForm(AuthenticationForm): username = forms.CharField(label='Имя пользователя', widget=forms.TextInput(attrs={'class': 'form-control'})) password = forms.CharField(label='Пароль', widget=forms.PasswordInput(attrs={'class': 'form-control'})) class QuestionForm(forms.Form): answer = forms.CharField(label='Введите ответ на вопрос', max_length=100) question_id = forms.IntegerField() class AskingForm(forms.Form): name = forms.CharField(max_length=60) question_text = forms.CharField(label='description', max_length=500) start_date = forms.DateTimeField(label='date start') finish_date = forms.DateTimeField(label='date finish') class QuestionChangeForm(forms.Form): question_text = forms.CharField(label='description', max_length=500) answer_type = forms.IntegerField(label='answer type') answer_choices = forms.CharField(label='answer choices')

[ "dstrannik10@gmail.com" ]

dstrannik10@gmail.com

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/sniffer/wsgi.py

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BraFei/sniffer

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refs/heads/master

2020-03-21T17:10:37.403760

2018-07-08T08:04:19

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""" WSGI config for sniffer project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/2.0/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "sniffer.settings") application = get_wsgi_application()

[ "1836088871@qq.com" ]

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/domiporta/category.py

8ca6b1c0a9d1ff931e2db55a10fe278b0022b414

[ "MIT" ]

permissive

nieruchomosci/pydomiporta

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refs/heads/master

2020-03-11T16:06:48.428938

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#!/usr/bin/python # -*- coding: utf-8 -*- import logging from bs4 import BeautifulSoup import domiporta import domiporta.utils log = logging.getLogger(__file__) logging.basicConfig(level=logging.DEBUG) def get_category(url=None, category='nieruchomosci', transaction_type='wszystkie', voivodeship=None, city=None, street=None, filters=None): """ Parses available offer urls from given category search page :param url: Url to search web page :param category: Type of property of interest (Mieszkanie/Dom/Garaż/Działka) :param transaction_type: Type of transaction :param voivodeship: Voivodeship :param city: City :param street: Street :param filters: Dictionary with additional filters :type url: str, None :type category:str, None :type transaction_type: str, None :type voivodeship: str, None :type city: str, None :type street: str, None :type filters: dict, None :return: List of urls of all offers for given parameters :rtype: list """ if url is None: url = domiporta.utils.get_url(category, transaction_type, voivodeship, city, street, filters) page, max_number_page, offers = 1, None, [] while max_number_page is None or page <= max_number_page: page_number = "PageNumber=" + str(page) join_param = '?' if '?' not in url else '&' page_url = "{0}{1}{2}".format(url, join_param, page_number) offers_urls, markup = get_offers_from_category(page_url) offers += offers_urls if page == 1: max_number_page = domiporta.utils.get_max_number_page(markup) # assert False page += 1 return offers def get_offers_from_category(url): """ Parses available offer urls from given category from given page :param url: Defined url for Domiporta page with offers :type url: str :return: List of urls from given page :rtype: list """ markup = BeautifulSoup(domiporta.utils.get_content_from_source(url), 'html.parser') offers_urls = [] offers = markup.find_all('div', class_='detail-card') for offer in offers: offers_urls.append(offer.find('a').get('href')) return offers_urls, markup

[ "mail@pythonic.ninja" ]

mail@pythonic.ninja

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/pyQtTuts-v04.py

c1cb44f6e9accb3892be1313d731ca913e2555c4

[]

no_license

SlyCodePanda/a-mess-of-pyqt-stuff

2e3404fa4833d39e1b9bac828f247028f583ffe4

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refs/heads/master

2021-01-01T06:56:46.071814

2017-07-18T05:45:36

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import sys from PyQt4 import QtGui, QtCore # Window inherits from the Qt widget 'MainWindow' class Window(QtGui.QMainWindow): # Any core application stuff goes in the 'init' function. def __init__(self): # With 'super' what we return is the parent object and so our parent object would be out main window object. super(Window, self).__init__() # x: 50, y: 50, width: 500, height: 300. self.setGeometry(50, 50, 500, 300) self.setWindowTitle("PyQt tuts") self.setWindowIcon(QtGui.QIcon('index.png')) # Main menu. # Adding Items to the main menu: extractAction = QtGui.QAction("&GET TO THE CHOPPAH!!!", self) extractAction.setShortcut("Ctrl+Q") extractAction.setStatusTip('Leave The App') extractAction.triggered.connect(self.close_application) anotherAction = QtGui.QAction("It's a trap!!", self) anotherAction.setShortcut("Ctrl+A") anotherAction.setStatusTip('This does nothing lol') anotherAction.triggered.connect(self.get_tricky) # Editor. openEditor = QtGui.QAction("&Editor", self) openEditor.setShortcut("Ctrl+E") openEditor.setStatusTip('Open Editor') openEditor.triggered.connect(self.editor) # Open. openFile = QtGui.QAction("&Open", self) openFile.setShortcut("Ctrl+O") openFile.setStatusTip('Open File') openFile.triggered.connect(self.file_open) # Save. saveFile = QtGui.QAction("&Save", self) saveFile.setShortcut("Ctrl+S") saveFile.setStatusTip('Save File') saveFile.triggered.connect(self.file_save) self.statusBar() # Menu definitions: mainMenu = self.menuBar() fileMenu = mainMenu.addMenu('&File') editMenu = mainMenu.addMenu('&Edit') fileMenu.addAction(extractAction) editMenu.addAction(anotherAction) fileMenu.addAction(openFile) fileMenu.addAction(saveFile) editorMenu = mainMenu.addMenu("&Editor") editorMenu.addAction(openEditor) self.home() # Stuff that is specific to that certain page that you are on. def home(self): btn = QtGui.QPushButton("Quit", self) # What happens when the button is clicked. btn.clicked.connect(self.close_application) btn.resize(btn.minimumSizeHint()) btn.move(0,100) # Adding the Toolbar: extractAction = QtGui.QAction(QtGui.QIcon('todachoppa.png'), 'Flea the Scene', self) extractAction.triggered.connect(self.close_application) self.toolBar = self.addToolBar("Extraction") self.toolBar.addAction(extractAction) # Adding Font Widget: fontChoice = QtGui.QAction('Font', self) fontChoice.triggered.connect(self.font_choice) self.toolBar.addAction(fontChoice) # Adding Colour Picker Widget. Changes the background colour of the font background: colour = QtGui.QColor(0, 0, 0) fontColour = QtGui.QAction('Font bg Colour', self) fontColour.triggered.connect(self.colour_picker) self.toolBar.addAction(fontColour) # Adding Check Box: checkBox = QtGui.QCheckBox('Enlarge Window', self) checkBox.move(300, 25) checkBox.stateChanged.connect(self.enlarge_window) # Adding Progress Bar: self.progress = QtGui.QProgressBar(self) self.progress.setGeometry(200, 80, 250, 20) self.btn = QtGui.QPushButton("Download", self) self.btn.move(200, 120) self.btn.clicked.connect(self.download) # Adding Drop Down Button. That allows you to set the GUI style : # This prints out the default style that you GUI is set to to your terminal. print(self.style().objectName()) self.styleChoice = QtGui.QLabel("GTK+", self) comboBox = QtGui.QComboBox(self) # A selection of GUI styles from the QStyleFactory. comboBox.addItem("motif") comboBox.addItem("Windows") comboBox.addItem("cde") comboBox.addItem("Plastique") comboBox.addItem("Cleanlooks") comboBox.addItem("gtk+") comboBox.move(20, 250) self.styleChoice.move(50, 150) comboBox.activated[str].connect(self.style_choice) # Adding Calendar Widget: cal = QtGui.QCalendarWidget(self) cal.move(500, 200) cal.resize(200, 200) self.show() def colour_picker(self): colour = QtGui.QColorDialog.getColor() self.styleChoice.setStyleSheet("QWidget { background-color: %s}" % colour.name()) def editor(self): self.textEdit = QtGui.QTextEdit() self.setCentralWidget(self.textEdit) def file_open(self): name = QtGui.QFileDialog.getOpenFileName(self, 'Open File') # Opening with the intention to read ('r'). file = open(name, 'r') # Need to call the editor, because it is not there by default. self.editor() with file: text = file.read() self.textEdit.setText(text) def file_save(self): name = QtGui.QFileDialog.getSaveFileName(self, 'Save File') # Intention to write the file ('w') file = open(name, 'w') text = self.textEdit.toPlainText() file.write(text) file.close() def font_choice(self): font, valid = QtGui.QFontDialog.getFont() if valid: self.styleChoice.setFont(font) def style_choice(self, text): self.styleChoice.setText(text) # Sets the style of your GUI. QtGui.QApplication.setStyle(QtGui.QStyleFactory.create(text)) def download(self): self.completed = 0 while self.completed < 100: self.completed += 0.0001 self.progress.setValue(self.completed) def enlarge_window(self, state): if state == QtCore.Qt.Checked: self.setGeometry(50, 50, 1000, 600) else: self.setGeometry(50, 50, 500, 300) def close_application(self): # Pop-Up Message: choice = QtGui.QMessageBox.question(self, 'Extract', "Get into the chopper?", QtGui.QMessageBox.Yes | QtGui.QMessageBox.No) if choice == QtGui.QMessageBox.Yes: print( "Extracting now") sys.exit() else: pass def get_tricky(self): self.setWindowTitle("OH SHIT DAT TRICKY!") # Our 'main'. def run(): app = QtGui.QApplication(sys.argv) GUI = Window() sys.exit(app.exec_()) run() '''NOTES: To convert a .ui file to a .py file you need to run the pyuic using the following command: pyuic4 -o ui_form.py form.ui

