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nlp/topic_modeling.py | innaiivanova/chatbot | 0 | 131286 | <gh_stars>0
# Codecademy's topic modeling
# <NAME>
import nltk, re
from sherlock_holmes import bohemia_ch1, bohemia_ch2, bohemia_ch3, boscombe_ch1, boscombe_ch2, boscombe_ch3
from preprocessing import preprocess_text
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.decomposition import LatentDirichletAllocation
# preparing the text
corpus = [bohemia_ch1, bohemia_ch2, bohemia_ch3, boscombe_ch1, boscombe_ch2, boscombe_ch3]
preprocessed_corpus = [preprocess_text(chapter) for chapter in corpus]
# Update stop_list:
stop_list = ["stop", "list", "free", "has", "words", "home", "six", "town", "random", "say"]
# filtering topics for stop words
def filter_out_stop_words(corpus):
no_stops_corpus = []
for chapter in corpus:
no_stops_chapter = " ".join([word for word in chapter.split(" ") if word not in stop_list])
no_stops_corpus.append(no_stops_chapter)
return no_stops_corpus
filtered_for_stops = filter_out_stop_words(preprocessed_corpus)
# creating the bag of words model
bag_of_words_creator = CountVectorizer()
bag_of_words = bag_of_words_creator.fit_transform(filtered_for_stops)
# creating the tf-idf model
tfidf_creator = TfidfVectorizer(min_df = 0.2)
tfidf = tfidf_creator.fit_transform(preprocessed_corpus)
# creating the bag of words LDA model
lda_bag_of_words_creator = LatentDirichletAllocation(learning_method='online', n_components=10)
lda_bag_of_words = lda_bag_of_words_creator.fit_transform(bag_of_words)
# creating the tf-idf LDA model
lda_tfidf_creator = LatentDirichletAllocation(learning_method='online', n_components=10)
lda_tfidf = lda_tfidf_creator.fit_transform(tfidf)
print("~~~ Topics found by bag of words LDA ~~~")
for topic_id, topic in enumerate(lda_bag_of_words_creator.components_):
message = "Topic #{}: ".format(topic_id + 1)
message += " ".join([bag_of_words_creator.get_feature_names()[i] for i in topic.argsort()[:-5 :-1]])
print(message)
print("\n\n~~~ Topics found by tf-idf LDA ~~~")
for topic_id, topic in enumerate(lda_tfidf_creator.components_):
message = "Topic #{}: ".format(topic_id + 1)
message += " ".join([tfidf_creator.get_feature_names()[i] for i in topic.argsort()[:-5 :-1]])
print(message)
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slash/utils/python.py | bheesham/slash | 70 | 1609339 | from collections import OrderedDict
from types import TracebackType
import functools
import warnings
import inspect
import sys
import ast
import pickle
import logbook
from sentinels import NOTHING
PYPY = hasattr(sys, 'pypy_version_info')
_logger = logbook.Logger(__name__)
def check_duplicate_functions(path):
code = None
with open(path, 'rb') as f:
code = f.read()
with warnings.catch_warnings():
warnings.simplefilter('ignore')
root = ast.parse(code, filename=path)
func_names = set()
duplicates = set()
for node in root.body:
if isinstance(node, ast.FunctionDef):
if node.name in func_names:
duplicates.add((path, node.name, node.lineno))
else:
func_names.add(node.name)
return duplicates
def wraps(func, preserve=()):
def decorator(new_func):
returned = functools.wraps(func)(new_func)
returned.__wraps__ = func
return returned
for p in preserve:
orig = getattr(func, p, NOTHING)
if orig is not NOTHING:
setattr(decorator, p, orig)
return decorator
def unpickle(thing):
if not thing:
return thing
return pickle.loads(thing.data)
def get_underlying_func(func):
while True:
underlying = getattr(func, "__wraps__", None)
if underlying is None:
return func
func = underlying
def get_argument_names(func):
return [arg.name for arg in get_arguments(func)]
def get_arguments_dict(func):
returned = OrderedDict()
for arg in get_arguments(func):
returned[arg.name] = arg
return returned
def get_arguments(func):
if PYPY:
func = get_underlying_func(func)
spec = inspect.getargspec(func) # pylint: disable=deprecated-method
returned = [FunctionArgument(name=name) for name in spec.args]
else:
if getattr(func, '__self__', None) is not None:
func = func.__func__ # signature() doesn't work on bound methods
returned = [FunctionArgument.from_parameter(p) for name, p in inspect.signature(func).parameters.items()] # pylint: disable=no-member
if returned and returned[0].name == 'self':
returned = returned[1:]
return returned
class FunctionArgument(object):
def __init__(self, name, annotation=NOTHING):
super(FunctionArgument, self).__init__()
self.name = name
self.annotation = annotation
@classmethod
def from_parameter(cls, parameter):
annotation = parameter.annotation
if annotation is parameter.empty:
annotation = NOTHING
return cls(name=parameter.name, annotation=parameter.annotation)
def call_all_raise_first(_funcs, *args, **kwargs):
exc_info = None
for func in _funcs:
try:
func(*args, **kwargs)
except Exception: # pylint: disable=broad-except
exc_info = sys.exc_info()
if exc_info is not None:
reraise(*exc_info)
def resolve_underlying_function(thing):
"""Gets the underlying (real) function for functions, wrapped functions, methods, etc.
Returns the same object for things that are not functions
"""
while True:
wrapped = getattr(thing, "__func__", None) or getattr(thing, "__wrapped__", None) or getattr(thing, "__wraps__", None)
if wrapped is None:
break
thing = wrapped
return thing
def reraise(tp, value, tb=None):
# A hacky way to check, whether we have a TracebackProxy here. Can't check directly, as
# it would lead to circular import.
if value.__traceback__ is not tb:
if not isinstance(tb, TracebackType):
tb = tb._tb # pylint: disable=protected-access
raise value.with_traceback(tb)
raise value
def get_underlying_classmethod_function(func):
return func.__func__
def safe_set_attribute(obj, name, value):
try:
setattr(obj, name, value)
except Exception: # pylint: disable=broad-except
try:
obj.__dict__[name] = value
except Exception: # pylint: disable=broad-except
_logger.debug("Failed to set attribute {!r} on {!r}", name, obj)
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mean_fits.py | kafkaphoenix/data-driven-astro | 0 | 140804 | <filename>mean_fits.py
from astropy.io import fits
import numpy as np
# Write your mean_fits function here:
def mean_fits(files):
all_data = []
for k, file in enumerate(files):
if k == 0:
all_data = fits.getdata(file)
else:
data = fits.getdata(file)
all_data = all_data + data
all_data = all_data / len(files)
return all_data
if __name__ == '__main__':
# Test your function with examples from the question
data = mean_fits(['image0.fits', 'image1.fits', 'image2.fits'])
print(data[100, 100])
# You can also plot the result:
import matplotlib.pyplot as plt
plt.imshow(data.T, cmap=plt.cm.viridis)
plt.colorbar()
plt.show() | [
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src/models/rankae.py | RowitZou/RankAE | 23 | 117624 | <filename>src/models/rankae.py<gh_stars>10-100
import copy
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_
from torch.nn.utils.rnn import pad_sequence
from models.decoder import TransformerDecoder
from models.encoder import Bert, TransformerEncoder, PositionalEncoding
from models.generator import Generator
from others.utils import tile
class RankAE(nn.Module):
def __init__(self, args, device, vocab, checkpoint=None):
super(RankAE, self).__init__()
self.args = args
self.device = device
self.vocab = vocab
self.vocab_size = len(vocab)
self.beam_size = args.beam_size
self.max_length = args.max_length
self.min_length = args.min_length
self.start_token = vocab['[unused1]']
self.end_token = vocab['[unused2]']
self.pad_token = vocab['[PAD]']
self.mask_token = vocab['[MASK]']
self.seg_token = vocab['[unused3]']
self.cls_token = vocab['[CLS]']
self.hidden_size = args.enc_hidden_size
self.embeddings = nn.Embedding(self.vocab_size, self.hidden_size, padding_idx=0)
if args.encoder == 'bert':
self.encoder = Bert(args.bert_dir, args.finetune_bert)
if(args.max_pos > 512):
my_pos_embeddings = nn.Embedding(args.max_pos, self.encoder.model.config.hidden_size)
my_pos_embeddings.weight.data[:512] = self.encoder.model.embeddings.position_embeddings.weight.data
my_pos_embeddings.weight.data[512:] = self.encoder.model.embeddings.position_embeddings.weight.data[-1][None, :].repeat(args.max_pos-512, 1)
self.encoder.model.embeddings.position_embeddings = my_pos_embeddings
tgt_embeddings = nn.Embedding(self.vocab_size, self.encoder.model.config.hidden_size, padding_idx=0)
else:
self.encoder = TransformerEncoder(self.hidden_size, args.enc_ff_size, args.enc_heads,
args.enc_dropout, args.enc_layers)
tgt_embeddings = nn.Embedding(self.vocab_size, self.hidden_size, padding_idx=0)
self.hier_encoder = TransformerEncoder(self.hidden_size, args.hier_ff_size, args.hier_heads,
args.hier_dropout, args.hier_layers)
self.cup_bilinear = nn.Bilinear(self.hidden_size, self.hidden_size, 1)
self.pos_emb = PositionalEncoding(0., self.hidden_size)
self.decoder = TransformerDecoder(
self.args.dec_layers,
self.args.dec_hidden_size, heads=self.args.dec_heads,
d_ff=self.args.dec_ff_size, dropout=self.args.dec_dropout,
embeddings=tgt_embeddings)
self.generator = Generator(self.vocab_size, self.args.dec_hidden_size, self.pad_token)
self.generator.linear.weight = self.decoder.embeddings.weight
if checkpoint is not None:
self.load_state_dict(checkpoint['model'], strict=True)
else:
if args.encoder == "transformer":
for module in self.encoder.modules():
self._set_parameter_tf(module)
xavier_uniform_(self.embeddings.weight)
for module in self.decoder.modules():
self._set_parameter_tf(module)
for module in self.hier_encoder.modules():
self._set_parameter_tf(module)
for p in self.generator.parameters():
self._set_parameter_linear(p)
for p in self.cup_bilinear.parameters():
self._set_parameter_linear(p)
if args.share_emb:
if args.encoder == 'bert':
self.embeddings = self.encoder.model.embeddings.word_embeddings
tgt_embeddings = nn.Embedding(self.vocab_size, self.encoder.model.config.hidden_size, padding_idx=0)
tgt_embeddings.weight = copy.deepcopy(self.encoder.model.embeddings.word_embeddings.weight)
else:
tgt_embeddings = self.embeddings
self.decoder.embeddings = tgt_embeddings
self.generator.linear.weight = self.decoder.embeddings.weight
self.to(device)
def _set_parameter_tf(self, module):
if isinstance(module, (nn.Linear, nn.Embedding)):
module.weight.data.normal_(mean=0.0, std=0.02)
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
def _set_parameter_linear(self, p):
if p.dim() > 1:
xavier_uniform_(p)
else:
p.data.zero_()
def _rebuild_tgt(self, origin, index, sep_token=None):
tgt_list = [torch.tensor([self.start_token], device=self.device)]
selected = origin.index_select(0, index)
for sent in selected:
filted_sent = sent[sent != self.pad_token][1:]
if sep_token is not None:
filted_sent[-1] = sep_token
else:
filted_sent = filted_sent[:-1]
tgt_list.append(filted_sent)
new_tgt = torch.cat(tgt_list, 0)
if sep_token is not None:
new_tgt[-1] = self.end_token
else:
new_tgt = torch.cat([new_tgt, torch.tensor([self.end_token], device=self.device)], 0)
return new_tgt
def _build_memory_window(self, ex_segs, keep_clss, replace_clss=None, mask=None, samples=None):
keep_cls_list = torch.split(keep_clss, ex_segs)
window_list = []
for ex in keep_cls_list:
ex_pad = F.pad(ex, (0, 0, self.args.win_size, self.args.win_size)).unsqueeze(1)
ex_context = torch.cat([ex_pad[:ex.size(0)], ex.unsqueeze(1),
ex_pad[self.args.win_size*2:]], 1)
window_list.append(ex_context)
memory = torch.cat(window_list, 0)
if replace_clss is not None:
replace_cls_list = torch.split(replace_clss, ex_segs)
window_list = []
for ex in replace_cls_list:
ex_pad = F.pad(ex, (0, 0, self.args.win_size, self.args.win_size)).unsqueeze(1)
ex_context = torch.cat([ex_pad[:ex.size(0)], ex.unsqueeze(1),
ex_pad[self.args.win_size*2:]], 1)
window_list.append(ex_context)
origin_memory = torch.cat(window_list, 0)
sample_list = torch.split(samples, ex_segs)
sample_tensor_list = []
for i in range(len(ex_segs)):
sample_index_ = torch.randint(0, samples.size(-1), [mask.size(-1)], device=self.device)
sample_index = torch.index_select(sample_list[i], 1, sample_index_)
sample_tensor = replace_cls_list[i][sample_index]
sample_tensor_list.append(sample_tensor)
sample_memory = torch.cat(sample_tensor_list, 0)
memory = memory * (mask == 2).unsqueeze(-1).float() + \
sample_memory * (mask == 0).unsqueeze(-1).float() + \
origin_memory * (mask == 1).unsqueeze(-1).float()
return memory
def _src_add_noise(self, sent, sampled_sent, expand_ratio=0.):
role_emb = sent[1:2]
filted_sent = sent[sent != self.pad_token][2:]
# filted_sent = sent[sent != self.pad_token][1:]
rand_size = sampled_sent.size(0)
length = max(int(filted_sent.size(0)*(1+expand_ratio)), filted_sent.size(0)+1)
while filted_sent.size(0) < length:
target_length = length - filted_sent.size(0)
rand_sent = sampled_sent[random.randint(0, rand_size-1)]
rand_sent = rand_sent[rand_sent != self.pad_token][2:] # remove cls and role embedding
# rand_sent = rand_sent[rand_sent != self.pad_token][1:] # no role embedding
start_point = random.randint(0, rand_sent.size(0)-1)
end_point = random.randint(start_point, rand_sent.size(0))
rand_segment = rand_sent[start_point:min(end_point, start_point+10, start_point+target_length)]
insert_point = random.randint(0, filted_sent.size(0)-1)
filted_sent = torch.cat([filted_sent[:insert_point],
rand_segment,
filted_sent[insert_point:]], 0)
# return filted_sent
return torch.cat([role_emb, filted_sent], 0)
def _build_noised_src(self, src, ex_segs, samples, expand_ratio=0.):
src_list = torch.split(src, ex_segs)
new_src_list = []
sample_list = torch.split(samples, ex_segs)
for i, ex in enumerate(src_list):
for j, sent in enumerate(ex):
sampled_sent = ex.index_select(0, sample_list[i][j])
expanded_sent = self._src_add_noise(sent, sampled_sent, expand_ratio)
new_src = torch.cat([torch.tensor([self.cls_token], device=self.device), expanded_sent], 0)
new_src_list.append(new_src)
new_src = pad_sequence(new_src_list, batch_first=True, padding_value=self.pad_token)
new_mask = new_src.data.ne(self.pad_token)
new_segs = torch.zeros_like(new_src)
return new_src, new_mask, new_segs
def _build_context_tgt(self, tgt, ex_segs, win_size=1, modify=False, mask=None):
tgt_list = torch.split(tgt, ex_segs)
new_tgt_list = []
if modify and mask is not None:
# 1 means keeping the sentence
mask_list = torch.split(mask, ex_segs)
for i in range(len(tgt_list)):
sent_num = tgt_list[i].size(0)
for j in range(sent_num):
if modify:
low = j-win_size
up = j+win_size+1
index = torch.arange(low, up, device=self.device)
index = index[mask_list[i][j] > 0]
else:
low = max(0, j-win_size)
up = min(sent_num, j+win_size+1)
index = torch.arange(low, up, device=self.device)
new_tgt_list.append(self._rebuild_tgt(tgt_list[i], index, self.seg_token))
new_tgt = pad_sequence(new_tgt_list, batch_first=True, padding_value=self.pad_token)
return new_tgt
def _build_doc_tgt(self, tgt, vec, ex_segs, win_size=1, max_k=6, sigma=1.0):
vec_list = torch.split(vec, ex_segs)
tgt_list = torch.split(tgt, ex_segs)
new_tgt_list = []
index_list = []
shift_list = []
accum_index = 0
for idx in range(len(ex_segs)):
ex_vec = vec_list[idx]
sent_num = ex_segs[idx]
ex_tgt = tgt_list[idx]
tgt_length = ex_tgt[:, 1:].ne(self.pad_token).sum(dim=1).float()
topk_ids = self._centrality_rank(ex_vec, sent_num, tgt_length, win_size, max_k, sigma)
new_tgt_list.append(self._rebuild_tgt(ex_tgt, topk_ids, self.seg_token))
shift_list.append(topk_ids)
index_list.append(topk_ids + accum_index)
accum_index += sent_num
new_tgt = pad_sequence(new_tgt_list, batch_first=True, padding_value=self.pad_token)
return new_tgt, index_list, shift_list
def _centrality_rank(self, vec, sent_num, tgt_length, win_size, max_k, sigma, eta=0.5, min_length=5):
assert vec.size(0) == sent_num
sim = torch.sigmoid(self.cup_bilinear(vec.unsqueeze(1).expand(sent_num, sent_num, -1).contiguous(),
vec.unsqueeze(0).expand(sent_num, sent_num, -1).contiguous())
).squeeze().detach()
# sim = torch.sigmoid(torch.mm(vec, vec.transpose(0, 1)))
# sim = torch.cosine_similarity(
# vec.unsqueeze(1).expand(sent_num, sent_num, -1).contiguous().view(sent_num * sent_num, -1),
# vec.unsqueeze(0).expand(sent_num, sent_num, -1).contiguous().view(sent_num * sent_num, -1)
# ).view(sent_num, sent_num).detach()
# calculate sim weight
k = min(max(sent_num // (win_size*2+1), 1), max_k)
var = sent_num / k * 1.
x = torch.arange(sent_num, device=self.device, dtype=torch.float).unsqueeze(0).expand_as(sim)
u = torch.arange(sent_num, device=self.device, dtype=torch.float).unsqueeze(1)
weight = torch.exp(-(x-u)**2 / (2. * var**2)) * (1. - torch.eye(sent_num, device=self.device))
# weight = 1. - torch.eye(sent_num, device=self.device)
sim[tgt_length < min_length, :] = -1e20
# Calculate centrality and select top k sentence.
topk_ids = torch.empty(0, dtype=torch.long, device=self.device)
mask = torch.zeros([sent_num, sent_num], dtype=torch.float, device=self.device)
for _ in range(k):
mean_score = torch.sum(sim * weight, dim=1) / max(sent_num-1, 1)
max_v, _ = torch.max(sim * weight * mask, dim=1)
centrality = eta*mean_score - (1-eta)*max_v
_, top_id = torch.topk(centrality, 1, dim=0, sorted=False)
topk_ids = torch.cat([topk_ids, top_id], 0)
sim[topk_ids, :] = -1e20
mask[:, topk_ids] = 1.
topk_ids, _ = torch.sort(topk_ids)
"""
centrality = torch.sum(sim * weight, dim=1)
_, topk_ids = torch.topk(centrality, k, dim=0, sorted=False)
topk_ids, _ = torch.sort(topk_ids)
"""
return topk_ids
def _add_mask(self, src, mask_src):
pm_index = torch.empty_like(mask_src).float().uniform_().le(self.args.mask_token_prob)
ps_index = torch.empty_like(mask_src[:, 0]).float().uniform_().gt(self.args.select_sent_prob)
pm_index[ps_index] = 0
# Avoid mask [PAD]
pm_index[(1-mask_src).byte()] = 0
# Avoid mask [CLS]
pm_index[:, 0] = 0
# Avoid mask [SEG]
pm_index[src == self.seg_token] = 0
src[pm_index] = self.mask_token
return src
def _build_cup(self, bsz, ex_segs, win_size=1, negative_num=2):
cup = torch.split(torch.arange(0, bsz, dtype=torch.long, device=self.device), ex_segs)
tgt = torch.split(torch.ones(bsz), ex_segs)
cup_list = []
cup_origin_list = []
tgt_list = []
negative_list = []
for i in range(len(ex_segs)):
sent_num = ex_segs[i]
cup_low = cup[i][0].item()
cup_up = cup[i][sent_num-1].item()
cup_index = cup[i].repeat(win_size*2*(negative_num+1))
tgt_index = tgt[i].repeat(win_size*2*(negative_num+1))
cup_origin_list.append(cup[i].repeat(win_size*2*(negative_num+1)))
tgt_index[sent_num*win_size*2:] = 0
for j in range(cup_index.size(0)):
if tgt_index[j] == 1:
cup_temp = cup_index[j]
window_list = [t for t in range(max(cup_index[j]-win_size, cup_low),
min(cup_index[j]+win_size, cup_up)+1)
if t != cup_index[j]]
cup_temp = window_list[(j // sent_num) % len(window_list)]
else:
cand_list = [t for t in range(cup_low, max(cup_index[j]-win_size, cup_low))] + \
[t for t in range(min(cup_index[j]+win_size, cup_up), cup_up)]
cup_temp = cand_list[random.randint(0, len(cand_list)-1)]
cup_index[j] = cup_temp
negative_list.append((cup_index[sent_num*win_size*2:]-cup_low).
view(negative_num*win_size*2, -1).transpose(0, 1))
cup_list.append(cup_index)
tgt_list.append(tgt_index)
tgt = torch.cat(tgt_list, dim=0).float().to(self.device)
cup_origin = torch.cat(cup_origin_list, dim=0)
cup = torch.cat(cup_list, dim=0)
negative_sample = torch.cat(negative_list, dim=0)
return cup, cup_origin, tgt[cup != -1], negative_sample
def _build_option_window(self, bsz, ex_segs, win_size=1, keep_ratio=0.1, replace_ratio=0.2):
assert keep_ratio + replace_ratio <= 1.
noise_ratio = 1 - keep_ratio - replace_ratio
window_size = 2*win_size+1
index = torch.split(torch.arange(1, bsz+1, dtype=torch.long, device=self.device), ex_segs)
# 2 means noise addition, 1 means keep the memory, 0 means replacement
tgt = torch.zeros([bsz, window_size], device=self.device, dtype=torch.int)
prob = torch.empty([bsz, window_size], device=self.device).uniform_()
tgt.masked_fill_(prob.lt(noise_ratio), 2)
tgt.masked_fill_(prob.ge(1-keep_ratio), 1)
tgt = torch.split(tgt, ex_segs)
for i in range(len(ex_segs)):
sent_num = ex_segs[i]
index_pad = F.pad(index[i], (self.args.win_size, self.args.win_size))
for j in range(sent_num):
window = index_pad[j:j+window_size]
# Avoiding that all elements are 0
if torch.sum(tgt[i][j].byte()*(window > 0)) == 0:
tgt[i][j][win_size] = 2
tgt[i][j][window == 0] = -1
tgt = torch.cat(tgt, 0)
return tgt
def _fast_translate_batch(self, batch, memory_bank, max_length, init_tokens=None, memory_mask=None,
min_length=2, beam_size=3, ignore_mem_attn=False):
batch_size = memory_bank.size(0)
dec_states = self.decoder.init_decoder_state(batch.src, memory_bank, with_cache=True)
# Tile states and memory beam_size times.
dec_states.map_batch_fn(
lambda state, dim: tile(state, beam_size, dim=dim))
memory_bank = tile(memory_bank, beam_size, dim=0)
init_tokens = tile(init_tokens, beam_size, dim=0)
memory_mask = tile(memory_mask, beam_size, dim=0)
batch_offset = torch.arange(
batch_size, dtype=torch.long, device=self.device)
beam_offset = torch.arange(
0,
batch_size * beam_size,
step=beam_size,
dtype=torch.long,
device=self.device)
alive_seq = torch.full(
[batch_size * beam_size, 1],
self.start_token,
dtype=torch.long,
device=self.device)
# Give full probability to the first beam on the first step.
topk_log_probs = (
torch.tensor([0.0] + [float("-inf")] * (beam_size - 1),
device=self.device).repeat(batch_size))
# Structure that holds finished hypotheses.
hypotheses = [[] for _ in range(batch_size)] # noqa: F812
results = [[] for _ in range(batch_size)] # noqa: F812
for step in range(max_length):
if step > 0:
init_tokens = None
# Decoder forward.
decoder_input = alive_seq[:, -1].view(1, -1)
decoder_input = decoder_input.transpose(0, 1)
dec_out, dec_states, _ = self.decoder(decoder_input, memory_bank, dec_states, init_tokens, step=step,
memory_masks=memory_mask, ignore_memory_attn=ignore_mem_attn)
# Generator forward.
log_probs = self.generator(dec_out.transpose(0, 1).squeeze(0))
vocab_size = log_probs.size(-1)
if step < min_length:
log_probs[:, self.end_token] = -1e20
if self.args.block_trigram:
cur_len = alive_seq.size(1)
if(cur_len > 3):
for i in range(alive_seq.size(0)):
fail = False
words = [int(w) for w in alive_seq[i]]
if(len(words) <= 3):
continue
trigrams = [(words[i-1], words[i], words[i+1]) for i in range(1, len(words)-1)]
trigram = tuple(trigrams[-1])
if trigram in trigrams[:-1]:
fail = True
if fail:
log_probs[i] = -1e20
# Multiply probs by the beam probability.
log_probs += topk_log_probs.view(-1).unsqueeze(1)
alpha = self.args.alpha
length_penalty = ((5.0 + (step + 1)) / 6.0) ** alpha
# Flatten probs into a list of possibilities.
curr_scores = log_probs / length_penalty
curr_scores = curr_scores.reshape(-1, beam_size * vocab_size)
topk_scores, topk_ids = curr_scores.topk(beam_size, dim=-1)
# Recover log probs.
topk_log_probs = topk_scores * length_penalty
# Resolve beam origin and true word ids.
topk_beam_index = topk_ids.div(vocab_size)
topk_ids = topk_ids.fmod(vocab_size)
# Map beam_index to batch_index in the flat representation.
batch_index = (
topk_beam_index
+ beam_offset[:topk_beam_index.size(0)].unsqueeze(1))
select_indices = batch_index.view(-1)
# Append last prediction.
alive_seq = torch.cat(
[alive_seq.index_select(0, select_indices),
topk_ids.view(-1, 1)], -1)
is_finished = topk_ids.eq(self.end_token)
if step + 1 == max_length:
is_finished.fill_(1)
# End condition is top beam is finished.
end_condition = is_finished[:, 0].eq(1)
# Save finished hypotheses.
if is_finished.any():
predictions = alive_seq.view(-1, beam_size, alive_seq.size(-1))
for i in range(is_finished.size(0)):
b = batch_offset[i]
if end_condition[i]:
is_finished[i].fill_(1)
finished_hyp = is_finished[i].nonzero().view(-1)
# Store finished hypotheses for this batch.
for j in finished_hyp:
hypotheses[b].append((
topk_scores[i, j],
predictions[i, j, 1:]))
# If the batch reached the end, save the n_best hypotheses.
if end_condition[i]:
best_hyp = sorted(
hypotheses[b], key=lambda x: x[0], reverse=True)
_, pred = best_hyp[0]
results[b].append(pred)
non_finished = end_condition.eq(0).nonzero().view(-1)
# If all sentences are translated, no need to go further.
if len(non_finished) == 0:
break
# Remove finished batches for the next step.
topk_log_probs = topk_log_probs.index_select(0, non_finished)
batch_index = batch_index.index_select(0, non_finished)
batch_offset = batch_offset.index_select(0, non_finished)
alive_seq = predictions.index_select(0, non_finished) \
.view(-1, alive_seq.size(-1))
# Reorder states.
select_indices = batch_index.view(-1)
if memory_bank is not None:
memory_bank = memory_bank.index_select(0, select_indices)
if memory_mask is not None:
memory_mask = memory_mask.index_select(0, select_indices)
if init_tokens is not None:
init_tokens = init_tokens.index_select(0, select_indices)
dec_states.map_batch_fn(
lambda state, dim: state.index_select(dim, select_indices))
results = [t[0] for t in results]
return results
def forward(self, batch):
src = batch.src
tgt = batch.tgt
segs = batch.segs
mask_src = batch.mask_src
ex_segs = batch.ex_segs
if self.training:
# Sample some dialogue utterances to do auto-encoder
ex_size = batch.src.size(0)
ex_index = [i for i in range(ex_size)]
random.shuffle(ex_index)
ex_indexs = torch.tensor(ex_index, dtype=torch.long, device=self.device)
ex_sample_indexs = ex_indexs[:max(int(ex_size * self.args.sample_ratio), 1)]
# Get Context utterance training samples and targets
cup_index, cup_original_index, cup_tgt, negative_samples = \
self._build_cup(src.size(0), ex_segs, self.args.win_size, self.args.negative_sample_num)
setattr(batch, 'cup_tgt', cup_tgt)
option_mask = self._build_option_window(src.size(0), ex_segs, win_size=self.args.win_size,
keep_ratio=self.args.ps if self.training else 1.,
replace_ratio=self.args.pr if self.training else 0.)
if self.training:
# Build noised src
noised_src, noised_src_mask, noised_src_segs = \
self._build_noised_src(src, ex_segs, samples=negative_samples,
expand_ratio=self.args.expand_ratio)
# build context tgt
context_tgt = self._build_context_tgt(tgt, ex_segs, self.args.win_size,
modify=self.training, mask=option_mask)
setattr(batch, 'context_tgt', context_tgt)
# DAE: Randomly mask tokens
if self.training:
src = self._add_mask(src.clone(), mask_src)
noised_src = self._add_mask(noised_src, noised_src_mask)
if self.args.encoder == "bert":
top_vec = self.encoder(src, segs, mask_src)
else:
src_emb = self.embeddings(src)
top_vec = self.encoder(src_emb, 1-mask_src)
clss = top_vec[:, 0, :]
# Hierarchical encoder
cls_list = torch.split(clss, ex_segs)
cls_input = nn.utils.rnn.pad_sequence(cls_list, batch_first=True, padding_value=0.)
cls_mask_list = [mask_src.new_zeros([length]) for length in ex_segs]
cls_mask = nn.utils.rnn.pad_sequence(cls_mask_list, batch_first=True, padding_value=1)
hier = self.hier_encoder(cls_input, cls_mask)
hier = hier.view(-1, hier.size(-1))[(1-cls_mask.view(-1)).byte()]
if self.training:
# calculate cup score
cup_tensor = torch.index_select(clss, 0, cup_index)
origin_tensor = torch.index_select(clss, 0, cup_original_index)
cup_score = torch.sigmoid(self.cup_bilinear(origin_tensor, cup_tensor)).squeeze()
# cup_score = torch.sigmoid(origin_tensor.unsqueeze(1).bmm(cup_tensor.unsqueeze(-1)).squeeze())
# noised src encode
if self.args.encoder == "bert":
noised_top_vec = self.encoder(noised_src, noised_src_segs, noised_src_mask)
else:
noised_src_emb = self.embeddings(noised_src)
noised_top_vec = self.encoder(noised_src_emb, 1-noised_src_mask)
noised_clss = noised_top_vec[:, 0, :]
noised_cls_mem = self._build_memory_window(ex_segs, noised_clss, clss, option_mask, negative_samples)
noised_cls_mem = self.pos_emb(noised_cls_mem)
# sample training examples
context_tgt_sample = torch.index_select(context_tgt, 0, ex_sample_indexs)
noised_cls_mem_sample = torch.index_select(noised_cls_mem, 0, ex_sample_indexs)
hier_sample = torch.index_select(hier, 0, ex_sample_indexs)
else:
cup_score = None
if self.training:
dec_state = self.decoder.init_decoder_state(noised_src, noised_cls_mem_sample)
decode_context, _, _ = self.decoder(context_tgt_sample[:, :-1], noised_cls_mem_sample, dec_state,
init_tokens=hier_sample)
doc_data = None
# For loss computation.
if ex_sample_indexs is not None:
batch.context_tgt = context_tgt_sample
else:
decode_context = None
# Build paragraph tgt based on centrality rank.
doc_tgt, doc_index, _ = self._build_doc_tgt(tgt, clss, ex_segs, self.args.win_size, self.args.ranking_max_k)
centrality_segs = [len(iex) for iex in doc_index]
centrality_index = [sum(centrality_segs[:i]) for i in range(len(centrality_segs)+1)]
doc_index = torch.cat(doc_index, 0)
setattr(batch, 'doc_tgt', doc_tgt)
doc_hier_sample = torch.index_select(hier, 0, doc_index)
# original cls mem
cls_mem = self._build_memory_window(ex_segs, clss)
cls_mem = self.pos_emb(cls_mem)
doc_cls_mem = torch.index_select(cls_mem, 0, doc_index)
# Context aware doc target
context_doc_tgt = torch.index_select(context_tgt, 0, doc_index)
setattr(batch, 'context_doc_tgt', context_doc_tgt)
setattr(batch, 'doc_segs', centrality_index)
doc_context_long = self._fast_translate_batch(batch, doc_cls_mem, self.max_length, init_tokens=doc_hier_sample,
min_length=2, beam_size=self.beam_size)
doc_context_long = [torch.cat(doc_context_long[centrality_index[i]:centrality_index[i+1]], 0) for i in range(len(centrality_segs))]
doc_data = doc_context_long
return cup_score, decode_context, doc_data
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src/decisions/fridgedecision.py | jussike/kuappi | 0 | 185937 | from abstract import AbstractDecision
from common import TEMP
class FridgeDecision(AbstractDecision):
soft_hi_limit = 4.8
soft_low_limit = 3.3
hard_hi_limit = 7
hard_low_limit = 0
def get_decision(self, data, output_state=None):
temp = data[TEMP]
if temp > self.hard_hi_limit:
return True
elif temp < self.hard_low_limit:
return False
elif output_state is None:
return None
elif temp >= self.soft_hi_limit and not output_state:
return True
elif temp <= self.soft_low_limit and output_state:
return False
return None
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Prediction.py | khayam-hafezi/CRNN-keras-persian | 0 | 3506 | <reponame>khayam-hafezi/CRNN-keras-persian
import cv2
import itertools, os, time
import numpy as np
from Model import get_Model
from parameter import letters
import argparse
from keras import backend as K
K.set_learning_phase(0)
Region = {"A": "서울 ", "B": "경기 ", "C": "인천 ", "D": "강원 ", "E": "충남 ", "F": "대전 ",
"G": "충북 ", "H": "부산 ", "I": "울산 ", "J": "대구 ", "K": "경북 ", "L": "경남 ",
"M": "전남 ", "N": "광주 ", "O": "전북 ", "P": "제주 "}
Hangul = {"dk": "아", "dj": "어", "dh": "오", "dn": "우", "qk": "바", "qj": "버", "qh": "보", "qn": "부",
"ek": "다", "ej": "더", "eh": "도", "en": "두", "rk": "가", "rj": "거", "rh": "고", "rn": "구",
"wk": "자", "wj": "저", "wh": "조", "wn": "주", "ak": "마", "aj": "머", "ah": "모", "an": "무",
"sk": "나", "sj": "너", "sh": "노", "sn": "누", "fk": "라", "fj": "러", "fh": "로", "fn": "루",
"tk": "사", "tj": "서", "th": "소", "tn": "수", "gj": "허"}
def decode_label(out):
# out : (1, 32, 42)
out_best = list(np.argmax(out[0, 2:], axis=1)) # get max index -> len = 32
out_best = [k for k, g in itertools.groupby(out_best)] # remove overlap value
outstr = ''
for i in out_best:
if i < len(letters):
outstr += letters[i]
return outstr
def label_to_hangul(label): # eng -> hangul
region = label[0]
two_num = label[1:3]
hangul = label[3:5]
four_num = label[5:]
try:
region = Region[region] if region != 'Z' else ''
except:
pass
try:
hangul = Hangul[hangul]
except:
pass
return region + two_num + hangul + four_num
parser = argparse.ArgumentParser()
parser.add_argument("-w", "--weight", help="weight file directory",
type=str, default="models/weights.best.hdf5")
parser.add_argument("-t", "--test_img", help="Test image directory",
type=str, default="./DB/test/")
args = parser.parse_args()
# Get CRNN model
model = get_Model(training=False)
try:
model.load_weights(args.weight)
print("...Previous weight data...")
except:
raise Exception("No weight file!")
test_dir =args.test_img
test_imgs = os.listdir(args.test_img)
total = 0
acc = 0
letter_total = 0
letter_acc = 0
start = time.time()
for test_img in test_imgs:
img = cv2.imread(test_dir + test_img, cv2.IMREAD_GRAYSCALE)
img_pred = img.astype(np.float32)
img_pred = cv2.resize(img_pred, (128, 64))
img_pred = (img_pred / 255.0) * 2.0 - 1.0
img_pred = img_pred.T
img_pred = np.expand_dims(img_pred, axis=-1)
img_pred = np.expand_dims(img_pred, axis=0)
net_out_value = model.predict(img_pred)
pred_texts = decode_label(net_out_value)
for i in range(min(len(pred_texts), len(test_img[0:-4]))):
if pred_texts[i] == test_img[i]:
letter_acc += 1
letter_total += max(len(pred_texts), len(test_img[0:-4]))
predOk = "True"
if pred_texts == test_img[0:-4]:
acc += 1
else:
predOk = "False"
total += 1
# print('Predicted: %s / True: %s / net_out_value: %s / ' % (label_to_hangul(pred_texts), label_to_hangul(test_img[0:-4])))
print('Predicted: %s / True: %s / predOk: %s ' % (pred_texts, test_img[0:-4], predOk ))
# cv2.rectangle(img, (0,0), (150, 30), (0,0,0), -1)
# cv2.putText(img, pred_texts, (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255,255,255),2)
#cv2.imshow("q", img)
#if cv2.waitKey(0) == 27:
# break
#cv2.destroyAllWindows()
end = time.time()
total_time = (end - start)
print("Time : ",total_time / total)
print("ACC : ", acc / total)
print("letter ACC : ", letter_acc / letter_total)
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02_oop/01_classes_and_objects/08_access_modifiers_public.py | doanthanhnhan/learningPY | 1 | 99253 | <reponame>doanthanhnhan/learningPY
# Public Attributes
class Employee:
def __init__(self, ID, salary):
# all properties are public
self.ID = ID
self.salary = salary
def displayID(self):
print("ID:", self.ID)
Steve = Employee(3789, 2500)
Steve.displayID()
print(Steve.salary) | [
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django-rgd-3d/rgd_3d/models/__init__.py | ResonantGeoData/ResonantGeoData | 40 | 170766 | from .point_cloud import PointCloud, PointCloudMeta, PointCloudSpatial # noqa
| [
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tests/testguid.py | grodansparadis/pyvscpws1 | 1 | 101575 | import sys
from ctypes import *
sys.path.append('../')
from vscp import *
# GUID conversion
gg = vscp.guid()
print("After creation:", gg.getAsString())
gg.setFromString("00:11:22:33:44:55:66:77:88:99:AA:BB:CC:DD:EE:FF")
print("guid = ", gg.getAsString())
gg.reverse()
print("reverse guid = ", gg.getAsString())
print("guid[2] = ", gg.getAt(2), format("%02X" % gg.getAt(2)))
gg.setAt(2,33)
print("setat(2,33) guid[2] = ", gg.getAt(2), format("%02X" % gg.getAt(2)))
print("LSB = ", gg.getLSB(), format("%02X" % gg.getLSB()))
gg.setLSB(12)
print("setLSB(12) LSB = ", gg.getLSB(), format("%02X" % gg.getLSB()))
print("setLSB(12) LSB = ", gg.getNickname(), format("%02X" % gg.getNickname()))
print("Nickname = ", format("%02X" % gg.getNickname()), "Nickname ID", format("%04X" % gg.getNicknameID()))
gg.setNickname(99)
print("After setNickname (99) - Nickname = ", format("%02X" % gg.getNickname()), "Nickname ID", format("%04X" % gg.getNicknameID()))
gg.setNicknameID(0xaa55)
print("After setNicknameID (0xAA55) - Nickname = ", format("%02X" % gg.getNickname()), "Nickname ID", format("%04X" % gg.getNicknameID()))
gg.clear()
print("After clear() guid = ", gg.getAsString())
print("isNULL() = ", gg.isNULL())
gg.setNicknameID(0xaa55)
print("(False) isNULL() = ", gg.isNULL())
print("(False) isSame() = ", \
gg.isSame( gg.getArrayFromString("99:99:99:00:00:00:00:00:00:00:00:10:10:10:10:00")))
print("guid = ", gg.getAsString())
print("(True) isSame() = ", \
gg.isSame( gg.getArrayFromString("00:00:00:00:00:00:00:00:00:00:00:00:00:00:AA:55")))
a = vscp.guidarray(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0xAA,0x55)
b = vscp.guidarray(11,0,0,0,0,0,0,0,0,0,0,0,0,0,0xAA,0x55)
print("(True) isSame() = ", gg.isSame(a))
print("(False) isSame() = ", gg.isSame(b))
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] |
straxen/mongo_storage.py | ahiguera-mx/straxen | 0 | 133896 | <reponame>ahiguera-mx/straxen
import os
import tempfile
from datetime import datetime
from warnings import warn
import pytz
from strax import exporter, to_str_tuple
import gridfs
from tqdm import tqdm
from shutil import move
import hashlib
from pymongo.collection import Collection as pymongo_collection
import utilix
from straxen import uconfig
export, __all__ = exporter()
@export
class GridFsInterface:
"""
Base class to upload/download the files to a database using GridFS
for PyMongo:
https://pymongo.readthedocs.io/en/stable/api/gridfs/index.html#module-gridfs
This class does the basic shared initiation of the downloader and
uploader classes.
"""
def __init__(self,
readonly=True,
file_database='files',
config_identifier='config_name',
collection=None,
_test_on_init=False,
):
"""
GridFsInterface
:param readonly: bool, can one read or also write to the
database.
:param file_database: str, name of the database. Default should
not be changed.
:param config_identifier: str, header of the files that are
saved in Gridfs
:param collection: pymongo.collection.Collection, (Optional)
PyMongo DataName Collection to bypass normal initiation
using utilix. Should be an object of the form:
pymongo.MongoClient(..).DATABASE_NAME.COLLECTION_NAME
:param _test_on_init: Test if the collection is empty on init
(only deactivate if you are using a brand new database)!
"""
if collection is None:
if not readonly:
# We want admin access to start writing data!
mongo_url = uconfig.get('rundb_admin', 'mongo_rdb_url')
mongo_user = uconfig.get('rundb_admin', 'mongo_rdb_username')
mongo_password = uconfig.get('rundb_admin', 'mongo_rdb_password')
else:
# We can safely use the Utilix defaults
mongo_url = mongo_user = mongo_password = None
# If no collection arg is passed, it defaults to the 'files'
# collection, see for more details:
# https://github.com/XENONnT/utilix/blob/master/utilix/rundb.py
mongo_kwargs = {
'url': mongo_url,
'user': mongo_user,
'password': <PASSWORD>,
'database': file_database,
}
# We can safely hard-code the collection as that is always
# the same with GridFS.
collection = utilix.rundb.xent_collection(
**mongo_kwargs,
collection='fs.files')
else:
# Check the user input is fine for what we want to do.
if not isinstance(collection, pymongo_collection):
raise ValueError('Provide PyMongo collection (see docstring)!')
if file_database is not None:
raise ValueError("Already provided a collection!")
# Set collection and make sure it can at least do a 'find' operation
self.collection = collection
if _test_on_init:
self.test_find()
# This is the identifier under which we store the files.
self.config_identifier = config_identifier
# The GridFS used in this database
self.grid_fs = gridfs.GridFS(collection.database)
def get_query_config(self, config):
"""
Generate identifier to query against. This is just the configs
name.
:param config: str, name of the file of interest
:return: dict, that can be used in queries
"""
return {self.config_identifier: config}
def document_format(self, config):
"""
Format of the document to upload
:param config: str, name of the file of interest
:return: dict, that will be used to add the document
"""
doc = self.get_query_config(config)
doc.update({
'added': datetime.now(tz=pytz.utc),
})
return doc
def config_exists(self, config):
"""
Quick check if this config is already saved in the collection
:param config: str, name of the file of interest
:return: bool, is this config name stored in the database
"""
query = self.get_query_config(config)
return self.collection.count_documents(query) > 0
def md5_stored(self, abs_path):
"""
NB: RAM intensive operation!
Carefully compare if the MD5 identifier is the same as the file
as stored under abs_path.
:param abs_path: str, absolute path to the file name
:return: bool, returns if the exact same file is already stored
in the database
"""
if not os.path.exists(abs_path):
# A file that does not exist does not have the same MD5
return False
query = {'md5': self.compute_md5(abs_path)}
return self.collection.count_documents(query) > 0
def test_find(self):
"""
Test the connection to the self.collection to see if we can
perform a collection.find operation.
"""
if self.collection.find_one(projection="_id") is None:
raise ConnectionError('Could not find any data in this collection')
def list_files(self):
"""
Get a complete list of files that are stored in the database
:return: list, list of the names of the items stored in this
database
"""
return [doc[self.config_identifier]
for doc in
self.collection.find(
projection=
{self.config_identifier: 1})
if self.config_identifier in doc
]
@staticmethod
def compute_md5(abs_path):
"""
NB: RAM intensive operation!
