I want to do pairwise alignment with uniprot and pdb sequences. I have an input file containing uniprot and pdb IDs like this.
pdb id uniprot id
1dbh Q07889
1e43 P00692
1f1s Q53591
first, I need to read each line in an input file
2) retrieve the pdb and uniprot sequences from pdb.fasta and uniprot.fasta files
3) Do alignment and calculate sequence identity.
Usually, I use the following program for pairwise alignment and seq.identity calculation.
library("seqinr")
seq1 <- "MDEKRRAQHNEVERRRRDKINNWIVQLSKIIPDSSMESTKSGQSKGGILSKASDYIQELRQSNHR"
seq2<- "MKGQQKTAETEEGTVQIQEGAVATGEDPTSVAIASIQSAATFPDPNVKYVFRTENGGQVM"
library(Biostrings)
globalAlign<- pairwiseAlignment(seq1, seq2)
pid(globalAlign, type = "PID3")
I need to print the output like this
pdbid uniprotid seq.identity
1dbh Q07889 99
1e43 P00692 80
1f1s Q53591 56
How can I change the above code ? your help would be appreciated!
'
This code is hopefully what your looking for:
class test():
def get_seq(self, pdb,fasta_file): # Get sequences
from Bio.PDB.PDBParser import PDBParser
from Bio import SeqIO
aa = {'ARG':'R','HIS':'H','LYS':'K','ASP':'D','GLU':'E','SER':'S','THR':'T','ASN':'N','GLN':'Q','CYS':'C','SEC':'U','GLY':'G','PRO':'P','ALA':'A','ILE':'I','LEU':'L','MET':'M','PHE':'F','TRP':'W','TYR':'Y','VAL':'V'}
p=PDBParser(PERMISSIVE=1)
structure_id="%s" % pdb[:-4]
structure=p.get_structure(structure_id, pdb)
residues = structure.get_residues()
seq_pdb = ''
for res in residues:
res = res.get_resname()
if res in aa:
seq_pdb = seq_pdb+aa[res]
handle = open(fasta_file, "rU")
for record in SeqIO.parse(handle, "fasta") :
seq_fasta = record.seq
handle.close()
self.seq_aln(seq_pdb,seq_fasta)
def seq_aln(self,seq1,seq2): # Align the sequences
from Bio import pairwise2
from Bio.SubsMat import MatrixInfo as matlist
matrix = matlist.blosum62
gap_open = -10
gap_extend = -0.5
alns = pairwise2.align.globalds(seq1, seq2, matrix, gap_open, gap_extend)
top_aln = alns[0]
aln_seq1, aln_seq2, score, begin, end = top_aln
with open('aln.fasta', 'w') as outfile:
outfile.write('> PDB_seq\n'+str(aln_seq1)+'\n> Uniprot_seq\n'+str(aln_seq2))
print aln_seq1+'\n'+aln_seq2
self.seq_id('aln.fasta')
def seq_id(self,aln_fasta): # Get sequence ID
import string
from Bio import AlignIO
input_handle = open("aln.fasta", "rU")
alignment = AlignIO.read(input_handle, "fasta")
j=0 # counts positions in first sequence
i=0 # counts identity hits
