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import smart_imports
smart_imports.all()
class SettingAdmin(django_admin.ModelAdmin):
list_display = ('key', 'value', 'updated_at')
def save_model(self, request, obj, form, change):
from . import settings
settings[obj.key] = obj.value
def delete_model(self, request, obj):
from . import settings
del settings[obj.key]
django_admin.site.register(models.Setting, SettingAdmin)
|
nilq/baby-python
|
python
|
from __future__ import division
import pandas as pd
import numpy as np
from sklearn.neighbors import NearestNeighbors
import sys
import pickle
__author__ = 'sladesal'
__time__ = '20171110'
"""
Parameters
----------
data : 原始数据
tag_index : 因变量所在的列数，以0开始
max_amount : 少类别类想要达到的数据量
std_rate : 多类:少类想要达到的比例
#如果max_amount和std_rate同时定义优先考虑max_amount的定义
kneighbor : 生成数据依赖kneighbor个附近的同类点，建议不超过5个
kdistinctvalue : 认为每列不同元素大于kdistinctvalue及为连续变量，否则为class变量
method ： 生成方法
"""
# smote unbalance dataset
def smote(data, tag_index=None, max_amount=0, std_rate=5, kneighbor=5, kdistinctvalue=10, method='mean'):
try:
data = pd.DataFrame(data)
except:
raise ValueError
case_state = data.iloc[:, tag_index].groupby(data.iloc[:, tag_index]).count()
case_rate = max(case_state) / min(case_state)
location = []
if case_rate < 5:
print('不需要smote过程')
return data
else:
# 拆分不同大小的数据集合
less_data = np.array(
data[data.iloc[:, tag_index] == np.array(case_state[case_state == min(case_state)].index)[0]])
more_data = np.array(
data[data.iloc[:, tag_index] == np.array(case_state[case_state == max(case_state)].index)[0]])
# 找出每个少量数据中每条数据k个邻居
neighbors = NearestNeighbors(n_neighbors=kneighbor).fit(less_data)
for i in range(len(less_data)):
point = less_data[i, :]
location_set = neighbors.kneighbors([less_data[i]], return_distance=False)[0]
location.append(location_set)
# 确定需要将少量数据补充到上限额度
# 判断有没有设定生成数据个数，如果没有按照std_rate(预期正负样本比)比例生成
if max_amount > 0:
amount = max_amount
else:
amount = int(max(case_state) / std_rate)
# 初始化，判断连续还是分类变量采取不同的生成逻辑
times = 0
continue_index = [] # 连续变量
class_index = [] # 分类变量
for i in range(less_data.shape[1]):
if len(pd.DataFrame(less_data[:, i]).drop_duplicates()) > kdistinctvalue:
continue_index.append(i)
else:
class_index.append(i)
case_update = list()
location_transform = np.array(location)
while times < amount:
# 连续变量取附近k个点的重心，认为少数样本的附近也是少数样本
new_case = []
pool = np.random.permutation(len(location))[1]
neighbor_group = location_transform[pool]
if method == 'mean':
new_case1 = less_data[list(neighbor_group), :][:, continue_index].mean(axis=0)
# 连续样本的附近点向量上的点也是异常点
if method == 'random':
away_index = np.random.permutation(len(neighbor_group) - 1)[1]
neighbor_group_removeorigin = neighbor_group[1:][away_index]
new_case1 = less_data[pool][continue_index] + np.random.rand() * (
less_data[pool][continue_index] - less_data[neighbor_group_removeorigin][continue_index])
# 分类变量取mode
new_case2 = np.array(pd.DataFrame(less_data[neighbor_group, :][:, class_index]).mode().iloc[0, :])
new_case = list(new_case1) + list(new_case2)
if times == 0:
case_update = new_case
else:
case_update = np.c_[case_update, new_case]
print('已经生成了%s条新数据，完成百分之%.2f' % (times, times * 100 / amount))
times = times + 1
less_origin_data = np.hstack((less_data[:, continue_index], less_data[:, class_index]))
more_origin_data = np.hstack((more_data[:, continue_index], more_data[:, class_index]))
data_res = np.vstack((more_origin_data, less_origin_data, np.array(case_update.T)))
label_columns = [0] * more_origin_data.shape[0] + [1] * (
less_origin_data.shape[0] + np.array(case_update.T).shape[0])
data_res = pd.DataFrame(data_res)
return data_res
if __name__ == '__main__':
data = pd.read_table('/Users/slade/Documents/GitHub/machine_learning/data/data_all.txt')
smote(data,tag_index=1)
|
nilq/baby-python
|
python
|
from django.contrib.auth.models import User
from django.shortcuts import render
from ..date_checker import update_all
from ..forms import UserForm, UserProfileForm
from ..models import UserProfile
from random import randint
def register(request):
"""Registration Page View
Displays registration form for user to fill up.