[ "reneemarsland@hotmail.com" ]

reneemarsland@hotmail.com

1738a57b1e32175ba5e0dc80f365532ff1433e5a

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/personal/RebeccaTippens/new_averaged_spectra.py

27966ca752346a02a995b53b74f51e5ff51beaaa

[]

no_license

gnarayan/astr596

81fe27bf67e7caa68e8c314cdbe68e818f0512dd

1f76df17903875162885308479831d8bb345c2b7

refs/heads/master

2021-01-20T22:02:00.941404

2017-08-29T17:00:21

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#Use numpy and scipy import math import numpy as np from scipy.interpolate import interp1d import matplotlib.pyplot as plt #Read data from first file into two arrays #could also use array = np.loadtext() x1=[] y1=[] for line in open('/Users/Rebecca/astr596/data/sn2011by-hst+lick.flm'): columns = line.split() if len(columns) == 2: if float(columns[0]) <= 5715: # truncation x1.append(float(columns[0])) y1.append(float(columns[1])) #Read data from second file into two arrays #could also use array = np.loadtext() x2 = [] y2 = [] for line in open('/Users/Rebecca/astr596/data/cfa/sn1995Y/sn1995Y-19950916.49-fast.flm'): columns = line.split() if len(columns) == 2: x2.append(float(columns[0])) y2.append(float(columns[1])) def avg(*vals): return float(sum(vals))/float(len(vals)) #print avg(1,2,3,9.5) #test line for avg function x3 = [] y3 = [] for x1_val in range(0,len(x1)): for x2_val in range (0,len(x2)): if x1[x1_val] == x2[x2_val]: x3.append(x1[x1_val]) y3.append(avg(y1[x1_val],y2[x2_val])) x3_string = str(x3) y3_string = str(y3) x2_val += 1 x1_val += 1 f3 = interp1d(x3, y3, kind='linear') x4 = x3 y4 = f3(x3) #--------------- the point at which my code becomes useful again #Read averaged data to file -- change this to .flm or whatever np.savetxt('averaged_spectra.txt', np.transpose([x3,y3]), fmt="%d %26.18e") #Plot arrays one by the other plt.subplot(111) plt.plot(x1, y1, color='c', linewidth=1) plt.plot(x2, y2, color='m', linewidth=1) plt.plot(x3, y3, color='#ff6600', linewidth=1) plt.plot(x4, y4, color='b', linewidth=1) plt.yscale('log') plt.xlabel('Wavelength ' + '(' + u'\u212B' + ')') plt.ylabel('log (Flux)') plt.subplot(112) #pyplot.savefig('averaged_spectra.eps', format='eps') # saves plot as .eps plt.savefig('averaged_spectra.pdf', format='PDF') plt.show()

[ "Rebecca.Tippens@gmail.com" ]

Rebecca.Tippens@gmail.com

83d0a32ef2d365d17caa9d311c367ed5828559ac

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/coding_python/2729.py

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[]

no_license

PaengE/AlgorithmPractice

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refs/heads/master

2023-07-08T10:09:15.874030

2021-08-11T08:10:15

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n, m = map(int, input().split()) li = list(map(int, input().split())) max = 0 for i in range(0, n): for j in range(i+1, n): for k in range(j+1, n): tmp = li[i] + li[j] + li[k] if(tmp <= m and max < tmp): max = tmp print(max)

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ckddn2820@gmail.com

38dca13d0bf9d00206d097874ed1f6efdea2317e

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/python programming/beginner/reverse number.py

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no_license

inbavalli/python

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refs/heads/master

2018-11-11T10:09:12.370195

2018-08-22T11:15:28

120,161,666

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null

UTF-8

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py

main() for v in range(20,0,-1): print(v)

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/models/multiatt/model_noPadding.py

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[]