for record in alignment:
#print record
for amino_acid in record.seq:
if amino_acid == '-':
pass
else:
if amino_acid == alignment[0].seq[j]:
i += 1
j += 1
j = 0
seq = str(record.seq)
gap_strip = seq.replace('-', '')
percent = 100*i/len(gap_strip)
print record.id+' '+str(percent)
i=0
a = test()
a.get_seq('1DBH.pdb','Q07889.fasta')
This outputs:
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------EQTYYDLVKAF-AEIRQYIRELNLIIKVFREPFVSNSKLFSANDVENIFSRIVDIHELSVKLLGHIEDTVE-TDEGSPHPLVGSCFEDLAEELAFDPYESYARDILRPGFHDRFLSQLSKPGAALYLQSIGEGFKEAVQYVLPRLLLAPVYHCLHYFELLKQLEEKSEDQEDKECLKQAITALLNVQSG-EKICSKSLAKRRLSESA-------------AIKK-NEIQKNIDGWEGKDIGQCCNEFI-EGTLTRVGAKHERHIFLFDGL-ICCKSNHGQPRLPGASNAEYRLKEKFF-RKVQINDKDDTNEYKHAFEIILKDENSVIFSAKSAEEKNNW-AALISLQYRSTL---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
MQAQQLPYEFFSEENAPKWRGLLVPALKKVQGQVHPTLESNDDALQYVEELILQLLNMLCQAQPRSASDVEERVQKSFPHPIDKWAIADAQSAIEKRKRRNPLSLPVEKIHPLLKEVLGYKIDHQVSVYIVAVLEYISADILKLVGNYVRNIRHYEITKQDIKVAMCADKVLMDMFHQDVEDINILSLTDEEPSTSGEQTYYDLVKAFMAEIRQYIRELNLIIKVFREPFVSNSKLFSANDVENIFSRIVDIHELSVKLLGHIEDTVEMTDEGSPHPLVGSCFEDLAEELAFDPYESYARDILRPGFHDRFLSQLSKPGAALYLQSIGEGFKEAVQYVLPRLLLAPVYHCLHYFELLKQLEEKSEDQEDKECLKQAITALLNVQSGMEKICSKSLAKRRLSESACRFYSQQMKGKQLAIKKMNEIQKNIDGWEGKDIGQCCNEFIMEGTLTRVGAKHERHIFLFDGLMICCKSNHGQPRLPGASNAEYRLKEKFFMRKVQINDKDDTNEYKHAFEIILKDENSVIFSAKSAEEKNNWMAALISLQYRSTLERMLDVTMLQEEKEEQMRLPSADVYRFAEPDSEENIIFEENMQPKAGIPIIKAGTVIKLIERLTYHMYADPNFVRTFLTTYRSFCKPQELLSLIIERFEIPEPEPTEADRIAIENGDQPLSAELKRFRKEYIQPVQLRVLNVCRHWVEHHFYDFERDAYLLQRMEEFIGTVRGKAMKKWVESITKIIQRKKIARDNGPGHNITFQSSPPTVEWHISRPGHIETFDLLTLHPIEIARQLTLLESDLYRAVQPSELVGSVWTKEDKEINSPNLLKMIRHTTNLTLWFEKCIVETENLEERVAVVSRIIEILQVFQELNNFNGVLEVVSAMNSSPVYRLDHTFEQIPSRQKKILEEAHELSEDHYKKYLAKLRSINPPCVPFFGIYLTNILKTEEGNPEVLKRHGKELINFSKRRKVAEITGEIQQYQNQPYCLRVESDIKRFFENLNPMGNSMEKEFTDYLFNKSLEIEPRNPKPLPRFPKKYSYPLKSPGVRPSNPRPGTMRHPTPLQQEPRKISYSRIPESETESTASAPNSPRTPLTPPPASGASSTTDVCSVFDSDHSSPFHSSNDTVFIQVTLPHGPRSASVSSISLTKGTDEVPVPPPVPPRRRPESAPAESSPSKIMSKHLDSPPAIPPRQPTSKAYSPRYSISDRTSISDPPESPPLLPPREPVRTPDVFSSSPLHLQPPPLGKKSDHGNAFFPNSPSPFTPPPPQTPSPHGTRRHLPSPPLTQEVDLHSIAGPPVPPRQSTSQHIPKLPPKTYKREHTHPSMHRDGPPLLENAHSS
PDB_seq 100 # pdb to itself would obviously have 100% identity
Uniprot_seq 24 # pdb sequence has 24% identity to the uniprot sequence
For this to work on you input file, you need to put my a.get_seq() in a for loop with the inputs from your text file.