Get the md5 hash of a file stored under abs_path
:param abs_path: str, absolute path to a file
:return: str, the md5-hash of the requested file
"""
# This function is copied from:
# stackoverflow.com/questions/3431825/generating-an-md5-checksum-of-a-file
if not os.path.exists(abs_path):
# if there is no file, there is nothing to compute
return ""
# Also, disable all the Use of insecure MD2, MD4, MD5, or SHA1
# hash function violations in this function.
# disable bandit
hash_md5 = hashlib.md5()
with open(abs_path, "rb") as f:
for chunk in iter(lambda: f.read(4096), b""):
hash_md5.update(chunk)
return hash_md5.hexdigest()
@export
class MongoUploader(GridFsInterface):
"""
Class to upload files to GridFs
"""
def __init__(self, readonly=False, *args, **kwargs):
# Same as parent. Just check the readonly_argument
if readonly:
raise PermissionError(
"How can you upload if you want to operate in readonly?")
super().__init__(*args, readonly=readonly, **kwargs)
def upload_from_dict(self, file_path_dict):
"""
Upload all files in the dictionary to the database.
:param file_path_dict: dict, dictionary of paths to upload. The
dict should be of the format:
file_path_dict = {'config_name': '/the_config_path', ...}
:return: None
"""
if not isinstance(file_path_dict, dict):
raise ValueError(f'file_path_dict must be dict of form '
f'"dict(NAME=ABSOLUTE_PATH,...)". Got '
f'{type(file_path_dict)} instead')
for config, abs_path in tqdm(file_path_dict.items()):
# We need to do this expensive check here. It is not enough
# to just check that the file is stored under the
# 'config_identifier'. What if the file changed? Then we
# want to upload a new file! Otherwise we could have done
# the self.config_exists-query. If it turns out we have the
# exact same file, forget about uploading it.
if self.config_exists(config) and self.md5_stored(abs_path):
continue
else:
# This means we are going to upload the file because its
# not stored yet.
try:
self.upload_single(config, abs_path)
except (CouldNotLoadError, ConfigTooLargeError):
# Perhaps we should fail then?
warn(f'Cannot upload {config}')
def upload_single(self, config, abs_path):
"""
Upload a single file to gridfs
:param config: str, the name under which this file should be
stored
:param abs_path: str, the absolute path of the file
"""
doc = self.document_format(config)
doc['md5'] = self.compute_md5(abs_path)
if not os.path.exists(abs_path):
raise CouldNotLoadError(f'{abs_path} does not exits')
print(f'uploading {config}')
with open(abs_path, 'rb') as file:
self.grid_fs.put(file, **doc)
@export
class MongoDownloader(GridFsInterface):
"""
Class to download files from GridFs
"""
def __init__(self,
store_files_at=None,
*args, **kwargs):
super().__init__(*args, **kwargs)
# We are going to set a place where to store the files. It's
# either specified by the user or we use these defaults:
if store_files_at is None:
store_files_at = ('/tmp/straxen_resource_cache/',
'/dali/lgrandi/strax/resource_cache',
'./resource_cache',
)
elif not isinstance(store_files_at, (tuple, str, list)):
raise ValueError(f'{store_files_at} should be tuple of paths!')
elif isinstance(store_files_at, str):
store_files_at = to_str_tuple(store_files_at)
self.storage_options = store_files_at
def download_single(self,
config_name: str,
human_readable_file_name=False):
"""
Download the config_name if it exists
:param config_name: str, the name under which the file is stored
:param human_readable_file_name: bool, store the file also under
it's human readable name. It is better not to use this as
the user might not know if the version of the file is the
latest.
:return: str, the absolute path of the file requested
"""
if self.config_exists(config_name):
# Query by name
query = self.get_query_config(config_name)
try:
# This could return multiple since we upload files if
# they have changed again! Therefore just take the last.
fs_object = self.grid_fs.get_last_version(**query)
except gridfs.NoFile as e:
raise CouldNotLoadError(
f'{config_name} cannot be downloaded from GridFs') from e
# Ok, so we can open it. We will store the file under it's
# md5-hash as that allows to easily compare if we already
# have the correct file.
if human_readable_file_name:
target_file_name = config_name
else:
target_file_name = fs_object.md5
for cache_folder in self.storage_options:
possible_path = os.path.join(cache_folder, target_file_name)
if os.path.exists(possible_path):
# Great! This already exists. Let's just return
# where it is stored.
return possible_path
# Apparently the file does not exist, let's find a place to
# store the file and download it.
store_files_at = self._check_store_files_at(self.storage_options)
destination_path = os.path.join(store_files_at, target_file_name)
# Let's open a temporary directory, download the file, and
# try moving it to the destination_path. This prevents
# simultaneous writes of the same file.
with tempfile.TemporaryDirectory() as temp_directory_name:
temp_path = os.path.join(temp_directory_name, target_file_name)
with open(temp_path, 'wb') as stored_file:
# This is were we do the actual downloading!
warn(f'Downloading {config_name} to {destination_path}')
stored_file.write(fs_object.read())
if not os.path.exists(destination_path):
# Move the file to the place we want to store it.
move(temp_path, destination_path)
return destination_path
else:
raise ValueError(f'Config {config_name} cannot be downloaded '
f'since it is not stored')
def get_abs_path(self, config_name):
return self.download_single(config_name)
def download_all(self):
"""Download all the files that are stored in the mongo collection"""
raise NotImplementedError('This feature is disabled for now')
# Disable the inspection of `Unreachable code`
# pylint: disable=unreachable
for config in self.list_files():
self.download_single(config)
@staticmethod
def _check_store_files_at(cache_folder_alternatives):
"""
Iterate over the options in cache_options until we find a folder
where we can store data. Order does matter as we iterate
until we find one folder that is willing.
:param cache_folder_alternatives: tuple, this tuple must be a
list of paths one can try to store the downloaded data
:return: str, the folder that we can write to.
"""
if not isinstance(cache_folder_alternatives, (tuple, list)):
raise ValueError('cache_folder_alternatives must be tuple')
for folder in cache_folder_alternatives:
if not os.path.exists(folder):
try:
os.makedirs(folder)
except (PermissionError, OSError):
continue
if os.access(folder, os.W_OK):
return folder
raise PermissionError(
f'Cannot write to any of the cache_folder_alternatives: '
f'{cache_folder_alternatives}')
class CouldNotLoadError(Exception):
"""Raise if we cannot load this kind of data"""
# Disable the inspection of 'Unnecessary pass statement'
# pylint: disable=unnecessary-pass
pass
class ConfigTooLargeError(Exception):
"""Raise if the data is to large to be uploaded into mongo"""
# Disable the inspection of 'Unnecessary pass statement'
# pylint: disable=unnecessary-pass
pass
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] |
skyhook/skyhook/middlewares/retry.py | wfgydbu/skyhook | 2 | 59477 | <filename>skyhook/skyhook/middlewares/retry.py
from scrapy.downloadermiddlewares.retry import RetryMiddleware
from scrapy.utils.response import response_status_message
class FeedBackRetryMiddleware(RetryMiddleware):
def process_response(self, request, response, spider):
if request.meta.get('dont_retry', False):
return response
if response.status in self.retry_http_codes:
reason = response_status_message(response.status)
return self._retry(request, reason, spider) or response
return response
def process_exception(self, request, exception, spider):
if isinstance(exception, self.EXCEPTIONS_TO_RETRY) and not request.meta.get('dont_retry', False):
return self._retry(request, exception, spider)
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] |
river/migrations/0012_auto_20191113_1550.py | xuziheng1002/django-river | 0 | 17471 | <filename>river/migrations/0012_auto_20191113_1550.py
# -*- coding: utf-8 -*-
# Generated by Django 1.11.25 on 2019-11-13 21:50
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('river', '0011_auto_20191110_1411'),
]
operations = [
migrations.AlterField(
model_name='onapprovedhook',
name='transition_approval',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='on_approved_hooks', to='river.TransitionApproval',
verbose_name='Transition Approval'),
),
migrations.AlterField(
model_name='onapprovedhook',
name='transition_approval_meta',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='on_approved_hooks', to='river.TransitionApprovalMeta', verbose_name='Transition Approval Meta'),
),
migrations.AlterField(
model_name='ontransithook',
name='transition',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='on_transit_hooks', to='river.Transition', verbose_name='Transition'),
),
migrations.AlterField(
model_name='ontransithook',
name='transition_meta',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='on_transit_hooks', to='river.TransitionMeta', verbose_name='Transition Meta'),
),
migrations.AlterField(
model_name='workflow',
name='content_type',
field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, to='contenttypes.ContentType', verbose_name='Content Type'),
),
migrations.AlterField(
model_name='workflow',
name='initial_state',
field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='workflow_this_set_as_initial_state', to='river.State', verbose_name='Initial State'),
),
]
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] |
attackMain.py | saurabhK99/substitution-cipher | 0 | 16593 | from tkinter import *
from attack import *
#calls letter frequency attack
def attack(on, cipherTxt):
plainTxt = str()
attack = LetterFrequencyAttack(cipherTxt, on)
for i in range(10):
plainTxt = plainTxt + attack.attack() + '\n\n'
answer.config(text = plainTxt)
#defining main window
root = Tk()
root.title('Letter Frequency Attack')
root.configure(
background='#221b1b',
)
root.option_add('*Font', 'helvatica 12')
root.option_add('*Foreground', 'whitesmoke')
root.option_add('*Background', '#221b1b')
root.option_add('*Entry.HighlightColor', 'whitesmoke')
#key value pairs for radio buttons
types = [
('MONOALPHABETIC_CIPHER', 'MONOALPHABETIC_CIPHER'),
('ADDITIVE_CIPHER', 'ADDITIVE_CIPHER')
]
#variable to store current selection of radio button
attackOn= StringVar()
attackOn.set('MONOALPHABETIC_CIPHER')
Label(root, text='ATTACK ON').grid(row=0, column=0, padx=20)
#radio buttons
for i in range(2):
Radiobutton(
root,
text=types[i][0],
value=types[i][1],
variable=attackOn,
highlightthickness=0,
activebackground='#221b1b',
activeforeground='whitesmoke'
).grid(
row=0,
column=i+1,
padx=20,
pady=20
)
#label to show the result
answer = Label(root, text='ANSWER HERE', wraplength=700, justify=CENTER)
answer.grid(row=1, column=0, columnspan=3, pady=20)
#entry widget to input cipher text to crack
Label(root, text='CIPHER TXT').grid(row=6, column=0)
cipherTxt = Entry(root)
cipherTxt.grid(row=6, column=1, columnspan=2, pady=20)
#button to call attack()
Button(
root,
text='DECRYPT',
justify=CENTER,
command=lambda: attack(
attackOn.get(),
cipherTxt.get()
)
).grid(
row=7,
column=0,
columnspan=3,
pady=20
)
#mainloop of tkinter window
root.mainloop()
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] |
src/waldur_auth_saml2/utils.py | geant-multicloud/MCMS-mastermind | 26 | 34735 | <filename>src/waldur_auth_saml2/utils.py<gh_stars>10-100
from django.conf import settings
from django.core.exceptions import ObjectDoesNotExist
from djangosaml2.conf import get_config
from djangosaml2.utils import available_idps
from saml2.attribute_converter import ac_factory
from saml2.mdstore import InMemoryMetaData, MetaDataFile
from saml2.mdstore import name as get_idp_name
from saml2.s_utils import UnknownSystemEntity
from . import models
def load_providers():
metadata = {}
for filename in settings.WALDUR_AUTH_SAML2['IDP_METADATA_LOCAL']:
mdf = MetaDataFile(ac_factory(), filename)
mdf.load()
metadata.update(mdf.items())
return metadata
def sync_providers():
providers = load_providers()
current_idps = list(models.IdentityProvider.objects.all().only('url', 'pk'))
backend_urls = set(providers.keys())
stale_idps = set(idp.pk for idp in current_idps if idp.url not in backend_urls)
models.IdentityProvider.objects.filter(pk__in=stale_idps).delete()
existing_urls = set(idp.url for idp in current_idps)
for url, metadata in providers.items():
name = get_idp_name(metadata)
if not name:
# It is expected that every provider has name. For corner cases check entity_id
name = metadata.get('entity_id')
if not name:
# Skip invalid identity provider
continue
if url in existing_urls:
# Skip identity provider if its url is already in the database
continue
models.IdentityProvider.objects.create(url=url, name=name, metadata=metadata)
for provider in models.IdentityProvider.objects.all().iterator():
backend_metadata = providers.get(provider.url)
if backend_metadata and provider.metadata != backend_metadata:
provider.metadata = backend_metadata
provider.save()
def is_valid_idp(value):
remote_providers = available_idps(get_config()).keys()
return (
value in remote_providers
or models.IdentityProvider.objects.filter(url=value).exists()
)
def get_idp_sso_supported_bindings(idp_entity_id, config):
try:
return config.metadata.service(
idp_entity_id, 'idpsso_descriptor', 'single_sign_on_service'
).keys()
except (UnknownSystemEntity, AttributeError):
return []
class DatabaseMetadataLoader(InMemoryMetaData):
def load(self, *args, **kwargs):
# Skip default parsing because data is not stored in file
pass
def __getitem__(self, item):
try:
return models.IdentityProvider.objects.get(url=item).metadata
except ObjectDoesNotExist:
raise KeyError
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MusicTager/components/dialog/auto_meatadata_dialog.py | Mai-icy/MusicTagger | 3 | 1601322 | <reponame>Mai-icy/MusicTagger<filename>MusicTager/components/dialog/auto_meatadata_dialog.py
#!/usr/bin/python
# -*- coding:utf-8 -*-
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
from ui.ui_source.AutoMetadataDialog import Ui_AutoMetadataDialog
from components.mask_widget import MaskWidget
class AutoMetadataDialog(QDialog, Ui_AutoMetadataDialog):
add_signal = pyqtSignal(str)
prepare_close_signal = pyqtSignal(str)
def __init__(self, parent=None):
super(AutoMetadataDialog, self).__init__(parent)
self.setupUi(self)
self.add_signal.connect(self.value_add)
self.prepare_close_signal.connect(self.prepare_reject)
def set_max(self, maximum):
self.progressBar.setMaximum(maximum)
def value_add(self):
now_value = self.progressBar.value()
if now_value + 1 == self.progressBar.maximum():
self.prepare_reject()
self.progressBar.setValue(now_value + 1)
def show(self) -> None:
self.progressBar.reset()
self.label.setText("正在为你自动补充元数据")
self._set_mask_visible(True)
super(AutoMetadataDialog, self).show()
def done(self, a0: int) -> None:
self._set_mask_visible(False)
super(AutoMetadataDialog, self).done(a0)
def reject(self) -> None:
self.parent().stop_auto = True
self.label.setText("正在中断操作...")
def prepare_reject(self):
super(AutoMetadataDialog, self).reject()
def _set_mask_visible(self, flag: bool):
if self.parent():
if not hasattr(self.parent(), "mask_widget"):
self.parent().mask_widget = MaskWidget(self.parent())
if flag:
self.parent().mask_widget.show()
else:
self.parent().mask_widget.hide()
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package.py | OSS-Pipeline/rez-boost | 0 | 53097 | <reponame>OSS-Pipeline/rez-boost<filename>package.py
name = "boost"
version = "1.61.0"
authors = [
"<NAME>",
"<NAME>"
]
description = \
"""
Boost is a set of libraries for the C++ programming language that provide support for tasks and structures such
as linear algebra, pseudorandom number generation, multithreading, image processing, regular expressions,
and unit testing.
"""
requires = [
"cmake-3+",
"gcc-6+",
"python-2.7+"
]
variants = [
["platform-linux"]
]
tools = [
"boost"
]
build_system = "cmake"
with scope("config") as config:
config.build_thread_count = "logical_cores"
uuid = "boost-{version}".format(version=str(version))
def commands():
env.LD_LIBRARY_PATH.prepend("{root}/lib")
# Helper environment variables.
env.BOOST_INCLUDE_PATH.set("{root}/include")
env.BOOST_LIBRARY_PATH.set("{root}/lib")
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] |
orbit_predictor/predictors/base.py | Juanlu001/orbit-predictor | 0 | 9794 | <gh_stars>0
# MIT License
#
# Copyright (c) 2017 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import datetime as dt
import logging
import warnings
from collections import namedtuple
from math import pi, acos, degrees, radians
import numpy as np
try:
from scipy.optimize import brentq, minimize_scalar
except ImportError:
warnings.warn('scipy module was not found, some features may not work properly.',
ImportWarning)
from orbit_predictor.constants import MU_E
from orbit_predictor.exceptions import NotReachable, PropagationError
from orbit_predictor import coordinate_systems
from orbit_predictor.keplerian import rv2coe
from orbit_predictor.utils import (
angle_between,
cross_product,
dot_product,
reify,
vector_diff,
vector_norm,
gstime_from_datetime,
get_shadow,
get_sun,
eclipse_duration,
get_satellite_minus_penumbra_verticals,
)
logger = logging.getLogger(__name__)
ONE_SECOND = dt.timedelta(seconds=1)
def round_datetime(dt_):
return dt_
class Position(namedtuple(
"Position", ['when_utc', 'position_ecef', 'velocity_ecef', 'error_estimate'])):
@reify
def position_llh(self):
"""Latitude (deg), longitude (deg), altitude (km)."""
return coordinate_systems.ecef_to_llh(self.position_ecef)
@reify
def osculating_elements(self):
"""Osculating Keplerian orbital elements.
Semimajor axis (km), eccentricity, inclination (deg),
right ascension of the ascending node or RAAN (deg),
argument of perigee (deg), true anomaly (deg).
"""
gmst = gstime_from_datetime(self.when_utc)
position_eci = coordinate_systems.ecef_to_eci(self.position_ecef, gmst)
velocity_eci = coordinate_systems.ecef_to_eci(self.velocity_ecef, gmst)
# Convert position to Keplerian osculating elements
p, ecc, inc, raan, argp, ta = rv2coe(
MU_E, np.array(position_eci), np.array(velocity_eci)
)
# Transform to more familiar semimajor axis
sma = p / (1 - ecc ** 2)
return sma, ecc, degrees(inc), degrees(raan), degrees(argp), degrees(ta)
class PredictedPass:
def __init__(self, location, sate_id,
max_elevation_deg,
aos, los, duration_s,
max_elevation_position=None,
max_elevation_date=None):
self.location = location
self.sate_id = sate_id
self.max_elevation_position = max_elevation_position
self.max_elevation_date = max_elevation_date
self.max_elevation_deg = max_elevation_deg
self.aos = aos
self.los = los
self.duration_s = duration_s
@property
def midpoint(self):
"""Returns a datetime of the midpoint of the pass"""
return self.aos + (self.los - self.aos) / 2
def __repr__(self):
return "<PredictedPass {} over {} on {}>".format(self.sate_id, self.location, self.aos)
def __eq__(self, other):
return all([issubclass(other.__class__, PredictedPass),
self.location == other.location,
self.sate_id == other.sate_id,
self.max_elevation_position == other.max_elevation_position,
self.max_elevation_date == other.max_elevation_date,
self.max_elevation_deg == other.max_elevation_deg,
self.aos == other.aos,
self.los == other.los,
self.duration_s == other.duration_s])
def get_off_nadir_angle(self):
warnings.warn("This method is deprecated!", DeprecationWarning)
return self.off_nadir_deg
@reify
def off_nadir_deg(self):
"""Computes off-nadir angle calculation
Given satellite position ``sate_pos``, velocity ``sate_vel``, and
location ``target`` in a common frame, off-nadir angle ``off_nadir_angle``
is given by:
t2b = sate_pos - target
cos(off_nadir_angle) = (sate_pos · t2b) # Vectorial dot product
_______________________
|| sate_pos || || t2b||
Sign for the rotation is calculated this way
cross = target ⨯ sate_pos
sign = cross · sate_vel
____________________
| cross · sate_vel |
"""
sate_pos = self.max_elevation_position.position_ecef
sate_vel = self.max_elevation_position.velocity_ecef
target = self.location.position_ecef
t2b = vector_diff(sate_pos, target)
angle = acos(
dot_product(sate_pos, t2b) / (vector_norm(sate_pos) * vector_norm(t2b))
)
cross = cross_product(target, sate_pos)
dot = dot_product(cross, sate_vel)
try:
sign = dot / abs(dot)
except ZeroDivisionError:
sign = 1
return degrees(angle) * sign
class Predictor:
@property
def sate_id(self):
raise NotImplementedError
def propagate_eci(self, when_utc=None):
raise NotImplementedError
def get_position(self, when_utc=None):
raise NotImplementedError("You have to implement it!")
def get_shadow(self, when_utc=None):
"""Gives illumination at given time (2 for illuminated, 1 for penumbra, 0 for umbra)."""
if when_utc is None:
when_utc = dt.datetime.utcnow()
return get_shadow(
self.get_position(when_utc).position_ecef,
when_utc
)
def get_normal_vector(self, when_utc=None):
"""Gets unitary normal vector (orthogonal to orbital plane) at given time."""
if when_utc is None:
when_utc = dt.datetime.utcnow()
position, velocity = self.propagate_eci(when_utc)
orbital_plane_normal = np.cross(position, velocity)
return orbital_plane_normal / vector_norm(orbital_plane_normal)
def get_beta(self, when_utc=None):
"""Gets angle between orbital plane and Sun direction (beta) at given time, in degrees."""
if when_utc is None:
when_utc = dt.datetime.utcnow()
# Here we calculate the complementary angle of beta,
# because we use the normal vector of the orbital plane
beta_comp = angle_between(
get_sun(when_utc),
self.get_normal_vector(when_utc)
)
# We subtract from 90 degrees to return the real beta angle
return 90 - beta_comp
class CartesianPredictor(Predictor):
def _propagate_ecef(self, when_utc=None):
"""Return position and velocity in the given date using ECEF coordinate system."""
if when_utc is None:
when_utc = dt.datetime.utcnow()
position_eci, velocity_eci = self.propagate_eci(when_utc)
gmst = gstime_from_datetime(when_utc)
position_ecef = coordinate_systems.eci_to_ecef(position_eci, gmst)
velocity_ecef = coordinate_systems.eci_to_ecef(velocity_eci, gmst)
return position_ecef, velocity_ecef
@reify
def mean_motion(self):
"""Mean motion, in radians per minute"""
raise NotImplementedError
@reify
def period(self):
"""Orbital period, in minutes"""
return 2 * pi / self.mean_motion
def get_position(self, when_utc=None):
"""Return a Position namedtuple in ECEF coordinate system"""
if when_utc is None:
when_utc = dt.datetime.utcnow()
position_ecef, velocity_ecef = self._propagate_ecef(when_utc)
return Position(when_utc=when_utc, position_ecef=position_ecef,
velocity_ecef=velocity_ecef, error_estimate=None)
def get_only_position(self, when_utc=None):
"""Return a tuple in ECEF coordinate system"""
return self.get_position(when_utc).position_ecef
def get_eclipse_duration(self, when_utc=None, tolerance=1e-1):
"""Gets eclipse duration at given time, in minutes"""
ecc = self.get_position(when_utc).osculating_elements[1]
if ecc > tolerance:
raise NotImplementedError("Non circular orbits are not supported")
beta = self.get_beta(when_utc)
return eclipse_duration(beta, self.period)
def passes_over(self, location, when_utc, limit_date=None, max_elevation_gt=0, aos_at_dg=0):
return LocationPredictor(location, self, when_utc, limit_date,
max_elevation_gt, aos_at_dg)
def get_next_pass(self, location, when_utc=None, max_elevation_gt=5,
aos_at_dg=0, limit_date=None):
"""Return a PredictedPass instance with the data of the next pass over the given location
location_llh: point on Earth we want to see from the satellite.
when_utc: datetime UTC after which the pass is calculated, default to now.
max_elevation_gt: filter passes with max_elevation under it.
aos_at_dg: This is if we want to start the pass at a specific elevation.
The next pass with a LOS strictly after when_utc will be returned,
possibly the current pass.
"""
if when_utc is None:
when_utc = dt.datetime.utcnow()
for pass_ in self.passes_over(location, when_utc, limit_date,
max_elevation_gt=max_elevation_gt,
aos_at_dg=aos_at_dg):
return pass_
else:
raise NotReachable('Propagation limit date exceeded')
def eclipses_since(self, when_utc=None, limit_date=None):
"""
An iterator that yields all eclipses start and end times between
when_utc and limit_date.
The next eclipse with a end strictly after when_utc will be returned,
possibly the current eclipse.
The last eclipse returned starts before limit_date, but it can end
strictly after limit_date.
No circular orbits are not supported, and will raise NotImplementedError.
"""
def _get_illumination(t):
my_start = start + dt.timedelta(seconds=t)
result = get_satellite_minus_penumbra_verticals(
self.get_only_position(my_start),
my_start
)
return result
if when_utc is None:
when_utc = dt.datetime.utcnow()
orbital_period_s = self.period * 60
# A third of the orbit period is used as the base window of the search.
# This window ensures the function get_satellite_minus_penumbra_verticals
# will not have more than one local minimum (one in the illuminated phase and
# the other in penumbra).
base_search_window_s = orbital_period_s / 3
start = when_utc
while limit_date is None or start < limit_date:
# a minimum negative value is aproximatelly the middle point of the eclipse
minimum_illumination = minimize_scalar(
_get_illumination,
bounds=(0, base_search_window_s),
method="bounded",
options={"xatol": 1e-2},
)
eclipse_center_candidate_delta_s = minimum_illumination.x
# If found a minimum that is not illuminated, there is an eclipse here
if _get_illumination(eclipse_center_candidate_delta_s) < 0:
# The small time interval to search zeros around the center
# is estimated with the expected eclipse duration (which generally
# is smaller than expected, and that is the reason of the 1.5 coeficient).
# Also a minimum of 180 seconds was added because
# in some cases the estimation is 0 even though there is an eclipse.
eclipse_duration_estimation_s = self.get_eclipse_duration(start) * 60
zero_search_window_s = max(180, 1.5 * eclipse_duration_estimation_s)
# Search now both zeros to get the start and end of the eclipse
eclipse_start_delta_s = brentq(
_get_illumination,
eclipse_center_candidate_delta_s - zero_search_window_s,
eclipse_center_candidate_delta_s,
xtol=1e-2,
full_output=False,
)
eclipse_end_delta_s = brentq(
_get_illumination,
eclipse_center_candidate_delta_s,
eclipse_center_candidate_delta_s + zero_search_window_s,
xtol=1e-2,
full_output=False,
)
eclipse_start = start + dt.timedelta(seconds=eclipse_start_delta_s)
eclipse_end = start + dt.timedelta(seconds=eclipse_end_delta_s)
yield eclipse_start, eclipse_end
start = eclipse_end + dt.timedelta(seconds=base_search_window_s)
else:
start += dt.timedelta(seconds=base_search_window_s)
class GPSPredictor(Predictor):
pass
class LocationPredictor:
"""Predicts passes over a given location
Exposes an iterable interface
"""
def __init__(self, location, predictor, start_date, limit_date=None,
max_elevation_gt=0, aos_at_dg=0, *, propagator=None):
if propagator is not None:
warnings.warn(
"propagator parameter was renamed to predictor "
"and will be removed in a future release",
DeprecationWarning
)
predictor = propagator
self.location = location
self.predictor = predictor
self.start_date = start_date
self.limit_date = limit_date
self.max_elevation_gt = radians(max([max_elevation_gt, aos_at_dg]))
self.aos_at = radians(aos_at_dg)
@property
def propagator(self):
warnings.warn(
"propagator parameter was renamed to predictor "
"and will be removed in a future release",
DeprecationWarning
)
return self.predictor
def __iter__(self):
"""Returns one pass each time"""
current_date = self.start_date
while True:
if self.is_ascending(current_date):
# we need a descending point
ascending_date = current_date
descending_date = self._find_nearest_descending(ascending_date)
pass_ = self._refine_pass(ascending_date, descending_date)
if pass_.valid:
if self.limit_date is not None and pass_.aos > self.limit_date:
break
yield self._build_predicted_pass(pass_)
if self.limit_date is not None and current_date > self.limit_date:
break
current_date = pass_.tca + self._orbit_step(0.6)
else:
current_date = self._find_nearest_ascending(current_date)
def _build_predicted_pass(self, accuratepass):
"""Returns a classic predicted pass"""
tca_position = self.predictor.get_position(accuratepass.tca)
return PredictedPass(self.location, self.predictor.sate_id,
max_elevation_deg=accuratepass.max_elevation_deg,
aos=accuratepass.aos,
los=accuratepass.los,
duration_s=accuratepass.duration.total_seconds(),
max_elevation_position=tca_position,
max_elevation_date=accuratepass.tca,
)
def _find_nearest_descending(self, ascending_date):
for candidate in self._sample_points(ascending_date):
if not self.is_ascending(candidate):
return candidate
else:
logger.error('Could not find a descending pass over %s start date: %s - TLE: %s',
self.location, ascending_date, self.predictor.tle)
raise PropagationError("Can not find an descending phase")
def _find_nearest_ascending(self, descending_date):
for candidate in self._sample_points(descending_date):
if self.is_ascending(candidate):
return candidate
else:
logger.error('Could not find an ascending pass over %s start date: %s - TLE: %s',
self.location, descending_date, self.predictor.tle)
raise PropagationError('Can not find an ascending phase')
def _sample_points(self, date):
"""Helper method to found ascending or descending phases of elevation"""
start = date
end = date + self._orbit_step(0.99)
mid = self.midpoint(start, end)
mid_right = self.midpoint(mid, end)
mid_left = self.midpoint(start, mid)
return [end, mid, mid_right, mid_left]
def _refine_pass(self, ascending_date, descending_date):
tca = self._find_tca(ascending_date, descending_date)
elevation = self._elevation_at(tca)
if elevation > self.max_elevation_gt:
aos = self._find_aos(tca)
los = self._find_los(tca)
else:
aos = los = None
return AccuratePredictedPass(aos, tca, los, elevation)
def _find_tca(self, ascending_date, descending_date):
while not self._precision_reached(ascending_date, descending_date):
midpoint = self.midpoint(ascending_date, descending_date)
if self.is_ascending(midpoint):
ascending_date = midpoint
else:
descending_date = midpoint
return ascending_date
def _precision_reached(self, start, end):
# TODO: Allow the precision to change from the outside
return end - start <= ONE_SECOND
@staticmethod
def midpoint(start, end):
"""Returns the midpoint between two dates"""
return start + (end - start) / 2
def _elevation_at(self, when_utc):
position = self.predictor.get_only_position(when_utc)
return self.location.elevation_for(position)
def is_passing(self, when_utc):
"""Returns a boolean indicating if satellite is actually visible"""
return bool(self._elevation_at(when_utc))
def is_ascending(self, when_utc):
"""Check is elevation is ascending or descending on a given point"""
elevation = self._elevation_at(when_utc)
next_elevation = self._elevation_at(when_utc + ONE_SECOND)
return elevation <= next_elevation
def _orbit_step(self, size):
"""Returns a time step, that will make the satellite advance a given number of orbits"""
step_in_radians = size * 2 * pi
seconds = (step_in_radians / self.predictor.mean_motion) * 60
return dt.timedelta(seconds=seconds)
def _find_aos(self, tca):
end = tca
start = tca - self._orbit_step(0.34) # On third of the orbit
elevation = self._elevation_at(start)
assert elevation < 0
while not self._precision_reached(start, end):
midpoint = self.midpoint(start, end)
elevation = self._elevation_at(midpoint)
if elevation < self.aos_at:
start = midpoint
else:
end = midpoint
return end
def _find_los(self, tca):
start = tca
end = tca + self._orbit_step(0.34)
while not self._precision_reached(start, end):
midpoint = self.midpoint(start, end)
elevation = self._elevation_at(midpoint)
if elevation < self.aos_at:
end = midpoint
else:
start = midpoint
return start
class AccuratePredictedPass:
def __init__(self, aos, tca, los, max_elevation):
self.aos = round_datetime(aos) if aos is not None else None
self.tca = round_datetime(tca)
self.los = round_datetime(los) if los is not None else None
self.max_elevation = max_elevation
@property
def valid(self):
return self.max_elevation > 0 and self.aos is not None and self.los is not None
@reify
def max_elevation_deg(self):
return degrees(self.max_elevation)
@reify
def duration(self):
return self.los - self.aos
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] |
pydov/util/hooks.py | TimFranken/pydov | 0 | 94603 | <gh_stars>0
# -*- coding: utf-8 -*-
"""Module implementing a simple hooks system to allow late-binding actions to
PyDOV events."""
import sys
import time
class AbstractHook(object):
"""Abstract base class for custom hook implementations.
Provides all available methods with a default implementation to do
nothing. This allows for hook subclasses to only implement the events
they need.
"""
def wfs_search_init(self, typename):
"""Called upon starting a WFS search.
Parameters
----------
typename : str
The typename (layername) of the WFS service used for searching.
"""
pass
def wfs_search_result(self, number_of_results):
"""Called after a WFS search finished.
Parameters
----------
number_of_results : int
The number of features returned by the WFS search.
"""
pass
def xml_requested(self, pkey_object):
"""Called upon requesting an XML document of an object.
This is either followed by ``xml_cache_hit`` or ``xml_downloaded``.
Parameters
----------
pkey_object : str
Permanent key of the requested object.
"""
pass
def xml_cache_hit(self, pkey_object):
"""Called when the XML document of an object is retrieved from the
cache.
Parameters
----------
pkey_object : str
Permanent key of the requested object.
"""
pass
def xml_downloaded(self, pkey_object):
"""Called when the XML document of an object is downloaded from the
DOV services.
Parameters
----------
pkey_object : str
Permanent key of the requested object.
"""
pass
class SimpleStatusHook(AbstractHook):
"""Simple hook implementation to print progress to stdout."""
def __init__(self):
"""Initialisation.
Initialise all variables to 0.
"""
self.result_count = 0
self.prog_counter = 0
self.init_time = None
self.previous_remaining = None
def _write_progress(self, char):
"""Write progress to standard output.
Progress is grouped on lines per 50 items, adding ``char`` for every
item processed.
Parameters
----------
char : str
Single character to print.
"""
if self.prog_counter == 0:
sys.stdout.write('[%03i/%03i] ' % (self.prog_counter,
self.result_count))
sys.stdout.flush()
elif self.prog_counter % 50 == 0:
time_elapsed = time.time() - self.init_time
time_per_item = time_elapsed/self.prog_counter
remaining_mins = int((time_per_item*(
self.result_count-self.prog_counter))/60)
if remaining_mins > 1 and remaining_mins != \
self.previous_remaining:
remaining = " (%i min. left)" % remaining_mins
self.previous_remaining = remaining_mins
else:
remaining = ""
sys.stdout.write('%s\n[%03i/%03i] ' % (
remaining, self.prog_counter, self.result_count))
sys.stdout.flush()
sys.stdout.write(char)
sys.stdout.flush()
self.prog_counter += 1
if self.prog_counter == self.result_count:
sys.stdout.write('\n')
sys.stdout.flush()
def wfs_search_init(self, typename):
"""When a new WFS search is started, reset all counters to 0.
Parameters
----------
typename : str
The typename (layername) of the WFS service used for searching.
"""
self.result_count = 0
self.prog_counter = 0
self.init_time = time.time()
self.previous_remaining = None
def wfs_search_result(self, number_of_results):
"""When the WFS search completes, set the total result count to
``number_of_results``.
Parameters
----------
number_of_results : int
The number of features returned by the WFS search.
"""
self.result_count = number_of_results
def xml_cache_hit(self, pkey_object):
"""When an XML document is retrieved from the cache, print 'c' to
the progress output.
Parameters
----------
pkey_object : str
Permanent key of the requested object.
"""
self._write_progress('c')
def xml_downloaded(self, pkey_object):
"""When an XML document is downloaded from the DOV services,
print '.' to the progress output.
Parameters
----------
pkey_object : str
Permanent key of the requested object.
"""
self._write_progress('.')
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] |
isobuilder/apptest.py | knight-ryu12/StarFoxAdventures | 22 | 132038 | <gh_stars>10-100
import os
import os.path
import sys
import inspect
import tempfile
from iso.gciso import GCISO
class AppTest:
"""Tests for this app."""
# TODO proper Python unit test framework
def __init__(self):
self.isoPath = None
self.filesPath = None
self.tempDir = None
self.iso = None
def runAllTests(self, isoPath:str, filesPath:str):
"""Run all tests."""
self.testExtractAndRebuild(isoPath, filesPath)
def testExtractAndRebuild(self, isoPath:str, filesPath:str):
"""Test that we can:
1) extract an ISO correctly
2) build an ISO correctly from extracted files
isoPath: path to a GameCube ISO.
filesPath: path to directory containing files extracted
from said ISO.
The files are not modified during the test.
"""
self.isoPath = isoPath
self.filesPath = filesPath
print("Self test: reading ISO...")
self.iso = GCISO().readFile(isoPath)
tempDirObj = tempfile.TemporaryDirectory()
self.tempDir = tempDirObj.name
#self.tempDir = './test'
#self.iso.bootBin.dump()
# extract ISO to temp dir and verify contents
# against known-good extracted directory.
#print("extract to temp dir", self.tempDir)
print("Extracting...")
self.iso.extract(self.tempDir)
self._verifyExtractedFiles()
self._verifyNoExtraFiles()
self._verifySystemFiles()
fst = self.iso.fstbin
# rebuild a new ISO from the extracted files
#testIsoPath = '/home/rena/projects/sfa/test.iso'
testIsoPath = os.path.join(self.tempDir, 'test.iso')
print("Rebuilding...")
self.iso = GCISO()
#self.iso.readDir(self.tempDir)
self.iso.addFilesFromFst(fst, self.tempDir)
self.iso.writeToFile(testIsoPath)
# extract the rebuilt ISO and verify again
# to ensure it was rebuilt correctly.
tempDirObj2 = tempfile.TemporaryDirectory()
self.tempDir = tempDirObj2.name
print("Reading rebuilt ISO...")
self.iso = GCISO().readFile(testIsoPath)
#print("extract to temp dir", self.tempDir)
print("Extracting rebuilt ISO...")
self.iso.extract(self.tempDir)
self._verifyExtractedFiles()
self._verifyNoExtraFiles()
self._verifySystemFiles()
print("Test completed")
def _verifyExtractedFiles(self):
print("Verify extracted files... ", end='', flush=True)
for file in self.iso.files:
target = os.path.join(self.tempDir, 'files', '.'+file.path)
if file.isDir:
if not os.path.isdir(target):
raise RuntimeError("Directory not extracted: %s" % str(file.path))
else:
if not os.path.isfile(target):
raise RuntimeError("File not extracted: %s" % str(file.path))
# verify data
self._verifyExtractedFileData(file)
print("OK")
def _verifyExtractedFileData(self, file):
origPath = os.path.join(self.filesPath, 'files', '.'+file.path)
newPath = os.path.join(self.tempDir, 'files', '.'+file.path)
self._verifyFileData(origPath, newPath)
#print("OK: %s" % str(file.path))
def _verifyFileData(self, origPath, newPath):
offs = 0
try: origFile = open(origPath, 'rb')
except FileNotFoundError:
raise RuntimeError("Extra file extracted: %s" % str(file.path))
try: newFile = open(newPath, 'rb')
except FileNotFoundError:
raise RuntimeError("File not extracted: %s" % str(file.path))
while True:
origData = origFile.read(4096)
newData = newFile .read(4096)
if len(origData) < len(newData):
raise RuntimeError("File extracted incorrectly: %s (extra data at 0x%X)" % (
newPath, offs + len(origData)
))
elif len(origData) > len(newData):
raise RuntimeError("File extracted incorrectly: %s (truncated at 0x%X)" % (
newPath, offs + len(newData)
))
if len(origData) == 0: break
if origData != newData:
dOffs = 0
for i in range(len(origData)):
if origData[i] != newData[i]:
dOffs = i
break
print("ERROR", origPath)
f = lambda d: ' '.join(map(lambda b: '%02X' % b, d))
s = max(dOffs-8, 0)
print("Got: ..." + f(newData[s:s+16]) + "...")
print("Expected: ..." + f(origData[s:s+16]) + "...")