no_license

tanjatang/DMVCR

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refs/heads/main

2023-07-10T10:34:20.693260

2021-08-23T10:11:34

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""" Let's get the relationships yo """ from typing import Dict, List, Any import math import torch import torch.nn as nn import numpy as np import torch.nn.functional as F import torch.nn.parallel from allennlp.data.vocabulary import Vocabulary from allennlp.models.model import Model from allennlp.modules import TextFieldEmbedder, Seq2SeqEncoder, FeedForward, InputVariationalDropout, TimeDistributed from allennlp.training.metrics import CategoricalAccuracy from allennlp.modules.matrix_attention import BilinearMatrixAttention from utils.detector import SimpleDetector from allennlp.nn.util import masked_softmax, weighted_sum, replace_masked_values from allennlp.nn import InitializerApplicator import os # import pickle import pickle import ipdb #######################################3 from PIL import Image import pylab import matplotlib.pyplot as plt import matplotlib.patches as patches from matplotlib import pyplot from matplotlib.patches import Rectangle ####################################### SAVE_ROOT = "/phys/ssd/tangxueq/tmp/vcr/vcrimage/rationale" @Model.register("MultiHopAttentionQA") class AttentionQA(Model): def __init__(self, vocab: Vocabulary, span_encoder: Seq2SeqEncoder, reasoning_encoder: Seq2SeqEncoder, # lstm_encoder: Seq2SeqEncoder, input_dropout: float = 0.3, hidden_dim_maxpool: int =512, class_embs: bool=True, reasoning_use_obj: bool=True, reasoning_use_answer: bool=True, reasoning_use_question: bool=True, pool_reasoning: bool = True, pool_answer: bool = True, pool_question: bool = False, initializer: InitializerApplicator = InitializerApplicator(), ): super(AttentionQA, self).__init__(vocab) ################################################################################################################################ path = '/home/tangxueq/MA_tang/r2c/models/saves/memory_cell.npz' self.memory_cell = torch.nn.Parameter(torch.Tensor(self.memory_cell_load(path))) print(self.memory_cell) ############################################################################################################################### # if os.path.isfile(self.memory_cell_path): # print('load memory_cell from {0}'.format(self.memory_cell_path)) # memory_init = np.load(self.memory_cell_path)['memory_cell'][()] # else: # print('create a new memory_cell') # memory_init = np.random.rand(1000, 1024) / 100 # memory_init = np.float32(memory_init) # self.memory_cell = torch.from_numpy(memory_init).cuda().requires_grad_() ################################################################################################################################# self.detector = SimpleDetector(pretrained=True, average_pool=True, semantic=class_embs, final_dim=512) ################################################################################################### INPUT_SIZE =512 #768 self.lstm = nn.LSTM( input_size=INPUT_SIZE, hidden_size=512, num_layers=2, # hidden_layer的数目 batch_first=True, # 输入数据的维度一般是（batch, time_step, input)，该属性表征batch是否放在第一个维度 bidirectional=True ) ############################################################################################################################3 self.linear = torch.nn.Linear(512,256) self.mask_linear = torch.nn.Linear(1,512) self.encoder_layer_vc = nn.TransformerEncoderLayer(d_model=512, nhead=8) self.transformer_encoder_vc = nn.TransformerEncoder(self.encoder_layer_vc, num_layers=3) self.AvgPool =nn.AdaptiveAvgPool1d(1) # self.encoder_layer_cv = nn.TransformerEncoderLayer(d_model=768, nhead=8) # self.transformer_encoder_cv = nn.TransformerEncoder(self.encoder_layer_cv, num_layers=2) self.image_AttFlat = AttFlat(512) self.qa_AttFlat = AttFlat(768) self.proj_norm = LayerNorm(1024) self.proj = nn.Linear(1024, 4) ############################################################################################################################ self.norm = torch.nn.BatchNorm1d(256) self.lstm_norm = torch.nn.BatchNorm1d(200) self.rnn_input_dropout = TimeDistributed(InputVariationalDropout(input_dropout)) if input_dropout > 0 else None self.span_encoder = TimeDistributed(span_encoder) self.span_reshape = TimeDistributed(torch.nn.Linear(512, 768)) self.qao_reshape = torch.nn.Linear(512,768) # self.out = nn.Linear(50, 1) # self.reasoning_encoder = TimeDistributed(lstm_encoder) self.span_attention = BilinearMatrixAttention( matrix_1_dim=span_encoder.get_output_dim(), matrix_2_dim=span_encoder.get_output_dim(), ) self.obj_attention = BilinearMatrixAttention( matrix_1_dim=span_encoder.get_output_dim(), matrix_2_dim=self.detector.final_dim, ) self.reasoning_use_obj = reasoning_use_obj self.reasoning_use_answer = reasoning_use_answer self.reasoning_use_question = reasoning_use_question self.pool_reasoning = pool_reasoning self.pool_answer = pool_answer self.pool_question = pool_question #[96,4,50,1024] dim = 512#768*2 #sum([d for d, to_pool in [(reasoning_encoder.get_output_dim(), self.pool_reasoning), # (span_encoder.get_output_dim(), self.pool_answer), # (span_encoder.get_output_dim(), self.pool_question)] if to_pool]) # self.final_mlp = torch.nn.Sequential( # torch.nn.Flatten(), # torch.nn.Dropout(input_dropout, inplace=False), # torch.nn.Linear(dim, hidden_dim_maxpool), # torch.nn.ReLU(inplace=True), # torch.nn.Dropout(input_dropout, inplace=False), # torch.nn.Linear(hidden_dim_maxpool, 4), # ) # self.final_mlp = torch.nn.Sequential( # torch.nn.Flatten(), # torch.nn.Dropout(input_dropout, inplace=False), # torch.nn.Linear(dim, hidden_dim_maxpool), # torch.nn.LeakyReLU(inplace=True), # # torch.nn.Dropout(input_dropout, inplace=False), # torch.nn.Linear(hidden_dim_maxpool, 4), # ) # self.final_mlp = torch.nn.Sequential( # torch.nn.Flatten(), # torch.nn.Dropout(input_dropout, inplace=False), # torch.nn.Linear(dim,4)) #------------------------------------------------------------------ self.final_mlp = torch.nn.Sequential( torch.nn.Dropout(input_dropout, inplace=False), torch.nn.Linear(dim, hidden_dim_maxpool), torch.nn.ReLU(inplace=True), torch.nn.Dropout(input_dropout, inplace=False), torch.nn.Linear(hidden_dim_maxpool, 1), ) #---------------------------------------------------------------------- self._accuracy = CategoricalAccuracy() self._loss = torch.nn.CrossEntropyLoss() ##################################################################### initializer(self) ############################################################################## # self.memory_cell_path = getattr(opt, 'memory_cell_path', '0') def memory_cell_load(self,path): if os.path.isfile(path): print('load memory_cell from {0}'.format(path)) # memory_init = np.load(self.memory_cell_path)['memory_cell'] memory_init = torch.load(path)['memory_cell'][()] else: print('create a new memory_cell') # memory_init = np.random.rand(10000, 1024)/ 100 # memory_init = torch.random(10000,1024)/100 memory_init = torch.rand(200, 256)/100 # memory_init = memory_init.long() # self.memory_cell = torch.from_numpy(memory_init).cuda().requires_grad_() return memory_init def _collect_obj_reps(self, span_tags, object_reps): """ Collect span-level object representations :param span_tags: [batch_size, ..leading_dims.., L] :param object_reps: [batch_size, max_num_objs_per_batch, obj_dim] :return: """ span_tags_fixed = torch.clamp(span_tags, min=0) # In case there were masked values here row_id = span_tags_fixed.new_zeros(span_tags_fixed.shape) row_id_broadcaster = torch.arange(0, row_id.shape[0], step=1, device=row_id.device)[:, None] # Add extra diminsions to the row broadcaster so it matches row_id leading_dims = len(span_tags.shape) - 2 for i in range(leading_dims): row_id_broadcaster = row_id_broadcaster[..., None] row_id += row_id_broadcaster return object_reps[row_id.view(-1), span_tags_fixed.view(-1)].view(*span_tags_fixed.shape, -1) def embed_span(self, span, span_tags, span_mask, object_reps): """ :param span: Thing that will get embed and turned into [batch_size, ..leading_dims.., L, word_dim] :param span_tags: [batch_size, ..leading_dims.., L] :param object_reps: [batch_size, max_num_objs_per_batch, obj_dim] :param span_mask: [batch_size, ..leading_dims.., span_mask :return: """ retrieved_feats = self._collect_obj_reps(span_tags, object_reps) span_rep = torch.cat((span['bert'], retrieved_feats), -1) # add recurrent dropout here if self.rnn_input_dropout: span_rep = self.rnn_input_dropout(span_rep) # x = self.span_encoder(span_rep, span_mask) # x = self.span_reshape(x) return self.span_encoder(span_rep, span_mask), retrieved_feats def padding(self,q_rep,a_rep): # max_len = max(max([i.size(2) for i in q_rep]), max([i.size(2) for i in a_rep])) max_len = max(a_rep.size(2),q_rep.size(2)) a1, b1, c1, d1 = a_rep.size() a2, b2, c2, d2 = q_rep.size() padding_a = torch.zeros(a1, b1, max_len - c1, d1).float().cuda() padding_q = torch.zeros(a2, b2, max_len - c2, d2).float().cuda() q_rep_new = torch.cat((q_rep, padding_q), dim=2) a_rep_new = torch.cat((a_rep, padding_a), dim=2) qa_rep = torch.cat((q_rep_new, a_rep_new), dim=3) # [batch_size, 8, seq_len, 1536] return qa_rep def Dictionary(self,h,M): #[96*4,768] M[10000,768] h_size = h.size() # [96*4,768] # h = h.view(-1, h_size[3]) # [(96*4),768] att = torch.mm(h, torch.t(M)) # [(96*4*50),768] * [768,10000] att = F.softmax(att, dim=1) # [96*4*50,10000] att_res = torch.mm(att, M) # [96*4,10000]*[10000,768] -> #[96*4,768] # att_res = att_res.view([-1, h_size[1]*h_size[2], h_size[3]]) #[96*4,768] return att_res def make_mask(self, feature): return (torch.sum( torch.abs(feature), dim=-1 ) == 0).unsqueeze(1).unsqueeze(2) def forward(self, training_mode, # images: torch.Tensor, obj_reps: Dict[str, torch.Tensor], objects: torch.LongTensor, segms: torch.Tensor, boxes: torch.Tensor, box_mask: torch.LongTensor, question: Dict[str, torch.Tensor], question_tags: torch.LongTensor, question_mask: torch.LongTensor, answers: Dict[str, torch.Tensor], answer_tags: torch.LongTensor, answer_mask: torch.LongTensor, metadata: List[Dict[str, Any]] = None, label: torch.LongTensor = None) -> Dict[str, torch.Tensor]: """ :param images: [batch_size, 3, im_height, im_width] :param objects: [batch_size, max_num_objects] Padded objects :param boxes: [batch_size, max_num_objects, 4] Padded boxes :param box_mask: [batch_size, max_num_objects] Mask for whether or not each box is OK :param question: AllenNLP representation of the question. [batch_size, num_answers, seq_length] :param question_tags: A detection label for each item in the Q [batch_size, num_answers, seq_length] :param question_mask: Mask for the Q [batch_size, num_answers, seq_length] :param answers: AllenNLP representation of the answer. [batch_size, num_answers, seq_length] :param answer_tags: A detection label for each item in the A [batch_size, num_answers, seq_length] :param answer_mask: Mask for the As [batch_size, num_answers, seq_length] :param metadata: Ignore, this is about which dataset item we're on :param label: Optional, which item is valid :return: shit """ # Trim off boxes that are too long. this is an issue b/c dataparallel, it'll pad more zeros that are # not needed # print("question:\n", len(question), "\n") # print(question['bert'].size()) # print("answers:\n", len(answers), "\n") # print(answers['bert'].size()) max_len = int(box_mask.sum(1).max().item()) objects = objects[:, :max_len] box_mask = box_mask[:, :max_len] boxes = boxes[:, :max_len] segms = segms[:, :max_len] for tag_type, the_tags in (('question', question_tags), ('answer', answer_tags)): if int(the_tags.max()) > max_len: raise ValueError("Oh no! {}_tags has maximum of {} but objects is of dim {}. Values are\n{}".format( tag_type, int(the_tags.max()), objects.shape, the_tags )) # obj_reps = self.detector(images=images, boxes=boxes, box_mask=box_mask, classes=objects, segms=segms) ################################################################################################################## '''try idea to create new model with dictionary''' '''padding q_rep and a_rep with 0''' # max_len = max(max([i.size(2) for i in question['bert']]),max([i.size(2) for i in answers['bert']])) # a, b, c, d = a_rep.size() # padding = torch.zeros(a,b,max_len,d).float() # q_rep_new = torch.cat((q_rep,padding),dim = 2) # a_rep_new = torch.cat((a_rep,padding), dim = 2) # print(question['bert'].size()) if training_mode == 0: # qa_rep = self.padding(question['bert'],answers['bert']) #[batch_size, 8, seq_len, 768] qa_rep = torch.cat((question['bert'],answers['bert']),dim = 2) elif training_mode == 1: q_rep, q_obj_reps = self.embed_span(question, question_tags, question_mask, obj_reps['obj_reps']) a_rep, a_obj_reps = self.embed_span(answers, answer_tags, answer_mask, obj_reps['obj_reps']) ##########################################################################3 qa_similarity = self.span_attention( q_rep.view(q_rep.shape[0] * q_rep.shape[1], q_rep.shape[2], q_rep.shape[3]), a_rep.view(a_rep.shape[0] * a_rep.shape[1], a_rep.shape[2], a_rep.shape[3]), ).view(a_rep.shape[0], a_rep.shape[1], q_rep.shape[2], a_rep.shape[2]) qa_attention_weights = masked_softmax(qa_similarity, question_mask[..., None], dim=2) # formula attended_q = torch.einsum('bnqa,bnqd->bnad', (qa_attention_weights, q_rep)) # Have a second attention over the objects, do A by Objs # [batch_size, 4, answer_length, num_objs] atoo_similarity = self.obj_attention(a_rep.view(a_rep.shape[0], a_rep.shape[1] * a_rep.shape[2], -1), obj_reps['obj_reps']).view(a_rep.shape[0], a_rep.shape[1], a_rep.shape[2], obj_reps['obj_reps'].shape[1]) atoo_attention_weights = masked_softmax(atoo_similarity, box_mask[:, None, None]) attended_o = torch.einsum('bnao,bod->bnad', (atoo_attention_weights, obj_reps['obj_reps'])) # print('qa_similarity.size(): ', qa_similarity.size(),'\n', 'attended_q.size(): ', attended_q.size(),'\n', # 'qa_attention_weights.size(): ', qa_attention_weights.size(),'\n','atoo_attention_weights.size() :',atoo_attention_weights.size(),'\n','attended_o.size() ',attended_o.size()) # reasoning_inp = torch.cat([x for x, to_pool in [(a_rep, self.reasoning_use_answer), # (attended_o, self.reasoning_use_obj), # (attended_q, self.reasoning_use_question)] # if to_pool], -1) # # # print("..........", q_rep,"////////",a_rep) # qa_rep = torch.cat((attended_q,attended_o),dim = 2) qa_rep = torch.cat((q_rep,a_rep),dim = 2) # reasoning_inp = torch.cat([x for x, to_pool in [(a_rep, self.reasoning_use_answer), # (attended_o, self.reasoning_use_obj), # (attended_q, self.reasoning_use_question)] # if to_pool], -1) # # print(qa_rep.size(),'reasoning_inp.size()') [96,4,29,1536] # qa_rep = self.qao_reshape(qao_rep) if self.rnn_input_dropout is not None: qa_rep = self.rnn_input_dropout(qa_rep) ################################################################################# #clip # a1, a2, a3, a4 = qa_rep.size() # if a3 >= 50: # qa_rep = qa_rep[:, :, 0:50, :].contiguous() # # else: # padding_i = torch.zeros(a1, a2, 50 - a3, a4).cuda() # qa_rep = torch.cat((qa_rep, padding_i), dim=2) # # print(qa_rep.size(),'qa_rep.size()') ################################################################## qa_rep_size = qa_rep.size() qa_rep = qa_rep.view(qa_rep_size[0]*qa_rep_size[1],qa_rep_size[2],qa_rep_size[3]) #qa_rep[96*4,50,768] ######################################################################################################### # # output, (h_n, h_c) = self.lstm(qa_rep) # self.reasoning_encoder(qa_rep) # # # # output = output[:, -1, :] # 取最后一层输出 [96*4,1024] ########################################################################################################### '''这一段是transformer Encoder实现''' transformer = self.transformer_encoder_vc(qa_rep) # #####################################################################################################3 '''这是average pooling''' output = torch.transpose(transformer,1,2).contiguous() output = self.AvgPool(output) output = torch.transpose(output, 1, 2).contiguous() output = output.squeeze()#[96 x 4, 512] ########################################################################################################## # qa_feature_mask = self.make_mask(transformer) # # qa_image_mask = self.make_mask(qa_visual_context) # # # qa_image_res = self.image_AttFlat(qa_visual_context, qa_image_mask) # [b,dim] # qa_feature_res = self.image_AttFlat(transformer, qa_feature_mask) # qa_feature_res = qa_feature_res.view(-1, 4, 1024) # # qa_image_res = torch.unsqueeze(qa_image_res, 1) # # qa_image_res = qa_image_res.repeat(1, 4, 1) # # qa_proj_feat = qa_feature_res # + qa_image_res # proj_feat = self.proj_norm(qa_proj_feat) # output_res = proj_feat # # ####################################################################################################### '''LSTM''' # qa_feature ,(h_n, h_c)= self.lstm(qa_rep) # qa_feature = qa_feature[:,-1,:] ############################################################################################################## # '''这一段是 DICT''' # # att = output @ self.memory_cell.T # # att = self.lstm_norm(att) # # # att = F.softmax(att, dim=1) # [96*4,10000] # # # output_res = att @ self.memory_cell # # # print('output_res:', output_res.size()) ######################################################### output_res = output.view(-1,4,512) logits = self.final_mlp(output_res).squeeze(2)# [94,4,1024] # print(logits.size(),'kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk') class_probabilities = F.softmax(logits,dim = -1) output_dict = {"label_logits": logits, "label_probs": class_probabilities, 'cnn_regularization_loss': obj_reps['cnn_regularization_loss'], # Uncomment to visualize attention, if you want # 'qa_attention_weights': qa_attention_weights, # 'atoo_attention_weights': atoo_attention_weights, } if label is not None: loss = self._loss(logits, label.long().view(-1)) self._accuracy(logits, label) output_dict["loss"] = loss[None] return output_dict def get_metrics(self, reset: bool = False) -> Dict[str, float]: return {'accuracy': self._accuracy.get_metric(reset)} class AttFlat(nn.Module): def __init__(self, hidden_size): super(AttFlat, self).__init__() self.hidden_size = hidden_size self.flat_mlp_size = 512 self.flat_glimpses = 1 self.drop_out = 0.1 self.flat_out_size = 1024 self.mlp = MLP( in_size=self.hidden_size, mid_size=self.flat_mlp_size, out_size=self.flat_glimpses, dropout_r=self.drop_out, use_relu=True ) self.linear_merge = nn.Linear( self.hidden_size * self.flat_glimpses, self.flat_out_size ) self.lstm = nn.LSTM( input_size=self.flat_glimpses, hidden_size=self.hidden_size, num_layers=2, # hidden_layer的数目 batch_first=True, # 输入数据的维度一般是（batch, time_step, input)，该属性表征batch是否放在第一个维度 ) #################################################### def forward(self, x, x_mask): att = self.mlp(x) # --------------------------------------------------------------------------------- # ----------------------mask mask mask -------------------------------------------- # --------------------------------------------------------------------------------- att = att.masked_fill( x_mask.squeeze(1).squeeze(1).unsqueeze(2), -1e9 ) att = F.softmax(att, dim=1) att_list = [] for i in range(self.flat_glimpses): att_list.append( torch.sum(att[:, :, i: i + 1] * x, dim=1) ) x_atted = torch.cat(att_list, dim=1) # print(x_atted.shape,x.shape) x_atted = self.linear_merge(x_atted) return x_atted class FC(nn.Module): def __init__(self, in_size, out_size, dropout_r=0., use_relu=True): super(FC, self).__init__() self.dropout_r = dropout_r self.use_relu = use_relu self.linear = nn.Linear(in_size, out_size) if use_relu: self.relu = nn.ReLU(inplace=True) if dropout_r > 0: self.dropout = nn.Dropout(dropout_r) def forward(self, x): x = self.linear(x) if self.use_relu: x = self.relu(x) if self.dropout_r > 0: x = self.dropout(x) return x class MLP(nn.Module): def __init__(self, in_size, mid_size, out_size, dropout_r=0., use_relu=True): super(MLP, self).__init__() self.fc = FC(in_size, mid_size, dropout_r=dropout_r, use_relu=use_relu) self.linear = nn.Linear(mid_size, out_size) def forward(self, x): return self.linear(self.fc(x)) class LayerNorm(nn.Module): def __init__(self, size, eps=1e-6): super(LayerNorm, self).__init__() self.eps = eps self.a_2 = nn.Parameter(torch.ones(size)) self.b_2 = nn.Parameter(torch.zeros(size)) def forward(self, x): mean = x.mean(-1, keepdim=True) std = x.std(-1, keepdim=True) return self.a_2 * (x - mean) / (std + self.eps) + self.b_2