EDIT:
Replace the seq_id function with this one:
def seq_id(self,aln_fasta):
import string
from Bio import AlignIO
from Bio import SeqIO
record_iterator = SeqIO.parse(aln_fasta, "fasta")
first_record = record_iterator.next()
print '%s has a length of %d' % (first_record.id, len(str(first_record.seq).replace('-','')))
second_record = record_iterator.next()
print '%s has a length of %d' % (second_record.id, len(str(second_record.seq).replace('-','')))
lengths = [len(str(first_record.seq).replace('-','')), len(str(second_record.seq).replace('-',''))]
if lengths.index(min(lengths)) == 0: # If both sequences have the same length the PDB sequence will be taken as the shortest
print 'PDB sequence has the shortest length'
else:
print 'Uniport sequence has the shortes length'
idenities = 0
for i,v in enumerate(first_record.seq):
if v == '-':
pass
#print i,v, second_record.seq[i]
if v == second_record.seq[i]:
idenities +=1
#print i,v, second_record.seq[i], idenities
print 'Sequence Idenity = %.2f percent' % (100.0*(idenities/min(lengths)))
to pass the arguments to the class use:
with open('input_file.txt', 'r') as infile:
next(infile)
next(infile) # Going by your input file
for line in infile:
line = line.split()
a.get_seq(segs[0]+'.pdb',segs[1]+'.fasta')
It might be something like this; a repeatable example (e.g., with short files posted on-line) would help...
library(Biostrings)
pdb = readAAStringSet("pdb.fasta")
uniprot = readAAStringSet("uniprot.fasta")
to input all sequences into two objects. pairwiseAlignment accepts a vector as first (query) argument, so if you were wanting to align all pdb against all uniprot pre-allocate a result matrix
pids = matrix(numeric(), length(uniprot), length(pdb),
dimnames=list(names(uniprot), names(pdb)))
and then do the calculations
for (i in seq_along(uniprot)) {
globalAlignment = pairwiseAlignment(pdb, uniprot[i])
pids[i,] = pid(globalAlignment)
}
Related
from torchvision_starter.engine import train_one_epoch, evaluate
from torchvision_starter import utils
import multiprocessing
import time
n_cpu = multiprocessing.cpu_count()
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
_ = model.to(device)
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.Adam(model.parameters(), lr=0.00001)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=3,
gamma=0.2,
verbose=True
)
# Let's train for 10 epochs
num_epochs = 1
start = time.time()
for epoch in range(10, 10 + num_epochs):
# train for one epoch, printing every 10 iterations
train_one_epoch(model, optimizer, data_loaders['train'], device, epoch, print_freq=10)
# update the learning rate
lr_scheduler.step()
# evaluate on the validation dataset
evaluate(model, data_loaders['valid'], device=device)
stop = time.time()
print(f"\n\n{num_epochs} epochs in {stop - start} s ({(stop-start) / 3600:.2f} hrs)")
Before I move on to this part, everything is OK. But after I run the part, the error is like below:
I have tried to add drop_last to the helper.py's function like:
data_loaders["train"] = torch.utils.data.DataLoader(
train_data,
batch_size=batch_size,
sampler=train_sampler,
num_workers=num_workers,
collate_fn=utils.collate_fn,
drop_last=True
)
But it doesn't work. By the way, the torch and torchvision are compatible and Cuda is available.
I wonder how to fix it.
The get_data_loaders function:
def get_data_loaders(
folder, batch_size: int = 2, valid_size: float = 0.2, num_workers: int = -1, limit: int = -1, thinning: int = None
):
"""
Create and returns the train_one_epoch, validation and test data loaders.