If it's a POST request, uses the user input to make a new User.
Returns: {% url 'register' %}
"""
registered = False
# If it's a HTTP POST, we're interested in processing form data.
update_all()
x = randint(0, 9)
y = randint(0, 9)
math_equation = str(x) + '+' + str(y) + '='
context_dict = {
'math': math_equation
}
if request.method == 'POST':
# Attempt to grab information from the raw form information.
user_form = UserForm(data=request.POST)
userprofile_form = UserProfileForm(data=request.POST)
eq = request.POST.get('eq', '')
form_ans = request.POST.get('answer')
ans = int(eq[0]) + int(eq[2])
if user_form.is_valid() and userprofile_form.is_valid() and int(form_ans) == int(ans):
user = User.objects.create_user(
first_name=user_form.cleaned_data['first_name'],
last_name=user_form.cleaned_data['last_name'],
username=user_form.cleaned_data['username'],
email=user_form.cleaned_data['email'],
password=user_form.cleaned_data['password'],
)
user.save()
profile = UserProfile.objects.get(user=user)
profile.bio = userprofile_form.cleaned_data['bio']
profile.phone = userprofile_form.cleaned_data['phone']
profile.city = userprofile_form.cleaned_data['city']
profile.country = userprofile_form.cleaned_data['country']
profile.credit_card = userprofile_form.cleaned_data['credit_card']
profile.save()
registered = True
else:
print(user_form.errors)
print(userprofile_form.errors)
else:
user_form = UserForm()
userprofile_form = UserProfileForm()
# Render the template depending on the context.
return render(request, 'registration.html',
{'user_form': user_form,
'userprofile_form': userprofile_form,
'registered': registered, 'math': math_equation,} )
|
nilq/baby-python
|
python
|
import numpy as np
import pickle
import tensorflow as tf
import os
import sys
sys.path.append("../../..")
sys.path.append("../..")
sys.path.append("..")
import utils
import random
import math
from mimic3models.multitask import utils as mt_utils
from waveform.WaveformLoader import WaveformDataset
from mimic3models.preprocessing import Discretizer, Normalizer
from text_utils import avg_emb
import torch
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter
from torch.optim.lr_scheduler import StepLR
from models.multi_modality_model_hy import Text_CNN, Text_RNN,LSTMModel, ChannelWiseLSTM, \
Waveform_Pretrained, Text_Only_DS, Text_AVG, LSTMAttentionModel
from models.loss import masked_weighted_cross_entropy_loss, masked_mse_loss
from readmit_dataloaders import MultiModal_Dataset, custom_collate_fn
import functools
import json
from tqdm import tqdm
from sklearn import metrics
from utils import BootStrap, BootStrapDecomp, BootStrapLos, BootStrapIhm, BootStrapPheno, BootStrapReadmit
#======================================Hyperparameters======================================#
# decomp_weight = 5.0
# los_weight = 3.0
# ihm_weight = 3.0
# pheno_weight = 2.0
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# class frequency for each task
readmit_class_weight = torch.FloatTensor([1.0599 ,17.5807])
readmit_class_weight = readmit_class_weight.to(device)
version = 1
experiment = "readmit_dev"
while os.path.exists(os.path.join('runs', experiment+"_v{}".format(version))):
version += 1
experiment = experiment + "_v{}".format(version)
print("Starting run {}".format(experiment))
writer = SummaryWriter(os.path.join('runs', experiment))
conf = utils.get_config()
args = utils.get_args()
vectors, w2i_lookup = utils.get_embedding_dict(conf)
if conf.padding_type == 'Zero':