raise RuntimeError("File extracted incorrectly: %s (at 0x%X: 0x%02X should be 0x%02X)" % (
newPath,
offs + dOffs, newData[dOffs], origData[dOffs]
))
offs += len(origData)
def _verifyNoExtraFiles(self):
print("Verify no extra files... ", end='', flush=True)
self._verifyNoExtraFilesInDir('/')
print("OK")
def _verifyNoExtraFilesInDir(self, path, _depth=0):
assert _depth < 10, "Maximum depth exceeded"
fullPath = os.path.join(self.tempDir, 'files', '.'+path)
for name in os.listdir(fullPath):
fullName = os.path.join(fullPath, name)
origName = os.path.join(self.filesPath, 'files', '.'+path, name)
relPath = os.path.join(path, name)
#print("check", fullName, "vs", origName)
if os.path.isdir(fullName):
if not os.path.isdir(origName):
raise RuntimeError("Extra directory: %s" % relPath)
self._verifyNoExtraFilesInDir(relPath)
else:
if not os.path.isfile(origName):
raise RuntimeError("Extra file: %s" % relPath)
def _verifySystemFiles(self):
print("Verify system files... ", end='', flush=True)
# skip fst.bin because the files can be in a different order.
files = ('apploader.img', 'bi2.bin', 'boot.bin', 'main.dol')
for name in files:
origPath = os.path.join(self.filesPath, 'sys', name)
newPath = os.path.join(self.tempDir, 'sys', name)
self._verifyFileData(origPath, newPath)
print("OK")
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] |
mesh/scale_test.py | melonwan/sphereHand | 53 | 52642 | # to test the gradient back-propagation
from __future__ import absolute_import, division, print_function
import torch
import pickle
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
import numpy as np
import cv2
import torch.utils.data as data
from mesh.multiview_utility import MutualProjectionLoss, MultiviewConsistencyLoss
from dataset.nyu_dataset import create_nyu_dataset
from network.util_modules import PosePriorLoss
from dataset.joint_angle import JointAngleDataset
import matplotlib.pyplot as plt
from mesh.kinematicsTransformation import HandTransformationMat
from mesh.pointTransformation import LinearBlendSkinning, RandScale
from network.constants import Constant
from network.util_modules import HandSynthesizer
constant = Constant()
synt_key_points = [[0,1],
[0,2],
[33,34]]
real_key_points = [[30,31],
[30,32],
[0,1]]
def bone_length(joints, indices):
bone_length = []
for [idx_1, idx_2] in indices:
diff = joints[idx_1] - joints[idx_2]
bone_length.append(diff.view(-1).norm())
bone_length = [bone_length[0]/bone_length[1], bone_length[0]/bone_length[2]]
return bone_length
dataset_dir = 'D:\\data\\nyu_hand_dataset_v2\\npy-64\\test'
nyu_dataset = create_nyu_dataset(dataset_dir)
with open('mesh/model/preprocessed_hand.pkl', 'rb') as f:
mesh = pickle.load(f)
hand_synthsizer = HandSynthesizer(mesh, 64, 16, 1.0, 0.01).cuda()
joint_data = JointAngleDataset()
for _ in range(10):
real_data = nyu_dataset[0]
real_joints = real_data[1][0][constant.real_key_points]
real_joints = real_joints * 64 / 300 + 32
fig = plt.figure()
ax = fig.add_subplot(1, 2, 1)
ax.scatter(real_joints[:,0], real_joints[:,1])
for idx in range(len(real_joints)):
ax.annotate('%d'%idx, (real_joints[idx,0], real_joints[idx,1]))
ax.imshow(real_data[0][0].squeeze())
para = joint_data[0].unsqueeze(dim=0).cuda()
synt_result = hand_synthsizer(para)
dm = synt_result[0].squeeze().detach().cpu().numpy()
synt_joints = synt_result[3].squeeze().detach().cpu().numpy()[constant.synt_key_points]
synt_joints = synt_joints * 64 / 300 + 32
ax = fig.add_subplot(1, 2, 2)
ax.scatter(synt_joints[:,0], synt_joints[:,1])
for idx in range(len(synt_joints)):
ax.annotate('%d'%idx, (synt_joints[idx,0], synt_joints[idx,1]))
ax.imshow(dm)
plt.show() | [
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4- Convolutional Neural Networks/Week 1/cnn_utils.py | ansuini/deeplearning.ai-pytorch | 0 | 159621 | import h5py
import numpy as np
import torch
import cv2
from torch.utils.data import DataLoader, TensorDataset
def get_data(batch_size=64):
train_dataset = h5py.File('datasets/train_signs.h5', "r")
x_train = np.array(train_dataset["train_set_x"][:]) # your train set features
x_train = np.transpose(x_train, (0, 3, 1, 2))
y_train = np.array(train_dataset["train_set_y"][:]) # your train set labels
y_train = y_train.reshape((1, y_train.shape[0])).T
test_dataset = h5py.File('datasets/test_signs.h5', "r")
x_test = np.array(test_dataset["test_set_x"][:]) # your test set features
x_test = np.transpose(x_test, (0, 3, 1, 2))
y_test = np.array(test_dataset["test_set_y"][:]) # your test set labels
y_test = y_test.reshape((1, y_test.shape[0])).T
classes = np.array(test_dataset["list_classes"][:]) # the list of classes
X_train_tensor = torch.tensor(x_train, dtype=torch.float)/255
Y_train_tensor = torch.tensor(y_train, dtype=torch.long)
X_test_tensor = torch.tensor(x_test, dtype=torch.float)/255
Y_test_tensor = torch.tensor(y_test, dtype=torch.long)
train_dataset = TensorDataset(X_train_tensor, Y_train_tensor)
test_dataset = TensorDataset(X_test_tensor, Y_test_tensor)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
return train_dataset, test_dataset, train_loader, test_loader, classes
def path_to_input(image_path, input_size, device):
img = cv2.imread(image_path)
img = cv2.resize(img, (input_size, input_size)) #Resize
img = img[..., ::-1].transpose((2, 0, 1)) #BGR -> RGB and HxWxC -> CxHxW
img = img[np.newaxis, ...] / 255.0 #Add a channel at 0, thus making it a batch
img = torch.tensor(img, dtype=torch.float, device=device) #Convert to Tensor
return img
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problems/p009.py | davisschenk/project-euler-python | 0 | 7486 | <gh_stars>0
from math import ceil, sqrt
from problem import Problem
from utils.math import gcd
class PythagoreanTriplet(Problem, name="Special Pythagorean triplet", expected=31875000):
@Problem.solution()
def brute_force(self, ts=1000):
for a in range(3, round((ts - 3) / 2)):
for b in range(a + 1, round((ts - 1 - a) / 2)):
c = ts - a - b
if c * c == a * a + b * b:
return a * b * c
@Problem.solution()
def parametrisation(self, ts=1000):
s2 = ts / 2
mlimit = ceil(sqrt(s2)) - 1
for m in range(2, mlimit):
if s2 % m == 0:
sm = s2 / m
while sm % 2 == 0:
sm /= 2
if m % 2 == 1:
k = m + 2
else:
k = m + 1
while k < 2 * m and k <= sm:
if sm % k == 0 and gcd(k, m) == 1:
d = s2 / (k * m)
n = k - m
a = d * (m * m - n * n)
b = 2 * d * m * n
c = d * (m * m + n * n)
return a * b * c
k += 2
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] |
hw/a2fomu_build.py | elecbrick/a2fomu | 1 | 186027 | <reponame>elecbrick/a2fomu<gh_stars>1-10
#!/usr/bin/env python3
#
# a2fomu_build.py - Part of a2fomu - Copyright (c) 2020-2021 <NAME>
#
# This file contains a significant contribution from foboot-bitstream.py
# which wass made available under the Apache License, Version 2.0:
# http://www.apache.org/licenses/LICENSE-2.0
# Import the litex build environment to integrate the deps/ directory
# external programs this lxbuildenv project relies on.
lx_dependencies = ["riscv", "icestorm", "yosys", "nextpnr-ice40"]
import lxbuildenv
# base hardware definitions: migen
from migen import Module, Signal, Instance, ClockDomain, If
from migen.fhdl.specials import TSTriple
from migen.fhdl.structure import ResetSignal
from migen.fhdl.decorators import ClockDomainsRenamer
# SoC modules and integrator: LiteX
from litex.build.lattice.platform import LatticePlatform
from litex.build.generic_platform import Pins, Subsignal
from litex.build.sim.platform import SimPlatform
from litex.soc.integration.doc import AutoDoc, ModuleDoc
from litex.soc.integration.soc_core import SoCCore
from litex.soc.cores.cpu import CPUNone
from litex.soc.integration.builder import Builder
from litex.soc.interconnect import wishbone
from litex.soc.cores import up5kspram, spi_flash
from litex_boards.targets.fomu import _CRG
import litex.soc.doc as lxsocdoc
# USB controller: ValentyUSB
from valentyusb.usbcore import io as usbio
from valentyusb.usbcore.cpu import epmem, unififo, epfifo, dummyusb, eptri
from valentyusb.usbcore.endpoint import EndpointType
# Fomu and ice40 modules:
from rtl.fomutouch import TouchPads
from rtl.romgen import RandomFirmwareROM, FirmwareROM
from rtl.sbwarmboot import SBWarmBoot
from rtl.apple2 import Apple2
from rtl.pdpspram import PDP_SPRAM
# Generic Python modules
import argparse
import os
# Simulations Interface: Create I/O pins that can interface with standard test
# suites.
sim_io = [
# define top level connection between FOMU and simulator
("clk", 0,
Subsignal("clk48", Pins(1)),
Subsignal("clk12", Pins(1)),
),
("reset", 0, Pins(1)),
("user_led_n", 0, Pins(1)),
("rgb_led", 0,
Subsignal("r", Pins(1)),
Subsignal("g", Pins(1)),
Subsignal("b", Pins(1)),
),
("touch_pins", 0,
Subsignal("user_touch_0", Pins(1)),
Subsignal("user_touch_1", Pins(1)),
Subsignal("user_touch_2", Pins(1)),
Subsignal("user_touch_3", Pins(1)),
),
("usb", 0,
Subsignal("d_p", Pins(1)),
Subsignal("d_n", Pins(1)),
Subsignal("pullup", Pins(1)),
Subsignal("tx_en", Pins(1)),
),
("spiflash", 0,
Subsignal("cs_n", Pins(1)),
Subsignal("clk", Pins(1)),
Subsignal("mosi", Pins(1)),
Subsignal("miso", Pins(1)),
Subsignal("wp", Pins(1)),
Subsignal("hold", Pins(1)),
),
("spiflash4x", 0,
Subsignal("cs_n", Pins(1)),
Subsignal("clk", Pins(1)),
Subsignal("dq", Pins(4)),
),
]
sim_connectors = [("touch_pins", "user_touch_0, user_touch_1, user_touch_2, user_touch_3")]
# Clock and reset signals that the simulator needs for proper emulation.
class sim_CRG(Module):
def __init__(self, platform):
clk = platform.request("clk")
rst = platform.request("reset")
clk12 = Signal()
self.clock_domains.cd_sys = ClockDomain()
self.clock_domains.cd_usb_12 = ClockDomain()
self.clock_domains.cd_usb_48 = ClockDomain()
self.clock_domains.cd_usb_48_to_12 = ClockDomain()
#clk12 = clk.clk12
clk48 = clk.clk48
#self.comb += clk.clk12.eq(clk12)
#self.comb += clk.clk48.eq(clk48)
self.comb += self.cd_usb_48.clk.eq(clk48)
self.comb += self.cd_usb_48_to_12.clk.eq(clk48)
# derive 12MHz clock by division of 48MHz clock
clk12_counter = Signal(2)
self.sync.usb_48_to_12 += clk12_counter.eq(clk12_counter + 1)
self.comb += clk12.eq(clk12_counter[1])
# Uncomment the following to enable 48MHz Risc-V for faster simulation
# Warning: it breaks USB communication as all data will be sent 4x
#self.comb += self.cd_sys.clk.eq(clk48)
# Use the following for FPGA timing
self.comb += self.cd_sys.clk.eq(clk12)
self.comb += self.cd_usb_12.clk.eq(clk12)
self.comb += [
ResetSignal("sys").eq(rst),
ResetSignal("usb_12").eq(rst),
ResetSignal("usb_48").eq(rst),
# Must not reset cd_usb_48_to_12 otherwise clock divider halts
# and sys_clk domain fails to reset
]
class sim_Platform(SimPlatform):
def __init__(self, revision=None, toolchain="verilator"):
default_clk_name = "clk12"
SimPlatform.__init__(self,
"sim",
sim_io,
connectors=sim_connectors,
toolchain=toolchain)
self.revision = revision
self.spi_size = 2 * 1024 * 1024
self.spi_dummy = 6
def create_programmer(self):
raise ValueError("programming is not supported")
def add_fsm_state_names():
"""Hack the FSM module to add state names to the output"""
from migen.fhdl.visit import NodeTransformer
from migen.genlib.fsm import NextState, NextValue, _target_eq
from migen.fhdl.bitcontainer import value_bits_sign
class My_LowerNext(NodeTransformer):
def __init__(self, next_state_signal, next_state_name_signal, encoding,
aliases):
self.next_state_signal = next_state_signal
self.next_state_name_signal = next_state_name_signal
self.encoding = encoding
self.aliases = aliases
# (target, next_value_ce, next_value)
self.registers = []
def _get_register_control(self, target):
for x in self.registers:
if _target_eq(target, x[0]):
return x[1], x[2]
raise KeyError
def visit_unknown(self, node):
if isinstance(node, NextState):
try:
actual_state = self.aliases[node.state]
except KeyError:
actual_state = node.state
return [
self.next_state_signal.eq(self.encoding[actual_state]),
self.next_state_name_signal.eq(
int.from_bytes(actual_state.encode(), byteorder="big"))
]
elif isinstance(node, NextValue):
try:
next_value_ce, next_value = self._get_register_control(
node.target)
except KeyError:
related = node.target if isinstance(node.target,
Signal) else None
next_value = Signal(bits_sign=value_bits_sign(node.target),
related=related)
next_value_ce = Signal(related=related)
self.registers.append(
(node.target, next_value_ce, next_value))
return next_value.eq(node.value), next_value_ce.eq(1)
else:
return node
import migen.genlib.fsm as fsm
def my_lower_controls(self):
self.state_name = Signal(len(max(self.encoding, key=len)) * 8,
reset=int.from_bytes(
self.reset_state.encode(),
byteorder="big"))
self.next_state_name = Signal(len(max(self.encoding, key=len)) * 8,
reset=int.from_bytes(
self.reset_state.encode(),
byteorder="big"))
self.comb += self.next_state_name.eq(self.state_name)
self.sync += self.state_name.eq(self.next_state_name)
return My_LowerNext(self.next_state, self.next_state_name,
self.encoding, self.state_aliases)
fsm.FSM._lower_controls = my_lower_controls
class Platform(LatticePlatform):
def __init__(self, revision=None, toolchain="icestorm"):
self.revision = revision
if revision == "evt":
from litex_boards.platforms.fomu_evt import _io, _connectors
LatticePlatform.__init__(self, "ice40-up5k-sg48", _io, _connectors, toolchain="icestorm")
self.spi_size = 16 * 1024 * 1024
self.spi_dummy = 6
elif revision == "dvt":
from litex_boards.platforms.fomu_pvt import _io, _connectors
LatticePlatform.__init__(self, "ice40-up5k-uwg30", _io, _connectors, toolchain="icestorm")
self.spi_size = 2 * 1024 * 1024
self.spi_dummy = 6
elif revision == "pvt":
from litex_boards.platforms.fomu_pvt import _io, _connectors
LatticePlatform.__init__(self, "ice40-up5k-uwg30", _io, _connectors, toolchain="icestorm")
self.spi_size = 2 * 1024 * 1024
self.spi_dummy = 6
elif revision == "hacker":
from litex_boards.platforms.fomu_hacker import _io, _connectors
LatticePlatform.__init__(self, "ice40-up5k-uwg30", _io, _connectors, toolchain="icestorm")
self.spi_size = 2 * 1024 * 1024
self.spi_dummy = 4
else:
raise ValueError("Unrecognized revision: {}. Known values: evt, dvt, pvt, hacker".format(revision))
def create_programmer(self):
raise ValueError("programming is not supported in this environment")
class BaseSoC(SoCCore, AutoDoc):
"""A2Fomu SoC and Bootloader
Fomu is an FPGA that fits entirely within a USB port.
A2Fomu is an Apple II clone inside of Fomu along with an operating
system for its control processor. The SoC contains a small ROM that
loads the OS from Flash memory into RAM.
"""
SoCCore.csr_map = {
#"ctrl": 0, # LiteX - many better uses for the space
"apple2": 0,
"crg": 1, # user - no registers in the default clock module
#"uart_phy": 2, # Fomu PVT has no pins for uart
#"uart": 3, # Fomu PVT has no pins for uart
#"identifier_mem": 4, # unnecessary
"timer0": 5, # provided by default (optional)
#"cpu_or_bridge": 8, # Nothing here
"usb": 9,
#"picorvspi": 10,
"touch": 11,
"reboot": 12,
"rgb": 13,
#"version": 14,
"lxspi": 15,
#"messible": 16,
}
SoCCore.mem_map = {
"rom": 0x00000000, # (default shadow @0x80000000)
"sram": 0x10000000, # (default shadow @0x90000000)
"spiflash": 0x20000000, # (default shadow @0xa0000000)
"a2ram": 0xC0000000, # (default shadow @0xc0000000)
"csr": 0xe0000000, # (default shadow @0xe0000000)
"vexriscv_debug": 0xf00f0000,
}
interrupt_map = {
"timer0": 2,
"usb": 3,
}
interrupt_map.update(SoCCore.interrupt_map)
def __init__(self, platform, boot_source="rand",
gdb_debug=None, usb_wishbone=False, bios_file=None,
use_dsp=False, placer="heap", output_dir="build",
pnr_seed=0,
warmboot_offsets=None,
**kwargs):
# Disable integrated RAM unless using simulator - we'll add it later
self.integrated_sram_size = 0
self.output_dir = output_dir
if kwargs["sim"]:
clk_freq = int(48e6)
self.submodules.crg = sim_CRG(platform)
self.integrated_sram_size = 0 # 0x8000,
else:
clk_freq = int(12e6)
self.submodules.crg = _CRG(platform, clk_freq)
SoCCore.__init__(self, platform, clk_freq,
integrated_sram_size=self.integrated_sram_size, with_uart=False,
with_ctrl=False, csr_data_width=32, **kwargs)
if gdb_debug is not None:
if gdb_debug == "uart":
from litex.soc.cores.uart import UARTWishboneBridge
self.submodules.uart_bridge = UARTWishboneBridge(platform.request("serial"), clk_freq, baudrate=115200)
self.add_wb_master(self.uart_bridge.wishbone)
elif gdb_debug == "usb":
usb_wishbone = True
elif gdb_debug == "spi":
import spibone
# Add SPI Wishbone bridge
debug_device = [
("spidebug", 0,
Subsignal("mosi", Pins("dbg:0")),
Subsignal("miso", Pins("dbg:1")),
Subsignal("clk", Pins("dbg:2")),
Subsignal("cs_n", Pins("dbg:3")),
)
]
platform.add_extension(debug_device)
spi_pads = platform.request("spidebug")
self.submodules.spibone = ClockDomainsRenamer("usb_12")(spibone.SpiWishboneBridge(spi_pads, wires=4))
self.add_wb_master(self.spibone.wishbone)
if hasattr(self, "cpu") and not isinstance(self.cpu, CPUNone):
self.cpu.use_external_variant("rtl/VexRiscv_Fomu_NoMMU_Debug.v")
os.path.join(output_dir, "gateware")
# This was needed for an earlier version of LiteX
#self.register_mem("vexriscv_debug", 0xf00f0000, self.cpu.debug_bus, 0x100)
else:
if hasattr(self, "cpu") and not isinstance(self.cpu, CPUNone):
#self.cpu.use_external_variant("rtl/VexRiscv_Fomu.v")
self.cpu.use_external_variant("rtl/VexRiscv_Fomu_NoMMU.v")
# SPRAM- UP5K has four blocks of Single Port RAM (SPRAM). This is split
# evenly between the Risc-V and the 6502 for main memory, 64kB each.
spram_size = 64*1024
if not kwargs["sim"]:
self.submodules.spram = up5kspram.Up5kSPRAM(size=spram_size)
else:
self.submodules.spram = wishbone.SRAM(spram_size, read_only=False, init=[])
self.register_mem("sram", self.mem_map["sram"], self.spram.bus, spram_size)
# Add a Messible for device->host communications
#Doug
#self.submodules.messible = Messible()
# Apple II specific modules here
a2mem_size = 64*1024
if not kwargs["sim"]:
a2mem = PDP_SPRAM(sim=kwargs["sim"])
else:
self.submodules.spram = wishbone.SRAM(spram_size, read_only=False, init=[])
self.submodules.a2mem = a2mem
self.register_mem("a2ram", self.mem_map["a2ram"], self.a2mem.bus, a2mem_size)
print("=====================\n", gdb_debug, gdb_debug!=None, "\n=====================\n")
self.submodules.apple2 = Apple2(platform, a2mem, minimal=(gdb_debug!=None))
if not kwargs["no_cpu"]:
bios_size = 0x2000 # Fomu standard 8 Kb ROM
if kwargs['sim']:
# 64kB ROM used in place of flash during simulation
bios_size = 0x10000
else:
# ROM consumes a large quantity of the limited EBR block RAM
# 1 KB ROM that just initializes flash and jumps to it
bios_size = 0x3000 # 12kB max size using all ebr
bios_size = 0x2400 # 9kB
bios_size = 0x2000 # 8kB foboot failsafe size
bios_size = 0x1000 # 4kB bootloader
bios_size = 0x0800 # 2kB bootloader
if boot_source == "bios" or bios_file is not None:
kwargs['cpu_reset_address'] = 0
if bios_file is None:
self.integrated_rom_size = bios_size
self.submodules.rom = wishbone.SRAM(bios_size, read_only=True, init=[])
self.register_rom(self.rom.bus, bios_size)
else:
self.firmware_rom_size = bios_size
self.submodules.firmware_rom = FirmwareROM(bios_size, bios_file)
self.add_constant("ROM_DISABLE", 1)
self.register_rom(self.firmware_rom.bus, bios_size)
elif boot_source == "rand":
kwargs['cpu_reset_address'] = 0
self.submodules.random_rom = RandomFirmwareROM(bios_size)
self.add_constant("ROM_DISABLE", 1)
self.register_rom(self.random_rom.bus, bios_size)
elif boot_source == "spi":
kwargs['cpu_reset_address'] = 0
self.integrated_rom_size = bios_size
gateware_size = 0x1a000
self.flash_boot_address = self.mem_map["spiflash"] + gateware_size
self.submodules.rom = wishbone.SRAM(bios_size, read_only=True, init=[])
self.register_rom(self.rom.bus, bios_size)
else:
raise ValueError("unrecognized boot_source: {}".format(boot_source))
# The litex SPI module supports memory-mapped reads, as well as a bit-banged mode
# for doing writes.
#if not kwargs['no_spi']:
spi_pads = platform.request("spiflash4x")
self.submodules.lxspi = spi_flash.SpiFlashDualQuad(spi_pads, dummy=platform.spi_dummy, endianness="little")
self.register_mem("spiflash", self.mem_map["spiflash"], self.lxspi.bus, size=platform.spi_size)
# Add USB pads, as well as the appropriate USB controller. If no CPU is
# present, use the DummyUsb controller.
usb_pads = platform.request("usb")
usb_iobuf = usbio.IoBuf(usb_pads.d_p, usb_pads.d_n, usb_pads.pullup)
if hasattr(self, "cpu") and not isinstance(self.cpu, CPUNone):
self.submodules.usb = eptri.TriEndpointInterface(usb_iobuf, debug=usb_wishbone)
else:
self.submodules.usb = dummyusb.DummyUsb(usb_iobuf, debug=usb_wishbone)
if kwargs['sim']:
self.comb += usb_pads.tx_en.eq(usb_iobuf.usb_tx_en)
if usb_wishbone:
self.add_wb_master(self.usb.debug_bridge.wishbone)
# For the EVT board, ensure the pulldown pin is tristated as an input
if hasattr(usb_pads, "pulldown"):
pulldown = TSTriple()
self.specials += pulldown.get_tristate(usb_pads.pulldown)
self.comb += pulldown.oe.eq(0)
if not kwargs['no_touch']:
# Add GPIO pads for the touch buttons
platform.add_extension(TouchPads.touch_device)
self.submodules.touch = TouchPads(platform.request("touch_pads"))
# Allow the user to reboot the ICE40.
self.submodules.reboot = SBWarmBoot(self, warmboot_offsets)
# If CPU has the debug option enabled, allow a register that changes the
# reset address that the CPU jumps to.
#if not kwargs['sim']:
if gdb_debug!=None and not isinstance(self.cpu, CPUNone):
try:
self.cpu.cpu_params.update(
i_externalResetVector=self.reboot.addr.storage,
)
except:
None
if not kwargs['no_rgb']:
from rtl.sbled import SBLED
rgb_pads = platform.request("rgb_led")
rgb = SBLED(platform.revision, rgb_pads)
self.submodules.rgb = rgb
if kwargs['sim']:
# The ice40 LED hardware is not emulated so route to testbench
self.comb += [
rgb_pads.r.eq(rgb.raw.storage[0]),
rgb_pads.g.eq(rgb.raw.storage[1]),
rgb_pads.b.eq(rgb.raw.storage[2]),
]
# Doug: We need space and this is not useful as it only specifies the
# model that the gateware was designed for and not the model that is
# actually in use. If the wrong gateware is loaded, the LED will not
# show the expected colors. This is the major difference.
#self.submodules.version = Version(platform.revision, self, pnr_seed, models=[
# ("0x45", "E", "Fomu EVT"),
# ("0x44", "D", "Fomu DVT"),
# ("0x50", "P", "Fomu PVT (production)"),
# ("0x48", "H", "Fomu Hacker"),
# ("0x3f", "?", "Unknown model"),
# ])
if not kwargs['sim']:
# Override default LiteX's yosys/build templates
assert hasattr(platform.toolchain, "yosys_template")
assert hasattr(platform.toolchain, "build_template")
platform.toolchain.yosys_template = [
"{read_files}",
"attrmap -tocase keep -imap keep=\"true\" keep=1 -imap keep=\"false\" keep=0 -remove keep=0",
"synth_ice40 -json {build_name}.json -top {build_name}",
]
platform.toolchain.build_template = [
#"set -x",
"set -x; yosys -q -w 'has an unprocessed .init. attribute.' -l {build_name}.rpt {build_name}.ys",
"nextpnr-ice40 --json {build_name}.json --pcf {build_name}.pcf --asc {build_name}.txt"
" --pre-pack {build_name}_pre_pack.py --{architecture} --package {package}",
"icepack {build_name}.txt {build_name}.bin"
]
# Add "-relut -dffe_min_ce_use 4" to the synth_ice40 command.
# The "-reult" adds an additional LUT pass to pack more stuff in,
# and the "-dffe_min_ce_use 4" flag prevents Yosys from generating a
# Clock Enable signal for a LUT that has fewer than 4 flip-flops.
# This increases density, and lets us use the FPGA more efficiently.
platform.toolchain.yosys_template[2] += " -relut -abc2 -dffe_min_ce_use 4 -relut"
# Disable final deep-sleep power down so firmware words are loaded
# onto softcore's address bus.
platform.toolchain.build_template[2] = "icepack -s {build_name}.txt {build_name}.bin"
# Allow us to set the nextpnr seed
platform.toolchain.build_template[1] += " --seed " + str(pnr_seed)
if placer is not None:
platform.toolchain.build_template[1] += " --placer {}".format(placer)
if use_dsp:
platform.toolchain.yosys_template[2] += " -dsp"
def copy_memory_file(self, src):
import os
from shutil import copyfile
if not os.path.exists(self.output_dir):
os.mkdir(self.output_dir)
if not os.path.exists(os.path.join(self.output_dir, "gateware")):
os.mkdir(os.path.join(self.output_dir, "gateware"))
copyfile(os.path.join("rtl", src), os.path.join(self.output_dir, "gateware", src))
def make_multiboot_header(filename, boot_offsets=[160]):
"""
ICE40 allows you to program the SB_WARMBOOT state machine by adding the following
values to the bitstream, before any given image:
[7e aa 99 7e] Sync Header
[92 00 k0] Boot mode (k = 1 for cold boot, 0 for warmboot)
[44 03 o1 o2 o3] Boot address
[82 00 00] Bank offset
[01 08] Reboot
[...] Padding (up to 32 bytes)
Note that in ICE40, the second nybble indicates the number of remaining bytes
(with the exception of the sync header).
The above construct is repeated five times:
INITIAL_BOOT The image loaded at first boot
BOOT_S00 The first image for SB_WARMBOOT
BOOT_S01 The second image for SB_WARMBOOT
BOOT_S10 The third image for SB_WARMBOOT
BOOT_S11 The fourth image for SB_WARMBOOT
"""
while len(boot_offsets) < 5:
boot_offsets.append(boot_offsets[0])
with open(filename, 'wb') as output:
for offset in boot_offsets:
# Sync Header
output.write(bytes([0x7e, 0xaa, 0x99, 0x7e]))
# Boot mode
output.write(bytes([0x92, 0x00, 0x00]))
# Boot address
output.write(bytes([0x44, 0x03,
(offset >> 16) & 0xff,
(offset >> 8) & 0xff,
(offset >> 0) & 0xff]))
# Bank offset
output.write(bytes([0x82, 0x00, 0x00]))
# Reboot command
output.write(bytes([0x01, 0x08]))
for x in range(17, 32):
output.write(bytes([0]))
def main():
parser = argparse.ArgumentParser(
description="Build A2Fomu Main Gateware")
parser.add_argument(
"--boot-source", choices=["spi", "rand", "bios"], default="rand",
help="where to have the CPU obtain its executable code from"
)
parser.add_argument(
"--document-only", default=False, action="store_true",
help="Don't build gateware or software, only build documentation"
)
parser.add_argument(
# The only difference between revisions is LED color so default to
# production version
"--revision", choices=["evt", "dvt", "pvt", "hacker"], default="pvt",
help="build a2fomu for a particular hardware model"
)
parser.add_argument(
"--name", help="set base name of output files", default="a2fomu"
)
parser.add_argument(
"--sim", help="build gateware suitable for simulation environment", action="store_true"
)
parser.add_argument(
"--bios", help="use specified file as a BIOS, rather than building one"
)
parser.add_argument(
"--gdb-debug", help="enable gdb debug support", choices=["usb", "uart", "spi", None], default=None
)
parser.add_argument(
"--wishbone-debug", help="enable debug support", action="store_true"
)
parser.add_argument(
"--with-dsp", help="use dsp inference in yosys (not all yosys builds have -dsp)", action="store_true"
)
parser.add_argument(
"--no-cpu", help="disable cpu generation for debugging purposes", action="store_true"
)
parser.add_argument(
"--no-spi", help="disable SPI flash", action="store_true"
)
parser.add_argument(
"--no-rgb", help="disable RGB LED", action="store_true"
)
parser.add_argument(
"--no-touch", help="disable touch pads", action="store_true"
)
parser.add_argument(
"--placer", choices=["sa", "heap"], default="heap", help="which placer to use in nextpnr"
)
parser.add_argument(
"--seed", default=0, help="seed to use in nextpnr"
)
parser.add_argument(
"--export-random-rom-file", help="Generate a random ROM file and save it to a file"
)
args = parser.parse_args()
output_dir = 'build'
if args.export_random_rom_file is not None:
size = 0x2000
def xorshift32(x):
x = x ^ (x << 13) & 0xffffffff
x = x ^ (x >> 17) & 0xffffffff
x = x ^ (x << 5) & 0xffffffff
return x & 0xffffffff
def get_rand(x):
out = 0
for i in range(32):
x = xorshift32(x)
if (x & 1) == 1:
out = out | (1 << i)
return out & 0xffffffff
seed = 1
with open(args.export_random_rom_file, "w", newline="\n") as output:
for d in range(int(size / 4)):
seed = get_rand(seed)
print("{:08x}".format(seed), file=output)
return 0
compile_software = False
if (args.boot_source == "bios" or args.boot_source == "spi") and args.bios is None:
compile_software = True
cpu_type = "vexriscv"
cpu_variant = "minimal"
if args.gdb_debug:
cpu_variant = cpu_variant + "+debug"
if args.no_cpu:
cpu_type = None
cpu_variant = None
compile_gateware = True
if args.document_only:
compile_gateware = False
compile_software = False
warmboot_offsets = [
160,
160,
157696,
262144,
262144 + 32768,
]
os.environ["LITEX"] = "1" # Give our Makefile something to look for
if args.sim:
platform = sim_Platform(revision=args.revision)
else:
platform = Platform(revision=args.revision)
soc = BaseSoC(platform, cpu_type=cpu_type, cpu_variant=cpu_variant,
gdb_debug=args.gdb_debug, usb_wishbone=args.wishbone_debug,
boot_source=args.boot_source,
bios_file=args.bios, sim=args.sim,
use_dsp=args.with_dsp, placer=args.placer,
pnr_seed=int(args.seed),
no_spi=args.no_spi,
no_rgb=args.no_rgb,
no_cpu=args.no_cpu,
no_touch=args.no_touch,
output_dir=output_dir,
warmboot_offsets=warmboot_offsets[1:])
builder = Builder(soc, output_dir=output_dir, csr_csv="build/csr.csv", csr_svd="build/soc.svd",
compile_software=compile_software, compile_gateware=compile_gateware)
if compile_software:
builder.software_packages = [
("all", os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "sw", "src")))
]
if args.sim:
vns = builder.build(run=False)
soc.do_exit(vns)
print("""Simulation build complete. Output files:
build/gateware/dut.v Run this Verilog file under Cocotb.
""")
return
vns = builder.build(build_name=args.name)
soc.do_exit(vns)
lxsocdoc.generate_docs(soc, "build/documentation/", project_name="A2Fomu", author="Elecbrick")
if not args.document_only:
make_multiboot_header(os.path.join(output_dir, "gateware", "multiboot-header.bin"),
warmboot_offsets)
with open(os.path.join(output_dir, 'gateware', 'multiboot-header.bin'), 'rb') as multiboot_header_file:
multiboot_header = multiboot_header_file.read()
with open(os.path.join(output_dir, 'gateware', args.name+'.bin'), 'rb') as top_file:
top = top_file.read()
with open(os.path.join(output_dir, 'gateware', args.name+'-multiboot.bin'), 'wb') as top_multiboot_file:
top_multiboot_file.write(multiboot_header)
top_multiboot_file.write(top)
print(
"""A2Fomu build complete. Output files:
build/gateware/top.bin Bitstream file. Use dfu-util to load.
build/gateware/top-multiboot.bin Multiboot-enabled bitstream file. May be
used with Booster to replace failsafe gateware. Caution: not reccommended
as it will likely brick device.
build/gateware/top.v Verilog representation of SoC.
build/software/include/generated/ Header files used by software.
""")
if __name__ == "__main__":
main()
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tests/test_pairwisevariationmphf.py | iqbal-lab-org/pangenome_variations | 0 | 163397 | from unittest import TestCase
from src.PairwiseVariationMPHF import PairwiseVariationMPHF
from src.PairwiseVariation import PairwiseVariation
class TestPairwiseVariationMPHF(TestCase):
def test___get_pairwise_variation_id_to_alleles_id(self):
mphf = PairwiseVariationMPHF()
pairwise_variation_1 = PairwiseVariation(0, 1)
pairwise_variation_2 = PairwiseVariation(1, 2)
pairwise_variation_3 = PairwiseVariation(2, 3)
mphf.add_object(pairwise_variation_1)
mphf.add_object(pairwise_variation_1)
mphf.add_object(pairwise_variation_3)
mphf.add_object(pairwise_variation_1)
mphf.add_object(pairwise_variation_2)
mphf.add_object(pairwise_variation_3)
mphf.add_object(pairwise_variation_1)
actual_pairwise_variation_id_to_alleles_id = mphf.get_pairwise_variation_id_to_alleles_id()
expected_pairwise_variation_id_to_alleles_id = [(0,1), (2,3), (1,2)]
self.assertListEqual(actual_pairwise_variation_id_to_alleles_id,
expected_pairwise_variation_id_to_alleles_id) | [
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users-backend/users/admin_api/schema.py | pythonitalia/pycon | 56 | 128189 | import strawberry
from pythonit_toolkit.api.extensions import SentryExtension
from users.admin_api.mutation import Mutation
from users.admin_api.query import Query
schema = strawberry.federation.Schema(
query=Query, mutation=Mutation, extensions=[SentryExtension]
)
| [
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] |
app/main/routes.py | EeOneDown/spbu4u | 30 | 193947 | <filename>app/main/routes.py
from flask import render_template, current_app
from app.main import bp
from app.models import User
@bp.route("/")
@bp.route("/index")
def index():
from requests import get
url = "https://api.rasp.yandex.net/v3.0/copyright/"
params = {"apikey": current_app.config['YANDEX_API_KEY'], "format": "json"}
data = get(url, params=params).json()["copyright"]
return render_template("index.html", bot_name="Spbu4UBot", url=data["url"],
text=data["text"])
@bp.route("/<tg_id>/<idate>/<n>")
def schedule(tg_id, idate, n):
from datetime import date
user = User.query.filter_by(tg_id=tg_id).first()
answers = user.create_answers_for_interval(
from_date=date(2018, 6, int(idate)),
to_date=date(2018, 6, int(idate) + int(n)))
return render_template("schedule.html", answers=answers)
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] |
openmaps_auth/urls.py | radiant-maxar/openmaps-auth | 0 | 63882 | from django.conf import settings
from django.urls import include, path
from . import views
urlpatterns = [
path(f"{settings.BASE_PATH}callback", views.callback, name="callback"),
path(f"{settings.BASE_PATH}index", views.index, name="index"),
path(f"{settings.BASE_PATH}v0/auth/login", views.login, name="openmaps_login"),
path(f"{settings.BASE_PATH}v0/auth/logout", views.logout, name="openmaps_logout"),
path(f"{settings.BASE_PATH}v0/auth/status", views.status, name="status"),
path(f"{settings.BASE_PATH}v0/auth/valid", views.valid, name="valid"),
path(
f"{settings.BASE_PATH}v0/social/",
include("social_django.urls", namespace="social"),
),
]
# Only care about using admin during development.
if settings.DEBUG:
from django.contrib import admin
urlpatterns += [
path(f"{settings.BASE_PATH}v0/admin/", admin.site.urls),
]
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] |
graphwalker/test/haltcond_test.py | spotify/python-graphwalker | 66 | 1612319 | <filename>graphwalker/test/haltcond_test.py
# -*- coding: utf-8 -*-
# Copyright (c) 2013 Spotify AB
import unittest
from graphwalker import halting
class TestSeconds(unittest.TestCase):
def test_ctor_smoke(self):
self.assertFalse(halting.Seconds().start(None))
self.assertFalse(halting.Seconds(20).start(None))
self.assertFalse(halting.Seconds(timeout=20).start(None))
def test_empty_done(self):
self.assertTrue(halting.Seconds(0).start(None))
def test_api(self):
ec = halting.Seconds(8).start(None)
ec.add(('cat', 'dog', ()))
self.assert_(type(ec.progress()) is str)
def test_step_through(self):
t, ec = 20, halting.Seconds(8)
ec.clock = lambda: t
self.assertFalse(ec.start(None))
for t, expect in ((23, False), (27, False), (28, True), (29, True)):
self.assertEqual(bool(ec), expect)
class TestSeenSteps(unittest.TestCase):
def test_ctor_smoke(self):
halting.SeenSteps()
halting.SeenSteps(*'abc')
def test_ctor_args(self):
self.assertEqual(halting.SeenSteps().targets, set([]))
self.assertEqual(halting.SeenSteps(*'abc').targets, set('abc'))
def test_empty_done(self):
self.assertTrue(halting.SeenSteps().start(None))
def test_api(self):
ec = halting.SeenSteps(*'abc').start(None)
ec.add(('cat', 'dog', ()))
self.assert_(type(ec.progress()) is str)
def test_step_through(self):
ec = halting.SeenSteps(*'abc').start(None)
self.assertFalse(ec)
ec.add('xa')
self.assertFalse(ec)
ec.add('xb')
self.assertFalse(ec)
ec.add('xc')
self.assertTrue(ec)
class TestCountSteps(unittest.TestCase):
def test_ctor_smoke(self):
self.assertFalse(halting.CountSteps().start(None))
self.assertFalse(halting.CountSteps(123).start(None))
self.assertFalse(halting.CountSteps(steps=123).start(None))
def test_zero_done(self):
self.assertTrue(halting.CountSteps(0).start(None))
def test_api(self):
ec = halting.CountSteps().start(None)
ec.add(('cat', 'dog', ()))
self.assert_(type(ec.progress()) is str)
def test_step_through(self):
ec = halting.CountSteps(3).start(None)
self.assertFalse(ec)
ec.add('xa')
self.assertFalse(ec)
ec.add('xb')
self.assertFalse(ec)
ec.add('xc')
self.assertTrue(ec)
class g:
V = {'a': ()}
E = {'e': ()}
class TestCoverage(unittest.TestCase):
def test_ctor_smoke(self):
self.assertFalse(halting.Coverage().start(g))
self.assertFalse(halting.Coverage(edges=10).start(g))
self.assertFalse(halting.Coverage(verts=20).start(g))
self.assertFalse(halting.Coverage(edges=10, verts=20).start(g))
self.assertFalse(halting.Coverage(edges=10, vertices=20).start(g))
def test_zero_done(self):
ec = halting.Coverage()
ec.edge_cov = 0.0
self.assertTrue(ec.start(g))
def test_api(self):
ec = halting.Coverage().start(g)
ec.add(('cat', 'dog', ()))
self.assert_(type(ec.progress()) is str)
def test_step_through(self):
ec = halting.Coverage(edges=1, verts=1).start(g)
self.assertFalse(ec)
ec.add('aaaa')
self.assertFalse(ec)
ec.add('eee')
self.assertTrue(ec)
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] |
tomef/tools/export_results.py | unlikelymaths/tomef | 0 | 88226 | <reponame>unlikelymaths/tomef
#!/usr/bin/env python
# coding: utf-8
# # Export Results
# <div style="position: absolute; right:0;top:0"><a href="./tools.ipynb" style="text-decoration: none"> <font size="5">←</font></a>
# <a href="../evaluation.ipynb" style="text-decoration: none"> <font size="5">↑</font></a></div>
#
# This script collects all HTML files and puts them into a `export.zip` archive in the results folder. Useful for sharing results outside of a jupyter lab.
#
# https://nbconvert.readthedocs.io/en/5.x/nbconvert_library.html
# In[ ]:
from init import setup
setup(vars())
import os
import zipfile
if _isMain:
try:
runvars = {'basepath': os.path.join(os.path.dirname(__file__),'../')}
except:
runvars = {'basepath': os.path.join(os.getcwd(),'../')}
def code_folders(runvars):
runvars['basepath'] = os.path.abspath(runvars['basepath'])
dir_list = ['./','./docs/','./tools/', './embedding/',
'./importer/', './tokenizer/', './vocab/',
'./vectorizer/', './models/', './distiller/',
'./metrics/']
file_list = []
file_types = ['.html', '.md', '.png']
for directory in dir_list:
directory_abs = os.path.abspath(os.path.join(runvars['basepath'],directory))
for filename in os.listdir(directory_abs):
if any([file_type for file_type in file_types if filename.endswith(file_type)]):
filename_full = os.path.join(directory,filename)
filename_abs= os.path.join(runvars['basepath'],filename_full)
file_list.append((filename_abs, filename_full))
zip_path = os.path.join(runvars['basepath'],'results/export.zip')
with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zip:
# writing each file one by one
for file in file_list:
zip.write(file[0], arcname = file[1])
def main(basepath):
runvars = {'basepath': basepath}
code_folders(runvars)
if _isMain:
code_folders(runvars)
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] |
prosumerPolicy/inputSetter.py | 0-k/prosumerpolicy | 6 | 144953 | <filename>prosumerPolicy/inputSetter.py
from __future__ import print_function
import logging
import numpy as np
from paths import *
from input import *
from pv import PV
from battery import Battery
totalPrices = import_Prices(path_Prices)
totalPvGen = import_PV(path_PvGen)
totalLoad = import_Load(path_Load)
class _InputSetter:
def __init__(self, duration=24, day=1, loadRow=0):
self.PV = PV()
self.Battery = Battery()
self. totalPrices = totalPrices
self.totalPvGen = totalPvGen
self.totalLoad = totalLoad
self.totalAverageLoad = totalLoad.mean(axis=1)
self.__timeDuration = duration
self.__day = day
self.loadRow = loadRow
@property
def pvGenList(self):
return self.get_pvGen_list()
@property
def priceList(self):
return self.get_price_list()
@property
def loadList(self):
return self.get_load_list()
@property
def timeDuration(self):
return self.__timeDuration
@timeDuration.setter
def timeDuration(self, time):
self.__timeDuration = time
@property
def day(self):
return self.__day
@day.setter
def day(self, day):
self.__day = day
@property
def loadRow(self):
return self.__loadRow
@loadRow.setter
def loadRow(self, loadrow):
logging.info('Load Row Changed to {}'.format(loadrow))
self.__loadRow = loadrow
def get_price_list(self, day=None, duration=None):
''' returns price list as np.array for specified day and duration'''
if day is None:
day = self.day
''' returns price list as np.array for specified day and duration'''
if duration is None:
duration = self.timeDuration
if day is None or duration is None:
raise ValueError("Please Specify a day and time series duration")
try:
hours = (day - 1) * 24
price = np.array(self.totalPrices['Price'][hours:hours + duration])
if np.nan in price:
raise IOError
return price / 1000 # in kWh
except IOError:
logging.WARNING('Price list in day {} contains missing values '.format(day)) #FIXME Logger
def get_load_list(self, day=None, duration=None, loadRow=None):
if day is None:
day = self.day
if duration is None:
duration = self.timeDuration
if loadRow is None:
loadRow = self.loadRow
if day is None or duration is None or loadRow is None:
raise ValueError("Please set day, duration and load Row")
try:
if loadRow == -1: ## LOAD ROW -1 gives average load row
hours = (day - 1) * 24
load = np.array(self.totalAverageLoad[:][hours:hours + duration])
if np.nan in load:
raise IOError
return load / 1000 # kWh
else:
hours = (day - 1) * 24
load = np.array(self.totalLoad[loadRow][hours:hours + duration])
if np.nan in load:
raise IOError
return load / 1000 # kWh
except IOError:
logging.warning('Load list in day {} contains missing values'.format(day))
def get_pvGen_list(self, day=None, duration=None):
if day is None:
day = self.day
if duration is None:
duration = self.timeDuration
if day is None or duration is None:
raise ValueError("Please Specify a day and time series duration")
try:
hours = (day - 1) * 24
result = np.array(self.PV._calculatedPvGen[0][hours:hours + duration])
if np.nan in result:
raise IOError
return result / 1000 # kW
except IOError:
logging.warning('PV Generation list day {} contains missing values.'.format(day))
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] |
parte2/P3/testRLsol.py | manuelsousa7/ia-project | 0 | 144171 | <filename>parte2/P3/testRLsol.py
import numpy as np
from sklearn.externals import joblib
from RL import *
import RLsol
for test in [('fmdp1.pkl','traj1.pkl'), ('fmdp2.pkl','traj2.pkl')]:
print("Testing " + test[0])
# funcoes auxiliarres
fmdp = joblib.load(test[0])
# ficheiro com a trajectório de treino
traj = joblib.load(test[1])
qlearn = RLsol.myRL(7,2,0.9)
Q = qlearn.traces2Q(traj)
print("Valores Q aprendidos")
print(qlearn.Q)
if np.linalg.norm(Q-fmdp.Q)<.3:
print("Erro nos Q dentro dos limites de tolerância. OK\n")
else:
print("Erro nos Q acima dos limites de tolerância. FAILED\n")
# gerar trajectoria aprendida
J,trajlearn = fmdp.runPolicy(6,4,RLsol.Q2pol(Q))
print("Trajectoria gerada com a politica aprendida")
print(trajlearn)
if J>.7:
print("Recompensa obtida dentro do previsto. OK\n")
else:
print("Recompensa obtida abaixo do previsto. FAILED\n") | [
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dataset/__init__.py | STomoya/animeface | 61 | 76330 |
from dataset.animeface import (
AnimeFace,
AnimeFaceCelebA,
AnimeFaceSR,
AnimeFaceXDoG,
AnimeFaceLabel,
AnimeFaceOneHot
)
from dataset.portrait import (
DanbooruPortrait,
DanbooruPortraitCelebA,
DanbooruPortraitSR,
DanbooruPortraitXDoG
)
from dataset.danbooru import (
Danbooru,
DanbooruSR,
DanbooruAutoPair
)
import os
import torch
from torch.utils.data import DataLoader, Dataset
from collections.abc import Iterable
def cycle(iterable: Iterable):
while True:
for i in iterable:
yield i
def to_loader(
dataset: Dataset,
batch_size: int,
shuffle: bool=True,
num_workers: int=os.cpu_count(),
pin_memory: bool=torch.cuda.is_available()
) -> DataLoader:
loader = DataLoader(
dataset, batch_size, shuffle=shuffle,
num_workers=num_workers, pin_memory=pin_memory
)
return loader
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] |
fasttask/models.py | huanjoyous/FasterRunner20190716 | 1 | 60960 | from django.db import models
# Create your models here.
from fastrunner.models import Project
from fastuser.models import BaseTable
class Schedule(BaseTable):
"""
定时任务信息表
"""
send_strategy = (
(1, "始终发送"),
(2, "仅失败发送"),
(3, "从不发送")
)
class Meta:
verbose_name = "定时任务"
db_table = "Schedule"
name = models.CharField("任务名称", unique=True, null=False, max_length=100)
identity = models.CharField("任务ID", null=False, unique=True, max_length=100)
send_type = models.IntegerField("发送策略", choices=send_strategy, default=3)
config = models.TextField("任务配置", null=False)
receiver = models.CharField("接收者", null=True, max_length=2048)
copy = models.CharField("抄送者", null=True, max_length=2048)
status = models.BooleanField("状态", default=True)
project = models.ForeignKey(Project, verbose_name='所属项目', null=False, on_delete=models.CASCADE)
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] |
nailgun/nailgun/test/unit/test_utils.py | Zipfer/fuel-web | 0 | 173305 | # -*- coding: utf-8 -*-
# Copyright 2013 Mirantis, Inc.
#
# 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.
from mock import mock_open
from mock import patch
import os
import tempfile
from nailgun.test.base import BaseIntegrationTest
from nailgun.utils import dict_merge
from nailgun.utils import extract_env_version
from nailgun.utils import get_fuel_release_versions
from nailgun.utils import migration
class TestUtils(BaseIntegrationTest):
def test_dict_merge(self):
custom = {"coord": [10, 10],
"pass": "<PASSWORD>",
"dict": {"body": "solid",
"color": "black",
"dict": {"stuff": "hz"}}}
common = {"parent": None,
"dict": {"transparency": 100,
"dict": {"another_stuff": "hz"}}}
result = dict_merge(custom, common)
self.assertEqual(result, {"coord": [10, 10],
"parent": None,
"pass": "<PASSWORD>",
"dict": {"body": "solid",
"color": "black",
"transparency": 100,
"dict": {"stuff": "hz",
"another_stuff": "hz"}}})
def test_upgrade_wizard_data(self):
fixture_path = os.path.join(os.path.dirname(__file__), '..', '..',
'fixtures', 'openstack.yaml')
wizard_meta = migration.upgrade_release_wizard_metadata_50_to_51(
fixture_path=fixture_path
)
network_settings = [
n['data'] for n in wizard_meta['Network']['manager']['values']
]
self.assertNotIn('neutron-nsx', network_settings)
def test_extract_env_version(self):
# format: input, output pairs
test_cases = [
('2014.1', '5.0'),
('2014.1-5.0', '5.0'),
('2014.1.1-5.0.1', '5.0.1'),
('2014.1.1-5.0.1-X', '5.0.1'),
('2014.1.1-5.1', '5.1'),
]
for input_, output in test_cases:
self.assertEqual(extract_env_version(input_), output)
@patch('nailgun.utils.glob.glob', return_value=['test.yaml'])
@patch('__builtin__.open', mock_open(read_data='test_data'))
def test_get_release_versions(self, _):
versions = get_fuel_release_versions(None)
self.assertDictEqual({'test': 'test_data'}, versions)
def test_get_release_versions_empty_file(self):
with tempfile.NamedTemporaryFile() as tf:
versions = get_fuel_release_versions(tf.name)
self.assertDictEqual({os.path.basename(tf.name): None}, versions)
def test_get_release_no_file(self):
with tempfile.NamedTemporaryFile() as tf:
file_path = tf.name
self.assertFalse(os.path.exists(file_path))
versions = get_fuel_release_versions(file_path)
self.assertDictEqual({}, versions)
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] |
tests/conftest.py | glemaitre/hexrd | 27 | 95706 | import os
from pathlib import Path
import pytest
@pytest.fixture
def example_repo_path():
if 'HEXRD_EXAMPLE_REPO_PATH' not in os.environ:
pytest.fail('Environment varable HEXRD_EXAMPLE_REPO_PATH not set!')
repo_path = os.environ['HEXRD_EXAMPLE_REPO_PATH']
return Path(repo_path)
| [
1,
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] |
vale/__init__.py | pyccel/vale | 3 | 55953 | __version__ = 0.9
from .parser import *
from .syntax import *
| [
1,
4770,
3259,
1649,
353,
29871,
29900,
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334,
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] |
TuRBO-Penicillin/turbo/turbo_m.py | HarryQL/TuRBO-Penicillin | 0 | 58401 | ###############################################################################
# Copyright (c) 2019 Uber Technologies, Inc. #
# #
# Licensed under the Uber Non-Commercial License (the "License"); #
# you may not use this file except in compliance with the License. #
# You may obtain a copy of the License at the root directory of this project. #
# #
# See the License for the specific language governing permissions and #
# limitations under the License. #
###############################################################################
import math
import sys
from copy import deepcopy
import gpytorch
import numpy as np
import torch
from .gp import train_gp
from .turbo_1 import Turbo1
from .utils import from_unit_cube, latin_hypercube, to_unit_cube
class TurboM(Turbo1):
"""The TuRBO-m algorithm.