:param foder: folder containing the dataset
:param batch_size: size of the mini-batches
:param valid_size: fraction of the dataset to use for validation. For example 0.2
means that 20% of the dataset will be used for validation
:param num_workers: number of workers to use in the data loaders. Use -1 to mean
"use all my cores"
:param limit: maximum number of data points to consider
:param thinning: take every n-th frame, instead of all frames
:return a dictionary with 3 keys: 'train_one_epoch', 'valid' and 'test' containing respectively the
train_one_epoch, validation and test data loaders
"""
if num_workers == -1:
# Use all cores
num_workers = multiprocessing.cpu_count()
# We will fill this up later
data_loaders = {"train": None, "valid": None, "test": None}
# create 3 sets of data transforms: one for the training dataset,
# containing data augmentation, one for the validation dataset
# (without data augmentation) and one for the test set (again
# without augmentation)
data_transforms = {
"train": get_transform(UdacitySelfDrivingDataset.mean, UdacitySelfDrivingDataset.std, train=True),
"valid": get_transform(UdacitySelfDrivingDataset.mean, UdacitySelfDrivingDataset.std, train=False),
"test": get_transform(UdacitySelfDrivingDataset.mean, UdacitySelfDrivingDataset.std, train=False),
}
# Create train and validation datasets
train_data = UdacitySelfDrivingDataset(
folder,
transform=data_transforms["train"],
train=True,
thinning=thinning
)
# The validation dataset is a split from the train_one_epoch dataset, so we read
# from the same folder, but we apply the transforms for validation
valid_data = UdacitySelfDrivingDataset(
folder,
transform=data_transforms["valid"],
train=True,
thinning=thinning
)
# obtain training indices that will be used for validation
n_tot = len(train_data)
indices = torch.randperm(n_tot)
# If requested, limit the number of data points to consider
if limit > 0:
indices = indices[:limit]
n_tot = limit
split = int(math.ceil(valid_size * n_tot))
train_idx, valid_idx = indices[split:], indices[:split]
# define samplers for obtaining training and validation batches
train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
valid_sampler = torch.utils.data.SubsetRandomSampler(valid_idx) # =
# prepare data loaders
data_loaders["train"] = torch.utils.data.DataLoader(
train_data,
batch_size=batch_size,
sampler=train_sampler,
num_workers=num_workers,
collate_fn=utils.collate_fn,
drop_last=True
)
data_loaders["valid"] = torch.utils.data.DataLoader(
valid_data, # -
batch_size=batch_size, # -
sampler=valid_sampler, # -
num_workers=num_workers, # -
collate_fn=utils.collate_fn,
drop_last=True
)
# Now create the test data loader
test_data = UdacitySelfDrivingDataset(
folder,
transform=data_transforms["test"],
train=False,
thinning=thinning
)
if limit > 0:
indices = torch.arange(limit)
test_sampler = torch.utils.data.SubsetRandomSampler(indices)
else:
test_sampler = None
data_loaders["test"] = torch.utils.data.DataLoader(
test_data,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
sampler=test_sampler,
collate_fn=utils.collate_fn,
drop_last=True
# -
)
return data_loaders
class UdacitySelfDrivingDataset(torch.utils.data.Dataset):
# Mean and std of the dataset to be used in nn.Normalize
mean = torch.tensor([0.3680, 0.3788, 0.3892])
std = torch.tensor([0.2902, 0.3069, 0.3242])
def __init__(self, root, transform, train=True, thinning=None):
super().__init__()
self.root = os.path.abspath(os.path.expandvars(os.path.expanduser(root)))
self.transform = transform
# load datasets
if train:
self.df = pd.read_csv(os.path.join(self.root, "labels_train.csv"))
else:
self.df = pd.read_csv(os.path.join(self.root, "labels_test.csv"))
# Index by file id (i.e., a sequence of the same length as the number of images)
codes, uniques = pd.factorize(self.df['frame'])
if thinning:
# Take every n-th rows. This makes sense because the images are
# frames of videos from the car, so we are essentially reducing
# the frame rate
thinned = uniques[::thinning]
idx = self.df['frame'].isin(thinned)
print(f"Keeping {thinned.shape[0]} of {uniques.shape[0]} images")
print(f"Keeping {idx.sum()} objects out of {self.df.shape[0]}")