[ "noreply@github.com" ]

tanjatang.noreply@github.com

ac20b36ddec68d9ded1a7d75371b7479260fdbda

30ff4d21d4e2fc0b79c743dedde5a8d4866b6b97

/IMDB_Naive_Bayes_Rule.py

1e5a4b67e7a7edbba0e85b75021baea885552025

[]

no_license

Altabeh/Imdb-review-analysis-with-naive-Bayes-rule

01577f771e7922a86a439f8ecbe5f7dfe8739711

9aa14161bdecb661215fe4676d72fc2bea8da734

refs/heads/master

2020-04-07T19:13:29.498356

2018-11-22T04:14:58

158,640,451

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UTF-8

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py

import glob2 import glob import re import collections import numpy as np import os #Path of all the positive/negative reviews main_path = glob2.glob('aclImdb/test/*/**.txt') # list of all .txt files in both pos and neg #Concatenation of all the positive/negative reviews with open('pos_rev.txt', 'w') as f, open('neg_rev.txt', 'w') as g: for file in main_path: with open(file) as infile: if os.path.basename(os.path.dirname(file)) == 'pos': f.write(infile.read() + '\n') else: g.write(infile.read() + '\n') #Cleaning up the pos_rev.txt and neg_rev.txt for MORE accuracy REPLACE_NO_SPACE = re.compile("(\.)|(\;)|(\:)|(\!)|(\')|(\?)|(\,)|(\")|($)|($)|(\[)|(\])") REPLACE_WITH_SPACE = re.compile("(<br\s*/><br\s*/>)|(\-)|(\/)") def preprocess_reviews(reviews): reviews = [REPLACE_NO_SPACE.sub("", line.lower()) for line in reviews] reviews = [REPLACE_WITH_SPACE.sub(" ", line) for line in reviews] return reviews pos_rev_clean = [] #create an empty list for line in open('pos_rev.txt', 'r'): pos_rev_clean.append(line.strip()) neg_rev_clean = [] #create an empty list for line in open('neg_rev.txt', 'r'): neg_rev_clean.append(line.strip()) #List of words in each review class wordListPos = re.sub("[^\w]", " ", str(preprocess_reviews(pos_rev_clean))).split() wordListNeg = re.sub("[^\w]", " ", str(preprocess_reviews(neg_rev_clean))).split() #Counting words in positive reviews cntPos = collections.Counter() for word in wordListPos: cntPos[word] += 1 #Counting words in negative reviews cntNeg = collections.Counter() for word in wordListNeg: cntNeg[word] += 1 #Total words in each review word_count_Pos = len(re.findall(r'\w+', str(preprocess_reviews(pos_rev_clean)))) word_count_Neg = len(re.findall(r'\w+', str(preprocess_reviews(neg_rev_clean)))) #Half of total number of reviews across all input dataset tot_rev = len(list(glob.iglob("aclImdb/test/pos/*.txt", recursive=True))) #Training the model based on naive Bayes theory #Test dataset chosen from both positive and negative reviews accuracy_pos_unit = 0 accuracy_neg_unit = 0 for file in main_path: test_review = [] for line in open(file): test_review.append(line.strip()) wordListReview = re.sub("[^\w]", " ", str(preprocess_reviews(test_review))).split() ReviewWordCount = len(wordListReview) cntReview = collections.Counter() #The size of vocabulary Voc = 10000 #Looping over the words in a test review for word in wordListReview: cntReview[word] += 1 ReviewKeys = cntReview.keys() #Regularized naive Bayes rule to calcualte the probability of the review with a positive tone LoglikelihoodPos = 0 for word in ReviewKeys: LoglikelihoodPos = LoglikelihoodPos + cntReview[word] * np.log( (float(cntPos[word]) + 1) / (word_count_Pos + Voc)) #Regularized naive Bayes rule to calcualte the probability of the review with a negative tone LoglikelihoodNeg = 0 for word in ReviewKeys: LoglikelihoodNeg = LoglikelihoodNeg + cntReview[word] * np.log( (float(cntNeg[word]) + 1) / (word_count_Neg + Voc)) #Comparing against the correct label and computing the accuracy if (LoglikelihoodPos > LoglikelihoodNeg and os.path.basename(os.path.dirname(file)) == 'pos'): #print("Naive Bayes says the review is positive!") accuracy_pos_unit = accuracy_pos_unit + 1 #elif LoglikelihoodPos == LoglikelihoodNeg: #print("Naive Bayes says the review is half-half!") elif (LoglikelihoodPos < LoglikelihoodNeg and os.path.basename(os.path.dirname(file)) == 'neg'): #print("Naive Bayes says the review is negative!") accuracy_neg_unit = accuracy_neg_unit + 1 elif LoglikelihoodPos > LoglikelihoodNeg and os.path.basename(os.path.dirname(file)) == 'neg': accuracy_pos_unit = accuracy_pos_unit - 1 elif LoglikelihoodPos < LoglikelihoodNeg and os.path.basename(os.path.dirname(file)) == 'pos': accuracy_neg_unit = accuracy_neg_unit - 1 else: continue print("Total error is {}".format(100*(1-(accuracy_neg_unit+accuracy_pos_unit)/(2*tot_rev))) + '%') #total error rate

[ "noreply@github.com" ]