Parameters
----------
f : function handle
lb : Lower variable bounds, numpy.array, shape (d,).
ub : Upper variable bounds, numpy.array, shape (d,).
n_init : Number of initial points *FOR EACH TRUST REGION* (2*dim is recommended), int.
max_evals : Total evaluation budget, int.
n_trust_regions : Number of trust regions
batch_size : Number of points in each batch, int.
verbose : If you want to print information about the optimization progress, bool.
use_ard : If you want to use ARD for the GP kernel.
max_cholesky_size : Largest number of training points where we use Cholesky, int
n_training_steps : Number of training steps for learning the GP hypers, int
min_cuda : We use float64 on the CPU if we have this or fewer datapoints
device : Device to use for GP fitting ("cpu" or "cuda")
dtype : Dtype to use for GP fitting ("float32" or "float64")
Example usage:
turbo5 = TurboM(f=f, lb=lb, ub=ub, n_init=n_init, max_evals=max_evals, n_trust_regions=5)
turbo5.optimize() # Run optimization
X, fX = turbo5.X, turbo5.fX # Evaluated points
"""
def __init__(
self,
f,
lb,
ub,
n_init,
max_evals,
n_trust_regions,
batch_size=1,
verbose=True,
use_ard=True,
max_cholesky_size=2000,
n_training_steps=50,
min_cuda=1024,
device="cpu",
dtype="float64",
):
self.n_trust_regions = n_trust_regions
super().__init__(
f=f,
lb=lb,
ub=ub,
n_init=n_init,
max_evals=max_evals,
batch_size=batch_size,
verbose=verbose,
use_ard=use_ard,
max_cholesky_size=max_cholesky_size,
n_training_steps=n_training_steps,
min_cuda=min_cuda,
device=device,
dtype=dtype,
)
self.succtol = 3
self.failtol = max(5, self.dim)
# Very basic input checks
assert n_trust_regions > 1 and isinstance(max_evals, int)
assert max_evals > n_trust_regions * n_init, "Not enough trust regions to do initial evaluations"
assert max_evals > batch_size, "Not enough evaluations to do a single batch"
# Remember the hypers for trust regions we don't sample from
self.hypers = [{} for _ in range(self.n_trust_regions)]
# Initialize parameters
self._restart()
def _restart(self):
self._idx = np.zeros((0, 1), dtype=int) # Track what trust region proposed what using an index vector
self.failcount = np.zeros(self.n_trust_regions, dtype=int)
self.succcount = np.zeros(self.n_trust_regions, dtype=int)
self.length = self.length_init * np.ones(self.n_trust_regions)
def _adjust_length(self, fX_next, i):
assert i >= 0 and i <= self.n_trust_regions - 1
fX_min = self.fX[self._idx[:, 0] == i, 0].min() # Target value
if fX_next.min() < fX_min - 1e-3 * math.fabs(fX_min):
self.succcount[i] += 1
self.failcount[i] = 0
else:
self.succcount[i] = 0
self.failcount[i] += len(fX_next) # NOTE: Add size of the batch for this TR
if self.succcount[i] == self.succtol: # Expand trust region
self.length[i] = min([2.0 * self.length[i], self.length_max])
self.succcount[i] = 0
elif self.failcount[i] >= self.failtol: # Shrink trust region (we may have exceeded the failtol)
self.length[i] /= 2.0
self.failcount[i] = 0
def _select_candidates(self, X_cand, y_cand):
"""Select candidates from samples from all trust regions."""
assert X_cand.shape == (self.n_trust_regions, self.n_cand, self.dim)
assert y_cand.shape == (self.n_trust_regions, self.n_cand, self.batch_size)
assert X_cand.min() >= 0.0 and X_cand.max() <= 1.0 and np.all(np.isfinite(y_cand))
X_next = np.zeros((self.batch_size, self.dim))
idx_next = np.zeros((self.batch_size, 1), dtype=int)
for k in range(self.batch_size):
i, j = np.unravel_index(np.argmin(y_cand[:, :, k]), (self.n_trust_regions, self.n_cand))
assert y_cand[:, :, k].min() == y_cand[i, j, k]
X_next[k, :] = deepcopy(X_cand[i, j, :])
idx_next[k, 0] = i
assert np.isfinite(y_cand[i, j, k]) # Just to make sure we never select nan or inf
# Make sure we never pick this point again
y_cand[i, j, :] = np.inf
return X_next, idx_next
def optimize(self):
"""Run the full optimization process."""
# Create initial points for each TR
for i in range(self.n_trust_regions):
X_init = latin_hypercube(self.n_init, self.dim)
X_init = from_unit_cube(X_init, self.lb, self.ub)
fX_init = np.array([[self.f(x)] for x in X_init])
# Update budget and set as initial data for this TR
self.X = np.vstack((self.X, X_init))
self.fX = np.vstack((self.fX, fX_init))
self._idx = np.vstack((self._idx, i * np.ones((self.n_init, 1), dtype=int)))
self.n_evals += self.n_init
if self.verbose:
fbest = fX_init.min()
print(f"TR-{i} starting from: {fbest:.4}")
sys.stdout.flush()
# Thompson sample to get next suggestions
while self.n_evals < self.max_evals:
# Generate candidates from each TR
X_cand = np.zeros((self.n_trust_regions, self.n_cand, self.dim))
y_cand = np.inf * np.ones((self.n_trust_regions, self.n_cand, self.batch_size))
for i in range(self.n_trust_regions):
idx = np.where(self._idx == i)[0] # Extract all "active" indices
# Get the points, values the active values
X = deepcopy(self.X[idx, :])
X = to_unit_cube(X, self.lb, self.ub)
# Get the values from the standardized data
fX = deepcopy(self.fX[idx, 0].ravel())
# Don't retrain the model if the training data hasn't changed
n_training_steps = 0 if self.hypers[i] else self.n_training_steps
# Create new candidates
X_cand[i, :, :], y_cand[i, :, :], self.hypers[i] = self._create_candidates(
X, fX, length=self.length[i], n_training_steps=n_training_steps, hypers=self.hypers[i]
)
# Select the next candidates
X_next, idx_next = self._select_candidates(X_cand, y_cand)
assert X_next.min() >= 0.0 and X_next.max() <= 1.0
# Undo the warping
X_next = from_unit_cube(X_next, self.lb, self.ub)
# Evaluate batch
fX_next = np.array([[self.f(x)] for x in X_next])
# Update trust regions
for i in range(self.n_trust_regions):
idx_i = np.where(idx_next == i)[0]
if len(idx_i) > 0:
self.hypers[i] = {} # Remove model hypers
fX_i = fX_next[idx_i]
if self.verbose and fX_i.min() < self.fX.min() - 1e-3 * math.fabs(self.fX.min()):
n_evals, fbest = self.n_evals, fX_i.min()
print(f"{n_evals}) New best @ TR-{i}: {fbest:.4}")
sys.stdout.flush()
self._adjust_length(fX_i, i)
# Update budget and append data
self.n_evals += self.batch_size
self.X = np.vstack((self.X, deepcopy(X_next)))
self.fX = np.vstack((self.fX, deepcopy(fX_next)))
self._idx = np.vstack((self._idx, deepcopy(idx_next)))
# Check if any TR needs to be restarted
for i in range(self.n_trust_regions):
if self.length[i] < self.length_min: # Restart trust region if converged
idx_i = self._idx[:, 0] == i
if self.verbose:
n_evals, fbest = self.n_evals, self.fX[idx_i, 0].min()
print(f"{n_evals}) TR-{i} converged to: : {fbest:.4}")
sys.stdout.flush()
# Reset length and counters, remove old data from trust region
self.length[i] = self.length_init
self.succcount[i] = 0
self.failcount[i] = 0
self._idx[idx_i, 0] = -1 # Remove points from trust region
self.hypers[i] = {} # Remove model hypers
# Create a new initial design
X_init = latin_hypercube(self.n_init, self.dim)
X_init = from_unit_cube(X_init, self.lb, self.ub)
fX_init = np.array([[self.f(x)] for x in X_init])
# Print progress
if self.verbose:
n_evals, fbest = self.n_evals, fX_init.min()
print(f"{n_evals}) TR-{i} is restarting from: : {fbest:.4}")
sys.stdout.flush()
# Append data to local history
self.X = np.vstack((self.X, X_init))
self.fX = np.vstack((self.fX, fX_init))
self._idx = np.vstack((self._idx, i * np.ones((self.n_init, 1), dtype=int)))
self.n_evals += self.n_init
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src/main.py | bhavanib/Music-Recommendation-System | 3 | 179551 | from SWIMMRS.src.TweetClassifier import Tokenizer, FeatureExtraction
from SWIMMRS.src.scraper.Datascraper import DataScraper
from SWIMMRS.src.scraper.OauthConnectionClient import OauthClient
userTweets_url = 'https://api.twitter.com/1.1/statuses/user_timeline.json?user_id=2597834149&count=20'
def main():
oauth_obj = OauthClient()
oauth_client = oauth_obj.getOauthClient()
data_scraper = DataScraper(oauth_client)
user_tweets = data_scraper.getUserTweets(userTweets_url)
for a in user_tweets:
print(a['text'])
tokenList = Tokenizer.preprocess(a['text'])
print(tokenList)
# for t in tokenList:
# print(t.encode('unicode-escape'))
featureList = FeatureExtraction.getfeatureVector(tokenList)
print(featureList)
if __name__ == '__main__':
main()
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mod_ci/cron.py | T1duS/sample-platform | 1 | 162413 | #!/usr/bin/python
import sys
from os import path
# Need to append server root path to ensure we can import the necessary files.
sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))
def cron():
from mod_ci.controllers import start_platform
from run import config, log
from database import create_session
from github import GitHub
log.info('Run the cron for kicking off CI platform(s).')
# Create session
db = create_session(config['DATABASE_URI'])
gh = GitHub(access_token=config['GITHUB_TOKEN'])
repository = gh.repos(config['GITHUB_OWNER'])(config['GITHUB_REPOSITORY'])
start_platform(db, repository)
cron()
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chargebee/models/site_migration_detail.py | koordinates/chargebee-python | 28 | 172046 | <filename>chargebee/models/site_migration_detail.py
import json
from chargebee.model import Model
from chargebee import request
from chargebee import APIError
class SiteMigrationDetail(Model):
fields = ["entity_id", "other_site_name", "entity_id_at_other_site", "migrated_at", "entity_type", \
"status"]
@staticmethod
def list(params=None, env=None, headers=None):
return request.send_list_request('get', request.uri_path("site_migration_details"), params, env, headers)
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memegen/memegen/routes/latest.py | flavienbwk/memegen | 3 | 74749 | from flask import Blueprint, render_template, current_app, make_response
from webargs import fields, flaskparser
from ._utils import route
REFRESH_SECONDS = 60
blueprint = Blueprint('latest-page', __name__)
@blueprint.route("/latest")
@flaskparser.use_kwargs({'nsfw': fields.Bool(missing=False)})
def get(nsfw):
filtered = 'false' if nsfw else 'true'
html = render_template(
'latest.html',
srcs=[route('image.get_latest', index=i, filtered=filtered)
for i in range(24)],
refresh=REFRESH_SECONDS,
config=current_app.config,
)
response = make_response(html)
response.headers['Cache-Control'] = f'max-age={REFRESH_SECONDS-1}'
return response
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dicomtrolley/core.py | michelkok/dicomtrolley | 0 | 43533 | <reponame>michelkok/dicomtrolley
"""Provides common base classes that allow different modules to talk to each other."""
from itertools import chain
from typing import Sequence
from pydantic.main import BaseModel
from pydicom.dataset import Dataset
from dicomtrolley.exceptions import DICOMTrolleyException
class DICOMObject(BaseModel):
"""An object in the DICOM world. Base for Study, Series, Instance.
dicomtrolley search methods always return instances based on DICOMObject
dicomtrolley download methods take instances based on DICOMObject as input
"""
uid: str
data: Dataset
def __str__(self):
return type(self).__name__ + " " + self.uid
def all_instances(self):
"""
Returns
-------
List[Instance]
All instances contained in this object
"""
raise NotImplementedError()
class Instance(DICOMObject):
parent: "Series"
def all_instances(self):
"""A list containing this instance itself. To match other signatures"""
return [self]
class Series(DICOMObject):
instances: Sequence[Instance]
parent: "Study"
def all_instances(self):
"""Each instance contained in this series"""
return self.instances
class Study(DICOMObject):
series: Sequence[Series]
def all_instances(self):
"""Return each instance contained in this study"""
return list(chain(*(x.instances for x in self.series)))
class Searcher:
"""Something that can search for DICOM studies. Base class."""
def find_studies(self, query) -> Sequence[Study]:
raise NotImplementedError()
def find_study(self, query) -> Study:
"""Like find_studies, but guarantees exactly one result. Exception if not.
This method is meant for searches that contain unique identifiers like
StudyInstanceUID, AccessionNumber, etc.
Raises
------
DICOMTrolleyException
If no results or more than one result is returned by query
"""
results = self.find_studies(query)
if len(results) == 0 or len(results) > 1:
raise DICOMTrolleyException(
f"Expected exactly one study for query '{query}', but"
f" found {len(results)}"
)
return results[0]
Instance.update_forward_refs() # enables pydantic validation
Series.update_forward_refs()
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] |
src/sqlite3_handler.py | simaoamorim/cnc_tooldb | 2 | 149467 | <reponame>simaoamorim/cnc_tooldb
#!/usr/bin/env python3
"""
Python module that handles the connection and actions to the local database
"""
import os.path
import sqlite3 as sql
class DB(object):
def __init__(self, dbname='database.db'):
"""Initialize the connection to the database and, if it doesn't exist, create the necessary tables"""
if os.path.isfile(dbname):
self.db_exists = True
else:
self.db_exists = False
self.conn = sql.connect(dbname)
self.cur = self.conn.cursor()
assert self.cur.connection == self.conn
if not self.db_exists:
self.init_db()
def init_db(self):
"""Create the needed tables in the new database file"""
self.cur.execute('''CREATE TABLE machine (ID INTEGER PRIMARY KEY, name VARCHAR(100));''')
self.cur.execute('''CREATE TABLE tool (ID INTEGER PRIMARY KEY, comment VARCHAR(100));''')
self.cur.execute('''
CREATE TABLE parameters (
IDMachine INTEGER NOT NULL,
IDTool INTEGER NOT NULL,
comment VARCHAR(100),
Xval REAL,
Yval REAL,
Zval REAL,
FOREIGN KEY (IDMachine) REFERENCES machine(ID) ON UPDATE CASCADE ON DELETE CASCADE,
FOREIGN KEY (IDTool) REFERENCES tool(ID) ON UPDATE CASCADE ON DELETE CASCADE,
PRIMARY KEY (IDMachine, IDTool)
);
''')
self.conn.commit()
def get_machines(self, cur=None):
if cur is None:
cur = self.cur
return cur.execute("SELECT name FROM machine")
def __del__(self):
"""Close the database connection and exit"""
self.conn.close()
| [
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] |
git_vote_cog/issues.py | dbford/votecog | 0 | 110044 | from typing import Optional, Set
import github
from github.PullRequest import PullRequest
from git_vote_cog.util import wrap_async
class Issue:
def __init__(self, pr: Optional[PullRequest]):
# class variables def
self._pr: Optional[PullRequest] = None
self.id: int = -1
self.url: str = ""
self.title: str = ""
self.description: str = ""
self.author: str = ""
self.labels: Set[str] = set()
self.exists: bool = False
# init class
self.pr = pr
@wrap_async
def remove_label(self, tag: [str]):
try:
self.pr.remove_from_labels(tag)
except github.GithubException as err:
if not err.status == 404:
raise err
try:
self.labels.remove(tag)
except KeyError:
pass
@wrap_async
def add_label(self, tag: str):
self.pr.add_to_labels(tag)
self.labels.add(tag)
@wrap_async
def update(self):
if self.pr is None:
return
try:
self.pr.update()
self.pr = self.pr
except github.UnknownObjectException:
self.pr = None
@property
def pr(self) -> Optional[PullRequest]:
return self._pr
@pr.setter
def pr(self, pr: Optional[PullRequest]):
self._pr = pr
if pr is None:
self.id = -1
self.exists = False
else:
self.id = pr.number
self.url = pr.html_url
self.title = pr.title
self.description = pr.body
self.author = pr.user.login
self.labels = {label.name for label in pr.labels}
self.exists = pr.state == "open" and not pr.is_merged()
def __str__(self) -> str:
return f"PR(id={self.id}, exists={self.exists})"
| [
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] |
tests/utils/frameworks/tensorflow_utils.py | matheusMoreno/BentoML | 0 | 195963 | import re
import string
import numpy as np
import tensorflow as tf
import tensorflow.keras as keras
def custom_activation(x):
return tf.nn.tanh(x) ** 2
class CustomLayer(keras.layers.Layer):
def __init__(self, units=32, **kwargs):
super(CustomLayer, self).__init__(**kwargs)
self.units = tf.Variable(units, name="units")
def call(self, inputs, training=False):
if training:
return inputs * self.units
else:
return inputs
def get_config(self):
config = super(CustomLayer, self).get_config()
config.update({"units": self.units.numpy()})
return config
def KerasSequentialModel() -> keras.models.Model:
net = keras.models.Sequential(
(
keras.layers.Dense(
units=1,
input_shape=(5,),
use_bias=False,
kernel_initializer=keras.initializers.Ones(),
),
)
)
opt = keras.optimizers.Adam(0.002, 0.5)
net.compile(optimizer=opt, loss="binary_crossentropy", metrics=["accuracy"])
return net
def KerasNLPModel() -> keras.models.Model:
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
def custom_standardization(input_data: str) -> tf.Tensor:
lowercase = tf.strings.lower(input_data)
stripped_html = tf.strings.regex_replace(lowercase, "<br />", " ")
return tf.strings.regex_replace(
stripped_html, "[%s]" % re.escape(string.punctuation), ""
)
max_features = 20000
embedding_dims = 50
vectorize_layer = TextVectorization(
standardize=custom_standardization,
max_tokens=max_features,
output_mode="int",
output_sequence_length=400,
)
# A text input with preprocessing layers
text_input = keras.Input(shape=(1,), dtype=tf.string, name="text")
x = vectorize_layer(text_input)
x = keras.layers.Embedding(max_features + 1, embedding_dims)(x)
x = keras.layers.Dropout(0.2)(x)
# Conv1D + global max pooling
x = keras.layers.Conv1D(128, 7, padding="valid", activation="relu", strides=1)(x)
x = keras.layers.GlobalMaxPooling1D()(x)
# We add a vanilla hidden layer:
x = keras.layers.Dense(128, activation="relu")(x)
x = keras.layers.Dropout(0.2)(x)
# We project onto a single unit output layer, and squash it with a sigmoid:
predictions = keras.layers.Dense(1, activation="sigmoid", name="predictions")(x)
model = keras.Model(text_input, predictions)
model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
return model
class NativeModel(tf.Module):
def __init__(self):
super().__init__()
self.weights = np.asfarray([[1.0], [1.0], [1.0], [1.0], [1.0]])
self.dense = lambda inputs: tf.matmul(inputs, self.weights)
@tf.function(
input_signature=[tf.TensorSpec(shape=[1, 5], dtype=tf.float64, name="inputs")]
)
def __call__(self, inputs):
return self.dense(inputs)
class NativeRaggedModel(NativeModel):
@tf.function(
input_signature=[
tf.RaggedTensorSpec(tf.TensorShape([None, None]), tf.float64, 1, tf.int64)
]
)
def __call__(self, inputs):
inputs = inputs.to_tensor(shape=[None, 5], default_value=0)
return self.dense(inputs)
class MultiInputModel(tf.Module):
def __init__(self):
super().__init__()
self.weights = np.asfarray([[1.0], [1.0], [1.0], [1.0], [1.0]])
self.dense = lambda tensor: tf.matmul(tensor, self.weights)
@tf.function(
input_signature=[
tf.TensorSpec(shape=[1, 5], dtype=tf.float64, name="x1"),
tf.TensorSpec(shape=[1, 5], dtype=tf.float64, name="x2"),
tf.TensorSpec(shape=(), dtype=tf.float64, name="factor"),
]
)
def __call__(self, x1: tf.Tensor, x2: tf.Tensor, factor: tf.Tensor):
return self.dense(x1 + x2 * factor)
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pixelpuncher/player/migrations/0020_auto_20160604_1602.py | ej2/pixelpuncher | 0 | 55356 | <reponame>ej2/pixelpuncher
# -*- coding: utf-8 -*-
# Generated by Django 1.9 on 2016-06-04 16:02
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('player', '0019_avatarlayer_playeravatar'),
]
operations = [
migrations.AlterField(
model_name='avatarlayer',
name='unlock_method',
field=models.CharField(choices=[(b'start', b'Starter'), (b'discvr', b'Discover'), (b'purcha', b'Purchase'), (b'secret', b'Secret')], default=b'discov', max_length=6),
),
migrations.AlterField(
model_name='playeravatar',
name='layer',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='+', to='player.AvatarLayer'),
),
migrations.AlterField(
model_name='playeravatar',
name='player',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='layers', to='player.Player'),
),
]
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conpaas-services/scripts/manager/notify_git_push.py | bopopescu/conpaas-2 | 5 | 132476 | <filename>conpaas-services/scripts/manager/notify_git_push.py
#!/usr/bin/env python
import simplejson
from conpaas.core.https import client
def main():
client.conpaas_init_ssl_ctx('/etc/cpsmanager/certs', 'user')
method = 'git_push_hook'
manager_ip = '127.0.0.1'
res = client.jsonrpc_post(manager_ip, 443, '/', method)
if res[0] == 200:
try:
data = simplejson.loads(res[1])
except simplejson.decoder.JSONDecodeError:
# Not JSON, simply return what we got
return res[1]
return data.get('result', data)
raise Exception("Call to method %s on %s failed with HTTP code %s: %s."
% (method, manager_ip, res[0], res[1]))
if __name__ == "__main__":
res = main()
print "%s" % res
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src/pyon/container/test/test_procs.py | scionrep/scioncc_new | 2 | 164856 | <gh_stars>1-10
#!/usr/bin/env python
__author__ = '<NAME>'
from unittest import SkipTest
from mock import Mock, patch, ANY, sentinel, call
from nose.plugins.attrib import attr
from gevent.event import AsyncResult, Event
import gevent
from ion.agent.agent import ResourceAgent
from pyon.container.procs import ProcManager
from pyon.core.exception import BadRequest, NotFound
from pyon.ion.endpoint import ProcessRPCServer
from pyon.ion.process import IonProcessError
from pyon.net.transport import NameTrio, TransportError
from pyon.public import PRED, CCAP, IonObject
from pyon.ion.service import BaseService
from pyon.util.int_test import IonIntegrationTestCase
from pyon.util.unit_test import PyonTestCase
from interface.objects import ProcessStateEnum
class FakeContainer(object):
def __init__(self):
self.id = "containerid"
self.node = None
self.name = "containername"
self.CCAP = CCAP
def has_capability(self, cap):
return True
class SampleProcess(BaseService):
name = 'sample'
dependencies = []
def call_process(self, *args, **kwargs):
pass
class BadProcess(BaseService):
name = 'bad'
dependencies = []
def on_quit(self):
bad = 3 / 0 # boom
return bad
def sleep_target(self, *args, **kwargs):
self.sleep_event = Event()
gevent.sleep(0.2)
self.sleep_event.set()
def fail_target(self, *args, **kwargs):
raise Exception("Blow up to test failure chain")
class SampleAgent(ResourceAgent):
dependencies = []
class TestRPCServer(ProcessRPCServer):
pass
@attr('UNIT')
class TestProcManager(PyonTestCase):
def setUp(self):
self.container = Mock()
self.pm = ProcManager(self.container)
self.container.resource_registry.create.return_value = (sentinel.rid, sentinel.rev)
self.container.resource_registry.find_resources.return_value = ([sentinel.oid], [sentinel.orev])
def test_start(self):
self.pm.start()
self.assertEquals(self.pm.cc_id, sentinel.rid)
def test_start_with_org(self):
self.patch_cfg('pyon.container.procs.CFG', {'container':{'org_name':'NOT_DEFAULT'}})
self.pm.start()
self.container.resource_registry.create_association.assert_called_once_with(sentinel.oid, PRED.hasResource, sentinel.rid)
@patch('pyon.container.procs.log')
def test_stop_with_error(self, mocklog):
self.pm.start()
self.pm.terminate_process = Mock(side_effect=BadRequest)
procmock = Mock()
procmock._proc_start_time = 0
procmock.id = sentinel.pid
self.pm.procs[sentinel.pid] = procmock
self.pm.procs_by_name['dummy'] = procmock
self.pm.stop()
self.pm.terminate_process.assert_called_once_with(sentinel.pid)
mocklog.warn.assert_has_calls([call("Failed to terminate process (%s): %s", sentinel.pid, ANY),
call("ProcManager procs not empty: %s", self.pm.procs),
call("ProcManager procs_by_name not empty: %s", self.pm.procs_by_name)])
def test_list_local_processes(self):
pmock = Mock()
pmock.process_type = sentinel.ptype
pmock2 = Mock()
pmock2.process_type = sentinel.ptype2
self.pm.procs = {'one':pmock,
'two':pmock2,
'three':pmock}
self.assertEquals(self.pm.list_local_processes(),
[pmock, pmock2, pmock])
def test_list_local_processes_proc_type_filter(self):
pmock = Mock()
pmock.process_type = sentinel.ptype
pmock2 = Mock()
pmock2.process_type = sentinel.ptype2
self.pm.procs = {'one':pmock,
'two':pmock2,
'three':pmock}
self.assertEquals(self.pm.list_local_processes(sentinel.ptype2),
[pmock2])
def test_get_a_local_process(self):
pmock = Mock()
pmock.name = sentinel.name
pmock2 = Mock()
pmock2.name = sentinel.name2
self.pm.procs = {'one':pmock,
'two':pmock2}
self.assertEquals(self.pm.get_a_local_process(sentinel.name2),
pmock2)
def test_get_a_local_process_for_agent_res_id(self):
pmock = Mock()
pmock.process_type = 'agent'
pmock.resource_type = sentinel.rtype
pmock2 = Mock()
pmock2.process_type = 'agent'
pmock2.resource_type = sentinel.rtype2
self.pm.procs = {'one':pmock,
'two':pmock2}
self.assertEquals(self.pm.get_a_local_process(sentinel.rtype2),
pmock2)
def test_get_a_local_process_no_match(self):
self.assertIsNone(self.pm.get_a_local_process())
def test_is_local_service_process(self):
pmock = Mock()
pmock.name = sentinel.name
pmock.process_type = 'simple'
pmock2 = Mock()
pmock2.name = sentinel.name2
pmock2.process_type = 'service'
pmock3 = Mock()
pmock3.name = sentinel.name3
pmock3.process_type = 'service'
self.pm.procs = {'one':pmock,
'two':pmock2,
'three':pmock3}
self.assertTrue(self.pm.is_local_service_process(sentinel.name3))
def test_is_local_service_process_name_matches_but_type_doesnt(self):
pmock = Mock()
pmock.name = sentinel.name
pmock.process_type = 'simple'
pmock2 = Mock()
pmock2.name = sentinel.name2
pmock2.process_type = 'notservice'
pmock3 = Mock()
pmock3.name = sentinel.name3
pmock3.process_type = 'notservice'
self.pm.procs = {'one':pmock,
'two':pmock2,
'three':pmock3}
self.assertFalse(self.pm.is_local_service_process(sentinel.name3))
def test_is_local_agent_process(self):
# agent is similar to above, but checks resource_type instead
pmock = Mock()
pmock.name = sentinel.name
pmock.process_type = 'simple'
pmock2 = Mock()
pmock2.resource_type = sentinel.name2
pmock2.process_type = 'agent'
pmock3 = Mock()
pmock3.name = sentinel.name3
pmock3.process_type = 'notservice'
self.pm.procs = {'one':pmock,
'two':pmock2,
'three':pmock3}
self.assertTrue(self.pm.is_local_agent_process(sentinel.name2))
def test_is_local_agent_process_not_found(self):
self.assertFalse(self.pm.is_local_agent_process(sentinel.one))
def test__unregister_process_errors(self):
pmock = Mock()
pmock._proc_name = '1'
pmock._proc_type = 'service'
pmock._proc_res_id = sentinel.presid
pmock._proc_svc_id = sentinel.psvcid
self.container.resource_registry.delete.side_effect = NotFound
self.pm.procs[sentinel.pid] = pmock
self.pm.procs_by_name['1'] = pmock
self.container.resource_registry.find_objects.reset_mock()
self.container.resource_registry.find_objects.side_effect = None
self.container.resource_registry.find_objects.return_value = ([],[])
self.pm._unregister_process(sentinel.pid, pmock)
# show we tried to interact with the RR
self.container.resource_registry.delete.assert_call(sentinel.presid, del_associations=True)
self.container.resource_registry.find_objects.assert_called_once_with(sentinel.psvcid, "hasProcess", "Process", id_only=True)
self.assertEquals(self.pm.procs, {})
self.assertEquals(self.pm.procs_by_name, {})
# NEXT: find_objects works fine and gives us an error deletion
self.container.resource_registry.delete.reset_mock()
self.container.resource_registry.find_objects.reset_mock()
self.container.resource_registry.find_objects.side_effect = None
self.container.resource_registry.find_objects.return_value = ([sentinel.svcid],[None])
self.pm.procs[sentinel.pid] = pmock
self.pm.procs_by_name['1'] = pmock
self.pm._unregister_process(sentinel.pid, pmock)
self.container.resource_registry.delete.assert_calls([call(sentinel.presid, del_associations=True),
call(sentinel.psvcid, del_associations=True)])
# NEXT: agent
pmock = Mock()
pmock.id = sentinel.pid
pmock._proc_name = '1'
pmock._proc_type = 'agent'
self.pm.procs[sentinel.pid] = pmock
self.pm.procs_by_name['1'] = pmock
self.pm._unregister_process(sentinel.pid, pmock)
self.container.directory.unregister_safe.assert_called_once_with("/Agents", sentinel.pid)
def test__create_listening_endpoint_with_cfg(self):
self.patch_cfg('pyon.container.procs.CFG', container=dict(messaging=dict(endpoint=dict(proc_listening_type='pyon.container.test.test_procs.TestRPCServer'))))
ep = self.pm._create_listening_endpoint(process=sentinel.process)
self.assertIsInstance(ep, TestRPCServer)
def test__create_listening_endpoint_without_cfg_and_no_conv(self):
self.patch_cfg('pyon.container.procs.CFG', container=dict(messaging=dict(endpoint=dict(proc_listening_type=None))))
ep = self.pm._create_listening_endpoint(process=sentinel.process)
self.assertIsInstance(ep, ProcessRPCServer)
def test_failed_process(self):
self.pm.start()
self.container.fail_fast = Mock()
self.pm.procs['pid1'] = Mock()
proc2 = BadProcess()
self.pm.proc_sup.spawn(name="bad", service=proc2, target=proc2.fail_target)
gevent.sleep(0) # Allow the new thread to fail and trigger the chain
self.assertFalse(self.container.fail_fast.called)
del self.pm.procs['pid1']
proc3 = BadProcess()
self.pm.proc_sup.spawn(name="bad", service=proc3, target=proc3.fail_target)
gevent.sleep(0) # Allow the new thread to fail and trigger the chain
self.assertTrue(self.container.fail_fast.called)
@attr('INT')
class TestProcManagerInt(IonIntegrationTestCase):
class ExpectedFailure(Exception):
pass
def test_proc_fails(self):
raise SkipTest("Fails with gevent 1.0, leaves bad container. Investigate")
self._start_container()
pm = self.container.proc_manager
ar = AsyncResult()
def failedhandler(proc, state, container):
if state == ProcessStateEnum.FAILED:
ar.set()
pm.add_proc_state_changed_callback(failedhandler)
pid = self._spawnproc(pm, 'service')
# cause a failure
pm.procs[pid]._process._ctrl_thread.proc.kill(exception=self.ExpectedFailure, block=False)
# wait for proc state changed notification
ar.get(timeout=5)
# make sure removed
self.assertNotIn(pid, pm.procs)
def test_procmanager_iso(self):
fakecc = FakeContainer()
fakecc.resource_registry = Mock()
fakecc.resource_registry.create.return_value=["ID","rev"]
fakecc.start_time = Mock()
fakecc.version = Mock()
pm = ProcManager(fakecc)
self.assertTrue(hasattr(fakecc, "spawn_process"))
pm.start()
pm.stop()
def test_procmanager(self):
self._start_container()
pm = self.container.proc_manager
pid1 = self._spawnproc(pm, 'service')
pid2 = self._spawnproc(pm, 'stream_process')
pid3 = self._spawnproc(pm, 'agent', 'SampleAgent')
pid4 = self._spawnproc(pm, 'standalone')
pid5 = self._spawnproc(pm, 'simple')
pid6 = self._spawnproc(pm, 'immediate')
with self.assertRaises(Exception) as ex:
config = {'process':{'type':'unknown_type'}}
pid = pm.spawn_process('sample1', 'pyon.container.test.test_procs', 'SampleProcess', config)
self.assertEqual(ex.exception, 'Unknown process type: BAMM')
self.assertEquals(len(pm.procs), 5) # service, stream_proc, (no agent), standalone, simple. NO IMMEDIATE
pm.terminate_process(pid1)
pm.terminate_process(pid2)
pm.terminate_process(pid3)
pm.terminate_process(pid4)
pm.terminate_process(pid5)
self.assertEquals(len(pm.procs), 0)
def _spawnproc(self, pm, ptype, pcls=None):
pcls = pcls or 'SampleProcess'
config = {'process':{'type':ptype}}
pid = pm.spawn_process('sample1', 'pyon.container.test.test_procs', pcls, config)
self.assertTrue(pid)
return pid
def test_proc_org(self):
self._start_container()
pm = self.container.proc_manager
config = {'process':{'type':'standalone'}}
pid1 = pm.spawn_process('sample1', 'pyon.container.test.test_procs', 'SampleProcess', config)
self.assertTrue(pid1)
config = {'process':{'type':'standalone'}, 'org_governance_name': 'Org2'}
pid2 = pm.spawn_process('sample2', 'pyon.container.test.test_procs', 'SampleProcess', config)
self.assertTrue(pid2)
proc = pm.procs_by_name['sample1']
self.assertEqual(proc.org_governance_name,'ION')
proc = pm.procs_by_name['sample2']
self.assertEqual(proc.org_governance_name,'Org2')
def test_procmanager_shutdown(self):
self.test_procmanager()
pm = self.container.proc_manager
pm.stop()
self.assertEquals(len(pm.procs), 0)
def test_procmanager_badquit_shutdown(self):
self._start_container()
pm = self.container.proc_manager
pid = pm.spawn_process('badprocess', 'pyon.container.test.test_procs', 'BadProcess', {'process':{'type':'service'}})
with patch('pyon.ion.service.log') as m:
pm.stop()
self.assertEquals(len(pm.procs), 0)
self.assertEquals(m.exception.call_count, 1)
def test_immediate_terminate(self):
self._start_container()
self._spawnproc(self.container.proc_manager, 'immediate')
self.assertEquals(len(self.container.proc_manager.procs), 0)
def test_terminate_process(self):
self._start_container()
pid = self._spawnproc(self.container.proc_manager, 'service')
self.container.terminate_process(pid)
self.assertEquals(len(self.container.proc_manager.procs), 0)
# now try to terminate it again, it shouldn't exist
self.assertRaises(BadRequest, self.container.terminate_process, pid)
def test_proc_state_change_callback(self):
self._start_container()
m = Mock()
pm = self.container.proc_manager
pm.add_proc_state_changed_callback(m)
pid = self._spawnproc(pm, 'service')
m.assert_called_with(ANY, ProcessStateEnum.RUNNING, self.container)
self.assertIsInstance(m.call_args[0][0], SampleProcess)
self.container.terminate_process(pid)
m.assert_called_with(ANY, ProcessStateEnum.TERMINATED, self.container)
self.assertIsInstance(m.call_args[0][0], SampleProcess)
pm.remove_proc_state_changed_callback(m)
cur_call_count = m.call_count
pid = self._spawnproc(pm, 'service')
self.assertEquals(m.call_count, cur_call_count) # should not have been touched
self.container.terminate_process(pid)
self.assertEquals(m.call_count, cur_call_count) # should not have been touched
def test_create_listening_endpoint(self):
self.patch_cfg('pyon.container.procs.CFG', {'container':{'messaging':{'endpoint':{'proc_listening_type':'pyon.container.test.test_procs.TestRPCServer'}}}})
fakecc = FakeContainer()
fakecc.resource_registry = Mock()
fakecc.resource_registry.create.return_value=["ID","rev"]
with patch('pyon.container.procs.ProcManager._get_execution_engine_config', Mock(return_value={})):
pm = ProcManager(fakecc)
pm._get_execution_engine_config = Mock()
ep = pm._create_listening_endpoint(node=sentinel.node,
service=sentinel.service,
process=sentinel.process)
self.assertIsInstance(ep, TestRPCServer)
def test_error_on_start_listeners_of_proc(self):
self._start_container()
m = Mock()
pm = self.container.proc_manager
pm.add_proc_state_changed_callback(m)
with patch('pyon.ion.process.IonProcessThread.start_listeners', Mock(side_effect=IonProcessError)):
pid = pm.spawn_process('sample1', 'pyon.container.test.test_procs', 'SampleProcess', {'process':{'type':'service'}})
self.assertIsNone(pid)
pid = pm.spawn_process('sample1', 'pyon.container.test.test_procs', 'SampleProcess', {'process':{'type':'stream_process'}})
self.assertIsNone(pid)
pid = pm.spawn_process('sample1', 'pyon.container.test.test_procs', 'SampleProcess', {'process':{'type':'standalone'}})
self.assertIsNone(pid)
m.assert_calls([call(ANY, ProcessStateEnum.FAILED),
call(ANY, ProcessStateEnum.FAILED),
call(ANY, ProcessStateEnum.FAILED)])
def test_process_config_reg(self):
self._start_container()
pm = self.container.proc_manager
# Test OK case
res_config = {'special':{'more':'exists'}}
res_obj = IonObject("AgentInstance", driver_config=res_config)
res_id, _ = self.container.resource_registry.create(res_obj)
config_ref = "resources:%s/driver_config" % res_id
config = {'process':{'config_ref':config_ref}}
pid1 = pm.spawn_process('sample1', 'pyon.container.test.test_procs', 'SampleProcess', config)
self.assertTrue(pid1)
proc = pm.procs_by_name['sample1']
self.assertTrue(proc)
self.assertIn('special', proc.CFG)
self.assertTrue(proc.CFG.get_safe("special.more"), 'exists')
pm.terminate_process(pid1)
# Test failure cases
config_ref = "XXXXX:%s/driver_config" % res_id
config = {'process':{'config_ref':config_ref}}
with self.assertRaises(BadRequest) as ex:
pid2 = pm.spawn_process('sample1', 'pyon.container.test.test_procs', 'SampleProcess', config)
config_ref = "resources:badbadbad/driver_config"
config = {'process':{'config_ref':config_ref}}
with self.assertRaises(NotFound) as ex:
pid3 = pm.spawn_process('sample1', 'pyon.container.test.test_procs', 'SampleProcess', config)
config_ref = "resources:%s/not_existing" % res_id
config = {'process':{'config_ref':config_ref}}
with self.assertRaises(BadRequest) as ex:
pid4 = pm.spawn_process('sample1', 'pyon.container.test.test_procs', 'SampleProcess', config)
config_ref = "resources:%s/name" % res_id
config = {'process':{'config_ref':config_ref}}
with self.assertRaises(BadRequest) as ex:
pid4 = pm.spawn_process('sample1', 'pyon.container.test.test_procs', 'SampleProcess', config)
# Test for objects method
obj_id = "test_" + res_id
self.container.object_store.create_doc(res_config, object_id=obj_id)
obj2 = self.container.object_store.read_doc(obj_id)
self.assertEquals(obj2, res_config)
config_ref = "objects:%s/" % obj_id
config = {'process':{'config_ref':config_ref}}
pid1 = pm.spawn_process('sample1', 'pyon.container.test.test_procs', 'SampleProcess', config)
self.assertTrue(pid1)
proc = pm.procs_by_name['sample1']
self.assertTrue(proc)
self.assertIn('special', proc.CFG)
self.assertTrue(proc.CFG.get_safe("special.more"), 'exists')
pm.terminate_process(pid1)
config_ref = "objects:%s/special" % obj_id
config = {'process':{'config_ref':config_ref}}
pid1 = pm.spawn_process('sample1', 'pyon.container.test.test_procs', 'SampleProcess', config)
self.assertTrue(pid1)
proc = pm.procs_by_name['sample1']
self.assertTrue(proc)
self.assertIn('more', proc.CFG)
self.assertTrue(proc.CFG.more, 'exists')
pm.terminate_process(pid1)
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boundaries/kml.py | MinnPost/represent-boundaries | 20 | 1607809 | <reponame>MinnPost/represent-boundaries
from __future__ import unicode_literals
from xml.sax.saxutils import escape
def generate_placemark(name, geom):
return "<Placemark><name>%s</name>%s</Placemark>" % (escape(name), geom.kml)
def generate_kml_document(placemarks):
return """<?xml version="1.0" encoding="UTF-8"?>
<kml xmlns="http://www.opengis.net/kml/2.2">
<Document>
%s
</Document>
</kml>""" % "\n".join(placemarks)
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constants.py | tyukesz/Command-word-recognition-keras- | 3 | 159415 | VERBOSITY = 1
SAMPLE_RATE = 16000
DURATION_MS = 1000
FEATURES_COUNT = 64
DESIRED_SAMPLES = int(SAMPLE_RATE * DURATION_MS / 1000)
DROPOUT_RATE = 0.5
TESING_PERCENTAGE = 15
VALIDATION_PERCENTAGE = 15
VALIDATION_FREQUENCY = 10
SAVE_PERIOD = 10
EPOCHS = 100
BATCH_SIZE = 512
DATASET_URL = 'http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz'
SOUNDS_DIR = './trainset/sounds/'
MFCCS_DIR = './trainset/MFCCS/'
MODELS_DIR = './models/'
LABELS_PATH = MODELS_DIR + 'labels.txt'
WANTED_WORDS = ['left', 'right', 'up', 'down']
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booktags/flaskapp/book/views.py | MagicSword/Booktags | 0 | 2498 | <reponame>MagicSword/Booktags
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
example.py
~~~~~~~~~
A simple command line application to run flask apps.