self.df = self.df[idx].reset_index(drop=True)
# Recompute codes
codes, uniques = pd.factorize(self.df['frame'])
self.n_images = len(uniques)
self.df['image_id'] = codes
self.df.set_index("image_id", inplace=True)
self.classes = ['car', 'truck', 'pedestrian', 'bicyclist', 'light']
self.colors = ['cyan', 'blue', 'red', 'purple', 'orange']
#property
def n_classes(self):
return len(self.classes)
def __getitem__(self, idx):
if idx in self.df.index:
row = self.df.loc[[idx]]
else:
return KeyError(f"Element {idx} not in dataframe")
# load images fromm file
img_path = os.path.join(self.root, "images", row['frame'].iloc[0])
img = Image.open(img_path).convert("RGB")
# Exclude bogus boxes with 0 height or width
h = row['ymax'] - row['ymin']
w = row['xmax'] - row['xmin']
filter_idx = (h > 0) & (w > 0)
row = row[filter_idx]
# get bounding box coordinates for each mask
boxes = row[['xmin', 'ymin', 'xmax', 'ymax']].values
# convert everything into a torch.Tensor
boxes = torch.as_tensor(boxes, dtype=torch.float32)
# get the labels
labels = torch.as_tensor(row['class_id'].values, dtype=int)
image_id = torch.tensor([idx])
area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
# assume no crowd for everything
iscrowd = torch.zeros((row.shape[0],), dtype=torch.int64)
target = {}
target["boxes"] = boxes
target["labels"] = labels
target["image_id"] = image_id
target["area"] = area
target["iscrowd"] = iscrowd
if self.transform is not None:
img, target = self.transform(img, target)
return img, target
def __len__(self):
return self.n_images
def plot(self, idx, renormalize=True, predictions=None, threshold=0.5, ax=None):
image, label_js = self[idx]
if renormalize:
# Invert the T.Normalize transform
unnormalize = T.Compose(
[
T.Normalize(mean = [ 0., 0., 0. ], std = 1 / type(self).std),
T.Normalize(mean = -type(self).mean, std = [ 1., 1., 1. ])
]
)
image, label_js = unnormalize(image, label_js)
if ax is None:
fig, ax = plt.subplots(figsize=(8, 8))
_ = ax.imshow(torch.permute(image, [1, 2, 0]))
for i, box in enumerate(label_js['boxes']):
xy = (box[0], box[1])
h, w = (box[2] - box[0]), (box[3] - box[1])
r = patches.Rectangle(xy, h, w, fill=False, color=self.colors[label_js['labels'][i]-1], lw=2, alpha=0.5)
ax.add_patch(r)
if predictions is not None:
# Make sure the predictions are on the CPU
for k in predictions:
predictions[k] = predictions[k].detach().cpu().numpy()
for i, box in enumerate(predictions['boxes']):
if predictions['scores'][i] > threshold:
xy = (box[0], box[1])
h, w = (box[2] - box[0]), (box[3] - box[1])
r = patches.Rectangle(xy, h, w, fill=False, color=self.colors[predictions['labels'][i]-1], lw=2, linestyle=':')
ax.add_patch(r)
_ = ax.axis("off")
return ax
I have a '.txt' file in which a list of genes are given and their sequence. I need to create a dictionary in which the keys are the names of the genes and the values are the sequences.
I want the output of the dictionary to be this:
dict = ('sequence1' : 'AATTGGCC', 'sequence2' : 'AAGGCCTT', ...)
So this is what I tried, but I ran into some problems:
dictionary = {}
accesion_number = ""
sequentie = ""
with open("6EP.fasta", "r") as proteoom:
for line in proteoom:
if line.startswith(">"):
line.strip()
dictionary[accesion_number] = sequentie
sequentie = ""
else:
sequentie = sequentie + line.rstrip().strip("\n").strip("\r")
dictionary[accesion_number] = sequentie
Does anyone know what went wrong here, and how I can fix it?
Thanks in advance!
I can think of two ways to do this:
High memory usage
If the file is not too large, you can use readlines() and then use the indexes like so:
IDs = []
sequences = []
with open('Proteome.fasta', 'r') as f:
raw_data = f.readlines()
for i, l in enumerate(raw_data):
if l[0] == '>':
IDs.append(l)
sequences.append(raw_data[i + 1])
Low memory usage
Now, if you don't want to load the contents of the file into memory, then I think you can read the file twice by saving the indexes of every ID line plus one, like so:
Get the '>' lines and their indexes, which will be the ID index plus one
Compare if the line number is in the indexes list and, if so, then append the content to your variable
In here, I'm taking advantage of the fact that the lists are, by definition, sorted.