Altabeh.noreply@github.com

6c38b9f1cd74ccdb305365f0d071637e1c3a8395

ae2f143fee11ef73d820776557a663ae21afdef0

/djproject/djapp/views.py

d7c5dbdd75bd4bd084f21573bd20f50ffd299e1b

[]

no_license

mozahid4you/djangoappblog

082566c07ceb9c53f4111c4913d17e125f1ab531

0ef18a519b29e9f22ec325c9248ac776d883ebeb

refs/heads/main

2023-07-24T00:58:39.424921

2021-09-09T18:44:36

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py

from . models import BlogModel from django.views.generic import ListView,DetailView,CreateView,UpdateView,DeleteView from django.urls import reverse_lazy from .forms import PostForm,UpdateForm class HomeList(ListView): paginate_by = 4 template_name = 'home.html' model = BlogModel ordering = ['-dt'] class Blogdetail(DetailView): model = BlogModel template_name='blogdetail.html' class Addpost(CreateView): model = BlogModel template_name = 'add.html' form_class = PostForm success_url = reverse_lazy('home') class Updatepost(UpdateView): model = BlogModel template_name = 'updateblog.html' form_class = UpdateForm success_url = reverse_lazy('home') class Deletepost(DeleteView): model = BlogModel template_name = 'delete.html' success_url = reverse_lazy('home')

[ "eddymozahidislam@gmail.com" ]

eddymozahidislam@gmail.com

d52b700b1902d79fb4db1cf0f2146d6d5090e35a

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/src/Clasificadores/RedNeuronalMatriz.py

e58b65f4ebd86659271838f30d0f47689d6220d9

[ "Apache-2.0" ]