vectors[utils.lookup(w2i_lookup, '<pad>')] = 0
train_val_ts_root_dir = '/home/yong/mutiltasking-for-mimic3/data/multitask_2/train'
test_ts_root_dir = '/home/yong/mutiltasking-for-mimic3/data/multitask_2/test'
train_val_text_root_dir = '/home/yong/mutiltasking-for-mimic3/data/root_2/train_text_ds'
test_text_root_dir = '/home/yong/mutiltasking-for-mimic3/data/root_2/test_text_ds'
train_listfile = 'listfile.csv'
val_listfile = '4k_val_listfile.csv'
test_listfile ='listfile.csv'
ihm_pos = 48
los_pos = 24
use_ts = False
use_text = True
decay = 0.1
max_text_length = 500
max_num_notes = 1
regression = False
discharge_summary_only = False
bin_type = 'coarse'
train_val_starttime_path = conf.starttime_path_train_val
test_starttime_path = conf.starttime_path_test
epochs = 50
learning_rate = 3e-4
batch_size = 8
bootstrap_decomp = BootStrapDecomp(k=1000, experiment_name = experiment)
bootstrap_los = BootStrapLos(experiment_name = experiment)
bootstrap_ihm = BootStrapIhm(experiment_name = experiment)
bootstrap_pheno = BootStrapPheno(experiment_name = experiment)
bootstrap_readmit = BootStrapReadmit(experiment_name = experiment)
# prepare discretizer and normalizer
conf = utils.get_config()
discretizer = Discretizer(timestep=conf.timestep,
store_masks=True,
impute_strategy='previous',
start_time='zero')
cont_channels = [2, 3, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58]
normalizer = Normalizer(fields=cont_channels)
normalizer_state = conf.normalizer_state
if normalizer_state is None:
normalizer_state = 'mult_ts{}.input_str:previous.start_time:zero.n5e4.normalizer'.format(
conf.timestep)
normalizer.load_params(normalizer_state)
# Model
text_model = Text_CNN(in_channels=1, out_channels=128, kernel_heights =[2,3,4], embedding_length =200, name ='cnn')
#text_model = Text_RNN(embedding_length =200, hidden_size =32, name = 'rnn')
#text_model = Text_AVG()
#text_model = LSTMAttentionModel(hidden_size =128,embedding_length =200, name = 'lstm attn')
model = Text_Only_DS(text_model= text_model)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
scheduler = StepLR(optimizer, step_size=10, gamma=0.3)
early_stopper = utils.EarlyStopping(experiment_name = experiment)
embedding_layer = nn.Embedding(vectors.shape[0], vectors.shape[1])
embedding_layer.weight.data.copy_(torch.from_numpy(vectors))
embedding_layer.weight.requires_grad = False
train_mm_dataset = MultiModal_Dataset(train_val_ts_root_dir, train_val_text_root_dir, train_listfile, discretizer, train_val_starttime_path,\
regression, bin_type, normalizer, ihm_pos, los_pos, use_text, use_ts, decay, w2i_lookup, max_text_length, max_num_notes, discharge_summary_only, True)
#train_mm_dataset = subsampling(train_mm_dataset)
#print(len(train_mm_dataset))
# val_mm_dataset = MultiModal_Dataset(train_val_ts_root_dir, train_val_text_root_dir, wf_root_dir, val_listfile, discretizer, train_val_starttime_path,\
# regression, bin_type, normalizer, ihm_pos, los_pos, use_wf, use_text, use_ts, wf_dim, decay, w2i_lookup, max_text_length, max_num_notes)
test_mm_dataset = MultiModal_Dataset(test_ts_root_dir, test_text_root_dir,test_listfile, discretizer, test_starttime_path,\
regression, bin_type, normalizer, ihm_pos, los_pos, use_text, use_ts, decay, w2i_lookup, max_text_length, max_num_notes, discharge_summary_only, True)
collate_fn_train = functools.partial(custom_collate_fn)
collate_fn_val = functools.partial(custom_collate_fn)
collate_fn_test = functools.partial(custom_collate_fn)