:copyright: 2019 Miller
:license: BSD-3-Clause
"""
# Known bugs that can't be fixed here:
# - synopsis() cannot be prevented from clobbering existing
# loaded modules.
# - If the __file__ attribute on a module is a relative path and
# the current directory is changed with os.chdir(), an incorrect
# path will be displayed.
from flask import render_template, redirect, request, url_for, flash,jsonify,current_app
from flask_login import login_user, logout_user, login_required, current_user
from . import book
from flask_sqlalchemy import get_debug_queries
from sqlalchemy.sql.expression import cast
from datatables import ColumnDT, DataTables
from .. import auth
from .. import db
from .forms import EditBookForm, HackmdMeta
# from booktags.db.basemodels import Book
from booktags.flaskapp.model.models import BookMain
# --------------------------------------------------------- common routines
@book.after_app_request
def after_request(response):
for query in get_debug_queries():
if query.duration >= current_app.config['PROJECT_SLOW_DB_QUERY_TIME']:
current_app.logger.warning(
'Slow query: %s\nParameters: %s\nDuration: %fs\nContext: %s\n'
% (query.statement, query.parameters, query.duration,
query.context))
return response
@book.route('/', methods=['GET', 'POST'])
def index():
# books=BookMain.get_all_book()
query = BookMain.query
page = request.args.get('page', 1, type=int)
pagination = query.order_by(cast(BookMain.id, db.Integer)).paginate(
page, per_page=current_app.config['PROJECT_BOOKS_PER_PAGE'],
error_out=False)
books = pagination.items
return render_template('book/index.html',books=books,pagination=pagination)
# @book.route('/list/', methods=['GET', 'POST'])
# def list_book():
# """
#
# :param field: col name
# :param order: asc or desc
# :return: renew query
# """
# books = BookMain.get_all_book()
# return render_template('book/list_book.html',books=books)
@book.route("/list")
def list_book():
"""List users with DataTables <= 1.10.x."""
return render_template('book/list_book.html')
@book.route('/data', methods=['GET', 'POST'])
def data():
"""Return server side data."""
# defining columns
# - explicitly cast date to string, so string searching the date
# will search a date formatted equal to how it is presented
# in the table
columns = [
# ColumnDT(cast(BookMain.id, db.Integer)),
ColumnDT(BookMain.id),
ColumnDT(BookMain.isbn),
ColumnDT(BookMain.title_short),
ColumnDT(BookMain.title),
ColumnDT(BookMain.catalogue),
ColumnDT(BookMain.cutter),
ColumnDT(BookMain.pub_year),
ColumnDT(BookMain.copy_info)
# ColumnDT(BookMain.get_link),
# ColumnDT(BookMain.note),
# ColumnDT(BookMain.reprint),
# ColumnDT(BookMain.removed),
# ColumnDT(BookMain.keepsite)
]
# defining the initial query depending on your purpose
query = db.session.query().select_from(BookMain)
# GET parameters
params = request.args.to_dict()
# instantiating a DataTable for the query and table needed
rowTable = DataTables(params, query, columns)
# returns what is needed by DataTable
return jsonify(rowTable.output_result())
@book.route('/get/<int:id>', methods=['GET', 'POST'])
def get_book():
return f"Hello book index : {id}"
@book.route('/post/', methods=['GET', 'POST'])
def post_book():
"""
post new book entry
:return:
"""
book = BookMain.query.all()
id = int(book[-1].id) + 1
print(f"id is : {id}")
form = EditBookForm()
if form.validate_on_submit():
book.id = form.id.data
book.isbn = form.isbn.data
book.title_short = form.title_short.data
book.title = form.title.data
book.catalogue = form.catalogue.data
book.cutter = form.cutter.data
book.pub_year = form.pub_year.data
book.copy_info = form.copy_info.data
book.get_link = form.get_link.data
book.note = form.note.data
book.reprint = form.reprint.data
book.removed = form.removed.data
book.keepsite = form.keepsite.data
db.session.add(book)
db.session.commit()
flash('Your book data has been added.', 'success')
return redirect(url_for('book.index'))
form.id.data = id
return render_template('book/edit_book.html', form=form)
@book.route('/edit/<int:id>', methods=['GET', 'POST'])
def edit_book(id):
"""
edit , put book data
:param id:
:return:
"""
form = EditBookForm()
book = BookMain.query.filter_by(id=id).first_or_404()
if form.validate_on_submit():
# book.id = form.id.data
book.isbn = form.isbn.data
book.title_short = form.title_short.data
book.title = form.title.data
book.catalogue = form.catalogue.data
book.cutter = form.cutter.data
book.pub_year = form.pub_year.data
book.copy_info = form.copy_info.data
book.get_link = form.get_link.data
book.note = form.note.data
book.reprint = form.reprint.data
book.removed = form.removed.data
book.keepsite = form.keepsite.data
db.session.add(book)
db.session.commit()
flash('Your book data has been updated.', 'success')
return redirect(url_for('book.index'))
form.id.data = book.id
form.isbn.data = book.isbn
form.title_short.data = book.title_short
form.title.data = book.title
form.catalogue.data = book.catalogue
form.cutter.data = book.cutter
form.pub_year.data = book.pub_year
form.copy_info.data = book.copy_info
form.get_link.data = book.get_link
form.note.data = book.note
form.reprint.data = book.reprint
form.removed.data = book.removed
form.keepsite.data = book.keepsite
return render_template('book/edit_book.html', form=form)
@book.route('/del/<int:id>', methods=['GET', 'POST'])
def del_book(id):
return f"Hello book index: del {id}"
@book.route('/hackmdmeta', methods=['GET', 'POST'])
def hackmd_meta():
"""
:return:
"""
from booktags.vendor.hackmd_meta import get_hackmdmeta
form = HackmdMeta()
if form.validate_on_submit():
booksn = str(form.booksn.data)
# print(f"booksn is : {booksn}")
temp = get_hackmdmeta(booksn)
# print(temp)
form.body.data = temp
# flash('Your book data has been updated.', 'success')
# return redirect(url_for('book.hackmd_meta'))
return render_template('book/hackmd_meta.html',form=form)
if __name__ == '__main__':
pass
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2396,
13,
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1209,
13,
2
] |
src/token/__init__.py | mingz2013/py.script | 1 | 12169 | <gh_stars>1-10
# -*- coding:utf-8 -*-
"""
"""
__date__ = "14/12/2017"
__author__ = "zhaojm"
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web-app/app/main/modules.py | sandbernar/anti-corona-crm | 0 | 48574 | <reponame>sandbernar/anti-corona-crm
from flask import request
import math
from wtforms import SelectField
from wtforms.validators import DataRequired
from flask_wtf import FlaskForm
class TableModule:
class WrongPageError(Exception):
pass
class WrongSortingParameterError(Exception):
pass
class TableForm(FlaskForm):
per_page = SelectField(choices=[(num, str(num)) for num in [5, 10, 25, 50, 100, 250, 500]],
default=5, validators=[DataRequired()])
def __init__(self, request, q, table_head, header_button = None, search_form = None, sort_param = None,
page = 1, per_page = 5, table_title = "", is_downloadable_xls = False, table_head_info = dict()):
if "page" in request.args:
try:
page = int(request.args["page"])
except ValueError:
raise self.WrongPageError
self.page = page
self.per_page = per_page
self.table_head_dict = table_head
self.table_head = []
self.request = request
self.q = q
self.header_button = header_button
self.search_form = search_form
self.sort_param = sort_param
self.search_params = []
self.is_downloadable_xls = is_downloadable_xls
self.xls_response = None
self.table_head_info = table_head_info
self.table_title = table_title
self.table_form = self.TableForm()
try:
per_page = request.args.get("per_page", None)
if per_page:
per_page = int(per_page)
self.table_form.per_page.default = per_page
self.per_page = per_page
except ValueError:
print("Wrong Per Page Value")
if page < 1:
raise self.WrongPageError
self.table_form.process()
self.total_len = q.count()
self.max_page = 0
self.search_table()
self.sort_table()
download_xls = request.args.get("download_xls", None)
if download_xls == self.__class__.__name__:
self.xls_response = self.download_xls()
# Should always be the last one to be called
self.entries = self.get_entries()
def print_entry(self, result):
pass
def get_entries(self):
if self.total_len:
entries = []
self.total_len = self.q.count()
for result in self.q.offset((self.page-1)*self.per_page).limit(self.per_page).all():
entry = {"data": self.print_entry(result)}
entries.append(entry)
entries = self.preprocess_entries(entries)
self.max_page = math.ceil(self.total_len/self.per_page)
return entries
def preprocess_entries(self, entries):
return entries
def search_table(self):
pass
def download_xls(self):
pass
def sort_table(self):
self.sort_by = None
self.sort_by_asc = True
if "sort_by_asc" in request.args:
self.sort_by = request.args["sort_by_asc"]
elif "sort_by_desc" in request.args:
self.sort_by = request.args["sort_by_desc"]
self.sort_by_asc = False
for i, th in enumerate(self.table_head_dict):
new_th = (th, th) if len(self.table_head_dict[th]) > 0 else th
self.table_head.append(new_th)
first = self.q.first()
if first and self.sort_param != None:
first = getattr(first, self.sort_param)
if len(self.q._entities) == 1:
first = [first]
if self.sort_by:
if first:
for m in first:
if self.sort_by in self.table_head_dict:
for s in self.table_head_dict[self.sort_by]:
if hasattr(m, "__dict__"):
if s in m.__dict__.keys():
param = getattr(type(m), s)
if self.sort_by_asc:
self.q = self.q.order_by(param.asc())
else:
self.q = self.q.order_by(param.desc()) | [
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] |
solutions/problem_101.py | ksvr444/daily-coding-problem | 1,921 | 135529 | def is_prime(num, primes):
for prime in primes:
if prime == num:
return True
if not num % prime:
return False
return True
def get_primes(num):
limit = (num // 2) + 1
candidates = list()
primes = list()
for i in range(2, limit):
if is_prime(i, primes):
primes.append(i)
candidates.append((i, num - i))
new_candidates = list()
for first, second in candidates[::-1]:
if is_prime(second, primes):
primes.append(second)
new_candidates.append((first, second))
return new_candidates[-1]
assert get_primes(4) == (2, 2)
assert get_primes(10) == (3, 7)
assert get_primes(100) == (3, 97)
| [
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] |
Linux_Android_Classification/Src/GridSearchCV/SVM/SVM_search.py | Syuukakou/DeepLook_into_Android-type_ELF | 0 | 1600741 | <reponame>Syuukakou/DeepLook_into_Android-type_ELF<gh_stars>0
from sklearn import svm
from Linux_Android_Classification.Src.data_process import get_firstN_rows_opcodes, data_process_opcodes
from Linux_Android_Classification.Src.common_libs import *
# from sklearn.ensemble import RandomForestClassifier
# from sklearn.tree import DecisionTreeClassifier
# from sklearn.naive_bayes import GaussianNB, MultinomialNB, ComplementNB, BernoulliNB, CategoricalNB
# from sklearn.neural_network import MLPClassifier
# r"../Files/opcodes624_fcns98-malicious.csv"
filepath = r"../../../Files/opcodes624_fcns249-malicious.csv"
data, label = data_process_opcodes(filepath)
# data, label = get_firstN_rows_opcodes(filepath)
print(data, label)
label = np.array(label)
result_params = []
lsvc_resluts = []
lsvc_recall = []
lsvc_f1 = []
lsvc_precision = []
# svc_results = []
# data_train, data_test, label_train, label_test = train_test_split(data, label, test_size=0.1, shuffle=True)
skf = StratifiedKFold(n_splits=10)
# skf.get_n_splits(data, label)
for train_index, test_index in skf.split(data, label):
results = {}
data_train, data_test = data[train_index], data[test_index]
label_train, label_test = label[train_index], label[test_index]
print("TRAIN: ", train_index, "TEST: ", test_index)
seed = 1
"""
'LinearSVC',
'SVC',
"""
models = ['LSVC']
# svm.NuSVC(random_state=seed, probability=True), ,
# svm.SVC(probability=True, random_state=seed)
clfs = [svm.LinearSVC(random_state=seed)]
# ,
# models[1]: {'C': [10, 100, 1000], 'kernel': ['linear', 'poly', 'rbf'], 'gamma': ['scale', 'auto']}
# models[0]: {'penalty': ['l2'], 'loss': ['hinge'], 'C': [1, 10, 100]
params = {
models[0]: {'penalty': ['l2'], 'loss': ['hinge'], 'C': [1, 100]}
}
for name, estimator in zip(models, clfs):
print(name)
clf = GridSearchCV(estimator, params[name], scoring='roc_auc', refit=True, n_jobs=-1, cv=10)
clf.fit(data_train, label_train)
print("best params: " + str(clf.best_params_))
print("best scores: " + str(clf.best_score_))
# print(clf.best_estimator_.feature_importance())
results[name] = {'best params': str(clf.best_params_), 'best scores': str(clf.best_score_)}
result_params.append(results)
# evaluate on the dataset
lsvc_params = ast.literal_eval(results["LSVC"]["best params"])
clf_lsvc = svm.LinearSVC(C=lsvc_params["C"], loss=lsvc_params["loss"], penalty=lsvc_params['penalty'])
clf_lsvc.fit(data_train, label_train)
label_predict = clf_lsvc.predict(data_test)
clf_lsvc_score = roc_auc_score(label_test, label_predict)
lsvc_resluts.append(clf_lsvc_score)
clf_lsvc_recall = recall_score(label_test, label_predict)
lsvc_recall.append(clf_lsvc_recall)
clf_lsvc_f1 = f1_score(label_test, label_predict)
lsvc_f1.append(clf_lsvc_f1)
clf_lsvc_precision = precision_score(label_test, label_predict)
lsvc_precision.append(clf_lsvc_precision)
print(clf_lsvc_score)
# print(clf_lsvc.coef_)
# svc_params = ast.literal_eval(results["SVC"]["best params"])
# clf_svc = svm.SVC(C=svc_params['C'], gamma=svc_params['gamma'], kernel=svc_params["kernel"])
# clf_svc.fit(data_train, label_train)
# clf_svc_score = clf_svc.score(data_test, label_test)
# svc_results.append(clf_svc_score)
# print(clf_svc_score)
# print(list(set(result_params)))
with open(r"results_1/opcodes_fcns_lsvc_best_params.json", "w") as bf:
json.dump(result_params, bf)
# with open(r"lsvc_results.txt", "w") as lf:
# for l in lsvc_resluts:
# lf.write("%s\n" % l)
with open(r"results_1/opcodes_fcns_lsvc_results.txt", "w") as sf:
for s in lsvc_resluts:
sf.write("%s\n" % s)
with open(r"results_1/opcodes_fcns_lsvc_recall.txt", "w") as rf:
for r in lsvc_recall:
rf.write("%s\n" % r)
with open(r"results_1/opcodes_fcns_lsvc_f1.txt", "w") as ff:
for f in lsvc_f1:
ff.write("%s\n" % f)
with open(r"results_1/opcodes_fcns_lsvc_precision.txt", "w") as pf:
for p in lsvc_precision:
pf.write("%s\n" % p)
# estimates = clf.predict_proba(data_test)
# y_test += estimates
# acc = accuracy_score()
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src/services/StudentAppServicer.py | comword/TCD20-DP-DeepModel | 0 | 185392 | <reponame>comword/TCD20-DP-DeepModel<filename>src/services/StudentAppServicer.py
import src.gen_proto.student_pb2_grpc as student_pb2__grpc
class StudentAppServicer(student_pb2__grpc.StudentAppServicer):
def __init__(self):
pass
def UpPredictResult(self, request, context):
pass
def GetExams(self, request, context):
pass
def GetPredicts(self, request, context):
pass
def GetUserDetail(self, request, context):
pass
def PutUserDetail(self, request, context):
pass
def StreamVideo(self, request_iterator, context):
pass | [
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steps/make/haxe.py | flipcoder/siege-tools | 6 | 85378 | #!/usr/bin/env python
import os
import sgmake
from common import Status
from common import Support
from common import Settings
from common.Plugin import Plugin
import subprocess
from common import call
def make(project):
cmd = [ 'haxe', 'compile.hxml' ]
try:
call(cmd)
except subprocess.CalledProcessError:
return Status.FAILURE
return Status.SUCCESS
def update(project):
pass
def compatible(project):
support = Support.MASK & (~Support.PROJECT)
if os.path.isfile("compile.hxml"):
support |= Support.PROJECT
return support
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flying_desktop/__main__.py | roee30/flying_desktop | 1 | 127257 | """
Run the Flying Desktop application.
"""
import asyncio
import sys
import threading
import tkinter as tk
from .log import logging_setup
logging_setup()
from .utils import loop
from flying_desktop.app.main_window import AppWindow
def loop_worker(loop_: asyncio.AbstractEventLoop):
"""
Thread for running the asyncio event loop
"""
asyncio.set_event_loop(loop_)
loop_.run_forever()
def main():
loop_thread = threading.Thread(target=loop_worker, args=(loop,), daemon=True)
loop_thread.start()
root = tk.Tk()
app = AppWindow(loop, root)
app.pack(fill="both", expand=True)
root.mainloop()
if __name__ == "__main__":
sys.exit(main())
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fellowcrm/__init__.py | Fellow-Consulting-AG/fellowcrm | 0 | 145236 | from flask import Flask, render_template, request, session
from flask_sqlalchemy import SQLAlchemy
from flask_bcrypt import Bcrypt
from flask_login import LoginManager
from flask_migrate import Migrate, MigrateCommand
from flask_script import Manager
from flask_babel import Babel
import os
from .config import DevelopmentConfig, TestConfig, ProductionConfig, DigitalocenDEV
# database handle
db = SQLAlchemy(session_options={"autoflush": False})
# encryptor handle
bcrypt = Bcrypt()
# manage user login
login_manager = LoginManager()
# function name of the login route that
# tells the path which facilitates authentication
login_manager.login_view = 'users.login'
def run_install(app_ctx):
from fellowcrm.install.routes import install
app_ctx.register_blueprint(install)
return app_ctx
def create_app(config_class=ProductionConfig):
app = Flask(__name__, instance_relative_config=True)
if os.getenv('FLASK_ENV') == 'development':
config_class = DevelopmentConfig()
elif os.getenv('FLASK_ENV') == 'DigitalocenDEV':
config_class = DigitalocenDEV()
elif os.getenv('FLASK_ENV') == 'production':
config_class = ProductionConfig()
elif os.getenv('FLASK_ENV') == 'testing':
config_class = TestConfig()
app.config.from_object(config_class)
configure_extensions(app)
with app.app_context():
# check if the config table exists, otherwise run install
engine = db.get_engine(app)
if not engine.dialect.has_table(engine, 'app_config'):
return run_install(app)
else:
from fellowcrm.settings.models import AppConfig
row = AppConfig.query.first()
if not row:
return run_install(app)
# application is installed so extends the config
from fellowcrm.settings.models import AppConfig, Currency, TimeZone
app_cfg = AppConfig.query.first()
app.config['def_currency'] = Currency.get_currency_by_id(app_cfg.default_currency)
app.config['def_tz'] = TimeZone.get_tz_by_id(app_cfg.default_timezone)
# include the routes
# from fellowcrm import routes
from fellowcrm.main.routes import main
from fellowcrm.users.routes import users
from fellowcrm.leads.routes import leads
from fellowcrm.accounts.routes import accounts
from fellowcrm.contacts.routes import contacts
from fellowcrm.deals.routes import deals
from fellowcrm.activities.routes import activities
from fellowcrm.settings.routes import settings
from fellowcrm.settings.app_routes import app_config
from fellowcrm.reports.routes import reports
from fellowcrm.picklists.routes import picklists
from fellowcrm.upgrade.routes import upgrade
# register routes with blueprint
app.register_blueprint(main)
app.register_blueprint(users)
app.register_blueprint(settings)
app.register_blueprint(app_config)
app.register_blueprint(leads)
app.register_blueprint(accounts)
app.register_blueprint(contacts)
app.register_blueprint(deals)
app.register_blueprint(activities)
app.register_blueprint(reports)
app.register_blueprint(picklists)
app.register_blueprint(upgrade)
return app
def configure_extensions(app):
"""configure flask extensions"""
#jwt.init_app(app)
app.url_map.strict_slashes = False
app.jinja_env.globals.update(zip=zip)
migrate = Migrate(app, db)
babel = Babel(app)
@babel.localeselector
def get_locale():
if 'language' in session:
if session['language'] is not None:
return session['language']
return 'en'
else:
return 'en'
@babel.timezoneselector
def get_timezone():
#user = getattr(g, 'user', None)
#if user is not None:
return user.timezone
manager = Manager(app)
manager.add_command('db', MigrateCommand)
login_manager.init_app(app)
db.init_app(app)
bcrypt.init_app(app)
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main_copy.py | Kevincj/TwitterCopyFollowing | 0 | 111744 | <gh_stars>0
from requests_oauthlib import OAuth1Session
import os
import json
import sys, traceback
import numpy as np
import time
import random
import threading
from datetime import datetime
import configparser
def getOAuth():
global account_name
def load_api_key_set():
config = configparser.ConfigParser()
config.read("api.keys")
return config
api_key_set = load_api_key_set()
oauth = OAuth1Session(
api_key_set["DEFAULT"]["api_key"],
client_secret=api_key_set["DEFAULT"]["api_key_secret"],
resource_owner_key=api_key_set["DEFAULT"]["access_token"],
resource_owner_secret=api_key_set["DEFAULT"]["access_token_secret"]
)
if oauth:
print("Default OAuth OK.")
params = {
"screen_name": account_name
}
response = oauth.get(
"https://api.twitter.com/1.1/lists/list.json",
params = params
)
if response.status_code != 200:
print("Authorizing...")
request_token_url = "https://api.twitter.com/oauth/request_token"
oauth = OAuth1Session(api_key_set["DEFAULT"]['api_key'], client_secret=api_key_set["DEFAULT"]['api_key_secret'])
try:
fetch_response = oauth.fetch_request_token(request_token_url)
except ValueError:
print(
"There may have been an issue with the api_key or api_key_secret you entered."
)
resource_owner_key = fetch_response.get("oauth_token")
resource_owner_secret = fetch_response.get("oauth_token_secret")
print("Got OAuth token: %s" % resource_owner_key)
# Get authorization
base_authorization_url = "https://api.twitter.com/oauth/authorize"
authorization_url = oauth.authorization_url(base_authorization_url)
print("Please authorize via: %s" % authorization_url)
verifier = input("Enter your PIN here:")
# Get the access token
access_token_url = "https://api.twitter.com/oauth/access_token"
oauth = OAuth1Session(
api_key_set["DEFAULT"]["api_key"],
client_secret=api_key_set["DEFAULT"]["api_key_secret"],
resource_owner_key=resource_owner_key,
resource_owner_secret=resource_owner_secret,
verifier=verifier,
)
oauth_tokens = oauth.fetch_access_token(access_token_url)
access_token = oauth_tokens["oauth_token"]
access_token_secret = oauth_tokens["oauth_token_secret"]
# Make the request
oauth = OAuth1Session(
api_key_set["DEFAULT"]["api_key"],
client_secret=api_key_set["DEFAULT"]["api_key_secret"],
resource_owner_key=access_token,
resource_owner_secret=access_token_secret,
)
api_key_set["DEFAULT"]["access_token"] = access_token
api_key_set["DEFAULT"]["access_token_secret"] = access_token_secret
with open("api.keys", 'w') as f:
api_key_set.write(f)
if oauth:
print("OAuth OK.")
return oauth
else:
print("OAuth failed. Please try again.")
return None
def get_list_members(list_id, count = 2000):
ids = []
cursor = -1
while cursor != 0:
params = {
"count": count,
"list_id": list_id,
"cursor": cursor
}
response = oauth.get(
"https://api.twitter.com/1.1/lists/members.json",
params = params
)
if response.status_code == 200:
contents = json.loads(response.content.decode('utf8'))
ids += [str(content['id']) for content in contents['users']]
cursor = contents['next_cursor']
else:
print("get_like_ids", response.status_code, response.content)
break
return ids
def get_user_following(target_user, count = 2000):
ids = []
cursor = -1
while cursor != 0:
params = {
"count": count,
"screen_name": target_user,
"cursor": cursor
}
response = oauth.get(
"https://api.twitter.com/1.1/friends/ids.json",
params = params
)
if response.status_code == 200:
contents = json.loads(response.content.decode('utf8'))
ids += [str(content) for content in contents['ids']]
cursor = contents['next_cursor']
else:
print("get_user_following", response.status_code, response.content)
break
return ids
def copyFollowing(target_user, account):
global oauth
print("Time: %s" % time.ctime())
if type(target_user) == str:
ids = get_user_following(target_user)
else:
ids = target_user
print("%d users followed in total" % (len(ids)))
if type(account) == str:
id_existing = get_user_following(account)
else:
id_existing = account
print("%d users followed existing" % (len(id_existing)))
diff = list(set(ids) - set(id_existing))
print("%d users to add" % len(diff))
err_ret_ids = []
count = 0
limit = False
while diff:
sampled_id = diff[-1]
if sampled_id in pending_users:
diff.pop()
continue
response = oauth.post(
"https://api.twitter.com/1.1/friendships/create.json",
params = {
"user_id": sampled_id
}
)
if response.status_code == 200:
# print("Successfully followed", sampled_id)
response = oauth.post(
"https://api.twitter.com/1.1/friendships/update.json",
params = {
"user_id": sampled_id,
"retweets": "false"
}
)
if response.status_code != 200:
print("Disable retweets", response.status_code, response.content)
err_ret_ids.append(sampled_id)
if json.loads(response.content.decode('utf8'))["errors"][0]["code"] != 167:
break
else:
diff.pop()
count += 1
print(count, "...OK")
id_existing.append(sampled_id)
limit = False
else:
code = json.loads(response.content.decode('utf8'))["errors"][0]["code"]
if code == 160 or code == 162:
print(sampled_id, code)
pending_users.append(sampled_id)
diff.pop()
limit = False
continue
elif code == 161:
print("Last limit:", limit)
if limit: break
print("API limit...")
limit = True
time.sleep(60)
continue
else:
print("Follow", response.status_code, response.content)
break
time.sleep(np.random.randint(256))
with open("following.users", "w") as f:
json.dump(ids, f)
next_hour = None
account_name = sys.argv[1]
oauth = getOAuth()
dt = datetime.now()
while True:
dt = datetime.now()
if not next_hour:
next_hour = dt.hour
else:
print("Current hour: %d, waiting until: %d" % (dt.hour, next_hour))
while dt.hour != next_hour:
dt = datetime.now()
time.sleep(30)
continue
print("Run @", dt)
try:
with open("pending.users", "r") as f:
pending_users = json.load(f)
except:
pending_users = []
try:
with open("following.users", "r") as f:
account = json.load(f)
except:
account = account_name
if len(sys.argv) >= 3:
target = sys.argv[2]
else:
try:
with open("all.users", "r") as f:
target = json.load(f)
except:
target = []
try:
copyFollowing(target_user = target, account = account)
print("Finished.")
except KeyboardInterrupt:
pass
except Exception:
traceback.print_exc()
finally:
print("Saving...")
with open("pending.users", "w") as f:
json.dump(pending_users, f)
time.sleep(3600) | [
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dataingest/main_pub_kafka.py | sodadata/soda-streaming | 22 | 118032 | <reponame>sodadata/soda-streaming<filename>dataingest/main_pub_kafka.py
from queue import Queue
import time
import os
from utils.io import CustomAvroKafkaPublisher
from utils.configurator import RestConfigurator
import logging
# Set proper logging
LOGLEVEL = os.environ.get('LOGLEVEL', 'DEBUG').upper()
logging.basicConfig(format="%(asctime)s | %(levelname)s | %(threadName)s | %(message)s",
level=LOGLEVEL,
datefmt="%H:%M:%S")
if __name__ == "__main__":
#todo: create the customavrokafkapublisher automatically and add queue
all_queues = {"travel": Queue(), "food": Queue()}
travel_kafka_config = {
"bootstrap_server": os.getenv("BOOTSTRAP_SERVER", 'localhost:9092'),
"topic_name": "travel",
"topic_description": "travel"
}
travel_data_generation_rate_msg_sec = int(os.getenv("TRAVEL_RATE_MSG_SEC", 10))
travel_publisher = CustomAvroKafkaPublisher(kafka_config=travel_kafka_config,
data_generation_rate_msg_sec=travel_data_generation_rate_msg_sec,
queue=all_queues["travel"])
food_kafka_config = {
"bootstrap_server": os.getenv("BOOTSTRAP_SERVER", 'localhost:9092'),
"topic_name": "food",
"topic_description": "food"
}
food_data_generation_rate_msg_sec = int(os.getenv("FOOD_RATE_MSG_SEC", 5))
food_publisher = CustomAvroKafkaPublisher(kafka_config=food_kafka_config,
data_generation_rate_msg_sec=food_data_generation_rate_msg_sec,
queue=all_queues["food"])
restconfigthread = RestConfigurator(all_queues=all_queues)
restconfigthread.start()
publishers = [travel_publisher, food_publisher]
for publisher in publishers:
publisher.start()
#publisher.join()
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custom_components/yahoofinance/__init__.py | Nag94/HomeAssistantConfig | 65 | 79755 | """
The Yahoo finance component.
https://github.com/iprak/yahoofinance
"""
from __future__ import annotations
from datetime import timedelta
import logging
from typing import Final, Union
from homeassistant.const import CONF_SCAN_INTERVAL
from homeassistant.core import HomeAssistant
from homeassistant.helpers import discovery
import homeassistant.helpers.config_validation as cv
from homeassistant.helpers.typing import ConfigType
import voluptuous as vol
from custom_components.yahoofinance.coordinator import YahooSymbolUpdateCoordinator
from .const import (
CONF_DECIMAL_PLACES,
CONF_INCLUDE_FIFTY_DAY_VALUES,
CONF_INCLUDE_POST_VALUES,
CONF_INCLUDE_PRE_VALUES,
CONF_INCLUDE_TWO_HUNDRED_DAY_VALUES,
CONF_SHOW_TRENDING_ICON,
CONF_SYMBOLS,
CONF_TARGET_CURRENCY,
DEFAULT_CONF_DECIMAL_PLACES,
DEFAULT_CONF_INCLUDE_FIFTY_DAY_VALUES,
DEFAULT_CONF_INCLUDE_POST_VALUES,
DEFAULT_CONF_INCLUDE_PRE_VALUES,
DEFAULT_CONF_INCLUDE_TWO_HUNDRED_DAY_VALUES,
DEFAULT_CONF_SHOW_TRENDING_ICON,
DOMAIN,
HASS_DATA_CONFIG,
HASS_DATA_COORDINATOR,
SERVICE_REFRESH,
)
_LOGGER = logging.getLogger(__name__)
DEFAULT_SCAN_INTERVAL: Final = timedelta(hours=6)
MINIMUM_SCAN_INTERVAL: Final = timedelta(seconds=30)
BASIC_SYMBOL_SCHEMA = vol.All(cv.string, vol.Upper)
COMPLEX_SYMBOL_SCHEMA = vol.All(
dict,
vol.Schema(
{
vol.Required("symbol"): BASIC_SYMBOL_SCHEMA,
vol.Optional(CONF_TARGET_CURRENCY): BASIC_SYMBOL_SCHEMA,
}
),
)
CONFIG_SCHEMA = vol.Schema(
{
DOMAIN: vol.Schema(
{
vol.Required(CONF_SYMBOLS): vol.All(
cv.ensure_list,
[vol.Any(BASIC_SYMBOL_SCHEMA, COMPLEX_SYMBOL_SCHEMA)],
),
vol.Optional(
CONF_SCAN_INTERVAL, default=DEFAULT_SCAN_INTERVAL
): vol.Any("none", "None", cv.positive_time_period),
vol.Optional(CONF_TARGET_CURRENCY): vol.All(cv.string, vol.Upper),
vol.Optional(
CONF_SHOW_TRENDING_ICON, default=DEFAULT_CONF_SHOW_TRENDING_ICON
): cv.boolean,
vol.Optional(
CONF_DECIMAL_PLACES, default=DEFAULT_CONF_DECIMAL_PLACES
): vol.Coerce(int),
vol.Optional(
CONF_INCLUDE_FIFTY_DAY_VALUES,
default=DEFAULT_CONF_INCLUDE_FIFTY_DAY_VALUES,
): cv.boolean,
vol.Optional(
CONF_INCLUDE_POST_VALUES, default=DEFAULT_CONF_INCLUDE_POST_VALUES
): cv.boolean,
vol.Optional(
CONF_INCLUDE_PRE_VALUES, default=DEFAULT_CONF_INCLUDE_PRE_VALUES
): cv.boolean,
vol.Optional(
CONF_INCLUDE_TWO_HUNDRED_DAY_VALUES,
default=DEFAULT_CONF_INCLUDE_TWO_HUNDRED_DAY_VALUES,
): cv.boolean,
}
)
},
# The complete HA configuration is passed down to`async_setup`, allow the extra keys.
extra=vol.ALLOW_EXTRA,
)
class SymbolDefinition:
"""Symbol definition."""
symbol: str
target_currency: str
def __init__(self, symbol: str, target_currency: Union[str, None] = None) -> None:
"""Create a new symbol definition."""
self.symbol = symbol
self.target_currency = target_currency
def __repr__(self) -> str:
"""Return the representation."""
return f"{self.symbol},{self.target_currency}"
def __eq__(self, other: any) -> bool:
"""Return the comparison."""
return (
isinstance(other, SymbolDefinition)
and self.symbol == other.symbol
and self.target_currency == other.target_currency
)
def __hash__(self) -> int:
"""Make hashable."""
return hash((self.symbol, self.target_currency))
def parse_scan_interval(scan_interval: Union[timedelta, str]) -> timedelta:
"""Parse and validate scan_interval."""
if isinstance(scan_interval, str):
if isinstance(scan_interval, str):
if scan_interval.lower() == "none":
scan_interval = None
else:
raise vol.Invalid(
f"Invalid {CONF_SCAN_INTERVAL} specified: {scan_interval}"
)
elif scan_interval < MINIMUM_SCAN_INTERVAL:
raise vol.Invalid("Scan interval should be at least 30 seconds.")
return scan_interval
def normalize_input(defined_symbols: list) -> tuple[list[str], list[SymbolDefinition]]:
"""Normalize input and remove duplicates."""
symbols = set()
symbol_definitions: list[SymbolDefinition] = []
for value in defined_symbols:
if isinstance(value, str):
if value not in symbols:
symbols.add(value)
symbol_definitions.append(SymbolDefinition(value))
else:
symbol = value["symbol"]
if symbol not in symbols:
symbols.add(symbol)
symbol_definitions.append(
SymbolDefinition(symbol, value.get(CONF_TARGET_CURRENCY))
)
return (list(symbols), symbol_definitions)
async def async_setup(hass: HomeAssistant, config: ConfigType) -> bool:
"""Set up the component."""
domain_config = config.get(DOMAIN, {})
defined_symbols = domain_config.get(CONF_SYMBOLS, [])
symbols, symbol_definitions = normalize_input(defined_symbols)
domain_config[CONF_SYMBOLS] = symbol_definitions
scan_interval = parse_scan_interval(domain_config.get(CONF_SCAN_INTERVAL))
# Populate parsed value into domain_config
domain_config[CONF_SCAN_INTERVAL] = scan_interval
coordinator = YahooSymbolUpdateCoordinator(symbols, hass, scan_interval)
# Refresh coordinator to get initial symbol data
_LOGGER.info(
"Requesting data from coordinator with update interval of %s.", scan_interval
)
await coordinator.async_refresh()
# Pass down the coordinator and config to platforms.
hass.data[DOMAIN] = {
HASS_DATA_COORDINATOR: coordinator,
HASS_DATA_CONFIG: domain_config,
}
async def handle_refresh_symbols(_call) -> None:
"""Refresh symbol data."""
_LOGGER.info("Processing refresh_symbols")
await coordinator.async_request_refresh()
hass.services.async_register(
DOMAIN,
SERVICE_REFRESH,
handle_refresh_symbols,
)
if not coordinator.last_update_success:
_LOGGER.debug("Coordinator did not report any data, requesting async_refresh")
hass.async_create_task(coordinator.async_request_refresh())
hass.async_create_task(
discovery.async_load_platform(hass, "sensor", DOMAIN, {}, config)
)
return True
def convert_to_float(value) -> float | None:
"""Convert specified value to float."""
try:
return float(value)
except: # noqa: E722 pylint: disable=bare-except
return None
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decimal to binary.py | Kshitijkrishnadas/haribol | 0 | 15737 | <gh_stars>0
a=''
n=int(input())
while n != 0:
a=str(n%2)+a
n//=2
print(a)
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hangpy/tests/repositories/test_server_repository.py | luizfernandomeier/hangpy | 5 | 61447 | import unittest
from hangpy.repositories import ServerRepository
class TestServerRepository(unittest.TestCase):
def test_instantiate(self):
server_repository = FakeServerRepository()
self.assertIsNone(server_repository.get_servers())
self.assertIsNone(server_repository.add_server(None))
self.assertIsNone(server_repository.update_server(None))
class FakeServerRepository(ServerRepository):
def get_servers(self):
return ServerRepository.get_servers(self)
def add_server(self, server):
return ServerRepository.add_server(self, server)
def update_server(self, server):
return ServerRepository.update_server(self, server)
if (__name__ == "__main__"):
unittest.main()
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] |
tests/test_softmax.py | kcosta42/Multilayer_Perceptron | 1 | 190230 | <filename>tests/test_softmax.py
import numpy as np
import matplotlib.pyplot as plt
from libft.activations import ReLU, Softmax
from libft.layers import Dense, Input
from libft.losses import BinaryCrossentropy
from libft.metrics import BinaryAccuracy
from libft.models import Sequential
from libft.optimizers import SGD
from libft.preprocessing import to_categorical
np.seterr(all='raise')
X = np.random.rand(500, 30)
X_std = np.copy(X)
X_std[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()
X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()
y = to_categorical(np.random.randint(0, 2, 500))
model = Sequential([
Input(input_shape=(30,)),
Dense(16),
ReLU(),
Dense(2),
Softmax(),
])
model.summary()
model.compile(loss=BinaryCrossentropy(),
optimizer=SGD(learning_rate=1e-4),
metrics=BinaryAccuracy())
epochs = 100
history = model.fit(X_std, y,
batch_size=128,
epochs=epochs,
verbose=1,
validation_split=0.1)
epoch_range = range(1, epochs + 1)
plt.plot(epoch_range, model.losses['training'], c='blue')
plt.plot(epoch_range, model.losses['validation'], c='red', ls='dashed')
plt.xlabel('Epochs')
plt.ylabel('Cost function')
plt.show()
plt.plot(epoch_range, model.metrics['training'], c='blue')
plt.plot(epoch_range, model.metrics['validation'], c='red', ls='dashed')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.show()
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] |
service/__init__.py | 2890841438/fast-index.py | 4 | 1393 | <filename>service/__init__.py
# -*- coding = utf-8 -*-
# @Time: 2020/9/4 18:52
# @Author: dimples_yj
# @File: __init__.py.py
# @Software: PyCharm
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1,
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29901,
10772,
1451,
2817,
13,
2
] |
Source_Code/Python/ConductedTest/case_generator.py | fenglwh/instruments | 0 | 12927 | <gh_stars>0
import json
from labinstrument.SS.CMW500.CMW500_WIFI.CMW500_WIFI import *
if __name__ == '__main__':
new_config_name='emm'
new_config=CMW_WIFI(17).get_parameters()
config=json.load(open('config.txt'))
config[new_config_name]=new_config
json.dump(config,open('config.txt','w')) | [
1,
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303,
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13,
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3166,
9775,
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3945,
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978,
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29918,
2917,
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2917,
29892,
3150,
877,
2917,
29889,
3945,
3788,
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2
] |
gen-po-comm-invoice.py | tschutter/cctools | 0 | 158295 | #!/usr/bin/env python2
"""
Generates a Purchase Order / Commercial Invoice.
All known products are included. If you are never ordering a product
again, then just don't order it. Or use the --exclude-sku option.
"""
from __future__ import print_function
import ConfigParser
import argparse
import datetime
import itertools
import logging
import os
import re
import openpyxl # sudo pip install openpyxl
import cctools
import notify_send_handler
CHECK_FOR_LACK_OF_ANY = False # until most "Any" variants have been added
NUMBER_FORMAT_USD = "$#,##0.00;-$#,##0.00"
RE_SIZE_VARIANT = re.compile("^(Length|Size)$")
# Column numbers of product values.
COL_LINE_NO = 1
COL_SKU = 2
COL_DESCRIPTION = 3
COL_SIZE = 4
COL_PRICE = 5
COL_QTY = 6
COL_TOTAL = 7
COL_HTSUS_NO = 8
COL_INSTRUCTIONS = 9
def set_cell(
worksheet,
row,
col,
value,
font_bold=False,
font_size=11,
alignment_horizontal="general",
alignment_vertical="bottom",
number_format="General"
):
"""Set cell value and style."""
cell = worksheet.cell(row=row, column=col)
cell.value = value
cell.font = openpyxl.styles.Font(bold=font_bold, size=font_size)
cell.alignment = openpyxl.styles.Alignment(
horizontal=alignment_horizontal,
vertical=alignment_vertical
)
if number_format != "General":
cell.number_format = number_format
def col_letter(col):
"""Return column letter for given column."""
return chr(ord("A") + col - 1)
def add_title(worksheet):
"""Add worksheet title."""
set_cell(worksheet, 1, 1, "Purchase Order", font_bold=True, font_size=20)
worksheet.merge_cells(
start_row=1,
start_column=1,
end_row=1,
end_column=3
)
worksheet.row_dimensions[1].height = 25
def set_label_value(
worksheet,
row,
col_label_start,
col_label_end,
col_value_start,
col_value_end,
label,
value
):
"""Add label: value."""
if col_label_start != col_label_end:
worksheet.merge_cells(
start_row=row,
start_column=col_label_start,
end_row=row,
end_column=col_label_end
)
col_label = col_label_start
else:
col_label = col_label_end
set_cell(
worksheet,
row,
col_label,
label,
font_bold=True,
alignment_horizontal="right"
)
if col_value_start != col_value_end:
worksheet.merge_cells(
start_row=row,
start_column=col_value_start,
end_row=row,
end_column=col_value_end
)
col_value = col_value_start
else:
col_value = col_value_end
cell = worksheet.cell(row=row, column=col_value)
cell.set_explicit_value(value) # Force type to be string.
def add_header(args, config, worksheet, row):
"""Add PO/Invoice header."""
col_label_start = 3
col_label_end = 3
col_value_start = col_label_end + 1
col_value_end = col_value_start
set_label_value(
worksheet,
row - 1,
col_label_start,
col_label_end,
col_value_start,
col_value_end,
"Invoice #: ",
args.number
)
row += 1
set_label_value(
worksheet,
row - 1,
col_label_start,
col_label_end,
col_value_start,
col_value_end,
"Associated PO #s: ",
args.number
)
row += 1
date_str = datetime.date.today().strftime("%B %d, %Y")
set_label_value(
worksheet,
row - 1,
col_label_start,
col_label_end,
col_value_start,
col_value_end,
"Date: ",
date_str
)
row += 1
set_label_value(
worksheet,
row - 1,
col_label_start,
col_label_end,
col_value_start,
col_value_end,
"Country of Origin: ",
config.get("invoice", "country_of_origin")
)
row += 1
set_label_value(
worksheet,
row - 1,
col_label_start,
col_label_end,
col_value_start,
col_value_end,
"Manufacturer ID: ",
config.get("invoice", "manufacturer_id")
)
row += 1
first_row = True
for key, value in config.items("invoice"):
if key.startswith("consignee"):
set_label_value(
worksheet,
row - 1,
col_label_start,
col_label_end,
col_value_start,
col_value_end,
"Ultimate Consignee: " if first_row else "",
value
)
first_row = False
row += 1
# Unit of measurement.
set_label_value(
worksheet,
row - 1,
col_label_start,
col_label_end,
col_value_start,
col_value_end,
"Unit of Measurement: ",
config.get("invoice", "unit_of_measurement")
)
row += 1
# Terms of sale.
set_label_value(
worksheet,
row - 1,
col_label_start,
col_label_end,
col_value_start,
col_value_end,
"Currency: ",
config.get("invoice", "currency")
)
row += 1
# Transport and delivery.
set_label_value(
worksheet,
row - 1,
col_label_start,
col_label_end,
col_value_start,
col_value_end,
"Transport and Delivery: ",
config.get("invoice", "transport_and_delivery")
)
row += 1
# Terms of sale.
set_label_value(
worksheet,
row - 1,
col_label_start,
col_label_end,
col_value_start,
col_value_end,
"Terms of Sale: ",
config.get("invoice", "terms_of_sale")
)
row += 1
return row
def add_variant(
worksheet,
row,
lineno,
sku,
description,
size,
cost,
htsus_no
):
"""Add a row for a variant."""
set_cell(worksheet, row, COL_LINE_NO, lineno)
set_cell(
worksheet,
row,
COL_SKU,
sku,
alignment_horizontal="left"
)
set_cell(worksheet, row, COL_DESCRIPTION, description)
set_cell(worksheet, row, COL_SIZE, size)
set_cell(worksheet, row, COL_PRICE, cost, number_format=NUMBER_FORMAT_USD)
total_formula = "=IF({}{}=\"\", \"\", {}{} * {}{})".format(
col_letter(COL_QTY),
row,
col_letter(COL_PRICE),
row,
col_letter(COL_QTY),
row
)
set_cell(
worksheet,
row,
COL_TOTAL,
total_formula,
number_format=NUMBER_FORMAT_USD
)
if htsus_no is not None:
set_cell(worksheet, row, COL_HTSUS_NO, htsus_no)
def get_product_variants(variants, sku):
"""Returns a list of variants for a product."""
product_variants = [
variant for variant in variants
if variant["Product SKU"] == sku and variant["Variant Enabled"] == "Y"
]
product_variants.sort(key=lambda variant: variant["Variant Sort"])
return product_variants
def add_product(worksheet, row, lineno, product, variants):
"""Add row for each variant."""
product_name = product["Product Name"]
sku = product["SKU"]
teaser = cctools.html_to_plain_text(product["Teaser"])
size = product["Size"]
cost = float(product["Cost"])
if "HTSUS No" in product:
htsus_no = product["HTSUS No"]
else:
htsus_no = None
product_variants = get_product_variants(variants, sku)
if len(product_variants) == 0:
description = "{}: {}".format(product_name, teaser)
add_variant(
worksheet,
row,
lineno,
sku,
description,
size,
cost,
htsus_no
)
row += 1
lineno += 1
else:
any_variant_exists = False
for variant in product_variants:
variant_sku = variant["Variant SKU"]
if variant_sku == "ANY" or variant_sku == "VAR":
any_variant_exists = True
variant_sku = "{}-{}".format(sku, variant_sku)
variant_add_cost = float(variant["Variant Add Cost"])
variant_name = variant["Variant Name"]
description = "{} ({}): {}".format(
product_name,
variant_name,
teaser
)
variant_group = variant["Variant Group"]
if RE_SIZE_VARIANT.match(variant_group):
size = variant_name
add_variant(
worksheet,
row,
lineno,
variant_sku,
description,
size,
cost + variant_add_cost,
htsus_no
)
row += 1
lineno += 1
if CHECK_FOR_LACK_OF_ANY and not any_variant_exists:
logging.getLogger().warning(
"No 'Any' or 'Variety' variant exists for {} {}".format(
sku,
product_name
)
)
return row, lineno
def add_products(worksheet, row, cc_browser, products, has_htsus_no):
"""Add row for each product."""