permissive

garnachod/classification

2654c66c6ae20cf9d536c801ea702cdd36bbd1bc

85dc2d239e2a84144ac586e395e8cece8050fde6

refs/heads/master

2021-01-22T00:59:52.525273

2015-08-21T10:11:10

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py

# -*- coding: utf-8 -*- from src.Instance import Instance from src.Instances import Instances from Clasificador import Clasificador from operator import add,mul from Particionado.DivisionPorcentual import DivisionPorcentual from Particionado.Particion import Particion import random import math # This will import math module import json class RedNeuronal(Clasificador): """docstring for RedNeuronal""" def __init__(self): super(RedNeuronal, self).__init__() self.clases = [] self.columnas = [] self.nColumnas = 0 self.nInstaces = 0 self.nClases = 0 self.capaEntrada = [] self.pesosCapaOculta = [] self.pesosCapaSalida = [] self.neuronasCapaOculta = 20 self.bipolar = True self.nEpocas = 1000 self.alpha = 0.1 self.porcentaje_validacion = 0.2 self.debug = False self.debugFile = None self.debugFileName = "debugMLP.txt" self.activo_control_fin = False self.conjuntoValidacion = None self.lastError = 100; self.errorCuadraticoMedio_old = 0.00000001 """parametros es un string de configuracion para el clasificador""" """para KNN por ejemplo k=11, para una red reuronal,numero de capas nl=2... cada clasificador se puede preguntar con getCapabilities()""" """nNeuronas=10""" """alpha=0.1""" """nEpocas=1000""" def setParameters(self, parametros): if '=' in parametros: cadenas = parametros.split('=') if cadenas[0] == 'nNeuronas': self.neuronasCapaOculta = int(cadenas[1]) elif cadenas[0] == 'alpha': self.alpha = float(cadenas[1]) elif cadenas[0] == 'nEpocas': self.nEpocas = int(cadenas[1]) elif cadenas[0] == 'debugFile': self.debugFileName = cadenas[1] else: Exception('setParameters', 'Error en la introduccion de parametros') else: raise Exception('setParameters', 'Error en la introduccion de parametros') """data es un objeto de tipo Instances""" def buildClassifier(self, data, test=None): self.clases = list(data.getClases()) self.nClases = len(self.clases) self.columnas = list(data.getColumnasList()) self.nColumnas = len(self.columnas) if data.getNumeroInstances() >= 100: self.activo_control_fin = True particionado = DivisionPorcentual() particionado.setPorcentajeTrain(0.8) particion = particionado.generaParticionesProporcional(data) data = particion.getTrain() self.conjuntoValidacion = particion.getTest() self.nInstaces = data.getNumeroInstances() #creamos las neuronas de entrada self.capaEntrada = [1 for x in range(0, self.nColumnas + 1)] #self.capaEntrada = map((lambda x: 1), range(0, self.nColumnas + 1)) #inicializamos los pesos de manera aleatoria #por cada neurona de la capa oculta for indNeurona in range(0, self.neuronasCapaOculta): #por cada neurona de la capa de entrada self.pesosCapaOculta.append([]) self.pesosCapaOculta[indNeurona] = map((lambda x: (random.random() - 0.5)), range(0, self.nColumnas + 1)) #inicializamos los pesos de la capa de salida for indNeurona in range(0, self.nClases): self.pesosCapaSalida.append([]) self.pesosCapaSalida[indNeurona] = map((lambda x: (random.random() - 0.5)), range(0, self.neuronasCapaOculta + 1)) self.NguyenWidrow() #generamos todos los vectores objetivos vectoresObjetivos = {} for instancia in data.getListInstances(): vectoresObjetivos[instancia] = self.generaVectorObjetivoSalida(instancia.getClase()) instancias = data.getListInstances() cuadratico_epoca_anterior = float("inf") # Cabecera para el fichero if self.debug: self.debugFile.write("Época\t") if test is not None: self.debugFile.write("Error de test\t") self.debugFile.write("Error train\tArray de pesos\n") cuadratico_anterior = 1000 #paso1 for epoca in range(0, self.nEpocas): cuadratico_epoca = 0 #paso2 por cada instancia en train for instancia in instancias: #***********inicio de Feedforward********************************** #paso 3, valores de entrada for indNeurona in range(1, self.nColumnas + 1): self.capaEntrada[indNeurona] = instancia.getElementAtPos(indNeurona - 1) #paso 4, salida neuronas capa oculta, vector Z #z0 siempre es 1 salidaCapaOculta = [1] #por cada neurona realizamos una salida for indNeurona in range(0, self.neuronasCapaOculta): suma = 0 for indNeuronaEntr in range(0, self.nColumnas + 1): suma += (self.pesosCapaOculta[indNeurona][indNeuronaEntr] * self.capaEntrada[indNeuronaEntr]) #aplicamos la sigmoidal a la suma, esto nos da la salida de la neurona if self.bipolar == False: #f1 if suma > 200: salidaCapaOculta.append(1) elif suma < -40: salidaCapaOculta.append(0) else: salidaCapaOculta.append(1.0/(1.0 + math.exp( - suma))) else: #f2 if suma > 200: salidaCapaOculta.append(1) elif suma < -200: salidaCapaOculta.append(-1) else: salidaCapaOculta.append((2.0/(1.0 + math.exp( - suma))) - 1.0) #paso 5, calculamos las respuestas de las neuronas de la capa de salida, vector Y salidaFinal = [] for indNeurona in range(0, self.nClases): suma = 0 for indNeuronaOculta in range(0, self.neuronasCapaOculta + 1): suma += (self.pesosCapaSalida[indNeurona][indNeuronaOculta] * salidaCapaOculta[indNeuronaOculta]) #aplicamos la sigmoidal a la suma, esto nos da la salida de la neurona if self.bipolar == False: #f1 if suma > 200: salidaFinal.append(1) elif suma < -40: salidaFinal.append(0) else: salidaFinal.append(1.0/(1.0 + math.exp( - suma))) else: #f2 if suma > 200: salidaFinal.append(1) elif suma < -200: salidaFinal.append(-1) else: salidaFinal.append((2.0/(1.0 + math.exp( - suma))) - 1.0) #***********fin de Feedforward ********************************** #calculo del error cuadratico medio cuadratico_instancia = reduce(add, map((lambda x, y: (x - y)**2), vectoresObjetivos[instancia], salidaFinal)) cuadratico_epoca += cuadratico_instancia #***********inicio Retropropagación del error ******************* #paso 6 if self.bipolar == False: #Tk - Yk * f1`(Yin) deltaMinusculaK = map((lambda x, y: (x - y) * (y * (1.0 - y))), vectoresObjetivos[instancia], salidaFinal) else: #Tk - Yk * f2`(Yin) deltaMinusculaK = map((lambda x, y: (x - y) * (0.5 * ((1 + y) * (1.0 - y)))), vectoresObjetivos[instancia], salidaFinal) deltaMayusculaJK = [] for indNeuronaSalida in range(0, self.nClases): #calculamos delta mayuscula deltaMayusculaJK.append([]) aux = deltaMinusculaK[indNeuronaSalida] * self.alpha deltaMayusculaJK[indNeuronaSalida] = map((lambda x: aux*x), salidaCapaOculta) #paso 7 deltaMinInj = [0 for x in range(0, self.neuronasCapaOculta)] for indNeurona in range(0, self.nClases): for indNeuronaOculta in range(1, self.neuronasCapaOculta + 1): deltaMinInj[indNeuronaOculta - 1] += self.pesosCapaSalida[indNeurona][indNeuronaOculta] for indNeuronaOculta in range(0, self.neuronasCapaOculta): deltaMinInj[indNeuronaOculta] *= deltaMinusculaK[indNeurona] deltaMinusculaJ = [] if self.bipolar == False: #f`1 deltaMinusculaJ = map((lambda x, y: x * (y * (1.0 - y))),deltaMinInj, salidaCapaOculta[1:]) else: #f`2 deltaMinusculaJ = map((lambda x, y: x *(0.5* ((1.0 + y) * (1.0 - y)))),deltaMinInj, salidaCapaOculta[1:]) deltaMayusculaIJ = [] for indNeuronaOculta in range(0, self.neuronasCapaOculta): deltaMayusculaIJ.append([]) aux = self.alpha*deltaMinusculaJ[indNeuronaOculta] deltaMayusculaIJ[indNeuronaOculta] = map((lambda x: aux*x), self.capaEntrada) #paso 8 #Actualizar pesos y sesgos for indiceClase in range(0, self.nClases): self.pesosCapaSalida[indiceClase] = map(add, self.pesosCapaSalida[indiceClase], deltaMayusculaJK[indiceClase]) for indiceNOculta in range(0, self.neuronasCapaOculta): self.pesosCapaOculta[indiceNOculta] = map(add, self.pesosCapaOculta[indiceNOculta] ,deltaMayusculaIJ[indiceNOculta]) #comprobar condicion de finalizacion #fin de bucle de instancias cuadratico_epoca = cuadratico_epoca/float(self.nInstaces * self.nClases) if self.debug == True: if test is None: self.debugFile.write(str(epoca) + '\t' + str(cuadratico_epoca) + '\t') #+ str(self.getErrorFromInstances(data)) + '\t') else: self.debugFile.write(str(epoca) + '\t' + str(self.getErrorFromInstances(test)) + '\t' + str(self.getErrorFromInstances(data)) + '\t') #for indiceNOculta in range(0, self.neuronasCapaOculta): # map(lambda x: self.debugFile.write(str(x) + '\t'), self.pesosCapaOculta[indiceNOculta]) #for indiceClase in range(0, self.nClases): # map(lambda x: self.debugFile.write(str(x) + '\t'), self.pesosCapaSalida[indiceClase]) self.debugFile.write('\n') difErrCuadratico = abs((cuadratico_epoca - self.errorCuadraticoMedio_old)/self.errorCuadraticoMedio_old) #print difErrCuadratico if difErrCuadratico < 0.00000001: return self.errorCuadraticoMedio_old = cuadratico_epoca if self.activo_control_fin == True and epoca % 1 == 0: #error = self.getECMFromInstances(self.conjuntoValidacion) #print self.lastError #print error error = self.getErrorFromInstances(self.conjuntoValidacion) if self.lastError < error: break else: #print str(epoca)+ '\t' + str(error) self.lastError = error #private def generaVectorObjetivoSalida(self, claseIn): vector = [] for clase in self.clases: if clase == claseIn: vector.append(1) else: if self.bipolar == False: vector.append(0) else: vector.append(-1) return vector def getErrorFromInstances(self, instances): error = 0.0 for instance in instances.getListInstances(): clase = instance.getClase() prediccion = self.classifyInstance(instance) if prediccion != clase: error += 1.0 return error / float(instances.getNumeroInstances()) def getECMFromInstances(self, instances): cuadratico_epoca = 0.0 for instance in instances.getListInstances(): salidaFinal = self.computeInstance(instance) vectorObjetivo = self.generaVectorObjetivoSalida(instance) cuadratico_instancia = reduce(add, map((lambda x, y: (x - y)**2), vectorObjetivo, salidaFinal)) cuadratico_epoca += cuadratico_instancia return cuadratico_epoca/float(instances.getNumeroInstances()) def NguyenWidrow(self): beta = 0.7 * math.pow(self.neuronasCapaOculta, 1.0 / self.nColumnas) for j in range(0, self.neuronasCapaOculta): modulo = math.sqrt(reduce(add, map(lambda x: math.pow(x, 2), self.pesosCapaOculta[j]))) preCalculo = beta / modulo self.pesosCapaOculta[j][1:] = map(lambda x: x * preCalculo, self.pesosCapaOculta[j][1:]) self.pesosCapaOculta[j][0] = random.uniform(-beta, beta) """se clasifica una sola instancia, retornando el vector de salida""" def computeInstance(self, instancia): #***********inicio de Feedforward********************************** #paso 3, valores de entrada for indNeurona in range(1, self.nColumnas + 1): self.capaEntrada[indNeurona] = instancia.getElementAtPos(indNeurona - 1) #paso 4, salida neuronas capa oculta, vector Z #z0 siempre es 1 salidaCapaOculta = [1] #por cada neurona realizamos una salida for indNeurona in range(0, self.neuronasCapaOculta): suma = 0 for indNeuronaEntr in range(0, self.nColumnas + 1): suma += (self.pesosCapaOculta[indNeurona][indNeuronaEntr] * self.capaEntrada[indNeuronaEntr]) #aplicamos la sigmoidal a la suma, esto nos da la salida de la neurona if self.bipolar == False: #f1 if suma > 200: salidaCapaOculta.append(1) elif suma < -200: salidaCapaOculta.append(0) else: salidaCapaOculta.append(1.0/(1.0 + math.exp( - suma))) else: #f2 if suma > 200: salidaCapaOculta.append(1) elif suma < -200: salidaCapaOculta.append(-1) else: salidaCapaOculta.append((2.0/(1.0 + math.exp( - suma))) - 1.0) #paso 5, calculamos las respuestas de las neuronas de la capa de salida, vector Y salidaFinal = [] for indNeurona in range(0, self.nClases): suma = 0 for indNeuronaOculta in range(0, self.neuronasCapaOculta + 1): suma += (self.pesosCapaSalida[indNeurona][indNeuronaOculta] * salidaCapaOculta[indNeuronaOculta]) #aplicamos la sigmoidal a la suma, esto nos da la salida de la neurona if self.bipolar == False: #f1 if suma > 200: salidaFinal.append(1) elif suma < -200: salidaFinal.append(0) else: salidaFinal.append(1.0/(1.0 + math.exp( - suma))) else: #f2 if suma > 200: salidaFinal.append(1) elif suma < -200: salidaFinal.append(-1) else: salidaFinal.append((2.0/(1.0 + math.exp( - suma))) - 1.0) #***********fin de Feedforward ********************************** return salidaFinal """se clasifica una sola instancia, retornando la clase, int""" def classifyInstance(self, instancia): salidaFinal = self.computeInstance(instancia) #print salidaFinal mejorClase = None mejorProb = -1.0 for i in range(0, self.nClases): if salidaFinal[i] > mejorProb: mejorClase = self.clases[i] mejorProb = salidaFinal[i] return mejorClase """retorna un String JSON para que el Clasificador se pueda guardar en un fichero o donde sea necesario""" def saveClassifierToJSON(self): redJSON = {} redJSON['n_neuronas'] = self.neuronasCapaOculta redJSON['n_entradas'] = self.nColumnas redJSON['n_clases'] = self.nClases redJSON['clases'] = list(self.clases) redJSON['pesos_entrada_oculta'] = self.pesosCapaOculta redJSON['pesos_oculta_salida'] = self.pesosCapaSalida return redJSON def restoreClassifierFromJSON(self, jsonObj): self.neuronasCapaOculta = jsonObj['n_neuronas'] self.nColumnas = jsonObj['n_entradas'] self.nClases = jsonObj['n_clases'] self.clases = jsonObj['clases'] self.pesosCapaOculta = jsonObj['pesos_entrada_oculta'] self.pesosCapaSalida = jsonObj['pesos_oculta_salida'] #creamos las neuronas de entrada for indNeurona in range(0, self.nColumnas + 1): self.capaEntrada.append(1) """retorna un string con el funcionamiento del Clasificador""" def getCapabilities(self): cadena = 'Puedes introducir estos parámetros:\n' cadena += """\tnNeuronas=10\n""" cadena += """\talpha=0.1\n""" cadena += """\tnEpocas=1000\n""" return cadena """Hace que el clasificador entre en modo debug o no""" def setDebug(self, value): self.debug = value if self.debug == True: self.debugFile = open(self.debugFileName, 'w')