train_data_loader = torch.utils.data.DataLoader(dataset=train_mm_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=5,
collate_fn = collate_fn_train)
# val_data_loader = torch.utils.data.DataLoader(dataset=val_mm_dataset,
# batch_size=batch_size,
# shuffle=True,
# num_workers=5,
# collate_fn = collate_fn_val)
test_data_loader = torch.utils.data.DataLoader(dataset=test_mm_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=5,
collate_fn = collate_fn_test)
def text_embedding(embedding_layer,data, device):
texts = torch.from_numpy(data['texts']).to(torch.int64)
texts = embedding_layer(texts) # [batch_size, num_docs, seq_len, emb_dim]
texts = texts.to(device)
if text_model.name == 'avg':
texts = avg_emb(texts, texts_weight_mat)
return texts
def retrieve_data(data, device):
"""
retrieve data from data loaders and reorganize its shape and obejects into desired forms
"""
ihm_mask = torch.from_numpy(np.array(data['ihm mask']))
ihm_mask = ihm_mask.to(device)
ihm_label = torch.from_numpy(np.array(data['ihm label'])).long()
ihm_label = ihm_label.reshape(-1,1).squeeze(1)
ihm_label = ihm_label.to(device)
decomp_mask = torch.from_numpy(data['decomp mask'])
decomp_mask = decomp_mask.to(device)
decomp_label = torch.from_numpy(data['decomp label']).long()
# the num valid data is used in case the last batch is smaller than batch size
num_valid_data = decomp_label.shape[0]
decomp_label = decomp_label.reshape(-1,1).squeeze(1) # (b*t,)
decomp_label = decomp_label.to(device)
los_mask = torch.from_numpy(np.array(data['los mask']))
los_mask = los_mask.to(device)
los_label = torch.from_numpy(np.array(data['los label']))
los_label = los_label.reshape(-1,1).squeeze(1)
los_label = los_label.to(device)
pheno_label = torch.from_numpy(np.array(data['pheno label'])).float()
pheno_label = pheno_label.to(device)
readmit_mask = torch.from_numpy(np.array(data['readmit mask']))
readmit_mask = readmit_mask.to(device)
readmit_label = torch.from_numpy(np.array(data['readmit label']))
readmit_label = readmit_label.reshape(-1,1).squeeze(1).long()
readmit_label = readmit_label.to(device)
return decomp_label, decomp_mask, los_label, los_mask, ihm_label, ihm_mask, pheno_label, readmit_label, readmit_mask, num_valid_data
def train(epochs, train_data_loader, test_data_loader, early_stopper, model, optimizer, scheduler, device):
criterion = nn.BCEWithLogitsLoss()
aucroc_readmit = utils.AUCROCREADMIT()
aucpr_readmit = utils.AUCPRREADMIT()
cfm_readmit = utils.ConfusionMatrixReadmit()
model.to(device)
train_b = 0
for epoch in range(epochs):
print('Epoch {}/{}'.format(epoch+1, epochs))
print('-' * 50)
model.train()
running_loss =0.0
epoch_metrics = utils.EpochWriter("Train", regression, experiment)
tk0 = tqdm(train_data_loader, total=int(len(train_data_loader)))
for i, data in enumerate(tk0):
if data is None:
continue
decomp_label, decomp_mask, los_label, los_mask, ihm_label, ihm_mask,\
pheno_label, readmit_label, readmit_mask, num_valid_data = retrieve_data(data, device)
if use_text:
texts = text_embedding(embedding_layer, data, device)
else:
texts = None
readmit_logits = model(texts = texts)
loss = masked_weighted_cross_entropy_loss(None, readmit_logits, readmit_label, readmit_mask )
train_b+=1
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_loss += loss.item()
m = nn.Softmax(dim=1)
sig = nn.Sigmoid()
readmit_pred = (sig(readmit_logits)[:,1]).cpu().detach().numpy()
readmit_label = readmit_label.cpu().detach().numpy()
if readmit_label is None:
print('bad')
readmit_mask = readmit_mask.cpu().detach().numpy()