# Add header row.
set_cell(
worksheet,
row,
COL_LINE_NO,
"Line No",
font_bold=True,
alignment_horizontal="right"
)
set_cell(
worksheet,
row,
COL_SKU,
"SKU",
font_bold=True,
alignment_horizontal="left"
)
set_cell(worksheet, row, COL_DESCRIPTION, "Description", font_bold=True)
set_cell(worksheet, row, COL_SIZE, "Size", font_bold=True)
set_cell(
worksheet,
row,
COL_PRICE,
"Price",
font_bold=True,
alignment_horizontal="right"
)
set_cell(
worksheet,
row,
COL_QTY,
"Qty",
font_bold=True,
alignment_horizontal="right"
)
set_cell(
worksheet,
row,
COL_TOTAL,
"Total",
font_bold=True,
alignment_horizontal="right"
)
if has_htsus_no:
set_cell(
worksheet,
row,
COL_HTSUS_NO,
"HTSUS No",
font_bold=True
)
set_cell(
worksheet,
row,
COL_INSTRUCTIONS,
"Item Special Instructions",
font_bold=True
)
row += 1
# Fetch variants list.
variants = cc_browser.get_variants()
# Group products by category.
first_product_row = row
lineno = 1
for _, product_group in itertools.groupby(
products,
key=cc_browser.product_key_by_category
):
# Leave a row for the category name.
category = "unknown"
category_row = row
row += 1
# Add product rows.
for product in product_group:
row, lineno = add_product(
worksheet,
row,
lineno,
product,
variants
)
category = product["Category"]
# Go back and insert category name.
if category == "":
category = "Uncategorized"
set_cell(
worksheet,
category_row,
COL_DESCRIPTION,
category,
font_bold=True
)
last_product_row = row - 1
# Set column widths.
worksheet.column_dimensions[col_letter(COL_LINE_NO)].width = 8
worksheet.column_dimensions[col_letter(COL_SKU)].width = 15
worksheet.column_dimensions[col_letter(COL_DESCRIPTION)].width = 100
worksheet.column_dimensions[col_letter(COL_SIZE)].width = 28
worksheet.column_dimensions[col_letter(COL_PRICE)].width = 7
worksheet.column_dimensions[col_letter(COL_QTY)].width = 5
worksheet.column_dimensions[col_letter(COL_TOTAL)].width = 10
if has_htsus_no:
worksheet.column_dimensions[col_letter(COL_HTSUS_NO)].width = 13
worksheet.column_dimensions[col_letter(COL_INSTRUCTIONS)].width = 30
return row, first_product_row, last_product_row
def set_label_dollar_value(
worksheet,
row,
col_label_start,
col_label_end,
col_total,
label,
value,
font_bold=False
):
"""Add label: value."""
worksheet.merge_cells(
start_row=row,
start_column=col_label_start,
end_row=row,
end_column=col_label_end
)
set_cell(
worksheet,
row,
col_label_start,
label,
font_bold=font_bold,
alignment_horizontal="right"
)
set_cell(
worksheet,
row,
col_total,
value,
number_format=NUMBER_FORMAT_USD,
font_bold=font_bold
)
def add_totals(
worksheet,
config,
row,
col_total,
first_product_row,
last_product_row
):
"""Add subtotals and totals."""
col_label_start = col_total - 3
col_label_end = col_total - 1
# Subtotal.
subtotal_formula = "=SUM({}{}:{}{})".format(
col_letter(col_total),
first_product_row,
col_letter(col_total),
last_product_row
)
set_label_dollar_value(
worksheet,
row,
col_label_start,
col_label_end,
col_total,
"Subtotal:",
subtotal_formula
)
subtotal_row = row
last_adjustment_row = row
row += 1
# Discount.
if config.has_option("invoice", "percent_discount"):
percent_discount = config.getfloat("invoice", "percent_discount")
discount_formula = "={}{} * {}".format(
col_letter(col_total),
subtotal_row,
-percent_discount / 100.0
)
set_label_dollar_value(
worksheet,
row,
col_label_start,
col_label_end,
col_total,
"{}% Discount:".format(percent_discount),
discount_formula
)
row += 1
# Shipping.
set_label_dollar_value(
worksheet,
row,
col_label_start,
col_label_end,
col_total,
"Shipping:",
0.0
)
row += 1
# Adjustments.
last_adjustment_row = row - 1
for key, value in config.items("invoice"):
if key.startswith("adjustment"):
set_label_dollar_value(
worksheet,
row,
col_label_start,
col_label_end,
col_total,
value + ":",
""
)
last_adjustment_row = row
row += 1
# Total.
total_formula = "=SUM({}{}:{}{})".format(
col_letter(col_total),
subtotal_row,
col_letter(col_total),
last_adjustment_row
)
set_label_dollar_value(
worksheet,
row,
col_label_start,
col_label_end,
col_total,
"Total:",
total_formula
)
# Create a data-validation object with list validation.
in_accounting_choices = [
"Not in accounting system",
"Entered into accounting system"
]
validator = openpyxl.worksheet.datavalidation.DataValidation(
type="list",
formula1="\"{}\"".format(",".join(in_accounting_choices)),
allow_blank=False
)
# Add the data-validation object to the worksheet.
worksheet.add_data_validation(validator)
# Set the cell and add to the data-validation object.
cell = worksheet.cell(row=row, column=col_total + 2)
cell.value = in_accounting_choices[0]
validator.add(cell)
row += 1
return row
def add_summary(
worksheet,
row,
first_product_row,
last_product_row,
htsus_numbers
):
"""
Add a section that summarizes total cost by HTSUS number. This
is intended to make the customs inspector's job easier and
therefore less likely to be grumpy and cause problems.
"""
# Section title.
worksheet.merge_cells(
start_row=row,
start_column=COL_PRICE,
end_row=row,
end_column=COL_HTSUS_NO
)
set_cell(
worksheet,
row,
COL_PRICE,
"Summary by HTSUS number:",
font_bold=True
)
row += 1
for htsus_no in sorted(htsus_numbers):
total_range = "{0}{1}:{0}{2}".format(
col_letter(COL_TOTAL),
first_product_row,
last_product_row
)
htsus_no_range = "{0}{1}:{0}{2}".format(
col_letter(COL_HTSUS_NO),
first_product_row,
last_product_row
)
if htsus_no == "":
htsus_no = "Not assigned"
# SUMIF() with "" test does not work.
total_formula = "=SUMPRODUCT({}, {}=\"\")".format(
total_range,
htsus_no_range
)
else:
total_formula = "=SUMIF({}, \"{}\", {})".format(
htsus_no_range,
htsus_no,
total_range,
)
set_cell(
worksheet,
row,
COL_TOTAL,
total_formula,
number_format=NUMBER_FORMAT_USD
)
set_cell(worksheet, row, COL_HTSUS_NO, htsus_no)
row += 1
return row
def add_special_instructions(worksheet, row):
"""Add special instructions."""
col_value_name = 3
# Special instructions.
set_cell(
worksheet,
row,
col_value_name,
"General Special Instructions:",
font_bold=True
)
def add_invoice(args, config, cc_browser, worksheet):
"""Create the PO-Invoice worksheet."""
# Prepare worksheet.
worksheet.title = "PO-Invoice"
# Add title.
add_title(worksheet)
# Add header.
row = 3
row = add_header(args, config, worksheet, row)
# Blank row.
row += 1
# Fetch products list.
products = cc_browser.get_products()
# Remove excluded SKUs.
if args.exclude_skus:
products = [
x for x in products if str(x["SKU"]) not in args.exclude_skus
]
# Sort products by category, product_name.
products = sorted(products, key=cc_browser.product_key_by_cat_and_name)
# Determine if products include HTSUS number.
has_htsus_no = len(products) > 0 and "HTSUS No" in products[0]
# Add products.
row, first_product_row, last_product_row = add_products(
worksheet,
row,
cc_browser,
products,
has_htsus_no
)
# Blank row.
row += 1
# Add subtotals and total.
row = add_totals(
worksheet,
config,
row,
COL_TOTAL,
first_product_row,
last_product_row
)
# Blank row.
row += 1
if has_htsus_no:
# Create list of unique HTSUS numbers.
htsus_numbers = []
for product in products:
if product["HTSUS No"] not in htsus_numbers:
htsus_numbers.append(product["HTSUS No"])
# Add summary by HTSUS.
row = add_summary(
worksheet,
row,
first_product_row,
last_product_row,
htsus_numbers
)
# Blank row.
row += 1
# Add special instructions.
add_special_instructions(worksheet, row)
def add_instructions(config, worksheet):
"""Create the Instructions worksheet."""
# Prepare worksheet.
worksheet.title = "Instructions"
col_instruction = 1
row = 1
# Add instructions.
for key, value in config.items("invoice"):
if key.startswith("instruction"):
if value == "[EMPTY]":
row += 1
continue
bold = False
if value.startswith("[BOLD]"):
bold = True
value = value[6:]
set_cell(worksheet, row, col_instruction, value, font_bold=bold)
row += 1
# Set column widths.
worksheet.column_dimensions[col_letter(col_instruction)].width = 120
def generate_xlsx(args, config, cc_browser):
"""Generate the XLS file."""
# Construct a document.
workbook = openpyxl.workbook.Workbook()
# Create PO-Invoice worksheet.
add_invoice(args, config, cc_browser, workbook.worksheets[0])
# Create Instructions worksheet.
add_instructions(config, workbook.create_sheet())
# Write to file.
workbook.save(args.xlsx_filename)
def main():
"""main"""
default_config = os.path.join(
os.path.dirname(os.path.abspath(__file__)),
"cctools.cfg"
)
now = datetime.datetime.now()
default_number = now.strftime("%y%m%d00")
default_xlsx_filename = now.strftime("%Y-%m-%d-PurchaseOrder.xlsx")
arg_parser = argparse.ArgumentParser(
description="Generates a Purchase Order / Commercial Invoice."
)
arg_parser.add_argument(
"--config",
action="store",
dest="config",
metavar="FILE",
default=default_config,
help="configuration filename (default=%(default)s)"
)
arg_parser.add_argument(
"--number",
action="store",
dest="number",
metavar="NUM",
default=default_number,
help="PO/Invoice number (default=%(default)s)"
)
arg_parser.add_argument(
"--outfile",
action="store",
dest="xlsx_filename",
metavar="FILE",
default=default_xlsx_filename,
help="output XLSX filename (default=%(default)s)"
)
arg_parser.add_argument(
"--exclude-sku",
action="append",
dest="exclude_skus",
metavar="SKU",
help="exclude SKU from output"
)
arg_parser.add_argument(
"--verbose",
action="store_true",
default=False,
help="display progress messages"
)
# Parse command line arguments.
args = arg_parser.parse_args()
# Configure logging.
logging.basicConfig(
level=logging.INFO if args.verbose else logging.WARNING
)
logger = logging.getLogger()
# Also log using notify-send if it is available.
if notify_send_handler.NotifySendHandler.is_available():
logger.addHandler(
notify_send_handler.NotifySendHandler(
os.path.splitext(os.path.basename(__file__))[0]
)
)
# Read config file.
config = ConfigParser.RawConfigParser()
config.readfp(open(args.config))
# Create a connection to CoreCommerce.
cc_browser = cctools.CCBrowser(
config.get("website", "base_url"),
config.get("website", "username"),
config.get("website", "password")
)
# Generate spreadsheet.
logger.debug("Generating {}\n".format(os.path.abspath(args.xlsx_filename)))
generate_xlsx(args, config, cc_browser)
logger.debug("Generation complete")
return 0
if __name__ == "__main__":
main()
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LeetCode-All-Solution/Python3/LC-0061-Rotate-List.py | YuweiYin/Algorithm_YuweiYin | 0 | 100693 | #!/usr/bin/env python
# -*- coding:utf-8 -*-
"""=================================================================
@Project : Algorithm_YuweiYin/LeetCode-All-Solution/Python3
@File : LC-0061-Rotate-List.py
@Author : [YuweiYin](https://github.com/YuweiYin)
@Date : 2022-03-11
=================================================================="""
import sys
import time
from typing import List, Optional
"""
LeetCode - 0061 - (Medium) - Rotate List
https://leetcode.com/problems/rotate-list/
Description & Requirement:
Given the head of a linked list,
rotate the list to the right by k places.
Example 1:
Input: head = [1,2,3,4,5], k = 2
Output: [4,5,1,2,3]
Example 2:
Input: head = [0,1,2], k = 4
Output: [2,0,1]
Constraints:
The number of nodes in the list is in the range [0, 500].
-100 <= Node.val <= 100
0 <= k <= 2 * 10^9
"""
# Definition for singly-linked list.
class ListNode:
def __init__(self, val=0, next=None):
self.val = val
self.next = next # this means (by default): end_node.next == None
@staticmethod
def build_singly_linked_list(val_list: List[int]):
if not isinstance(val_list, list) or len(val_list) <= 0:
return None
head_node = ListNode(val=val_list[0])
ptr = head_node
len_val = len(val_list)
val_index = 1
while val_index < len_val:
new_node = ListNode(val=val_list[val_index]) # create new node
ptr.next = new_node # singly link
ptr = new_node # move
val_index += 1
return head_node
@staticmethod
def show_val_singly_linked_list(head_node) -> None:
# exception case
if (not isinstance(head_node, ListNode)) and (head_node is not None):
return None # Error head_node type
if not isinstance(head_node, ListNode):
return None # Error n type or needn't delete
ptr = head_node
while ptr:
print(ptr.val)
ptr = ptr.next
class Solution:
def rotateRight(self, head: Optional[ListNode], k: int) -> Optional[ListNode]:
# exception case
if not isinstance(head, ListNode):
return None # Error head type
if not isinstance(head.next, ListNode):
return head # only one element
assert isinstance(k, int) and k >= 0
if k == 0:
return head
# main method: (scan, record the head, tail, and K-th node, where K = k % len(singly-linked list))
return self._rotateRight(head, k)
def _rotateRight(self, head_node: Optional[ListNode], k: int) -> Optional[ListNode]:
assert isinstance(head_node, ListNode) and isinstance(head_node.next, ListNode)
pseudo_head = ListNode(val=sys.maxsize, next=head_node) # Constraint: -100 <= Node.val <= 100
tail = pseudo_head
sll_len = 0
while isinstance(tail.next, ListNode): # get len(singly-linked list)
sll_len += 1
tail = tail.next
# now ptr is the tail node
k %= sll_len
if k == 0:
return head_node
# find the two breaking nodes
breaking_left = pseudo_head
step_counter = sll_len - k
for _ in range(step_counter):
breaking_left = breaking_left.next
breaking_right = breaking_left.next
# head...breaking_left breaking_right...tail -> breaking_right...tail head...breaking_left
pseudo_head.next = breaking_right
breaking_left.next = None
tail.next = head_node
return pseudo_head.next
def main():
# Example 1: Output: [4,5,1,2,3]
# head = [1, 2, 3, 4, 5]
# k = 2
# Example 2: Output: [2,0,1]
head = [0, 1, 2]
k = 4
head_node = ListNode.build_singly_linked_list(head)
# init instance
solution = Solution()
# run & time
start = time.process_time()
ans = solution.rotateRight(head_node, k)
end = time.process_time()
# show answer
print('\nAnswer:')
# print(ans.val)
ListNode.show_val_singly_linked_list(ans)
# show time consumption
print('Running Time: %.5f ms' % ((end - start) * 1000))
if __name__ == "__main__":
sys.exit(main())
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app/health.py | statisticsnorway/bip-initializer | 3 | 1608701 | from fastapi import APIRouter
router = APIRouter()
@router.get("/health/alive")
async def alive():
return {"status": "I'm not dead!"}
@router.get("/health/ready")
async def ready():
return {"status": "Ready"}
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robot.py | dragonrobotics/2018-PowerUp | 2 | 23314 | import wpilib
import constants
import swerve
import lift
import winch
import sys
from teleop import Teleop
from autonomous.baseline_simple import Autonomous
from sensors.imu import IMU
def log(src, msg):
try:
full_msg = "[{:.3f}] [{}] {}".format(
wpilib.Timer.getMatchTime(), str(src), str(msg)
)
print(full_msg, file=sys.stderr)
except: # noqa: E772
full_msg = "[{:.3f}] [log] Caught exception when logging: {} {}".format( # noqa: E501
wpilib.Timer.getMatchTime(),
str(sys.exc_info()[0]),
str(sys.exc_info()[1])
)
print(full_msg, file=sys.stderr)
def log_exception(src, locstr):
# i.e. caught {ValueError} {in my_method}: {could not cast X to Y}
log(src, "Caught {} {}: {}".format(
str(sys.exc_info()[0]), locstr, str(sys.exc_info()[1])
))
class Robot(wpilib.IterativeRobot):
def robotInit(self):
constants.load_control_config()
wpilib.CameraServer.launch('driver_vision.py:main')
self.autoPositionSelect = wpilib.SendableChooser()
self.autoPositionSelect.addDefault('Middle-Baseline', 'Middle-Baseline')
self.autoPositionSelect.addObject('Middle-Placement', 'Middle-Placement') # noqa: E501
self.autoPositionSelect.addObject('Left', 'Left')
self.autoPositionSelect.addObject('Right', 'Right')
wpilib.SmartDashboard.putData(
'Robot Starting Position',
self.autoPositionSelect)
self.drivetrain = swerve.SwerveDrive(
constants.chassis_length,
constants.chassis_width,
constants.swerve_config
)
self.drivetrain.load_config_values()
self.lift = lift.ManualControlLift(
constants.lift_ids['left'],
constants.lift_ids['right'],
constants.lift_limit_channel,
constants.start_limit_channel
)
self.winch = winch.Winch(
constants.winch_id
)
self.throttle = wpilib.Joystick(1)
self.claw = lift.Claw(
constants.claw_id,
constants.claw_follower_id
)
self.imu = IMU(wpilib.SPI.Port.kMXP)
self.sd_update_timer = wpilib.Timer()
self.sd_update_timer.reset()
self.sd_update_timer.start()
def disabledInit(self):
pass
def disabledPeriodic(self):
try:
self.lift.load_config_values()
self.drivetrain.load_config_values()
except: # noqa: E772
log_exception('disabled', 'when loading config')
try:
self.drivetrain.update_smart_dashboard()
self.imu.update_smart_dashboard()
self.lift.update_smart_dashboard()
self.winch.update_smart_dashboard()
wpilib.SmartDashboard.putNumber(
"Throttle Pos", self.throttle.getRawAxis(constants.liftAxis)
)
except: # noqa: E772
log_exception('disabled', 'when updating SmartDashboard')
try:
self.lift.checkLimitSwitch()
pass
except: # noqa: E772
log_exception('disabled', 'when checking lift limit switch')
self.drivetrain.update_smart_dashboard()
def autonomousInit(self):
try:
self.drivetrain.load_config_values()
self.lift.load_config_values()
except: # noqa: E772
log_exception('auto-init', 'when loading config')
self.autoPos = None
try:
self.autoPos = self.autoPositionSelect.getSelected()
except: # noqa: E772
self.autoPos = None
log_exception('auto-init', 'when getting robot start position')
try:
if self.autoPos is not None and self.autoPos != 'None':
self.auto = Autonomous(self, self.autoPos)
else:
log('auto-init', 'Disabling autonomous...')
except: # noqa: E772
log_exception('auto-init', 'in Autonomous constructor')
try:
self.lift.checkLimitSwitch()
pass
except: # noqa: E772
log_exception('auto-init', 'when checking lift limit switch')
def autonomousPeriodic(self):
try:
if self.sd_update_timer.hasPeriodPassed(0.5):
self.auto.update_smart_dashboard()
self.imu.update_smart_dashboard()
self.drivetrain.update_smart_dashboard()
self.lift.update_smart_dashboard()
self.winch.update_smart_dashboard()
except: # noqa: E772
log_exception('auto', 'when updating SmartDashboard')
try:
if self.autoPos is not None and self.autoPos != 'None':
self.auto.periodic()
except: # noqa: E772
# Stop everything.
self.drivetrain.immediate_stop()
self.lift.setLiftPower(0)
self.claw.set_power(0)
self.winch.stop()
log_exception('auto', 'in auto :periodic()')
try:
self.lift.checkLimitSwitch()
pass
except: # noqa: E772
log_exception('auto', 'when checking lift limit switch')
def teleopInit(self):
try:
self.teleop = Teleop(self)
except: # noqa: E772
log_exception('teleop-init', 'in Teleop constructor')
try:
self.drivetrain.load_config_values()
self.lift.load_config_values()
constants.load_control_config()
except: # noqa: E772
log_exception('teleop-init', 'when loading config')
try:
self.lift.checkLimitSwitch()
pass
except: # noqa: E772
log_exception('teleop-init', 'when checking lift limit switch')
def teleopPeriodic(self):
try:
self.teleop.drive()
except: # noqa: E772
log_exception('teleop', 'in drive control')
self.drivetrain.immediate_stop()
try:
self.teleop.buttons()
except: # noqa: E772
log_exception('teleop', 'in button handler')
try:
self.teleop.lift_control()
except: # noqa: E772
log_exception('teleop', 'in lift_control')
self.lift.setLiftPower(0)
try:
self.teleop.claw_control()
except: # noqa: E772
log_exception('teleop', 'in claw_control')
self.claw.set_power(0)
try:
self.teleop.winch_control()
except: # noqa: E772
log_exception('teleop', 'in winch_control')
self.winch.stop()
try:
self.lift.checkLimitSwitch()
pass
except: # noqa: E772
log_exception('teleop', 'in lift.checkLimitSwitch')
if self.sd_update_timer.hasPeriodPassed(0.5):
try:
constants.load_control_config()
self.drivetrain.load_config_values()
self.lift.load_config_values()
except: # noqa: E772
log_exception('teleop', 'when loading config')
try:
self.drivetrain.update_smart_dashboard()
self.teleop.update_smart_dashboard()
self.imu.update_smart_dashboard()
self.lift.update_smart_dashboard()
self.winch.update_smart_dashboard()
except: # noqa: E772
log_exception('teleop', 'when updating SmartDashboard')
# for module in self.drivetrain.modules:
# module.set_steer_angle(0)
if __name__ == "__main__":
wpilib.run(Robot)
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python/mzcloud/models/historical_deployment_delta.py | MaterializeInc/cloud-sdks | 0 | 115646 | from typing import Any, BinaryIO, Dict, List, Optional, TextIO, Tuple, Type, TypeVar, cast
import attr
from ..models.historical_deployment_change import HistoricalDeploymentChange
from ..models.historical_deployment_metadata import HistoricalDeploymentMetadata
from ..types import UNSET, Unset
T = TypeVar("T", bound="HistoricalDeploymentDelta")
@attr.s(auto_attribs=True)
class HistoricalDeploymentDelta:
""" """
changes: HistoricalDeploymentChange
metadata: HistoricalDeploymentMetadata
additional_properties: Dict[str, Any] = attr.ib(init=False, factory=dict)
def to_dict(self) -> Dict[str, Any]:
changes = self.changes.to_dict()
metadata = self.metadata.to_dict()
field_dict: Dict[str, Any] = {}
field_dict.update(self.additional_properties)
field_dict.update(
{
"changes": changes,
"metadata": metadata,
}
)
return field_dict
@classmethod
def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:
d = src_dict.copy()
changes = HistoricalDeploymentChange.from_dict(d.pop("changes"))
metadata = HistoricalDeploymentMetadata.from_dict(d.pop("metadata"))
historical_deployment_delta = cls(
changes=changes,
metadata=metadata,
)
historical_deployment_delta.additional_properties = d
return historical_deployment_delta
@property
def additional_keys(self) -> List[str]:
return list(self.additional_properties.keys())
def __getitem__(self, key: str) -> Any:
return self.additional_properties[key]
def __setitem__(self, key: str, value: Any) -> None:
self.additional_properties[key] = value
def __delitem__(self, key: str) -> None:
del self.additional_properties[key]
def __contains__(self, key: str) -> bool:
return key in self.additional_properties
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scripts/labtainer-instructor/bin/regresstest.py | jakuta-tech/Labtainers | 0 | 62472 | <gh_stars>0
#!/usr/bin/env python3
'''
This software was created by United States Government employees at
The Center for Cybersecurity and Cyber Operations (C3O)
at the Naval Postgraduate School NPS. Please note that within the
United States, copyright protection is not available for any works
created by United States Government employees, pursuant to Title 17
United States Code Section 105. This software is in the public
domain and is not subject to copyright.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
'''
# Filename: regresstest.py
# Description:
# Regression Testing script. This script will make use of labutils.
#
#
import sys
import os
import getpass
import time
import imp
gradelab = imp.load_source('gradelab', 'bin/gradelab')
instructor_cwd = os.getcwd()
student_cwd = instructor_cwd.replace('labtainer-instructor', 'labtainer-student')
# Append Student CWD to sys.path
sys.path.append(student_cwd+"/bin")
import labutils
import logging
import LabtainerLogging
import ParseLabtainerConfig
def usage():
sys.stderr.write("Usage: regresstest.py [<labname> | -a <labname>]\n")
sys.stderr.write(" <labname> : regression test on <labname> only\n")
sys.stderr.write(" -a <labname> : continue running regression test from <labname>\n")
sys.exit(1)
LABS_ROOT = os.path.abspath('../../labs')
def compareGrades(GradesGold, Grades):
GradesGoldLines = {}
GradesLines = {}
with open(GradesGold) as gradesgoldfile:
for line in gradesgoldfile:
linestrip = line.strip()
if not linestrip or linestrip.startswith("#"):
continue
linetoken = linestrip.split()
student_email = linetoken[0]
if "_at_" in student_email:
if student_email in GradesGoldLines:
logger.error("GradesGold file error: Multiple entries for the same student's e-mail %s" % student_email)
return False
else:
new_line = line.strip().replace(" ", "")
GradesGoldLines[student_email] = new_line
with open(Grades) as gradesfile:
for line in gradesfile:
linestrip = line.strip()
if not linestrip or linestrip.startswith("#"):
continue
linetoken = linestrip.split()
student_email = linetoken[0]
if "_at_" in student_email:
if student_email in GradesLines:
logger.error("Grades file error: Multiple entries for the same student's e-mail %s" % student_email)
return False
else:
new_line = line.strip().replace(" ", "")
GradesLines[student_email] = new_line
if GradesGoldLines == GradesLines:
return True
else:
return False
def RegressTest(lab_path, standard, logger):
labname = os.path.basename(lab_path)
labtainer_config_dir = os.path.join(os.path.dirname(os.path.dirname(lab_path)), 'config', 'labtainer.config')
labtainer_config = ParseLabtainerConfig.ParseLabtainerConfig(labtainer_config_dir, logger)
labutils.is_valid_lab(lab_path)
regresstest_lab_path = os.path.join(labtainer_config.testsets_root, labname, standard)
host_home_xfer = os.path.join(labtainer_config.host_home_xfer, labname)
logger.debug("Host Xfer directory for labname %s is %s" % (labname, host_home_xfer))
logger.debug("Regression Test path for labname %s is %s" % (labname, regresstest_lab_path))
GradesGold = "%s/%s.grades.txt" % (regresstest_lab_path, labname)
username = getpass.getuser()
Grades = "/home/%s/%s/%s.grades.txt" % (username, host_home_xfer, labname)
logger.debug("GradesGold is %s - Grades is %s" % (GradesGold, Grades))
is_regress_test = standard
check_watermark = False
auto_grade = True
debug_grade = False
gradelab.doGrade(labname, False, False, True, False, regress_test=GradesGold)
# for name, container in start_config.containers.items():
# mycontainer_name = container.full_name
# container_user = container.user
#
# if mycontainer_name == start_config.grade_container:
# logger.debug('about to RunInstructorCreateDradeFile for container %s' % start_config.grade_container)
# RunInstructorCreateGradeFile(start_config.grade_container, container_user, labname, check_watermark)
# Pass 'True' to ignore_stop_error (i.e., ignore stop error)
CompareResult = False
# GradesGold and Grades must exist
logger.debug('compare %s to %s' % (GradesGold, Grades))
if not os.path.exists(GradesGold):
logger.error("GradesGold %s file does not exist!" % GradesGold)
elif not os.path.exists(Grades):
logger.error("Grades %s file does not exist!" % Grades)
else:
CompareResult = compareGrades(GradesGold, Grades)
return CompareResult
def main():
labnamelist = []
num_args = len(sys.argv)
choplist = False
if num_args == 1:
labnamelist = os.listdir(LABS_ROOT)
elif num_args == 2:
labnamelist.append(sys.argv[1])
elif num_args == 3:
dash_a = sys.argv[1]
if dash_a != "-a":
usage()
labnamelist = os.listdir(LABS_ROOT)
labnamestart = sys.argv[2]
if labnamestart not in labnamelist:
sys.stderr.write("Using non-existent <labname> with -a option!\n")
usage()
choplist = True
else:
usage()
finallabnamelist = []
if choplist:
startfound = False
for labname in sorted(labnamelist):
if not startfound:
if labname == labnamestart:
finallabnamelist.append(labname)
startfound = True
else:
continue
else:
finallabnamelist.append(labname)
else:
finallabnamelist = labnamelist
for labname in sorted(finallabnamelist):
labutils.logger = LabtainerLogging.LabtainerLogging("labtainer-regress.log", labname, "../../config/labtainer.config")
labutils.logger.info("Begin logging regresstest.py for %s lab" % labname)
labutils.logger.debug("Current name is (%s)" % labname)
fulllabname = os.path.join(LABS_ROOT, labname)
if labname == "etc" or labname == "bin":
labutils.logger.debug("skipping etc or bin")
continue
if os.path.isdir(fulllabname):
labutils.logger.debug("(%s) is directory - assume (%s) is a labname" % (fulllabname, labname))
# RegressTest will do test following:
dir_path = os.path.dirname(os.path.realpath(__file__))
dir_path = dir_path[:dir_path.index("scripts")]
dir_path += "testsets/labs/" + labname
if not os.path.isdir(dir_path):
labutils.logger.info("no tests found for "+labname)
continue
crude_standards = os.listdir(dir_path)
standards = []
for items in crude_standards:
if "." not in items:
standards.append(items)
lab_path = os.path.join(LABS_ROOT, labname)
if len(standards) == 0:
print('Did not find any subdirectories under %s' % lab_path)
print('Test paths should be testsets/labs/[lab]/GOLD/...')
for standard in standards:
RegressTestResult = RegressTest(lab_path, standard, labutils.logger)
if RegressTestResult == False:
# False means grades.txt.GOLD != grades.txt, output error then break
print("RegressTest fails on %s lab %s" % (labname, standard))
sys.exit(1)
else:
print("RegressTest on %s lab SUCCESS %s" % (labname, standard))
return 0
if __name__ == '__main__':
sys.exit(main())
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lambdata_robdbennett/__example_module__.py | RobDBennett/lambdata | 0 | 71585 | #!/usr/bin/env python
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import plot_confusion_matrix
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.impute import SimpleImputer
import category_encoders as ce
x=2
def increment(number):
"""A toy function that increases a given number by 1"""
return number + 1
class Animal:
"""A toy Class layout, designed to create an animal.
Parameters:
-----------
: name- The name of the critter.
: weight- the weight, in pounds, of the critter.
: diet_type- what the critter eats.
Outputs:
-----------
How the animal runs.
"""
def __init__(self, name, weight, diet_type):
self.name = str(name)
if weight > 0:
self.weight = weight
else:
raise Exception
self.diet_type = diet_type
def run(self):
return 'Vroom!'
class Tiger(Animal):
"""A toy class that inherits the attributes of the Animal class.
Parameters:
----------
: num_stripes- The number of stripes on the tiger.
Outputs:
----------
What does the tiger say?
"""
def __init__(self, name, weight, diet_type, num_stripes):
super().__init__(name, weight, diet_type)
self.num_stripes = num_stripes
def say_great(self):
return 'Its Greeattt!'
def split_it(dataframe):
"""A function that takes a dataframe and splits into a train
, validate, test. Test is 15% of the total, validate is 20%
of the remainder.
This will return the variables of train, val, test."""
base, test = train_test_split(dataframe, test_size=.15)
train, val = train_test_split(base, test_size=.2)
return train, val, test
def pipe_it(train, val, features, target):
"""A function that inputs train, val, features and target. It is
recommended that you establish features and target as lists before
running this function.
The function will break the train and validate database into the
X and y categories, apply a Logistic Regression machine learning
function in a pipeline. The pipeline also applies an ordinal encoder,
an imputer, and scaler. This isn't appropriate for all machine
learning applications.
The function returns the pipe, X_val, y_val variables, for easy
scoring or graphing."""
pipe = make_pipeline(ce.OrdinalEncoder(),
SimpleImputer(),
StandardScaler(),
LogisticRegression(max_iter=200))
X_train = train[features]
X_val = val[features]
y_train = train[target]
y_val = val[target]
pipe.fit(X_train, y_train)
return pipe, X_val, y_val
def make_df(csv):
"""A proof of concept function that turns a csv file into a
Pandas database."""
df = pd.read_csv(csv)
return df
def confuse_me(train, val, features, target):
"""A function that takes a train, validate database, with listed features
and target. It runs this through a pipeline, and then produces a
confusion matrix."""
pipe, X_val, y_val = pipe_it(train, val, features, target)
return plot_confusion_matrix(pipe, X_val, y_val, values_format='.0f', xticks_rotation='vertical')
COLORS = ('Blue', 'Orange', 'Green', 'Red', 'Black', 'Cyan', 'Purple')
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] |
tests/unit/test_plugin_type_manager.py | thijskramer/flake8 | 2,013 | 195654 | """Tests for flake8.plugins.manager.PluginTypeManager."""
from unittest import mock
import pytest
from flake8 import exceptions
from flake8.plugins import manager
TEST_NAMESPACE = "testing.plugin-type-manager"
def create_plugin_mock(raise_exception=False):
"""Create an auto-spec'd mock of a flake8 Plugin."""
plugin = mock.create_autospec(manager.Plugin, instance=True)
if raise_exception:
plugin.load_plugin.side_effect = exceptions.FailedToLoadPlugin(
plugin_name="T101",
exception=ValueError("Test failure"),
)
return plugin
def create_mapping_manager_mock(plugins):
"""Create a mock for the PluginManager."""
# Have a function that will actually call the method underneath
def fake_map(func):
for plugin in plugins.values():
yield func(plugin)
# Mock out the PluginManager instance
manager_mock = mock.Mock(spec=["map"])
# Replace the map method
manager_mock.map = fake_map
# Store the plugins
manager_mock.plugins = plugins
return manager_mock
class FakeTestType(manager.PluginTypeManager):
"""Fake PluginTypeManager."""
namespace = TEST_NAMESPACE
@mock.patch("flake8.plugins.manager.PluginManager", autospec=True)
def test_instantiates_a_manager(PluginManager): # noqa: N803
"""Verify we create a PluginManager on instantiation."""
FakeTestType()
PluginManager.assert_called_once_with(TEST_NAMESPACE, local_plugins=None)
@mock.patch("flake8.plugins.manager.PluginManager", autospec=True)
def test_proxies_names_to_manager(PluginManager): # noqa: N803
"""Verify we proxy the names attribute."""
PluginManager.return_value = mock.Mock(names=["T100", "T200", "T300"])
type_mgr = FakeTestType()
assert type_mgr.names == ["T100", "T200", "T300"]
@mock.patch("flake8.plugins.manager.PluginManager", autospec=True)
def test_proxies_plugins_to_manager(PluginManager): # noqa: N803
"""Verify we proxy the plugins attribute."""
PluginManager.return_value = mock.Mock(plugins=["T100", "T200", "T300"])
type_mgr = FakeTestType()
assert type_mgr.plugins == ["T100", "T200", "T300"]
def test_generate_call_function():
"""Verify the function we generate."""
optmanager = object()
plugin = mock.Mock(method_name=lambda x: x)
func = manager.PluginTypeManager._generate_call_function(
"method_name",
optmanager,
)
assert callable(func)
assert func(plugin) is optmanager
@mock.patch("flake8.plugins.manager.PluginManager", autospec=True)
def test_load_plugins(PluginManager): # noqa: N803
"""Verify load plugins loads *every* plugin."""
# Create a bunch of fake plugins
plugins = {"T10%i" % i: create_plugin_mock() for i in range(8)}
# Return our PluginManager mock
PluginManager.return_value.plugins = plugins
type_mgr = FakeTestType()
# Load the plugins (do what we're actually testing)
type_mgr.load_plugins()
# Assert that our closure does what we think it does
for plugin in plugins.values():
plugin.load_plugin.assert_called_once_with()
assert type_mgr.plugins_loaded is True
@mock.patch("flake8.plugins.manager.PluginManager")
def test_load_plugins_fails(PluginManager): # noqa: N803
"""Verify load plugins bubbles up exceptions."""
plugins_list = [create_plugin_mock(i == 1) for i in range(8)]
plugins = {"T10%i" % i: plugin for i, plugin in enumerate(plugins_list)}
# Return our PluginManager mock
PluginManager.return_value.plugins = plugins
type_mgr = FakeTestType()
with pytest.raises(exceptions.FailedToLoadPlugin):
type_mgr.load_plugins()
# Assert we didn't finish loading plugins
assert type_mgr.plugins_loaded is False
# Assert the first two plugins had their load_plugin method called
plugins_list[0].load_plugin.assert_called_once_with()
plugins_list[1].load_plugin.assert_called_once_with()
# Assert the rest of the plugins were not loaded
for plugin in plugins_list[2:]:
assert plugin.load_plugin.called is False
@mock.patch("flake8.plugins.manager.PluginManager")
def test_register_options(PluginManager): # noqa: N803
"""Test that we map over every plugin to register options."""
plugins = {"T10%i" % i: create_plugin_mock() for i in range(8)}
# Return our PluginManager mock
PluginManager.return_value = create_mapping_manager_mock(plugins)
optmanager = object()
type_mgr = FakeTestType()
type_mgr.register_options(optmanager)
for plugin in plugins.values():
plugin.register_options.assert_called_with(optmanager)
@mock.patch("flake8.plugins.manager.PluginManager")
def test_provide_options(PluginManager): # noqa: N803
"""Test that we map over every plugin to provide parsed options."""
plugins = {"T10%i" % i: create_plugin_mock() for i in range(8)}
# Return our PluginManager mock
PluginManager.return_value = create_mapping_manager_mock(plugins)
optmanager = object()
options = object()
type_mgr = FakeTestType()
type_mgr.provide_options(optmanager, options, [])
for plugin in plugins.values():
plugin.provide_options.assert_called_with(optmanager, options, [])
@mock.patch("flake8.plugins.manager.PluginManager", autospec=True)
def test_proxy_contains_to_managers_plugins_dict(PluginManager): # noqa: N803
"""Verify that we proxy __contains__ to the manager's dictionary."""
plugins = {"T10%i" % i: create_plugin_mock() for i in range(8)}
# Return our PluginManager mock
PluginManager.return_value.plugins = plugins
type_mgr = FakeTestType()
for i in range(8):
key = "<KEY>
assert key in type_mgr
@mock.patch("flake8.plugins.manager.PluginManager")
def test_proxies_getitem_to_managers_plugins_dict(PluginManager): # noqa: N803
"""Verify that we can use the PluginTypeManager like a dictionary."""
plugins = {"T10%i" % i: create_plugin_mock() for i in range(8)}
# Return our PluginManager mock
PluginManager.return_value.plugins = plugins
type_mgr = FakeTestType()
for i in range(8):
key = "<KEY>
assert type_mgr[key] is plugins[key]
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web/index/migrations/0003_auto_20200407_1756.py | laozhudetui/LSpider | 311 | 70615 | <reponame>laozhudetui/LSpider<gh_stars>100-1000
# Generated by Django 3.0.1 on 2020-04-07 09:56
import datetime
from django.db import migrations, models
from django.utils.timezone import utc
class Migration(migrations.Migration):
dependencies = [
('index', '0002_scantask_last_scan_time'),
]
operations = [
migrations.AlterField(
model_name='scantask',
name='last_scan_time',
field=models.DateTimeField(default=datetime.datetime(2020, 4, 7, 9, 56, 27, 101552, tzinfo=utc)),
),
]
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] |
Hl7_Parser.py | AnaniSkywalker/HL7_Parser | 1 | 53455 | ## Author: <NAME>
import json
import io
import os
import re
from hl7apy.parser import parse_message
from hl7apy.exceptions import UnsupportedVersion
#receives the name of the file and reads the messages in the file
def readMessageFile(filename):
#read the file
message = open(filename, 'r').read()
print("Step 1: File read successfully")
return message
#This method splits the 3 messages based on the blank line that is between messages
def splitMessages(strmsg):
messageslist = re.split('\\n\\n', strmsg)
print("Step 2: Messages splitted successfully")
return messageslist
def hl7StrToDictionary(hl7string, use_long_name=True):
""" Takes a string parameter and converts it to a Dictionary
:param hl7string: HL7 string that is passed to the method
:returns: A dictionary representation of the HL7 message
"""
hl7string = hl7string.replace("\n", "\r")
try :
m = parse_message(hl7string)
except UnsupportedVersion:
print(" Error! : The specified version in the file is unsurpoted.")