IDs = []
indexes = []
sequences = []
with open('Proteome.fasta', 'r') as f:
for i, l in enumerate(f):
IDs.append(l) # Get your IDs
indexes.append(i + 1) # Get the index of the ID + 1
with open('Proteome.fasta', 'r') as f:
for i, l in enumerate(f):
if i == indexes[0]: # Check whether line matches with the index
sequences.append(l) # Get your sequence
indexes.pop(0) # Remove the first element of the indexes
I hope this helps! ;)
Code
ids = []
seq = []
char = ['_', ':', '*', '#'] #invalid in sequence
seqs = ''
with open('fasta.txt', 'r') as f: #open sample fasta
for line in f:
if line.startswith('>'):
ids.append(line.strip('\n'))
if seqs != '': #if there's previous seq
seq.append(seqs) #append the seq
seqs = '' #then start a new seq
elif line not in char:
seqs = seqs + line.strip('\n') #build seq with each line until '>'
seq.append(seqs) #append any remaining seq
print(ids)
print(seq)
Result
['>SeqABCD [organism=Mus musculus]', '>SeqABCDE [organism=Plasmodium]']
['ACGTCAGTCACGTACGTCAGTTCAGTC...', 'GGTACTGCAAAGTTCTTCCGCCTGATTA...']
Sample File
>SeqABCD [organism=Mus musculus]
ACGTCAGTCACGTACGTCAGTTCAGTCARYSTYSATCASMBMBDH
ATCGTTTTTATGTAATTGCTTATTGTTGTGTGTAGATTTTTTAA
AAATATCATTTGAGGTCAATACAAATCCTATTTCTATCGTTTTT
CCCTAAACCCTAAACCCTAAACCCTAAACCTCTGAATCCTTAAT
>SeqABCDE [organism=Plasmodium falciparum]
GGTACTGCAAAGTTCTTCCGCCTGATTAATTATCCATTTTACCTT
TTGTTTTGCTTCTTTGAAGTAGTTTCTCTTTGCAAAATTCCTCTT
GGTACTGCAAAGTTCTTCCGCCTGATTAATTATCCGGTACTGCAA
AGTCAATTTTATATAATTTAATCAAATAAATAAGTTTATGGTTAA
import random
while True:
calc_1 = (random.randint(1,50)) #generates random variables
calc_2 = (random.randint(1,50))
print (calc_1,"+",calc_2) #prints the random question
a = ((calc_1)+(calc_2)) #calculates the random question
q = input ("? ")
if q == a :
print ("right")
break
else:
print ("wrong")
It won't say right, when the answer is right. I already tested a few other possibilities but I couldn't figure it out.
input() gives you str so convert it to int before comparison
import random
while True:
calc_1 = (random.randint(1, 50)) # generates random variables
calc_2 = (random.randint(1, 50))
print(calc_1, "+", calc_2) # prints the random question
a = ((calc_1) + (calc_2)) # calculates the random question
q = input("? ")
try:
q = int(q)
if q == a:
print("right")
break
else:
print("wrong")
except:
print('Not a number')
pyparsing: The below is the code i put up which can parse a nested function call , a logical function call or a hybrid call which nests both the function and a logical function call. The dump() data adds too many unnecessary levels of braces because of grouping. Removing the Group() results in a wrong output. Is there a guideline to use Group(parsers)?
Also the Pyparsing document does'nt detail on how to walk the tree created and not much of data is available out there. Please point me to a link/guide which helps me write the tree walker for recursively parsed data for my test cases.
I will be translating this parsed data to a valid tcl code.
from pyparsing import *
from pyparsing import OneOrMore, Optional, Word, delimitedList, Suppress
# parse action -maker; # from Paul's example
def makeLRlike(numterms):
if numterms is None:
# None operator can only by binary op
initlen = 2
incr = 1
else:
initlen = {0:1,1:2,2:3,3:5}[numterms]
incr = {0:1,1:1,2:2,3:4}[numterms]
# define parse action for this number of terms,
# to convert flat list of tokens into nested list
def pa(s,l,t):
t = t[0]
if len(t) > initlen:
ret = ParseResults(t[:initlen])
i = initlen
while i < len(t):
ret = ParseResults([ret] + t[i:i+incr])
i += incr
return ParseResults([ret])
return pa
line = Forward()
fcall = Forward().setResultsName("fcall")
flogical = Forward()
lparen = Literal("(").suppress()
rparen = Literal(")").suppress()
arg = Word(alphas,alphanums+"_"+"."+"+"+"-"+"*"+"/")
args = delimitedList(arg).setResultsName("arg")
fargs = delimitedList(OneOrMore(flogical) | OneOrMore(fcall) |
OneOrMore(arg))
fname = Word(alphas,alphanums+"_")