aucpr_readmit.add(readmit_pred, readmit_label, readmit_mask)
aucroc_readmit.add(readmit_pred, readmit_label, readmit_mask)
cfm_readmit.add(readmit_pred, readmit_label, readmit_mask)
interval = 50
if i %interval == interval-1:
writer.add_scalar('training loss',
running_loss/(interval-1),
train_b)
print('readmission aucpr is {}'.format(aucpr_readmit.get()))
print('readmission aucroc is {}'.format(aucroc_readmit.get()))
print('readmission cfm is {}'.format(cfm_readmit.get()))
#scheduler.step()
#evaluate(epoch, val_data_loader, model, 'val', early_stopper, device, train_b)
evaluate(epoch, test_data_loader, model, 'test', early_stopper, device, train_b)
# if early_stopper.early_stop:
# evaluate(epoch, test_data_loader, model, 'test', early_stopper, device, train_b)
# bootstrap_pheno.get()
# print("Early stopping")
# break
def evaluate(epoch, data_loader, model, split, early_stopper, device, train_step=None):
aucroc_readmit = utils.AUCROCREADMIT()
aucpr_readmit = utils.AUCPRREADMIT()
cfm_readmit = utils.ConfusionMatrixReadmit()
if split == 'val':
epoch_metrics = utils.EpochWriter("Val", regression, experiment)
else:
epoch_metrics = utils.EpochWriter("Test", regression, experiment)
model.to(device)
model.eval()
running_loss = 0.0
tk = tqdm(data_loader, total=int(len(data_loader)))
criterion = nn.BCEWithLogitsLoss()
for i, data in enumerate(tk):
if data is None:
continue
decomp_label, decomp_mask, los_label, los_mask, ihm_label, ihm_mask,\
pheno_label, readmit_label, readmit_mask, num_valid_data = retrieve_data(data, device)
if use_ts:
ts = torch.from_numpy(data['time series'])
ts = ts.permute(1,0,2).float().to(device)
else:
ts = None
if use_text:
texts = text_embedding(embedding_layer, data, device)
else:
texts = None
readmit_logits = model(texts = texts)
loss = masked_weighted_cross_entropy_loss(None, readmit_logits, readmit_label, readmit_mask)
running_loss += loss.item()
sigmoid = nn.Sigmoid()
readmit_pred = (sigmoid(readmit_logits)[:,1]).cpu().detach().numpy()
readmit_label = readmit_label.cpu().detach().numpy()
readmit_mask = readmit_mask.cpu().detach().numpy()
aucpr_readmit.add(readmit_pred, readmit_label, readmit_mask)
aucroc_readmit.add(readmit_pred, readmit_label, readmit_mask)
cfm_readmit.add(readmit_pred, readmit_label, readmit_mask)
print('readmission aucpr is {}'.format(aucpr_readmit.get()))
print('readmission aucroc is {}'.format(aucroc_readmit.get()))
print('readmission cfm is {}'.format(cfm_readmit.get()))
if train_step is not None:
xpoint = train_step
else:
xpoint = epoch+1
writer.add_scalar('{} readmit loss'.format(split),
running_loss/ (i),
xpoint)
if split == 'val':
early_stopper(running_loss_pheno/(i), model)
train(epochs, train_data_loader, test_data_loader, early_stopper, model, optimizer, scheduler, device)
#bootstrap_pheno.get()
evaluate(0, test_data_loader, model, 'test', early_stopper, device, None)
#bootstrap_los.get()
|
nilq/baby-python
|
python
|
import math
def get_client_ip(request) -> str:
"""Get real client IP address."""
x_forwarded_for = request.META.get('HTTP_X_FORWARDED_FOR')
if x_forwarded_for:
ip = x_forwarded_for.split(',')[0]
else:
ip = request.META.get('REMOTE_ADDR')
return ip
def get_pretty_file_size(size_in_bytes: int) -> str:
exponent = math.floor(math.log(size_in_bytes, 1024))
unit = {
0: 'B',
1: 'KB',
2: 'MB',
3: 'GB',
}[exponent]
size = size_in_bytes / (1024 ** exponent)
return '{size} {unit}'.format(size=size, unit=unit)
|
nilq/baby-python
|
python
|
"""
mmic
A short description of the project.