print(" Kindly change the version number in the text file to 2.5")
#We create a dictionary to ensure it is json serializable
return hl7MessageToDictionary(m, use_long_name=use_long_name)
def hl7MessageToDictionary(m, use_long_name=True):
"""Convert an HL7 message to a dictionary
"""
if m.children:
d = {}
for c in m.children:
name = c.name.lower()
if use_long_name:
name = c.long_name.lower() if c.long_name else name
dictified = hl7MessageToDictionary(c, use_long_name=use_long_name)
if name in d:
if not isinstance(d[name], list):
d[name] = [d[name]]
d[name].append(dictified)
else:
d[name] = dictified
return d
else:
return m.to_er7()
def writeJsonFile(dictionary):
# Write JSON file
try:
to_unicode = unicode
except NameError:
to_unicode = str
#we want to write all messages into one file so we append
#to file first and then delete any previously writen file
with io.open('ml7tojson.json', 'a', encoding='utf8') as outfile:
str_ = json.dumps(dictionary,
indent=4, sort_keys=False,
separators=(',', ':'), ensure_ascii=False)
outfile.write(to_unicode(str_))
#read messages from file
strmsg = readMessageFile("HL7_Final.hl7")
#split the messages based on the blank line between messages
msgList = splitMessages(strmsg)
#lets remove a previously writen json file if the file exists, delete it
if os.path.isfile('ml7tojson.json'):
os.remove('ml7tojson.json')
print("Step 3: Previous json file deleted Successfully")
#Loop through the message to handle each message at a time
for message in msgList:
# Convert it to a dictionary
d = hl7StrToDictionary(message)
#write JSON file
writeJsonFile(d)
print ("A jason file with the message has been created")
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modules/SOM_F1.[Scatter Changes in Temp and GDP Sulfate].py | YixuanZheng/Aerosol_Inequality_2019 | 3 | 107870 | <reponame>YixuanZheng/Aerosol_Inequality_2019
# -*- coding: utf-8 -*-
'''
This code generates Fig. S11
Country-level temperature changes and the associated GDP increment induced by anthropogenic SULFATE aerosols
by <NAME> (<EMAIL>)
'''
import _env
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import seaborn.apionly as sns
from matplotlib.colors import ListedColormap
from netCDF4 import Dataset
matplotlib.rcParams['font.family'] = 'sans-serif'
matplotlib.rcParams['font.sans-serif'] = 'Helvetica'
gdp_year = _env.year
sgdp_year = str(gdp_year)
p_scen = 'No-Sulfate' #aerosol removal scenario
ds = 'ERA-Interim'
if_temp = _env.odir_root + '/summary_' + ds + '/country_specific_statistics_Temp_' + ds + '_' + p_scen + '.csv'
if_gdp = _env.odir_root + '/summary_' + ds + '/country_specific_statistics_GDP_' + ds + '_' + p_scen + '_Burke.xls'
odir_plot = _env.odir_root + '/plot/'
_env.mkdirs(odir_plot)
of_plot = odir_plot + 'SOM_F1.Scatter_Changes_in_Temp_and_GDP_Sulfate.png'
itbl_temp = pd.read_csv(if_temp,index_col = 0)
itbl_gdp = pd.read_excel(if_gdp,'country-lag0')
mtbl_tg = itbl_temp[['Temp_mean_climatological', 'Temp_mean_noaero', 'Temp_Changes']].copy()
for gc in ['iso', sgdp_year + '_gdp', sgdp_year + '_pop', 'GDP_median_benefit_ratio']:
mtbl_tg[gc] = itbl_gdp[gc].copy()
##############################################################################
##########################Panel a###########################
fig=plt.figure(figsize=(9,13))
ax=plt.subplot(211)
my_cmap = ListedColormap(sns.color_palette('viridis',11).as_hex()) #[::-1]) #modified by yz mar21,2019, suggested by SJD
pscat = plt.scatter(-mtbl_tg['Temp_Changes'],mtbl_tg['GDP_median_benefit_ratio'],c = mtbl_tg['Temp_mean_climatological'],vmin=-3,vmax=30,cmap=my_cmap,s=np.sqrt(mtbl_tg['2010_gdp']/1e7),
edgecolors='none',alpha=0.8,label=None)
##global mean results
g_pop = mtbl_tg['2010_pop'].sum()
g_gdp = (mtbl_tg['2010_gdp'].sum())
inc_temp = Dataset(_env.odir_root + '/sim_temperature/' + 'Simulated_Global_and_Country_TREFHT_20yravg.nc')
g_t_change = np.mean(inc_temp['TREFHT_Global_PW'][:,2]-inc_temp['TREFHT_Global_PW'][:,0])
g_gdp_change = ((mtbl_tg['GDP_median_benefit_ratio']*mtbl_tg['2010_gdp']).sum())/(mtbl_tg['2010_gdp'].sum())
g_gdp_size = 3*1e10
plt.scatter(-g_t_change,g_gdp_change,c = 'black',vmin=-3,vmax=30,s=np.sqrt(g_gdp_size/1e7),alpha=0.8)
#generate legend to indicate GDP size
gdp_labels = ['10$^9$','10$^{10}$','10$^{11}$','10$^{12}$','10$^{13}$']
for igdp,gdp in enumerate([1e9, 1e10, 1e11,1e12,1e13]):
plt.scatter([], [], c='',edgecolor='k', s=np.sqrt(gdp/1e7),
label=gdp_labels[igdp])
plt.legend(scatterpoints=1, frameon=False, labelspacing=0.5, title='GDP ($)',loc=6)
def plot_rec(llx,lly,urx,ury):
plt.vlines(llx,lly,ury,colors='grey',linestyle = 'dashed',lw=1)
plt.hlines(lly,llx,urx,colors='grey',linestyle = 'dashed',lw=1)
plt.vlines(urx,lly,ury,colors='grey',linestyle = 'dashed',lw=1)
plt.hlines(ury,llx,urx,colors='grey',linestyle = 'dashed',lw=1)
ollx = -1.2
olly = -0.5
ourx = -0.4
oury = 1.5
plot_rec(ollx,olly,ourx,oury)
ax.set_ylim(-2,2.2)
ax.set_xlim(-1.8,-0.2)
#set font size
plt.xticks(size=14)
plt.yticks(size=14)
ax.set_xlabel('Sulfate-induced temperature changes (\N{DEGREE SIGN}C)',fontsize=16)
ax.set_ylabel('GDP changes due to sulfate-induced cooling (%)',fontsize=16)
cbar = plt.colorbar(pscat,ticks=[0,3,6,9,12,15,18,21,24,27])
cbar.set_label('2010 temperature (\N{DEGREE SIGN}C)',fontsize = 16,rotation=270,labelpad=18)
cbar.ax.set_yticklabels([0,3,6,9,12,15,18,21,24,27],size=14)
plt.text(0.03,0.98, (chr(ord('a') + 0)),size=16, horizontalalignment='center',#fontweight = 'bold',
verticalalignment='top',transform=ax.transAxes,fontweight='bold')
##############################################################################
##########################Panel b###########################
#
ax=plt.subplot(212)
pscat = plt.scatter(-mtbl_tg['Temp_Changes'],mtbl_tg['GDP_median_benefit_ratio'],c = mtbl_tg['Temp_mean_climatological'],vmin=-3,vmax=30,cmap=my_cmap,s=np.sqrt(mtbl_tg['2010_gdp']/1e7),
edgecolors='none',alpha=0.8)
plt.scatter(-g_t_change,g_gdp_change,c = 'black',vmin=-3,vmax=30,s=np.sqrt(g_gdp_size/1e7),alpha=0.8)
#set font size
plt.xticks(size=14)
plt.yticks(size=14)
ax.set_ylim([olly,oury])
ax.set_xlim([ollx,ourx])
ax.set_xlabel('Sulfate-induced temperature changes (\N{DEGREE SIGN}C)',fontsize=16)
ax.set_ylabel('GDP changes due to sulfate-induced cooling (%)',fontsize=16)
cbar = plt.colorbar(pscat,ticks=[0,3,6,9,12,15,18,21,24,27])
cbar.set_label('2010 temperature (\N{DEGREE SIGN}C)',fontsize = 16,rotation=270,labelpad=18)
cbar.ax.set_yticklabels([0,3,6,9,12,15,18,21,24,27],size=14)
plt.text(0.03,0.98, (chr(ord('a') + 1)),size=16, horizontalalignment='center',#fontweight = 'bold',
verticalalignment='top',transform=ax.transAxes,fontweight='bold')
plt.savefig(of_plot, dpi=300,bbox_inches='tight')
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] |
Research/encoders.py | ylzhang29/GAN-for-tabular-data | 272 | 194662 | from typing import List
import numpy as np
import pandas as pd
from category_encoders.backward_difference import BackwardDifferenceEncoder
from category_encoders.cat_boost import CatBoostEncoder
from category_encoders.helmert import HelmertEncoder
from category_encoders.james_stein import JamesSteinEncoder
from category_encoders.leave_one_out import LeaveOneOutEncoder
from category_encoders.m_estimate import MEstimateEncoder
from category_encoders.one_hot import OneHotEncoder
from category_encoders.ordinal import OrdinalEncoder
from category_encoders.sum_coding import SumEncoder
from category_encoders.target_encoder import TargetEncoder
from category_encoders.woe import WOEEncoder
from sklearn.model_selection import RepeatedStratifiedKFold
def get_single_encoder(encoder_name: str, cat_cols: list):
"""
Get encoder by its name
:param encoder_name: Name of desired encoder
:param cat_cols: Cat columns for encoding
:return: Categorical encoder
"""
if encoder_name == "FrequencyEncoder":
encoder = FrequencyEncoder(cols=cat_cols)
if encoder_name == "WOEEncoder":
encoder = WOEEncoder(cols=cat_cols)
if encoder_name == "TargetEncoder":
encoder = TargetEncoder(cols=cat_cols)
if encoder_name == "SumEncoder":
encoder = SumEncoder(cols=cat_cols)
if encoder_name == "MEstimateEncoder":
encoder = MEstimateEncoder(cols=cat_cols)
if encoder_name == "LeaveOneOutEncoder":
encoder = LeaveOneOutEncoder(cols=cat_cols)
if encoder_name == "HelmertEncoder":
encoder = HelmertEncoder(cols=cat_cols)
if encoder_name == "BackwardDifferenceEncoder":
encoder = BackwardDifferenceEncoder(cols=cat_cols)
if encoder_name == "JamesSteinEncoder":
encoder = JamesSteinEncoder(cols=cat_cols)
if encoder_name == "OrdinalEncoder":
encoder = OrdinalEncoder(cols=cat_cols)
if encoder_name == "CatBoostEncoder":
encoder = CatBoostEncoder(cols=cat_cols)
if encoder_name == "MEstimateEncoder":
encoder = MEstimateEncoder(cols=cat_cols)
if encoder_name == "OneHotEncoder":
encoder = OneHotEncoder(cols=cat_cols)
if encoder is None:
raise NotImplementedError("To be implemented")
return encoder
class DoubleValidationEncoderNumerical:
"""
Encoder with validation within
"""
def __init__(self, cols, encoders_names_tuple=()):
"""
:param cols: Categorical columns
:param encoders_names_tuple: Tuple of str with encoders
"""
self.cols, self.num_cols = cols, None
self.encoders_names_tuple = encoders_names_tuple
self.n_folds, self.n_repeats = 5, 3
self.model_validation = RepeatedStratifiedKFold(
n_splits=self.n_folds, n_repeats=self.n_repeats, random_state=0
)
self.encoders_dict = {}
self.storage = None
def fit_transform(self, X: pd.DataFrame, y: np.array) -> pd.DataFrame:
self.num_cols = [col for col in X.columns if col not in self.cols]
self.storage = []
for encoder_name in self.encoders_names_tuple:
for n_fold, (train_idx, val_idx) in enumerate(
self.model_validation.split(X, y)
):
encoder = get_single_encoder(encoder_name, self.cols)
X_train, X_val = (
X.loc[train_idx].reset_index(drop=True),
X.loc[val_idx].reset_index(drop=True),
)
y_train, y_val = y[train_idx], y[val_idx]
_ = encoder.fit_transform(X_train, y_train)
# transform validation part and get all necessary cols
val_t = encoder.transform(X_val)
val_t = val_t[
[col for col in val_t.columns if col not in self.num_cols]
].values
if encoder_name not in self.encoders_dict.keys():
cols_representation = np.zeros((X.shape[0], val_t.shape[1]))
self.encoders_dict[encoder_name] = [encoder]
else:
self.encoders_dict[encoder_name].append(encoder)
cols_representation[val_idx, :] += val_t / self.n_repeats
cols_representation = pd.DataFrame(cols_representation)
cols_representation.columns = [
f"encoded_{encoder_name}_{i}"
for i in range(cols_representation.shape[1])
]
self.storage.append(cols_representation)
for df in self.storage:
X = pd.concat([X, df], axis=1)
X.drop(self.cols, axis=1, inplace=True)
return X
def transform(self, X: pd.DataFrame) -> pd.DataFrame:
self.storage = []
for encoder_name in self.encoders_names_tuple:
cols_representation = None
for encoder in self.encoders_dict[encoder_name]:
test_tr = encoder.transform(X)
test_tr = test_tr[
[col for col in test_tr.columns if col not in self.num_cols]
].values
if cols_representation is None:
cols_representation = np.zeros(test_tr.shape)
cols_representation = (
cols_representation + test_tr / self.n_folds / self.n_repeats
)
cols_representation = pd.DataFrame(cols_representation)
cols_representation.columns = [
f"encoded_{encoder_name}_{i}"
for i in range(cols_representation.shape[1])
]
self.storage.append(cols_representation)
for df in self.storage:
X = pd.concat([X, df], axis=1)
X.drop(self.cols, axis=1, inplace=True)
return X
class MultipleEncoder:
"""
Multiple encoder for categorical columns
"""
def __init__(self, cols: List[str], encoders_names_tuple=()):
"""
:param cols: List of categorical columns
:param encoders_names_tuple: Tuple of categorical encoders names. Possible values in tuple are:
"FrequencyEncoder", "WOEEncoder", "TargetEncoder", "SumEncoder", "MEstimateEncoder", "LeaveOneOutEncoder",
"HelmertEncoder", "BackwardDifferenceEncoder", "JamesSteinEncoder", "OrdinalEncoder""CatBoostEncoder"
"""
self.cols = cols
self.num_cols = None
self.encoders_names_tuple = encoders_names_tuple
self.encoders_dict = {}
# list for storing results of transformation from each encoder
self.storage = None
def fit_transform(self, X: pd.DataFrame, y: np.array) -> pd.DataFrame:
self.num_cols = [col for col in X.columns if col not in self.cols]
self.storage = []
for encoder_name in self.encoders_names_tuple:
encoder = get_single_encoder(encoder_name=encoder_name, cat_cols=self.cols)
cols_representation = encoder.fit_transform(X, y)
self.encoders_dict[encoder_name] = encoder
cols_representation = cols_representation[
[col for col in cols_representation.columns if col not in self.num_cols]
].values
cols_representation = pd.DataFrame(cols_representation)
cols_representation.columns = [
f"encoded_{encoder_name}_{i}"
for i in range(cols_representation.shape[1])
]
self.storage.append(cols_representation)
# concat cat cols representations with initial dataframe
for df in self.storage:
X = pd.concat([X, df], axis=1)
# remove all columns as far as we have their representations
X.drop(self.cols, axis=1, inplace=True)
return X
def transform(self, X) -> pd.DataFrame:
self.storage = []
for encoder_name in self.encoders_names_tuple:
# get representation of cat columns and form a pd.DataFrame for it
cols_representation = self.encoders_dict[encoder_name].transform(X)
cols_representation = cols_representation[
[col for col in cols_representation.columns if col not in self.num_cols]
].values
cols_representation = pd.DataFrame(cols_representation)
cols_representation.columns = [
f"encoded_{encoder_name}_{i}"
for i in range(cols_representation.shape[1])
]
self.storage.append(cols_representation)
# concat cat cols representations with initial dataframe
for df in self.storage:
X = pd.concat([X, df], axis=1)
# remove all columns as far as we have their representations
X.drop(self.cols, axis=1, inplace=True)
return X
class FrequencyEncoder:
def __init__(self, cols):
self.cols = cols
self.counts_dict = None
def fit(self, X: pd.DataFrame, y=None) -> pd.DataFrame:
counts_dict = {}
for col in self.cols:
values, counts = np.unique(X[col], return_counts=True)
counts_dict[col] = dict(zip(values, counts))
self.counts_dict = counts_dict
def transform(self, X: pd.DataFrame) -> pd.DataFrame:
counts_dict_test = {}
res = []
for col in self.cols:
values, counts = np.unique(X[col], return_counts=True)
counts_dict_test[col] = dict(zip(values, counts))
# if value is in "train" keys - replace "test" counts with "train" counts
for k in [
key
for key in counts_dict_test[col].keys()
if key in self.counts_dict[col].keys()
]:
counts_dict_test[col][k] = self.counts_dict[col][k]
res.append(X[col].map(counts_dict_test[col]).values.reshape(-1, 1))
res = np.hstack(res)
X[self.cols] = res
return X
def fit_transform(self, X: pd.DataFrame, y=None) -> pd.DataFrame:
self.fit(X, y)
X = self.transform(X)
return X
if __name__ == "__main__":
df = pd.DataFrame({})
df["cat_col"] = [1, 2, 3, 1, 2, 3, 1, 1, 1]
df["target"] = [0, 1, 0, 1, 0, 1, 0, 1, 0]
#
temp = df.copy()
enc = CatBoostEncoder(cols=["cat_col"])
print(enc.fit_transform(temp, temp["target"]))
#
temp = df.copy()
enc = MultipleEncoder(cols=["cat_col"], encoders_names_tuple=("CatBoostEncoder",))
print(enc.fit_transform(temp, temp["target"]))
#
temp = df.copy()
enc = DoubleValidationEncoderNumerical(
cols=["cat_col"], encoders_names_tuple=("CatBoostEncoder",)
)
print(enc.fit_transform(temp, temp["target"]))
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1054,
14664,
5975,
29889,
690,
14443,
6278,
29896,
29892,
29871,
29896,
876,
13,
4706,
620,
353,
7442,
29889,
29882,
1429,
29898,
690,
29897,
13,
13,
4706,
1060,
29961,
1311,
29889,
22724,
29962,
353,
620,
13,
4706,
736,
1060,
13,
13,
1678,
822,
6216,
29918,
9067,
29898,
1311,
29892,
1060,
29901,
10518,
29889,
17271,
29892,
343,
29922,
8516,
29897,
1599,
10518,
29889,
17271,
29901,
13,
4706,
1583,
29889,
9202,
29898,
29990,
29892,
343,
29897,
13,
4706,
1060,
353,
1583,
29889,
9067,
29898,
29990,
29897,
13,
4706,
736,
1060,
13,
13,
13,
361,
4770,
978,
1649,
1275,
376,
1649,
3396,
1649,
1115,
13,
1678,
4489,
353,
10518,
29889,
17271,
3319,
1800,
13,
1678,
4489,
3366,
4117,
29918,
1054,
3108,
353,
518,
29896,
29892,
29871,
29906,
29892,
29871,
29941,
29892,
29871,
29896,
29892,
29871,
29906,
29892,
29871,
29941,
29892,
29871,
29896,
29892,
29871,
29896,
29892,
29871,
29896,
29962,
13,
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29892,
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29871,
29900,
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29871,
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29900,
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29871,
29896,
29892,
29871,
29900,
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29871,
29896,
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29900,
29962,
13,
13,
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396,
13,
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5694,
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29889,
8552,
580,
13,
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2094,
353,
10459,
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520,
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22724,
29922,
3366,
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29918,
1054,
20068,
13,
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1596,
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3977,
29889,
9202,
29918,
9067,
29898,
7382,
29892,
5694,
3366,
5182,
3108,
876,
13,
13,
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13,
1678,
5694,
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8566,
6119,
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1054,
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414,
29918,
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23583,
29922,
703,
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6119,
613,
876,
13,
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1596,
29898,
3977,
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9202,
29918,
9067,
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7382,
29892,
5694,
3366,
5182,
3108,
876,
13,
13,
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396,
13,
1678,
5694,
353,
4489,
29889,
8552,
580,
13,
1678,
2094,
353,
11599,
19448,
8566,
6119,
29940,
4680,
936,
29898,
13,
4706,
28730,
29922,
3366,
4117,
29918,
1054,
12436,
2094,
397,
414,
29918,
7039,
29918,
23583,
29922,
703,
9694,
8431,
520,
8566,
6119,
613,
29897,
13,
1678,
1723,
13,
1678,
1596,
29898,
3977,
29889,
9202,
29918,
9067,
29898,
7382,
29892,
5694,
3366,
5182,
3108,
876,
13,
2
] |
python算法/5.6如何对由大小写字母组成的字符数组排序.py | RobinYaoWenbin/Python-CommonCode | 12 | 134334 | # -*- coding: utf-8 -*-
"""
Created on Wed Feb 12 19:36:03 2020
@author: Administrator
"""
"""
有一个由大小写字母组成的字符串,请对它进行重新组合,使得其中的所有小写字母排在大写字母的前面
(大写字母或小写字母之间不要求保持原来的次序)
"""
def SortStr(s):
i = 0 ; j = len(s) - 1
while i < j:
if s[i].isupper():
pass
else:
i += 1
if s[j].islower():
pass
else:
j -= 1
if s[i].isupper() and s[j].islower():
if j < len(s) - 1:
s = s[0:i] + s[j] + s[i+1:j] + s[i] + s[j+1:]
else:
s = s[0:i] + s[j] + s[i+1:j] + s[i]
i+=1 ; j-=1
return s
if __name__ == "__main__":
s = "AbcDefa"
s = SortStr(s)
print("The string after sorting is : " , s) | [
1,
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448,
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14137,
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565,
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275,
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13,
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13,
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13,
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432,
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13,
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565,
269,
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275,
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580,
322,
269,
29961,
29926,
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275,
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13,
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565,
432,
529,
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448,
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13,
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353,
269,
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29900,
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30004,
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736,
269,
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13,
30004,
13,
361,
4770,
978,
1649,
1275,
376,
1649,
3396,
1649,
1115,
30004,
13,
1678,
269,
353,
376,
4920,
29883,
3206,
29874,
19451,
13,
1678,
269,
353,
20025,
5015,
29898,
29879,
8443,
13,
1678,
1596,
703,
1576,
1347,
1156,
16548,
338,
584,
376,
1919,
269,
29897,
2
] |
transcendental-maps/program/processor.py | alexn11/complex-dynamics-progs | 3 | 155909 | #!/usr/bin/python
# todo: preview doesnt work
# TODO: new types: pixel (rgba)
# todo: legacy? e.g. compute image of points, computation illustration etc.
# todo: doesn't detect wrong number of args
# todo: set color
# todo: filled julia with the number of it
# todo: implement missing rect and arg and line complex
# todo: filled julia: nb it as output also
# todo: copy, useless?
# todo: lyapunov
# todo: implement arg_density + option set_smooth_argument
# todo: "rect_cdensity" : 7, # real src, double density, bool, bool, real, bool
# todo: "set_color" : 5,
# todo: "set_edge_thickness" : 1,
# todo: #"set_arg_shading" : 1,
# todo: "set_rect_shading" : 1,
# todo: "set_imaginary_part" : 1,
# todo: "draw_rect_edge" : 2,
# todo: "draw_rect_cross" : 2,
# todo: "draw_shade_arg" : 2,
# todo: implement load/save
from ezInputConf import convert_string_to_boolean
from ezInputConf import convert_string_to_string
# todo: this should be external
instruction_descriptors = {
# name : (arg types, var types)
"abs" : (
("variable", "variable"),
("complex", "real")),
"add_test_enter_disk" : (
("complex", "real"),
()),
"add_test_leave_annulus" : (
("complex", "real", "real"),
()),
"add_test_leave_disk" : (
("complex", "real"),
()),
"add_test_leave_left_half_plane" : (
("real", ),
()),
"add_test_leave_right_half_plane" : (
("real", ),
()),
"annulus_index" : (
("complex", "real", "real", "variable", "variable"),
("complex", "integer")),
"arg" : (
("complex", "variable", "variable"),
("complex", "real")),
"arg_density" : (
("variable", "variable"),
("real", "real")),
"boundaries" : (
("variable", "variable"),
("integer", "real")),
"cauliflower_julia" : ( # nit, pts, stop
("integer", "variable", "variable"),
("complex", "integer")),
"coord_rect" : (
("complex", "complex", "variable", "variable"),
("complex", "complex")),
"draw_complex_density" : (
("variable", "string"),
("complex", )),
"draw_density" : (
("variable", "string"),
("real", )),
"draw_indexes" : (
("variable", "string"),
("integer", ),
),
"draw_integers" : (
("variable", "string"),
("integer", )),
"draw_reals" : (
("variable", "string"),
("real", )),
"eval_main_map" : (
("variable", "variable"),
("complex", "complex")),
"exit" : (
("string", ),
()),
"is_in_annulus" : (
("complex", "real", "real", "variable", "variable"),
("complex", "integer")),
"is_in_disk" : (
("complex", "real", "variable", "variable"),
("complex", "integer")),
"is_in_filled_julia_set" : (
("integer", "real", "variable", "variable"),
("complex", "integer")),
"is_nan" : (
("variable", "variable"),
("complex", "integer")),
"is_where_wrt_annulus" : (
("complex", "real", "real", "variable", "variable"),
("complex", "integer")),
"iterate_main_map" : ( # z, z_n, n=nb_it, stop_value
("integer", "variable", "variable", "variable", "variable"),
("complex", "complex", "integer", "integer")),
"load_complexes" : (
("string", "variable"),
("complex", )),
"load_integers" : (
("string", "variable"),
("integer", )),
"load_reals" : (
("string", "variable"),
("real", )),
"make_grid" : (
("variable", ),
("complex", )),
"name" : (
("string", ),
()),
"nop" : (
(),
()),
"print_complexes" : (
("variable", ),
("complex", )),
"replace_nans" : (
("complex", "variable", "variable"),
("complex", "complex")),
"save_complexes" : (
("variable", "string"),
("complex", )),
"save_integers" : (
("variable", "string"),
("integer", )),
"save_reals" : (
("variable", "string"),
("real", )),
"reset_tests" : (
(),
()),
"set_critical_value" : (
("complex", ),
()),
"set_drawing_threshold" : (
("real", ),
()),
"set_drawing_type" : (
("string", ),
()),
"set_grid_tlbr" : (
("real", "real", "real", "real"),
()),
"set_grid_width" : (
("integer", ),
()),
"set_main_map_type" : (
("string", ),
()),
"set_number_of_indexes" : (
("integer", ),
()),
"set_number_of_iterations" : (
("integer", ),
()),
"set_preview_parameters" : (
("boolean", "integer", "integer", "real", "string", "string"),
()),
"set_shade_enhance_power" : (
("real", ),
()),
"set_shade_max" : (
("string", ),
()),
"set_shade_type" : (
("string", ),
()),
"set_smooth_argument" : (
("boolean",),
()),
"setup_fatou_inverse_data" : (
("complex", "real"),
()),
"setup_linearizer_data" : (
("integer", "complex", "real", "integer"),
()),
"show_help" : (
("string", ),
()),
}
# -----------------------------------------------------------------------------
instruction_number_args = {
name : len (instruction_descriptors [name] [0])
for name in instruction_descriptors }
variable_types_in_arguments = {
name : instruction_descriptors [name] [1]
for name in instruction_descriptors }
instruction_argument_types = {
name : instruction_descriptors [name] [0]
for name in instruction_descriptors }
def make_variable_positions_in_arguments (instruction_descriptors):
positions = {}
for instr_name in instruction_descriptors:
pos = []
args = instruction_descriptors [instr_name] [0]
for pos_id in range (len (args)):
if (args [pos_id] == "variable"):
pos . append (pos_id)
positions [instr_name] = tuple (pos)
return positions
variable_positions_in_arguments = (
make_variable_positions_in_arguments (instruction_descriptors))
def cat_dict (dict0, dict1):
return { ** dict0, ** dict1 }
class Processor:
def __init__ (self, program, external_variables):
self . program = program
self . split_program ()
self . cursor = 0
self . external_variables = external_variables
self . original_variables = {
"Results" : "complex",
"Iterations" : "integer", }
self . original_variable_names = (
list (self . original_variables . keys ()))
self . requested_variables = {}
#self . requested_variables_names = []
self . instructions = []
self . assemble ()
self . variables = cat_dict (self . original_variables,
self . requested_variables)
return
def assemble (self):
current_line = self . get_next_line ()
while (current_line != ""):
self . interpret_line (current_line)
current_line = self . get_next_line ()
return
def split_program (self):
prog_lines = self . program . splitlines ()
self . prog_lines = []
self . line_numbers = []
line_number = 0
for line in prog_lines:
line_number += 1
line = line . strip ()
if (line == ""):
continue
if (line [0] == "#"):
continue
comment_start = line . find ("#")
# NOTE: this forbid "#" in arguments
if (comment_start > 0):
line = line [:comment_start]
self . prog_lines . append (line)
self . line_numbers . append (line_number)
self . prog_len = len (self . prog_lines)
def get_next_line (self):
if (self . cursor == self . prog_len):
return ""
line = self . prog_lines [self . cursor]
self . cursor += 1
return line
def describe_position (self):
if (self . cursor == 0):
return "(nowhere)"
return "(@ " + str (self . line_numbers [self . cursor - 1]) + ")"
def add_requested_variables (self, arg_variables):
#print ("arg_variables="+str(arg_variables))
previous_variable_names = list (self . requested_variables . keys ())
for variable in arg_variables:
var_name = variable [0]
var_type = variable [1]
try:
expected_var_type = self . original_variables [var_name]
except (KeyError):
pass
else:
if (var_type == expected_var_type):
continue
else:
raise Exception (self . describe_position () + " Wrong var type")
try:
expected_var_type = self . requested_variables [var_name]
except (KeyError):
self . requested_variables [var_name] = var_type
else:
if (expected_var_type == "any"):
continue
elif (var_type != expected_var_type):
raise Exception (self . describe_position () + " Wrong var type")
return
def skip_spaces_in_line (self, cursor):
while ((cursor < len (self . instruction_text))
and (self . instruction_text [cursor] == " ")):
cursor += 1
return cursor
def find_end_of_argument_string (self, cursor):
line_len = len (self . instruction_text)
while (self . instruction_text [cursor] not in ['"']):
if (cursor == line_len - 1):
raise Exception ("Unended string "
+ self . describe_position ()
+ " in \""
+ self . instruction_text
+ "\"")
else:
if (self . instruction_text [cursor] == '\\'):
cursor += 2
if (cursor == line_len):
raise Exception ("Unended string "
+ self . describe_position ()
+ " in \""
+ self . instruction_text
+ "\"")
else:
cursor += 1
return cursor + 1
def find_argument_end (self, argument_start):
if (self . instruction_text [argument_start] == '"'):
argument_end = (
self . find_end_of_argument_string (argument_start + 1))
else:
c = argument_start
while ((c < len (self . instruction_text))
and (self . instruction_text [c] != ",")
and (self . instruction_text [c] != " ")):
c += 1
argument_end = c
return argument_end
def split_instuction_line (self):
raise Exception ("not here yet")
line = self . instruction_text
len_line = len (line)
next_space = line . find (" ")
if (next_space < 0):
first_word = line
arguments = []
else:
first_word = line [: next_space]
next_nonspace = self . skip_spaces_in_line (next_space)
if (next_nonspace == len_line):
arguments = []
else:
while (False):
#todo
pass
self . structured_line = [first_word] + arguments
def get_next_word (self):
cursor = self . instruction_cursor
line = self . instruction_text
len_line = len (line)
#print (cursor)
if (cursor == len_line):
return ""
word_start = cursor
#print ("line="+line)
#print ("start="+line [word_start:])
if (cursor == 0):
word_end = line . find (" ")
if (word_end < 0):
word_end = len_line
next_cursor = len_line
else:
next_cursor = self . skip_spaces_in_line (word_end)
else:
# NOTE: this restrict the use of coma only between arguments
# NOTE: that is forbid it in strings (=only file names)
# it's changing
#NOTE: no empty string allowed?
#if (line [word_start] != ","):
# raise Exception ("Missing coma in \""
# + line
# + "\" -> "
# + line [word_start :])
if (line [word_start] == ","):
word_start += 1
word_start = self . skip_spaces_in_line (word_start)
word_end = self . find_argument_end (word_start)
#print ("word_end="+str(word_end))
if (word_end == len_line):
next_cursor = len_line
else:
next_cursor = self . skip_spaces_in_line (word_end)
if ((next_cursor < len_line) and (line [next_cursor] != ",")):
raise Exception (self . describe_position ()
+ " Expected coma, had:"
+ line [word_end :]
+ " <- "
+ line)
if (next_cursor != len_line):
# note: line has been stripped of trailing spaces
# NOTE: no "," at the end of the line (will provocke an error)
while (line [next_cursor] == " "):
next_cursor += 1
word = line [word_start : word_end]
#print ("*** word="+word)
if (word == ""):
raise Exception (self . describe_position () + " Empty word: " + line)
self . instruction_cursor = next_cursor
return word
def get_instruction_and_arguments (self, line):
self . instruction_cursor = 0
self . instruction_text = line
instruction = self . get_next_word ()
nb_args = instruction_number_args [instruction]
arguments = nb_args * [""]
for arg_index in range (nb_args):
arguments [arg_index] = self . get_next_word ()
if (self . instruction_cursor != len (self . instruction_text)):
raise Exception (self . describe_position () + " Extra arguments: " + self . instruction_text)
return (instruction, arguments)
def determine_variable_type (self,
instruction,
variable_index):
var_type = variable_types_in_arguments [instruction] [variable_index]
if (var_type == "any"):
var_type = "any"
#raise Exception ("Could not determine type of variable")
return var_type
def convert_variable_id_to_full_variable_object (self,
argument,
instruction,
variable_index):
if (argument [0] == "$"):
argument = argument [1:]
type_char = argument [0]
var_type = self . determine_variable_type (instruction,
variable_index)
var_name = argument
return (var_name, var_type)
def get_instruction_variables (self, instruction_and_arguments):
(instruction, arguments) = instruction_and_arguments
variable_positions = variable_positions_in_arguments [instruction]
variables = []
for variable_index in range (len (variable_positions)):
position = variable_positions [variable_index]
variables . append (
self . convert_variable_id_to_full_variable_object (
arguments [position],
instruction,
variable_index))
return variables
def interpret_arguments (self,
instruction_and_arguments,
variables,
external_variables_dict):
instruction = instruction_and_arguments [0]
arguments = instruction_and_arguments [1]
argument_types = instruction_argument_types [instruction]
variable_index = 0
interpreted_arguments = []
for arg_type, arg in zip (argument_types, arguments):
if (arg [0] == "$"):
arg = external_variables_dict [arg [1:]]
if (arg_type == "boolean"):
interpreted_argument = (convert_string_to_boolean (arg))
elif (arg_type == "integer"):
interpreted_argument = (int (arg))
elif (arg_type == "real"):
interpreted_argument = (float (arg))
elif (arg_type == "complex"):
interpreted_argument = (complex (arg))
elif (arg_type == "string"):
interpreted_argument = (convert_string_to_string (arg))
elif (arg_type == "variable"):
interpreted_argument = (variables [variable_index])
variable_index += 1
else:
raise Exception (self . describe_position () + " Unknown argument type: " + str (arg_type))
interpreted_arguments . append (interpreted_argument)
return interpreted_arguments
def interpret_line (self, line):
instruction_and_arguments = self . get_instruction_and_arguments (line)
arg_variables = (
self . get_instruction_variables (instruction_and_arguments))
self . add_requested_variables (arg_variables)
instruction = instruction_and_arguments [0]
arguments = self . interpret_arguments (instruction_and_arguments,
arg_variables,
self . external_variables)
self . instructions . append ((instruction, arguments))
return
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] |
scripts/lidar_constants.py | priyablue/lidar_navigation | 2 | 1606381 | <filename>scripts/lidar_constants.py
import os
import sys
__path = os.path.abspath(sys.modules[__name__].__file__)
__dirname = os.path.dirname(__path)
HTTP_TEMPLATE_DEFAULT = __dirname + "/html/plot-image.html"
HTTP_PORT_DEFAULT = 8080
HTTP_DELAY_SECS_DEFAULT = 0
HTTP_PORT = "http_port"
TEMPLATE_FILE = "template_file"
HTTP_DELAY_SECS = "http_delay_secs"
HTTP_VERBOSE = "http_verbose"
LOG_LEVEL = "loglevel"
SLICE_SIZE = "slice_size"
SLICE_OFFSET = "slice_offset"
MAX_DIST_MULT = "max_dist_mult"
MAX_DIST_MULT_DEFAULT = 1.1
SLICE_SIZE_DEFAULT = 5
SLICE_OFFSET_DEFAULT = 0
PUBLISH_RATE = "publish_rate"
PUBLISH_RATE_DEFAULT = 30
SCAN_TOPIC = "scan_topic"
SCAN_TOPIC_DEFAULT = "/scan"
CONTOUR_TOPIC = "contour_topic"
CONTOUR_TOPIC_DEFAULT = "/contour"
CENTROID_TOPIC = "centroid_topic"
CENTROID_TOPIC_DEFAULT = "/centroid"
PC_TOPIC = "pc_topic"
PC_TOPIC_DEFAULT = "/pc2"
MAX_AXIS_MULT = "max_axis_mult"
MAX_AXIS_MULT_DEFAULT = 1.05
PLOT_ALL = "plot_all"
PLOT_CONTOUR = "plot_contour"
PLOT_CENTROID = "plot_centroid"
PLOT_POINTS = "plot_points"
PLOT_SLICES = "plot_slices"
PUBLISH_PC = "publish_pc"
PUBLISH_PC_DEFAULT = False
MAX_LINEAR = "max_linear"
MAX_LINEAR_DEFAULT = .35
MAX_ANGULAR = "max_angular"
MAX_ANGULAR_DEFAULT = 2.75
VEL_TOPIC = "vel_topic"
VEL_TOPIC_DEFAULT = "/cmd_vel"
STOP_ANGLE = "stop_angle"
STOP_ANGLE_DEFAULT = 70
PAUSE = "pause"
PAUSE_DEFAULT = 0
ITERATIONS = "iterations"
ITERATIONS_DEFAULT = 20
MIN_POINTS = "min_points"
MIN_POINTS_DEFAULT = 20
THRESHOLD = "threshold"
THRESHOLD_DEFAULT = 0.025
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tests/test_calculate_branch.py | ivergara/python-abc | 2 | 20073 | <reponame>ivergara/python-abc
import pytest
from tests import assert_source_returns_expected
BRANCH_CASES = [
# Call
('print("hello world")', 'b | print("hello world")'),
# Await
("await noop()", "b | await noop()"),
# Class instantiation
("Noop()", "b | Noop()"),
]
@pytest.mark.parametrize("source,expected", BRANCH_CASES)
def test_branch(capsys, source, expected):
assert_source_returns_expected(capsys, source, expected) is True
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spinoff/actor/validate.py | eallik/spinoff | 6 | 59393 | import re
_VALID_IP_RE = '(?:(?:[0-9]|[1-9][0-9]|1[0-9]{2}|2[0-4][0-9]|25[0-5])\.){3}(?:[0-9]|[1-9][0-9]|1[0-9]{2}|2[0-4][0-9]|25[0-5])'
_VALID_HOSTNAME_RE = '(?:(?:[a-zA-Z]|[a-zA-Z][a-zA-Z0-9\-]*[a-zA-Z0-9])\.)*(?:[A-Za-z]|[A-Za-z][A-Za-z0-9\-]*[A-Za-z0-9])'
_VALID_NODEID_RE = re.compile('(?:%s|%s):(?P<port>[0-9]+)$' % (_VALID_HOSTNAME_RE, _VALID_IP_RE))
def _validate_nodeid(nodeid):
if '|' in nodeid:
nodeid = nodeid.split('|', 1)[0]
# call from app code
m = _VALID_NODEID_RE.match(nodeid)
if not m: # pragma: no cover
raise ValueError("Node IDs should be in the format '<ip-or-hostname>:<port>': %s" % (nodeid,))
port = int(m.group(1))
if not (0 <= port <= 65535): # pragma: no cover
raise ValueError("Ports should be in the range 0-65535: %d" % (port,))
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ProgramacaoFuncional/desafio_mdc.py | VictorMello1993/CursoPythonUdemy | 0 | 43511 | #Desafio MDC
def mdc(numeros):
pass
if __name__ == '__main__':
print(mdc([21, 7])) #7
print(mdc([125, 40])) #5
print(mdc([9, 564, 66, 3])) #3
print(mdc([55, 22])) #11
print(mdc([15, 150])) #15
print(mdc([7, 9])) #1 | [
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] |
haweb/libs/drf/api_views.py | edilio/tobeawebproperty | 0 | 54046 | <filename>haweb/libs/drf/api_views.py
from __future__ import unicode_literals
import warnings
from django.http import Http404
from django.conf import settings
from rest_framework import viewsets, mixins, status, serializers
from rest_framework.response import Response
from .error_responses import ErrorResponse
DEFAULT_LIST_LIMIT = settings.API_SETTINGS.get('DEFAULT_LIST_LIMIT', 30)
def get_offset_limit_params(request, default_limit=DEFAULT_LIST_LIMIT):
range_errors = []
offset, limit = 0, default_limit
try:
offset = int(request.GET.get('offset', 0))
if offset < 0:
raise ValueError
except ValueError:
range_errors.append("Invalid value '{0}' for offset. Expected >=0.".format(request.GET.get('offset')))
try:
limit = int(request.GET.get('limit', default_limit))
if limit < 1:
raise ValueError
except ValueError:
range_errors.append("Invalid value '{0}' for limit. Expected >0.".format(request.GET.get('limit')))
return limit, offset, range_errors
def get_object_list(offset, limit, queryset_or_list):
return queryset_or_list[offset:(offset+limit)]
class OffsetLimitMixin(object):
def get_offset_limit_params(self, default_limit=DEFAULT_LIST_LIMIT):
return get_offset_limit_params(self.request, default_limit)
class JEDListRetrieve(mixins.RetrieveModelMixin,
mixins.ListModelMixin,
viewsets.GenericViewSet,
OffsetLimitMixin):
"""
A viewset that provides default `retrieve()` and `list()` actions.
but the list will contains the way smart bear api should work with limit, offset
"""
include_total = False
@staticmethod
def get_object_not_found_error_response(object_name, object_lookup_key):
return ErrorResponse(data="{0} with ID '{1}' not found.".format(object_name, object_lookup_key),
status=status.HTTP_404_NOT_FOUND)
@staticmethod
def get_fields_for_sencha(serializer):
fields = serializer.get_fields().keys()
return [{'header': f, 'name': f} for f in fields]
def generate_list_response(self, query, object_list, serializer, offset, limit):
"""
:rtype : object
:param query: The whole query before limiting using offset and limit params
:param object_list: the result of applying offset and limit to the query
:param serializer: serialize or serialized_data for some uses
:param offset:
:param limit:
:return: a response object with the data serialized
"""
object_list_count = len(object_list) if isinstance(object_list, list) else object_list.count()
serialized_data = serializer.data if isinstance(serializer, serializers.Serializer) else serializer
json_dict = {
'success': True,
'metaData': {
'root': 'rows',
'totalProperty': 'totalCount',
'successProperty': 'success',
'idProperty': 'id',
'sortInfo': {
"field": 'id',
"direction": 'asc'
},
'fields': self.get_fields_for_sencha(serializer)
},
'rows': serialized_data,
'totalCount':object_list_count
}
return Response(json_dict)
def list(self, request, *args, **kwargs):
"""
list all
limit -- limit
offset -- offset
"""
query = self.filter_queryset(self.get_queryset())
if isinstance(query, ErrorResponse):
return query
default_limit = DEFAULT_LIST_LIMIT
limit, offset, range_errors = self.get_offset_limit_params(default_limit)
if range_errors:
return ErrorResponse(data=range_errors)
self.object_list = get_object_list(offset, limit, query)
# Default is to allow empty querysets. This can be altered by setting
# `.allow_empty = False`, to raise 404 errors on empty querysets.
if not self.allow_empty and not self.object_list:
warnings.warn(
'The `allow_empty` parameter is due to be deprecated. '
'To use `allow_empty=False` style behavior, You should override '
'`get_queryset()` and explicitly raise a 404 on empty querysets.',
PendingDeprecationWarning
)
class_name = self.__class__.__name__
error_msg = self.empty_error % {'class_name': class_name}
raise Http404(error_msg)
# the pagination is not supported, use offset and limit
serializer = self.get_serializer(self.object_list, many=True)
return self.generate_list_response(query, self.object_list, serializer, offset, limit)
class JEDModelViewSet(mixins.CreateModelMixin,
mixins.UpdateModelMixin,
mixins.DestroyModelMixin,
JEDListRetrieve):
"""
A viewset that provides default `create()`, `retrieve()`, `update()`,
`partial_update()`, `destroy()` and `list()` actions.
but the list will contains the way smart bear api should work with limit, offset
"""
pass
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archived-stock-trading-bot-v1/yf_extender.py | Allcallofduty10/stock-trading-bot | 101 | 35857 | <reponame>Allcallofduty10/stock-trading-bot
import sys
from datetime import datetime
import yfinance as yf
def get_ticker_symbol(ticker: yf.Ticker) -> str:
try:
return ticker.get_info()['symbol']
except ImportError:
return ""
def get_stock_state(ticker: yf.Ticker) -> {}:
stock_info = ticker.history("1d").iloc[0].to_dict()
stock_info['Time'] = datetime.now().strftime("%H:%M:%S")
del stock_info['Dividends']
del stock_info['Stock Splits']
return stock_info
# Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
def previous_high(ticker: yf.Ticker, time_period: str) -> float:
high = 0
stock_history = ticker.history(time_period)
for i in range(0, len(stock_history) - 2):
temp_high = stock_history.iloc[i].to_dict()['High']
if temp_high > high:
high = temp_high
return high
# Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
def calculate_sma(ticker: yf.Ticker, time_period="1mo", interval="1d") -> float:
stock_history = ticker.history(period=time_period, interval=interval)
summation = 0
time_period_days = 0
for i in range(0, len(stock_history) - 1):
summation += stock_history.iloc[i].to_dict()['Close']
time_period_days += 1
if time_period_days > 0:
return summation / time_period_days
return sys.maxsize
# Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
def calculate_ema(ticker: yf.Ticker, time_period="1mo") -> float:
stock_history = ticker.history(period=time_period)
return stock_history.iloc[len(stock_history) - 1].to_dict()['Close'] * (
2.5 / (1 + len(stock_history))) + calculate_sma(ticker, time_period) * (
1 - (2.5 / (1 + len(stock_history))))
# Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
def calculate_previous_ema(ticker: yf.Ticker, time_period="1mo", days_previous=1) -> float:
time_period_days = len(ticker.history(period=time_period))
stock_history = ticker.history(period=time_period)
return stock_history.iloc[time_period_days - days_previous - 1].to_dict()['Close'] * (
2.5 / (1 + time_period_days)) + calculate_sma(ticker, time_period) * (
1 - (2.5 / (1 + time_period_days)))
def get_high2current_price_change_percent(ticker: yf.Ticker) -> float:
stock_info = ticker.history("1d").iloc[0].to_dict()
return (stock_info['Close'] - stock_info['High']) / stock_info['High']
def get_direction(ticker: yf.Ticker) -> float:
stock_history = ticker.history(period="1d", interval="1m")
return (stock_history.iloc[len(stock_history) - 1].to_dict()['Close'] - stock_history.iloc[len(stock_history) - 2].to_dict()['Close'])/stock_history.iloc[len(stock_history) - 2].to_dict()['Close']
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regenerate_api.py | gregsmi/sample-metadata | 0 | 144522 | #!/usr/bin/env python3
# pylint: disable=logging-not-lazy,subprocess-popen-preexec-fn,consider-using-with
import argparse
import logging
import os
import re
import shutil
import signal
import subprocess
import tempfile
import time
from typing import List, Optional
import requests
MODULE_NAME = 'sample_metadata'
LOCAL_SCHEMA_PORT = os.getenv('PORT', '8000')
DEFAULT_SCHEMA_URL = f'http://localhost:{LOCAL_SCHEMA_PORT}/openapi.json'
DEFAULT_HOST_URL = 'http://localhost:8000'
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)
def check_if_server_is_accessible(schema_url: str) -> bool:
"""Check if request to 'schema_url' returns OK"""
try:
return requests.get(schema_url).ok
except requests.ConnectionError:
return False
def start_server(
schema_url: str,
api_host_url: Optional[str],
docker_image: Optional[str]
) -> Optional[subprocess.Popen]:
"""Start the API server if necessary, and return a process when it's started"""
# Check if the schema endpoint is already running.
if check_if_server_is_accessible(schema_url):
if docker_image:
raise Exception(f'Can\'t run docker image - server already running at {schema_url}')
if api_host_url:
logger.warning(f'Server already running - can\'t apply api_host_url {api_host_url}')
logger.info(f'Using already running server {schema_url}')
return None
if not api_host_url:
api_host_url = os.getenv('SM_HOST_URL', DEFAULT_HOST_URL)
command = ['python', '-m', 'api.server']
if docker_image is not None:
command = [
'docker',
'run',
'-p8000:8000',
f'-eSM_HOST_URL={api_host_url}',
docker_image,
*command
]
logger.info('Starting local API server with: ' + ' '.join(command))
logger.info('Generated schema server endpoint will be ' + api_host_url)
_process = subprocess.Popen(
command,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
env={**os.environ, 'SM_HOST_URL':api_host_url},
# The os.setsid() is passed in the argument preexec_fn so
# it's run after the fork() and before exec() to run the shell.
preexec_fn=os.setsid,
)
assert _process.stdout
for c in iter(_process.stdout.readline, 'b'):
line = None
if c:
line = c.decode('utf-8').rstrip()
if line is not None:
logger.info('API: ' + line)
if 'running on http' in line.lower():
# server has been started
logger.info('Started process')
return _process
rc = _process.poll()
if rc is not None:
# the process exited early
logger.error(f'Server exited with rc={rc}')
raise SystemExit(1)
return None
def check_openapi_version(openapi_command: List[str]):
"""
Check compatible OpenAPI version
"""
command = [*openapi_command, '--version']
out = subprocess.check_output(command).decode().split('\n', maxsplit=1)[0].strip()
version_match = re.search(pattern=r'\d+\.\d+\.\d+', string=out)
if not version_match:
raise Exception(f'Could not detect version of openapi-generator from "{out}"')
version = version_match.group()
major = version.split('.')[0]
if int(major) != 5:
raise Exception(f'openapi-generator must be version 5.x.x, received: {version}')
def generate_api_and_copy(gen_command: List[str], output_copyer):
"""
Use OpenApiGenerator to generate the installable API
"""
tmpdir = tempfile.mkdtemp()
command = gen_command + ['-o', tmpdir]
logger.info('Generating with command: ' + ' '.join(command))
n_attempts = 1
succeeded = False
for i in range(n_attempts, 0, -1):
try:
stdout = subprocess.check_output(command)
logger.info('Generated API: ' + str(stdout.decode()))
succeeded = True
break
except subprocess.CalledProcessError as e:
logger.warning(f'openapi generation failed, trying {i-1} more times: {e}')
time.sleep(2)
if succeeded:
output_copyer(tmpdir)
shutil.rmtree(tmpdir)
def copy_typescript_files_from(tmpdir):
"""Copy typescript files to web/src/sm-api/"""
files_to_ignore = {
'README.md',
'.gitignore',
'.npmignore',
'.openapi-generator',
'.openapi-generator-ignore',
'git_push.sh',
}
dir_to_copy_to = 'web/src/sm-api/' # should be relative to this script
dir_to_copy_from = tmpdir
if not os.path.exists(dir_to_copy_to):
os.makedirs(dir_to_copy_to)
if not os.path.exists(dir_to_copy_from):
raise FileNotFoundError(
f"Directory to copy from doesn't exist ({dir_to_copy_from})"
)
# remove everything from dir_to_copy_to except those in files_to_ignore
logger.info('Removing files from dest directory ' + dir_to_copy_to)
for file_to_remove in os.listdir(dir_to_copy_to):
if file_to_remove in files_to_ignore:
continue
path_to_remove = os.path.join(dir_to_copy_to, file_to_remove)
if os.path.isdir(path_to_remove):
shutil.rmtree(path_to_remove)
else:
os.remove(path_to_remove)
files_to_copy = os.listdir(dir_to_copy_from)
logger.info(f'Copying {len(files_to_copy)} files / directories to {dir_to_copy_to}')
for file_to_copy in files_to_copy:
if file_to_copy in files_to_ignore:
continue
path_to_copy = os.path.join(dir_to_copy_from, file_to_copy)
output_path = os.path.join(dir_to_copy_to, file_to_copy)
if os.path.isdir(path_to_copy):
shutil.copytree(path_to_copy, output_path)
else:
shutil.copy(path_to_copy, output_path)
def copy_python_files_from(tmpdir):
"""
Copy a selection of API files generated from openapi-generator:
FROM: $tmpdir/sample_metadata
TO: ./sample_metadata
This clears the ./sample_metadata folder except for 'files_to_ignore'.