fcall << Group(fname.setResultsName('func') + Group(lparen +
Optional(fargs) + rparen).setResultsName('fargs'))
flogic = Keyword("or") | Keyword("and") | Keyword("not")
logicalArg = delimitedList(Group(fcall.setResultsName("fcall")) |
Group(arg.setResultsName("arg")))
#logicalArg.setDebug()
flogical << Group(logicalArg.setResultsName('larg1') +
flogic.setResultsName('flogic') + logicalArg.setResultsName('larg2'))
#logical = operatorPrecedence(flogical, [(not, 1, opAssoc.RIGHT,
makeLRlike(2)),
# (and, 2, opAssoc.LEFT,
makeLRlike(2)),
# (or , 2, opAssoc.LEFT,
makeLRlike(2))])
line = flogical | fcall #change to logical if operatorPrecedence is used
# Works fine
print line.parseString("f(x, y)").dump()
print line.parseString("f(h())").dump()
print line.parseString("a and b").dump()
print line.parseString("f(a and b)").dump()
print line.parseString("f(g(x))").dump()
print line.parseString("f(a and b) or h(b not c)").dump()
print line.parseString("f(g(x), y)").dump()
print line.parseString("g(f1(x), a, b, f2(x,y, k(x,y)))").dump()
print line.parseString("f(a not c) and g(f1(x), a, b, f2(x,y,
k(x,y)))").dump()
#Does'nt work fine yet;
#try changing flogical assignment to logicalArg | flogic
#print line.parseString("a or b or c").dump()
#print line.parseString("f(a or b(x) or c)").dump()
I'm trying to write a function that accepts a 2-dimensional (2D) list of characters (like a crossword puzzle) and a string as input arguments, the function must then search the columns of the 2D list to find a match of the word. If a match is found, the function should then return a list containing the row index and column index of the start of the match, otherwise it should return the value None.
For example if the function is called as shown below:
crosswords = [['s','d','o','g'],['c','u','c','m'],['a','c','a','t'],['t','e','t','k']]
word = 'cat'
find_word_vertical(crosswords,word)
then the function should return:
[1,0]
def find_word_vertical(crosswords,word):
columns = []
finished = []
for col in range(len(crosswords[0])):
columns.append( [crosswords[row][col] for row in
range(len(crosswords))])
for a in range(0, len(crosswords)):
column = [crosswords[x][a] for x in range(len(crosswords))]
finished.append(column)
for row in finished:
r=finished.index(row)
whole_row = ''.join(row)
found_at = whole_row.find(word)
if found_at >=0:
return([found_at, r])
This one is for finding horizontal... could switching this around help?
def find_word_horizontal(crosswords, word):
list1=[]
row_index = -1
column_index = -1
refind=''
for row in crosswords:
index=''
for column in row:
index= index+column
list1.append(index)
for find_word in list1:
if word in find_word:
row_index = list1.index(find_word)
refind = find_word
column_index = find_word.index(word)
ret = [row_index,column_index]
if row_index!= -1 and column_index != -1:
return ret
The simple version is:
def find_word_vertical(crosswords,word):
z=[list(i) for i in zip(*crosswords)]
for rows in z:
row_index = z.index(rows)
single_row = ''.join(rows)
column_index = single_row.find(word)
if column_index >= 0:
return([column_index, row_index])
This gives correct output [1,0]
To find a word vertically:
def find_word_vertical(crosswords,word):
if not crosswords or not word:
return None
for col_index in range(len(crosswords[0])):
str = ''
for row_index in range(len(crosswords)):
str = str + crosswords[row_index][col_index]
if temp_str.find(word) >= 0:
return [str.find(word),col_index]
To find a word Horizontaly:
def find_word_horizontal(crosswords, word):
if not crosswords or not word:
return None
for index, row in enumerate(crosswords):
str = ''.join(row)
if str.find(word) >= 0:
return [index,str.find(word)]
#find vertical word in 2d
def find_it(li,wo):
out_list=[]
for row in range(len(li)):
print(row)
chek_word=""
for item in range(len(li)):
chek_word=chek_word + li[item][row]
print(chek_word)
if wo in chek_word:
print(chek_word.find(wo))
out_list=[ chek_word.find(wo) , row]
print(out_list)
break
this is mine and yes it work