"""
# Add imports here
from . import components
# Handle versioneer
from ._version import get_versions
versions = get_versions()
__version__ = versions["version"]
__git_revision__ = versions["full-revisionid"]
del get_versions, versions
|
nilq/baby-python
|
python
|
#!/usr/bin/env python
from __tools__ import MyParser
from __tools__ import XmlParser
#import matplotlib.pyplot as plt
import lxml.etree as lxml
import subprocess as sp
import numpy as np
parser=MyParser(description="Tool to create histogramm for iexcitoncl from jobfile")
parser.add_argument("--format",type=str,default="Hist_{}",help="Title of histogramm and filename")
parser.add_argument("--printing",action='store_const', const=1, default=0,help="Print histogramms to txt file")
parser.add_argument("--bins",type=int,default=50,help="Number of bins")
parser.add_argument("--jobfiles",type=str, nargs="+",required=True,help="Name of jobfile")
parser.add_argument("--min",type=int, default=-14,help="Minimum log10(J2) to still count")
args=parser.parse_args()
if type(args.jobfiles)==str:
args.jobfiles=[args.jobfiles]
for i,jobfile in enumerate(args.jobfiles):
job=[]
toosmall=0
print "Reading in {}".format(jobfile)
root=XmlParser(jobfile)
for entry in root.iter('job'):
status=entry.find("status").text
if status=="COMPLETE":
coupling=entry.find("output")[0][0].get("jABstatic")
j2=float(coupling)**2
#if j2>10**args.min:
job.append(j2)
#else:
#toosmall+=1
job=np.array(job)
if i==0:
total=job
else:
total+=job
print "Read in {} jobs".format(len(job))
print "{} Jobs have a coupling below 10^{} eV**2".format(toosmall,args.min)
value,bins=np.histogram(np.log10(job),args.bins,density=True)
if args.printing:
bins=0.5*(bins[1:]+bins[:-1])
result=np.array([bins,value])
np.savetxt(args.format.format(jobfile)+".txt",result.T,header="Number of Integrals; J2 [eV**2]")
else:
print "Currently not implemented"
total=total/float(len(args.jobfiles))
value,bins=np.histogram(np.log10(total),args.bins,density=True)
bins=0.5*(bins[1:]+bins[:-1])
result=np.array([bins,value])
np.savetxt("Total_hist.txt",result.T,header="Number of Integrals; J2 [eV**2]")
|
nilq/baby-python
|
python
|
#!/usr/bin/env python3
import sys
import os
import logging
import numpy as np
import PIL.Image as Image
import vboard as vb
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO)
kl = vb.detect_key_lines()
for filename in sys.argv[1:]:
name = os.path.basename(filename)
try:
mid = int(name[4:name.index('.')])
except ValueError:
logging.exception('')
continue
basedir = os.path.dirname(os.path.realpath(filename))
todir = os.path.join(basedir, f'cells{mid}')
try:
os.mkdir(todir)
except FileExistsError:
logging.exception('')
continue
img = np.asarray(Image.open(filename).convert('L'))
cells, sh = vb.partition_board(kl, img)
for i, x in enumerate(map(Image.fromarray, cells)):
x.save(os.path.join(todir, f'c{i:0>3}.png'))
logging.info('Made %s', os.path.basename(todir))
|
nilq/baby-python
|
python
|
import argparse
import os
import json
from bs4 import BeautifulSoup
from tqdm import tqdm
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Script to process podcasts trascripts to json file')
parser.add_argument('--input_file', type=str, required=True)
parser.add_argument('--output_file', type=str, required=True)
parser.add_argument('--doc_file', type=str)
parser.add_argument('--meta_file', type=str)
args = parser.parse_args()
if args.doc_file:
out_docs = set()
with open(args.doc_file) as f:
for line in f:
line = line.strip()
out_docs.add(line)
meta_dict = {}
with open(args.meta_file) as f:
flag = False
for line in f:
if not flag:
flag = True
continue
temp = line.strip().split("\t")
meta_dict[temp[6]] = temp[8]
doc_lines=[]
flag = False
temp = []
with open(args.input_file) as f:
for line in tqdm(f):
line = line.strip()
if args.doc_file and "<DOCNO>" in line:
docno = line.replace("<DOCNO>","").replace("</DOCNO>","")
if docno not in out_docs:
flag = False
temp = []
continue
if flag and "</DOC>" in line:
flag = False
soup = BeautifulSoup("\n".join(temp), features="lxml")
docno = soup.find("docno").text.strip()
body = soup.find("text").text.strip()
doc_dict = {}
doc_dict["docno"] = docno
# body, title = text.split("\n")
# doc_dict["title"] = title
doc_dict["body"] = body
doc_dict["title"] = meta_dict[docno.split("_")[0]]
doc_lines.append(json.dumps(doc_dict))
temp = []
elif "<DOC>" in line:
flag = True
temp = [line]
elif flag:
temp.append(line)
with open(args.output_file, "w") as f:
for line in doc_lines:
f.write(line+"\n")
|
nilq/baby-python
|
python
|
#!/usr/bin/env python
# encoding: utf-8
"""
File: thin_mrbayes_runs.py
Author: Brant Faircloth
Created by Brant Faircloth on 29 March 2012 09:03 PDT (-0700)
Copyright (c) 2012 Brant C. Faircloth. All rights reserved.