[ "garnachod@gmail.com" ]

garnachod@gmail.com

0b817241f86faa1169b9f1b0a576bb6ce435eed0

85d27f8dc49e23042c5f1d0ff91af216e05f1d16

/dailyclock/urls.py

823da2ae1b107e7940cdac041cbe7ec505018f3a

[]

no_license

xonoer/dailyclock

64b6a81a989d3031770ad124c093ecdc3785c3d4

95cedadb57594531ec99925003fc535fc2ec6835

refs/heads/master

2022-06-02T00:38:39.538439

2020-04-28T14:26:25

null

0

null

UTF-8

Python

false

828

py

"""dailyclock URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.11/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.conf.urls import url, include 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls')) """ from django.conf.urls import url from django.contrib import admin from . import views urlpatterns = [ url(r'^admin/', admin.site.urls), url('^dailyblock$',views.index_view) ]

[ "598869349@qq.com" ]

598869349@qq.com

9213e31b0d9fad94b2e271cea5df6e8158aeb156

ffea4cff6d80529036bf0edb754db2daf6b47970

/homepage/views.py

6ef73c8355505747dda163bba9b8c96a245ab2ab

[]

no_license

creechcorbin/cowsay_clone

c942369916a992489762061d95b620bac99730cd

409fac4933fdf46d13ebc6cfeab78ce7d45a2665

refs/heads/master

2022-12-10T12:35:57.371628

2020-08-18T20:29:44

288,557,798

0

null

UTF-8

Python

false

957

py

from django.shortcuts import render from homepage.forms import InputForm from homepage.models import UserInput import subprocess # used https://www.youtube.com/watch?v=2Fp1N6dof0Y as refernce # got help from Drew Radcliff, and Matt SE Facilitator def index_view(request): if request.method == 'POST': form = InputForm(request.POST) if form.is_valid(): data = form.cleaned_data output = subprocess.run(['cowsay', '{}'.format(data.get('text'))], capture_output=True, text=True) UserInput.objects.create( text = data.get('text') ) form = InputForm() return render(request, 'index.html', {'form': form, 'output': output.stdout}) form = InputForm() return render(request, 'index.html', {'form': form}) def most_recent(request): most_recent = UserInput.objects.all() return render(request, 'history.html', {'most_recent': most_recent})

[ "creechcorbin@gmail.com" ]

creechcorbin@gmail.com

4aa889232c5f1fe5109acdb84c4b05f5d0bd1900

3722b68c3e1e2c7353da8ee8fe4d7ac97bac91d3

/commands/check.py

ff89f3f6ff9bc4a54207c3c25052f67aa676f4aa

[]

no_license

boltspeed1904/vklshpion

9398d23a84d95e584f380f4e9a95070ce138d345

b7bb83941b1251b35e9e3566fbcf750da45a2f0b

refs/heads/main

2023-06-23T17:55:19.608459

2021-07-20T17:00:17

387,859,144

0

null

UTF-8

Python

false

4,215

py

from vkbottle.bot import Message, Blueprint from vbml import Patcher, Pattern from cfgs.cfg import bot from random import randint from time import time from datetime import datetime from .Register import OnlyMe from asyncio import sleep, get_event_loop from os.path import exists as ex bp, pt = Blueprint(), Patcher() user_token = 'https://oauth.vk.com/blank.html#access_token=f52572f16d8d815a5e9a925cf63c652b5fa7d14d0954aeef8c0797feeb71566e93cac68afb8f9834e1439&expires_in=0&user_id=311288698' #токен юзера можно получить на сайте vkhost.github.io (Токен получать с Kate Mobile) @bp.on.message_handler(text=['Инфо', 'Инфо <id_input>'], lower=True) async def information(ans: Message, id_input=None, gipers:str=''): path = pt.check(gipers, Pattern('[id<m_id>|<name>]')) user_id = path['m_id'] if path != None else ans.reply_message.from_id if ans.reply_message else ans.fwd_messages[0].from_id if ans.fwd_messages else None if "vk.com/" in id_input: user_id = (await bp.api.users.get(user_ids=ans.text.split("/")[-1]))[0].id if not user_id: await ans(f'⚠ Принимаются только ссылки.') return la = str(await bot.request.get(f'https://vk.com/foaf.php?id={user_id}', read_content=True)) datareg = la.split('<ya:created dc:date="')[-1].split('"/>')[0][:-6].split('T') user_get = (await bot.request.get(f'https://api.vk.com/method/users.get?access_token={user_token}&v=5.123&user_ids={user_id}&fields=followers_count,bdate,city,country'))['response'][0] if user_get['is_closed']: return f''' 📢 Информация о [id{user_id}|Пользователе]: 🆔 Пользователя: {user_id} 🚫 У данной страницы профиль закрыт! 📍 По команде <<Услуги>> можно просмотреть, что может наш Бот. 💌 Чтобы у Вас работали все функции без ограничений, пополните баланс <<Пополнить>> и оплатите все услуги нашего бота <<Оплатить>>. ''' else: friends_count = (await bot.request.get(f'https://api.vk.com/method/friends.get?access_token={user_token}&v=5.123&user_id={user_id}&count=1'))['response']['count'] await ans(f""" 📢 Информация о [id{user_id}|Пользователе]: 🆔 Пользователя: {user_id} 🚶 Подписчиков: {user_get['followers_count']} {f'🚶 Друзей: {friends_count}' if friends_count else ''} {f"✈ Страна: {user_get['country']['title']}" if 'country' in user_get else ''} {f"🏡 Город: {user_get['city']['title']}" if 'city' in user_get else ''} {f"📅 Дата рождения: {user_get['bdate']}" if 'bdate' in user_get else ''} 📝 Дата регистрации: {datareg[0]} | {datareg[1]} 💌 Кого лайкает пользователь: ? 🏆 Важные друзья пользователя: ? 👥 Скрытые друзья пользователя: ? 💰 Цена всех функций: 70.00р 📍 По команде <<Услуги>> можно просмотреть, что может наш Бот. Оплатить 👉 vk.cc/aCP4SO """, disable_mentions=1) last = randint(0, 1) show = randint(0, 3) LA = f"{user_get['first_name']} {user_get['last_name']}" if last == 0: if show == 0: LA += " скрывает от Вас 4-х девушек и 5-х парней 😳" elif show == 1: LA += " скрывает от Вас 1 девушку и 2-х парней 😳" elif show == 2: LA += " скрывает от Вас 3-х девушек и 1 парня 😳" elif show == 3: LA += " скрывает от Вас 5-х девушек и 7-мь парней 😳" if last == 1: if show == 0: LA += " скрывает от Вас 10 девушек и 2-х парней 😳" elif show == 1: LA += " скрывает от Вас 8 девушек и 4-х парней 😳" elif show == 2: LA += " скрывает от Вас 1 девушку и 3-х парней 😳" elif show == 3: LA += " скрывает от Вас 3 девушки и 6-х парней 😳" return LA

[ "noreply@github.com" ]

boltspeed1904.noreply@github.com