"""
files_to_ignore = {'README.md', 'parser'}
module_dir = MODULE_NAME.replace('.', '/')
dir_to_copy_to = module_dir # should be relative to this script
dir_to_copy_from = os.path.join(tmpdir, module_dir)
if not os.path.exists(dir_to_copy_to):
raise FileNotFoundError(
f"Directory to copy to doesn't exist ({dir_to_copy_to})"
)
if not os.path.exists(dir_to_copy_from):
raise FileNotFoundError(
f"Directory to copy from doesn't exist ({dir_to_copy_from})"
)
# remove everything from dir_to_copy_to except those in files_to_ignore
logger.info('Removing files from dest directory ' + dir_to_copy_to)
for file_to_remove in os.listdir(dir_to_copy_to):
if file_to_remove in files_to_ignore:
continue
path_to_remove = os.path.join(dir_to_copy_to, file_to_remove)
if os.path.isdir(path_to_remove):
shutil.rmtree(path_to_remove)
else:
os.remove(path_to_remove)
files_to_copy = os.listdir(dir_to_copy_from)
logger.info(f'Copying {len(files_to_copy)} files / directories to {dir_to_copy_to}')
for file_to_copy in files_to_copy:
if file_to_copy in files_to_ignore:
continue
path_to_copy = os.path.join(dir_to_copy_from, file_to_copy)
output_path = os.path.join(dir_to_copy_to, file_to_copy)
if os.path.isdir(path_to_copy):
shutil.copytree(path_to_copy, output_path)
else:
shutil.copy(path_to_copy, output_path)
docs_dir = os.path.join(tmpdir, 'docs')
static_dir = 'web/src/static'
output_docs_dir = os.path.join(static_dir, 'sm_docs')
if os.path.exists(output_docs_dir):
shutil.rmtree(output_docs_dir)
if not os.path.exists(static_dir):
os.makedirs(static_dir)
shutil.copytree(docs_dir, output_docs_dir)
shutil.copy(
os.path.join(tmpdir, 'README.md'), os.path.join(output_docs_dir, 'README.md')
)
shutil.copy('README.md', os.path.join(output_docs_dir, 'index.md'))
def stop_server(process):
if process:
pid = process.pid
logger.info(f'Stopping self-managed server by sending sigkill to {pid}')
os.killpg(
os.getpgid(process.pid), signal.SIGTERM
) # Send the signal to all the process groups
def main(args):
"""
Generates installable python API using:
- Start API server (if applicable);
- Call openapi-generator to generate python API to temp folder;
- Empty the 'sample_metadata' folder (except for some files);
- Copy relevant files to 'sample_metadata' in CWD;
- Stop the server (if applicable)
"""
openapi_command = args.openapi_command.split(' ')
check_openapi_version(openapi_command)
with open('deploy/python/version.txt', encoding='utf-8') as f:
version = f.read().strip()
process = start_server(args.schema_url, args.api_host_url, args.docker_image)
base_command = [
*openapi_command,
'generate',
*('-i', args.schema_url),
*('--package-name', MODULE_NAME),
*('--artifact-version', version),
'--skip-validate-spec',
]
try:
# Generate Python client, copying md docs to web/static/sm_docs directory.
command = base_command + ['-g', 'python'] + ['--template-dir', 'openapi-templates']
generate_api_and_copy(command, copy_python_files_from)
# Generate Typescript client and copy to web/sm-api directory.
command = base_command + ['-g', 'typescript-axios']
generate_api_and_copy(command, copy_typescript_files_from)
# pylint: disable=broad-except
except BaseException as e:
logger.error(str(e))
stop_server(process)
raise e
stop_server(process)
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description=(
"Generates sample_metadata Python and Typescript library code from a "
"sample-metadata API server OpenAPI schema endpoint. Starts a "
"local server if the schema endpoint is not already available."
)
)
parser.add_argument(
"-o",
"--openapi-command",
action="store",
type=str,
default="openapi-generator",
help=(
"OpenApi command syntax used to generate the library from the server endpoint. "
"(Default: 'openapi-generator')"
)
)
parser.add_argument(
"-s",
"--schema-url",
action="store",
type=str,
default=DEFAULT_SCHEMA_URL,
help=f"OpenApi schema host endpoint. (Default: '{DEFAULT_SCHEMA_URL}')"
)
parser.add_argument(
"-a",
"--api-host-url",
action="store",
type=str,
help=(
"Url configured as the default host API endpoint in the generated Python/Typescript code."
f"(Default: the value of SM_HOST_URL, if set, or '{DEFAULT_HOST_URL}' otherwise)"
)
)
parser.add_argument(
"-d",
"--docker-image",
action="store",
type=str,
help="[optional] Docker image to run to serve up the OpenApi endpoint. Cannot be already running."
)
main(parser.parse_args())
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main.py | duyvu1991/license-plate-detection- | 0 | 50268 | <reponame>duyvu1991/license-plate-detection-
from flask import Flask, render_template
from blueprints import *
app = Flask(__name__)
app.register_blueprint(home)
app.register_blueprint(upload_api)
if __name__ == '__main__':
app.run(debug = True, host='127.0.0.1', port=5001)
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evoTurb_python/CalcCohx.py | YC-SWE/evoTurb | 2 | 153273 | # -*- coding: utf-8 -*-
"""
CalcCohx
function: calculate the longitudinal coherence
------------------------------------------------------------------------------------
Usage
Cohx,ConfigParameters = CalcCohx(ConfigParameters)
-----------------------------------------------------------------------------------
Inputs
ConfigParameters: -dict, configuration parameters
---------------------------------------------------------------------------------
Outputs
ConfigParameters: configuration parameters, -dict
Cohx: longitudinal coherence
- 3D array, with size of (Nplanes,Nplanes,number of freq)
--------------------------------------------------------------------------------
References
1.'Exp-UserDefined' uses the wind evolution model (Eq.4) and
'Exp-Simley' uses the wind evolution model (Eq.7) and in
# Simley, E., & Pao, L. Y. (2015).
# A longitudinal spatial coherence model for wind evolution based on large-eddy simulation.
# In 2015 American Control Conference (ACC) (pp. 3708–3714). IEEE.
# https://doi.org/10.1109/ACC.2015.7171906
This model is acquired from LES simulations.
2.'Kristensen' uses the wind evolution model (Eq.20) and G-function (Eq.29) in
# Kristensen, L. (1979).
# On longitudinal spectral coherence.
# Boundary-Layer Meteorology, 16(2), 145–153.
# https://doi.org/10.1007/BF02350508
This model is based on physical deduction.
3.'Exp-GPR' uses the wind evolution model (Eq.6) and
the GPR models case 15 for a and case 17 for b (Table5) in
# Chen, Y., Schlipf, D., & Cheng, P. W. (2021).
# Parameterization of wind evolution using lidar.
# Wind Energy Science, 6(1), 61–91.
# https://doi.org/10.5194/wes-6-61-2021
The GPR models are trained with measurement data from an onshore flat site.
Due to the limitation of the training data, it is not recommended to
use the GPR models for the cases where the separations between the unfrozen planes exceed 109 m.
----------------------------------------------------------------------------------------------------
Created on 20.06.2021
<NAME> (c) University of Stuttgart
<NAME> (c) Flensburg University of Applied Sciences
----------------------------------------------------------------------------------------------------
Modified
"""
# import libirary
from scipy.spatial.distance import cdist
import numpy as np
import math
# import scipy.io as sio
def CalcCohx(ConfigParameters):
# spatial distance x
X = np.reshape(ConfigParameters['Xpos'],(ConfigParameters['Nplanes'],1),order="F")
X = np.concatenate((X,0*X),axis=1)
r_x = np.reshape(cdist(X, X),(ConfigParameters['Nplanes']**2,1),order="F")
if ConfigParameters['EvoModel']!='Exp-UserDefined':
# calculate wind statistics to determine wind evolution model parameters
if ConfigParameters['TurbModel']=='Kaimal':
# Check Turbulence Class
# Iref: expected value of the turbulence intensity at 15 m/s. (IEC61400-1:2005 p.22)
# Note that IRef is defined as the mean value in this edition of the standard rather than as a representative value.
if ConfigParameters['TurbClass']=='A+':
Iref=0.18
elif ConfigParameters['TurbClass']=='A':
Iref=0.16
elif ConfigParameters['TurbClass']=='B':
Iref=0.14
elif ConfigParameters['TurbClass']=='C':
Iref=0.12
else:
raise ValueError('Wrong turbulence class. Please define IEC turbulence Class as A+, A, B, or C.')
# sigma_u: the representative value of the turbulence standard deviation,
# shall be given by the 90# quantile for the given hub height wind speed (IEC61400-1:2005 p.24)
sigma_u = Iref*(0.75*ConfigParameters['Uref']+5.6)
sigma_v = sigma_u*0.8
sigma_w = sigma_u*0.5
sigma_total = math.sqrt(sigma_u**2+sigma_v**2+sigma_w**2)
# Lambda = longitudinal turbulence scale parameter
if ConfigParameters['Href'] > 60:
Lambda = 42
else:
Lambda = 0.7*ConfigParameters['Href']
# Integral length scale
Lu = 8.1*Lambda
# save the parameters
ConfigParameters['sigma_u'] = sigma_u
ConfigParameters['sigma_v'] = sigma_v
ConfigParameters['sigma_w'] = sigma_w
ConfigParameters['L_u'] = Lu
elif ConfigParameters['TurbModel']=='Mann':
sigma_total = math.sqrt(ConfigParameters['sigma_u']**2+ConfigParameters['sigma_v']**2+ConfigParameters['sigma_w']**2)
Lu = ConfigParameters['L_u']
# coherence x
if ConfigParameters['EvoModel'] == 'Exp-UserDefined':
Cohx_squared = np.exp(-ConfigParameters['evo_a']*np.sqrt((ConfigParameters['f']*r_x/ConfigParameters['Uref'])**2+\
(ConfigParameters['evo_b']*r_x)**2))
elif ConfigParameters['EvoModel'] == 'Exp-Simley':
ConfigParameters['evo_a'] = 8.4*sigma_total/ConfigParameters['Uref']+0.05
ConfigParameters['evo_b'] = 0.25*Lu**(-1.24)
Cohx_squared = np.exp(-ConfigParameters['evo_a']*np.sqrt((ConfigParameters['f']*r_x/ConfigParameters['Uref'])**2+\
(ConfigParameters['evo_b']*r_x)**2))
elif ConfigParameters['EvoModel'] == 'Kristensen':
xi = ConfigParameters['f']*Lu/ConfigParameters['Uref']
alpha = sigma_total/ConfigParameters['Uref']*r_x/Lu
G = 33**(-2/3)*(33*xi)**2*(33*xi+3/11)**0.5/(33*xi+1)**(11/6)
m = 2*(alpha<=1)+1*(alpha>1)
Cohx_squared = np.exp(-2*alpha*G)*(1-np.exp(-1/(2*alpha**m*xi**2)))**2
# =============================================================================
# elif ConfigParameters['EvoModel'] == 'Exp-GPR':
# GPRmdl = sio.loadmat('ExpGPR.mat')
# predictor_a = struct2table(struct('V_long_mean',ConfigParameters.Uref,...
# 'V_vert_std',ConfigParameters.sigma_w,'DirError',0))
# predictor_b = struct2table(struct('V_long_mean',ConfigParameters.Uref*ones(size(r_x)),...
# 'V_long_TI_U',ConfigParameters.sigma_u/ConfigParameters.Uref*ones(size(r_x)),...
# 'V_long_skew',zeros(size(r_x)),'V_long_kurt',zeros(size(r_x)),...
# 'V_lat_skew',zeros(size(r_x)),'V_vert_skew',zeros(size(r_x)),...
# 'vlos_d',r_x))
# ConfigParameters.evo_a = predict(cgprMdl_a,predictor_a)
# ConfigParameters.evo_b = predict(cgprMdl_b,predictor_b)
# ConfigParameters.evo_b(predictor_b.vlos_d==0)=0
# Cohx_squared = exp(-sqrt(ConfigParameters.evo_a.^2.*(ConfigParameters.f.*r_x./ConfigParameters.Uref).^2+...
# ConfigParameters.evo_b.^2))
# =============================================================================
Cohx = np.reshape(np.sqrt(Cohx_squared),(ConfigParameters['Nplanes'],ConfigParameters['Nplanes'],len(ConfigParameters['f'])),order="F")
return Cohx,ConfigParameters
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] |
core/utils/network/sms.py | vsilent/smarty-bot | 1 | 113412 | <filename>core/utils/network/sms.py
from twilio.rest import TwilioRestClient
# Your Account Sid and Auth Token from twilio.com/user/account
account_sid = "AC32a3c49700934481addd5ce1659f04d2"
auth_token = ""
client = TwilioRestClient(account_sid, auth_token)
message = client.sms.messages.create(body="Jenny please?! I love you <3",
to="+14159352345", # Replace with your phone number
from_="+14158141829") # Replace with your Twilio number
print message.sid
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BBmap_scrapper/main.py | Cpt-Graby/web_scrapping | 0 | 72291 | import requests
from bs4 import BeautifulSoup
import random
import time
random.seed(60)
def get_page(url):
"""
Simple funtion that get the url with requests and return a bs4 object.
:param (str): The string of the url
:return: bs4 object
"""
try:
req = requests.get(url)
return BeautifulSoup(req.text, 'html.parser')
except requests.exceptions.RequestException:
print("Could load the page: {}".format(url))
def get_link_dicegrimorium():
"""
Funtion that returns all the links where to download the sample
:return: list of links
"""
bs = get_page('https://dicegrimorium.com/free-rpg-map-library/')
try:
content_images = bs.find_all('figure', {'class': 'wp-block-image size-large'})
links_page = []
for content in content_images:
links_page.append(content.find('a').get('href'))
return links_page
except:
print("""Some thing went wrong at the import of 'figure',
{'class': 'wp-block-image size-large'}""")
def dl_zip_dicegrimorium(url, title):
"""
:param url:
:param title:
:return:
"""
try:
r = requests.get(url, allow_redirects=True)
with open(title + ".zip", 'wb') as dl_file:
dl_file.write(r.content)
except:
print(f'Fail download: {title}')
def import_link_zip_dicegrimorium(links_pages):
"""
Given a list, this function will get all the href off the zip file in the url that are in the list.
"""
list_dl = []
list_wait_time = []
total_dl = len(links_pages)
iteration = 1
for i in links_pages:
index_fail = 0
if index_fail > 4:
break
try:
"""Making some verbose"""
print(f'{iteration}/{total_dl}')
index_fail = 0
wait_time = random.randint(10, 90)
list_wait_time.append(wait_time)
"""Main scraper"""
bs = get_page(i)
title_zip = bs.h2.text.replace(" ", "-").replace(".", "-").replace('\t', '').replace('\n', '').replace('--',
'')
print(title_zip)
dl_link = bs.find('figcaption').a.get('href')
dl_zip_dicegrimorium(dl_link, title_zip)
list_dl.append((title_zip, dl_link))
time.sleep(wait_time)
"""verbose _done"""
print(f'DL{iteration}/{total_dl}: done in {wait_time}')
iteration += 1
except:
print(f"Somethig went wrong with the {iteration}:{i}.\n")
index_fail += 1
return list_dl
def main_dicegrimorium():
all_links = get_link_dicegrimorium()
list_zip = import_link_zip_dicegrimorium(all_links)
return None
links = get_link_dicegrimorium()
print(import_link_zip_dicegrimorium(links))
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] |
stencils/diffterm/kernel.py | pengdada/vecscatter-artifact | 0 | 189790 | from st.expr import Index, ConstRef
from st.grid import Grid
# Declare indices
i = Index(0)
j = Index(1)
k = Index(2)
# Declare grid
ux = Grid("ux", 3)
vx = Grid("vx", 3)
wx = Grid("wx", 3)
uy = Grid("uy", 3)
vy = Grid("vy", 3)
wy = Grid("wy", 3)
uz = Grid("uz", 3)
vz = Grid("vz", 3)
wz = Grid("wz", 3)
q_1 = Grid("q_1", 3)
q_2 = Grid("q_2", 3)
q_3 = Grid("q_3", 3)
q_5 = Grid("q_5", 3)
diffflux1 = Grid("diffflux1", 3)
diffflux2 = Grid("diffflux2", 3)
diffflux3 = Grid("diffflux3", 3)
diffflux4 = Grid("diffflux4", 3)
dxinv0 = ConstRef("dxinv0")
dxinv1 = ConstRef("dxinv1")
dxinv2 = ConstRef("dxinv2")
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] |
appr/commands/logout.py | sergeyberezansky/appr | 31 | 6418 | <filename>appr/commands/logout.py
from __future__ import absolute_import, division, print_function
from appr.auth import ApprAuth
from appr.commands.command_base import CommandBase, PackageSplit
class LogoutCmd(CommandBase):
name = 'logout'
help_message = "logout"
def __init__(self, options):
super(LogoutCmd, self).__init__(options)
self.status = None
self.registry_host = options.registry_host
self.package_parts = options.package_parts
pname = self.package_parts.get('package', None)
namespace = self.package_parts.get('namespace', None)
self.package = None
if pname:
self.package = "%s/%s" % (namespace, pname)
elif namespace:
self.package = namespace
@classmethod
def _add_arguments(cls, parser):
cls._add_registryhost_option(parser)
parser.add_argument('registry', nargs='?', default=None, action=PackageSplit,
help="registry url: quay.io[/namespace][/repo]\n" +
"If namespace and/or repo are passed, creds only logout for them")
def _call(self):
client = self.RegistryClient(self.registry_host)
ApprAuth().delete_token(client.host, scope=self.package)
self.status = "Logout complete"
if self.registry_host != '*':
self.status += " from %s" % self.registry_host
def _render_dict(self):
return {"status": self.status, 'host': self.registry_host, "scope": self.package}
def _render_console(self):
return " >>> %s" % self.status
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temboardui/plugins/pgconf/__init__.py | missmoiselle/temboard | 294 | 78124 | <filename>temboardui/plugins/pgconf/__init__.py
import logging
from os import path
import tornado.web
from tornado.escape import url_escape, url_unescape
from temboardui.web import (
Blueprint,
HTTPError,
Redirect,
TemplateRenderer,
)
PLUGIN_NAME = 'pgconf'
logger = logging.getLogger(__name__)
blueprint = Blueprint()
blueprint.generic_proxy("/pgconf/configuration", methods=["POST"])
plugin_path = path.dirname(path.realpath(__file__))
render_template = TemplateRenderer(plugin_path + "/templates")
def configuration(config):
return {}
def get_routes(config):
routes = blueprint.rules + [
(
r"/js/pgconf/(.*)",
tornado.web.StaticFileHandler,
{'path': plugin_path + "/static/js"}
),
(
r"/css/pgconf/(.*)",
tornado.web.StaticFileHandler,
{'path': plugin_path + "/static/css"}
),
]
return routes
@blueprint.instance_route("/pgconf/configuration(?:/category/(.+))?",
methods=["GET", "POST"])
def configuration_handler(request, category=None):
request.instance.check_active_plugin(PLUGIN_NAME)
profile = request.instance.get_profile()
agent_username = profile['username']
template_vars = {}
# Deduplicate HTTP prefix of plugin on agent.
prefix = "/pgconf/configuration"
query_filter = request.handler.get_argument('filter', None, strip=True)
status = request.instance.get(prefix + "/status")
categories = request.instance.get(prefix + "/categories")
if category:
category = url_unescape(category)
else:
category = categories['categories'][0]
logger.debug("category=%s", category)
if query_filter:
query = {'filter': query_filter}
configuration_url = prefix
else:
query = {}
configuration_url = prefix + "/category/" + url_escape(category)
configuration = request.instance.get(configuration_url, query=query)
if "POST" == request.method:
settings = {'settings': [
{'name': name, 'setting': value[0]}
for name, value in request.arguments.iteritems()
# 'filter' is not a setting, just ignore it.
if name != 'filter'
]}
try:
request.instance.post(prefix, body=settings)
# Redirect to GET page, same URI.
return Redirect(request.uri)
except HTTPError as e:
# Rerender HTML page with errors.
template_vars['error_code'] = e
template_vars['error_message'] = e.log_message
return render_template(
'configuration.html',
nav=True,
role=request.current_user,
instance=request.instance,
agent_username=agent_username,
plugin=PLUGIN_NAME,
xsession=request.instance.xsession,
current_cat=category,
configuration_categories=categories,
configuration_status=status,
data=configuration,
query_filter=query_filter,
**template_vars
)
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src/ros/spirit/src/helpers.py | masasin/spirit | 0 | 1606605 | # -*- coding: utf-8 -*-
"""
Helper functions and classes for general use.
"""
from __future__ import division
from functools import partial, update_wrapper
from time import localtime, strftime
import numpy as np
from numpy.linalg import norm
import rospy
from geometry_msgs.msg import Point, PoseStamped, Quaternion
from sensor_msgs.msg import Image
from std_msgs.msg import Header
import tf
d2r = np.deg2rad
r2d = np.rad2deg
EULER_CONVENTION = "rxyz"
def unit_vector(v):
"""
Change the length of the vector to unity in the same direction.
Parameters
----------
v : array-like
A vector to be normalized.
Returns
-------
np.ndarray
The normalized vector, or the original vector if it has a length of 0.
"""
norm = np.linalg.norm(v)
if norm:
return v / norm
else:
return np.asarray(v)
# noinspection PyPep8Naming
class memoize(object):
def __init__(self, func):
self.func = func
update_wrapper(self, func)
def __get__(self, instance, owner):
if instance is None:
return self.func
return partial(self, instance)
def __call__(self, *args, **kwargs):
obj = args[0]
try:
cache = obj.__cache__
except AttributeError:
cache = obj.__cache__ = {}
key = (self.func, args[1:], frozenset(kwargs.items()))
try:
res = cache[key]
except KeyError:
res = cache[key] = self.func(*args, **kwargs)
return res
class Pose(object):
"""
Convenience wrapper for PoseStamped.
Parameters
----------
pose_stamped : PoseStamped
The pose message.
Attributes
----------
pose_stamped : PoseStamped
The pose message.
position : np.ndarray
The x, y, and z coordinates contained in the pose.
orientation : np.ndarray
The x, y, z, and w quaternion contained in the pose.
header : Header
The header from the pose message
"""
def __init__(self, pose_stamped):
self.pose_stamped = pose_stamped
self.position, self.orientation = self._components(self.pose_stamped)
self.header = self.pose_stamped.header
def rel_position(self, pose, rotation_matrix=None):
"""
Calculate the relative position with another pose, with local reference.
Parameters
----------
pose : Pose
The target pose.
rotation_matrix : Optional[np.ndarray]
The rotation matrix to use. If not provided, the rotation matrix of
the current pose is used.
Returns
-------
np.ndarray
The x, y, z relative positions.
"""
if rotation_matrix is None:
rotation_matrix = Quat.rotation_matrix(self.orientation)
return rotation_matrix.dot(pose.position - self.position)
def rel_euler(self, pose):
"""
Calculate the relative angle with another pose.
Parameters
----------
pose : Pose
The target pose.
Returns
-------
np.ndarray
The relative angle as Euler, in the order of pitch, roll, yaw.
"""
return Quat.to_euler(Quat.rel_rotation(pose.orientation,
self.orientation))
def distance(self, pose):
"""
Calculate the distance to another pose.
Parameters
----------
pose : Pose
The target pose.
Returns
-------
float
The distance to the target pose.
"""
return norm(pose.position - self.position)
@staticmethod
def _components(pose_stamped):
"""
Return the position and orientation of a PoseStamped as numpy arrays.
Parameters
----------
pose_stamped : Pose(WithCovariance)?(Stamped)?
The pose to be decomposed.
Returns
-------
position : np.ndarray
The x, y, and z coordinates contained in the pose.
orientation : np.ndarray
The x, y, z, and w quaternion contained in the pose.
"""
position = np.array([pose_stamped.pose.position.x,
pose_stamped.pose.position.y,
pose_stamped.pose.position.z])
orientation = np.array([pose_stamped.pose.orientation.x,
pose_stamped.pose.orientation.y,
pose_stamped.pose.orientation.z,
pose_stamped.pose.orientation.w])
return position, orientation
@classmethod
def from_components(cls, position, orientation, sequence=0):
"""
Generate a Pose from its components.
Parameters
----------
position : Sequence[float]
The x, y, and z coordinates of the pose.
orientation : Sequence[float]
The x, y, z, and w quaternion of the pose.
sequence : Optional[int]
The sequence number of the pose.
Returns
-------
Pose
The generated pose.
"""
return cls(cls.generate_stamped(position, orientation, sequence))
@staticmethod
def generate_stamped(position, orientation, sequence=0):
"""
Generate a PoseStamped from its components.
Parameters
----------
position : Sequence[float]
The x, y, and z coordinates of the pose.
orientation : Sequence[float]
The x, y, z, and w quaternion of the pose.
sequence : Optional[int]
The sequence number of the pose.
Returns
-------
PoseStamped
The generated pose.
"""
pose_stamped = PoseStamped()
pose_stamped.header.seq = sequence
try:
pose_stamped.header.stamp = rospy.Time.now()
except rospy.exceptions.ROSInitException:
pass
pose_stamped.pose.position = Point(*position)
pose_stamped.pose.orientation = Quaternion(*orientation)
return pose_stamped
def __repr__(self):
return "<Pose ({position}, {orientation})>".format(
position=self.position.tolist(),
orientation=self.orientation.tolist(),
time=self.header.stamp)
def __str__(self):
return "<Pose ({position}, {orientation}): {time}>".format(
position=self.position.tolist(),
orientation=self.orientation.tolist(),
time=self.header.stamp)
class Frame(object):
"""
Encapsulate an image and the pose it was taken in.
Parameters
----------
pose_stamped : PoseStamped
The pose of the drone when the image was taken.
image : Image
The image that was taken.
Attributes
----------
pose_stamped : PoseStamped
The raw pose message of the drone at which the image was taken.
pose : Pose
The pose of the drone at which the image was taken.
rotation_matrix : np.ndarray
The rotation matrix of the frame orientation.
image : Image
The image that was taken.
stamp : rospy.rostime.Time
The timestamp of the pose.
stamp_str : str
The timestamp of the pose, in human readable format.
"""
def __init__(self, pose_stamped, image):
self.pose_stamped = pose_stamped
self.pose = Pose(pose_stamped)
self.rotation_matrix = Quat.rotation_matrix(self.pose.orientation)
self.image = image
self.stamp = self.pose.header.stamp
self.stamp_str = strftime("%Y-%m-%d %H:%M:%S",
localtime(self.stamp.to_time()))
@memoize
def rel_position(self, pose):
"""
Calculate the relative position with another pose, with local reference.
Parameters
----------
pose : Pose
The target pose.
Returns
-------
np.ndarray
The x, y, z relative positions.
"""
return self.pose.rel_position(pose,
rotation_matrix=self.rotation_matrix)
@memoize
def rel_euler(self, pose):
"""
Calculate the relative angle with another pose.
Parameters
----------
pose : Pose
The target pose.
Returns
-------
np.ndarray
The relative angle as Euler, in the order of pitch, roll, yaw.
"""
return self.pose.rel_euler(pose)
@memoize
def distance(self, pose):
"""
Calculate the distance to another pose.
Parameters
----------
pose : Pose
The target pose.
Returns
-------
float
The distance to the target pose.
"""
return self.pose.distance(pose)
def __repr__(self):
return "<Frame({pose})>".format(pose=self.pose)
class Fov(object):
"""
Field of view methods.
"""
@staticmethod
def d2v(fov_diagonal, aspect_ratio=4 / 3):
"""
Convert a diagonal field of view to vertical.
Parameters
----------
fov_diagonal : float
The diagonal field of view.
aspect_ratio: Optional[float]
The aspect ratio of the display. Default is 4:3.
Returns
-------
float
The vertical field of view.
"""
ratio_diagonal = np.sqrt(1 + aspect_ratio**2)
return 2 * r2d(np.arctan(np.tan(d2r(fov_diagonal) / 2)
/ ratio_diagonal))
@staticmethod
def v2h(fov_vertical, aspect_ratio=4 / 3):
"""
Convert a vertical field of view to horizontal.
Parameters
----------
fov_vertical : float
The vertical field of view.
aspect_ratio: Optional[float]
The aspect ratio of the display. Default is 4:3.
Returns
-------
float
The horizontal field of view.
"""
return 2 * r2d(np.arctan(np.tan(d2r(fov_vertical) / 2) * aspect_ratio))
class Quat(object):
"""
Quaternion methods.
"""
@staticmethod
def to_euler(quaternion):
"""
Change a quaternion to an Euler angle representation.
Parameters
----------
quaternion : np.ndarray
A quaternion in the order of x, y, z, w.
Returns
-------
np.ndarray
The Euler angle, in the order of pitch, roll, yaw.
"""
# noinspection PyUnresolvedReferences
return tf.transformations.euler_from_quaternion(quaternion,
EULER_CONVENTION)
@staticmethod
def to_axis(quaternion):
"""
Change a quaternion to an axis-angle representation.
Parameters
----------
quaternion : np.ndarray
A quaternion in the order of x, y, z, w.
Notes
-----
θ is in degrees rather than radians, for ease of integration in OpenGL.
Returns
-------
tuple
The angle in axis-angle representation, with the order of θ, x, y,
z. θ is in degrees.
"""
x, y, z, w = unit_vector(quaternion)
angle = r2d(2 * np.arccos(w))
if angle == 0:
axis_x = 1
axis_y = axis_z = 0
elif angle % 180 == 0:
axis_x, axis_y, axis_z = x, y, z
else:
axis_x = x / np.sqrt(1 - w**2)
axis_y = y / np.sqrt(1 - w**2)
axis_z = z / np.sqrt(1 - w**2)
return angle, axis_x, axis_y, axis_z
@staticmethod
def product(a, b):
"""
Find the product of two quaternions.
Parameters
----------
a : Sequence[float]
A quaternion, in the order of x, y, z, w
b : Sequence[float]
A quaternion, in the order of x, y, z, w
Returns
-------
np.ndarray
A quaternion, in the order of x, y, z, w
"""
imaginary_part = a[3] * b[:3] + b[3] * a[:3] + np.cross(a[:3], b[:3])
real_part = a[3] * b[3] - np.dot(a[:3], b[:3])
return np.append(imaginary_part, real_part)
@staticmethod
def inverse(quaternion):
"""
Return the inverse of a quaternion
Parameters
----------
quaternion : Sequence[float]
A quaternion, in the order of x, y, z, w
Returns
-------
np.ndarray
The inverse of the quaternion.
"""
return (quaternion * np.array([-1, -1, -1, 1])
/ np.linalg.norm(quaternion)**2)
@staticmethod
def rel_rotation(a, b):
"""
Find the quaternion which produces a rotation from `a` to `b`.
Parameters
----------
a : Sequence[float]
A quaternion, in the order of x, y, z, w
b : Sequence[float]
A quaternion, in the order of x, y, z, w
Returns
-------
np.ndarray
A quaternion, in the order of x, y, z, w
"""
return Quat.product(unit_vector(a), Quat.inverse(unit_vector(b)))
@staticmethod
def rotation_matrix(quaternion):
"""
Create the rotation matrix of a quaternion.
Parameters
----------
quaternion : np.ndarray
A quaternion in the order of x, y, z, w.
Returns
-------
np.ndarray
A 3x3 rotation matrix representing the quaternion.
References
----------
.. [1] Wikipedia, Rotation Matrix.
https://en.wikipedia.org/wiki/Rotation_matrix#Quaternion
"""
x, y, z, w = quaternion
n = sum(quaternion**2)
if n == 0:
return np.identity(3)
s = 2 / n
wx = s * w * x
wy = s * w * y
wz = s * w * z
xx = s * x * x
xy = s * x * y
xz = s * x * z
yy = s * y * y
yz = s * y * z
zz = s * z * z
return np.array([[1 - (yy + zz), xy - wz, wy],
[wz, 1 - (xx + zz), yz - wx],
[xz - wy, yz + wx, 1 - (xx + yy)]])
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] |
train.py | Omrigan/shiny-language-learner | 2 | 166113 | <reponame>Omrigan/shiny-language-learner<gh_stars>1-10
import random
import telegram
import datetime
import study_settings
import copy
params = {}
langs = {
1: {'original': 'en',
'candidacies': 'ru'
},
2: {'original': 'ru',
'candidacies': 'en'
}
}
choose = 'Choose train type:\n' \
'1-foreign->native\n' \
'2-native->foreign\n' \
'3-write foreign'
def do_train(user, string):
if user['train']['type'] == 0:
user['train']['type'] = -1
telegram.send_message(user['chat_id'], choose, reply_markup=telegram.chooseTrainKeyboard)
elif user['train']['type'] == -1:
if string == 'end':
end_train(user, string)
else:
user['train']['type'] = int(string)
trains[user['train']['type']](user, string, overwrite=True)
else:
trains[user['train']['type']](user, string)
def do_variant_train(user, string, overwrite=False):
out_str = ""
was_incorrect = False
lang_original = langs[user['train']['type']]['original']
lang_candidacies = langs[user['train']['type']]['candidacies']
if string[0] == '/':
overwrite = True
if not overwrite:
try:
a = int(string) - 1
except ValueError:
telegram.send_message(user['chat_id'], "Error parse!")
return
for w in user['words']:
if w == user['train']['word']:
if user['train']['correct'] == a:
out_str += "Correct\n"
if w['stage'] < study_settings.max_stage:
w['stage'] += 1
w['expiration_date'] = datetime.datetime.utcnow() + study_settings.stages[w['stage']]
else:
out_str += "Incorrect\nThe correct one is %s \n" % (w[lang_candidacies],)
if w['stage'] > study_settings.min_stage:
w['stage'] -= 1
was_incorrect = True
if len(list(filter(lambda _: _['expiration_date'] < datetime.datetime.utcnow(), user['words']))) > 8:
if not was_incorrect:
word_list = sorted(user['words'], key=lambda _: _['expiration_date'])[0:8]
random.shuffle(word_list)
word_list = word_list[0:4]
user['train']['word_list'] = word_list
cnt = random.randint(0, 3)
user['train']['word'] = word_list[cnt]
user['train']['correct'] = cnt
out_str += user['train']['word'][lang_original] + "\n"
for i, w in zip(range(4), user['train']['word_list']):
out_str += "%s - %s\n" % (i + 1, w[lang_candidacies])
else:
out_str += "Not enough words\n"
user['train']['type'] = 0
if user['train']['type'] == 0:
telegram.send_message(user['chat_id'], out_str, reply_markup=telegram.hideKeyboard)
else:
telegram.send_message(user['chat_id'], out_str, reply_markup=telegram.variantTrainKeyboard)
def do_translate_train(user, string, overwrite=False):
out_str = ""
was_incorrect = False
if not overwrite:
for w in user['words']:
if w['ru'] == user['train']['word']['ru']:
if w['en'].lower() == string.lower():
out_str += "Correct\n"
if w['stage'] < study_settings.max_stage:
w['stage'] += 1
w['expiration_date'] = datetime.datetime.utcnow() + study_settings.stages[w['stage']]
else:
out_str += "Incorrect\nThe correct one is %s \n" % (w['en'],)
if w['stage'] > study_settings.min_stage:
w['stage'] -= 1
was_incorrect = True
sup = list(filter(lambda _: _['expiration_date'] < datetime.datetime.utcnow() and
_['stage'] >= study_settings.min_translation_stage, user['words']))
if len(sup) > 1:
if not was_incorrect:
word = copy.deepcopy(random.choice(sup))
user['train']['word'] = word
user['train']['shuffled'] = ''.join(random.sample(word['en'].lower(), len(word['en'])))
out_str += user['train']['word']['ru'] + "\n"
if 'shuffled' in user['train']:
out_str += user['train']['shuffled'] + "\n"
else:
out_str += "Not enough words\n"
user['train']['type'] = 0
telegram.send_message(user['chat_id'], out_str, reply_markup=telegram.hideKeyboard)
trains = {
1: do_variant_train,
2: do_variant_train,
3: do_translate_train
}
def end_train(user, string):
if user['train']['type'] == 0:
out_str = "No train is in process"
else:
out_str = "Train ended"
user['train']['type'] = 0
telegram.send_message(user['chat_id'], out_str, reply_markup=telegram.hideKeyboard)
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2
] |
server.py | RaphaelOlivier/deepspeech.pytorch | 1 | 105056 | import logging
import os
from tempfile import NamedTemporaryFile
import hydra
import torch
from flask import Flask, request, jsonify
from hydra.core.config_store import ConfigStore
from deepspeech_pytorch.configs.inference_config import ServerConfig
from deepspeech_pytorch.inference import run_transcribe
from deepspeech_pytorch.loader.data_loader import SpectrogramParser
from deepspeech_pytorch.utils import load_model, load_decoder
app = Flask(__name__)
ALLOWED_EXTENSIONS = set(['.wav', '.mp3', '.ogg', '.webm'])
cs = ConfigStore.instance()
cs.store(name="config", node=ServerConfig)
@app.route('/transcribe', methods=['POST'])
def transcribe_file():
if request.method == 'POST':
res = {}
if 'file' not in request.files:
res['status'] = "error"
res['message'] = "audio file should be passed for the transcription"
return jsonify(res)
file = request.files['file']
filename = file.filename
_, file_extension = os.path.splitext(filename)
if file_extension.lower() not in ALLOWED_EXTENSIONS:
res['status'] = "error"
res['message'] = "{} is not supported format.".format(file_extension)
return jsonify(res)
with NamedTemporaryFile(suffix=file_extension) as tmp_saved_audio_file:
file.save(tmp_saved_audio_file.name)
logging.info('Transcribing file...')
transcription, _ = run_transcribe(
audio_path=tmp_saved_audio_file,
spect_parser=spect_parser,
model=model,
decoder=decoder,
device=device,
precision=config.model.precision
)
logging.info('File transcribed')
res['status'] = "OK"
res['transcription'] = transcription
return jsonify(res)
@hydra.main(config_name="config")
def main(cfg: ServerConfig):
global model, spect_parser, decoder, config, device
config = cfg
logging.getLogger().setLevel(logging.DEBUG)
logging.info('Setting up server...')
device = torch.device("cuda" if cfg.model.cuda else "cpu")
model = load_model(
device=device,
model_path=cfg.model.model_path
)
decoder = load_decoder(
labels=model.labels,
cfg=cfg.lm
)
spect_parser = SpectrogramParser(
audio_conf=model.spect_cfg,
normalize=True
)
logging.info('Server initialised')
app.run(
host=cfg.host,
port=cfg.port,
debug=True,
use_reloader=False
)
if __name__ == "__main__":
main()
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1678,
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2
] |
tests/prefect_vault_secrets/conftest.py | vcdi/prefect-vault-secrets | 1 | 110753 | import pytest
import tempfile
import os
from prefect.utilities import configuration
@pytest.fixture()
def server_api():
"""
backend settings don't seem to be getting respected. As a work-around
set export PREFECT__BACKEND=server where running the tests to enforce
use of the server endpoint
"""
with tempfile.TemporaryDirectory() as tmp:
tmp = os.path.join(tmp, ".prefect")
os.makedirs(tmp)
with configuration.set_temporary_config({
"home_dir": tmp,
"backend": "server",
"cloud.api": "https:/localhost:4200",
"cloud.auth_token": "<PASSWORD>",
"cloud.use_local_secrets": False}):
yield
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1037,
1372,
1115,
7700,
29913,
1125,
13,
9651,
7709,
13,
2
] |
Comment/views.py | yqji/DJNeverDie | 0 | 73835 | <reponame>yqji/DJNeverDie
from django.shortcuts import render
from .models import Comment
def get_comments():
pass
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