Description:
"""
import os
import sys
import glob
import shutil
import argparse
from phyluce.helpers import FullPaths, is_dir
import pdb
def get_args():
"""Get arguments from CLI"""
parser = argparse.ArgumentParser(
description="""Thin a folder of mrbayes output""")
parser.add_argument(
"input",
action=FullPaths,
type=is_dir,
help="""Input directory"""
)
parser.add_argument(
"output",
action=FullPaths,
help="""Output directory"""
)
parser.add_argument(
"--thin",
type=int,
default=100,
help="""Thinning factor""",
)
return parser.parse_args()
def thin_p_files(input, output, thin = 100):
for line in open(input, 'rU'):
if not line.split('\t')[0].isdigit():
output.write(line)
else:
if int(line.split('\t')[0]) == 1:
output.write(line)
elif int(line.split('\t')[0]) % thin == 0:
output.write(line)
def thin_t_files(input, output, thin = 100):
for line in open(input, 'rU'):
if not line.startswith(' tree rep.'):
output.write(line)
else:
#pdb.set_trace()
if int(line.split('=')[0].strip().split('.')[1]) == 1:
output.write(line)
elif int(line.split('=')[0].strip().split('.')[1]) % thin == 0:
output.write(line)
def main():
args = get_args()
files = [f for f in glob.glob(os.path.join(args.input, '*')) if os.path.splitext(f)[1] in ['.t', '.p']]
# check if outputdir
try:
assert os.path.exists(args.output)
except:
inp = raw_input('Output directory does not exist. Create [Y/n]: ')
if inp == 'Y':
os.makedirs(args.output)
else:
print "Exiting"
sys.exit()
nexus = glob.glob(os.path.join(args.input, '*.nex'))
assert len(nexus) == 1, "There is more than one nexus file"
output_name = os.path.basename(nexus[0])
output_file = os.path.join(args.output, output_name)
shutil.copyfile(nexus[0], output_file)
for input in files:
output_name = os.path.basename(input)
output_file = os.path.join(args.output, output_name)
output = open(output_file, 'w')
if os.path.splitext(input)[1] == '.p':
thin_p_files(input, output, args.thin)
elif os.path.splitext(input)[1] == '.t':
thin_t_files(input, output, args. thin)
output.close()
if __name__ == '__main__':
main()
|
nilq/baby-python
|
python
|
# Copyright 2013-2018 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class Panda(CMakePackage):
"""PANDA: Parallel AdjaceNcy Decomposition Algorithm"""
homepage = "http://comopt.ifi.uni-heidelberg.de/software/PANDA/index.html"
url = "http://comopt.ifi.uni-heidelberg.de/software/PANDA/downloads/panda-2016-03-07.tar"
version('2016-03-07', 'b06dc312ee56e13eefea9c915b70fcef')
# Note: Panda can also be built without MPI support
depends_on('cmake@2.6.4:', type='build')
depends_on('mpi')
|
nilq/baby-python
|
python
|
import argparse
import json
import pickle
from bz2 import BZ2File
from pprint import pprint
# with open("wiki_data/properties.pkl", "rb") as props:
# properties_mapper = pickle.load(props)
properties_mapper = {}
properties_mapper.update(
{
"Q6581097": "male",
"Q6581072": "female",
}
)
def parse_person(data):
item_id = data["id"]
# Try to get the label
labels = data["labels"]
if "en" in labels:
label = labels["en"]["value"]
else:
# If there's no english label, bail out
return None
# Try to get the description
descriptions = data["descriptions"]
if "en" in descriptions:
description = descriptions["en"]["value"]
else:
description = ""
claims = data["claims"]
print(f"{label.ljust(20)} ({str(item_id).ljust(12)}):", description)
# Try to get the gender
gender_id = claims["P21"][0]['mainsnak']['datavalue']['value']['id']
# Try to get date of birth
dob = claims["P569"][0]['mainsnak']['datavalue']['value']['time']
# Try to get date of death
if "P570" in claims:
dod = claims["P570"][0]['mainsnak']['datavalue']['value']['time']
else:
dod = None
print(" -", properties_mapper.get(gender_id, gender_id))
print(" - ", dob, "::", dod)
def parse(line):
data = json.loads(line.strip().rstrip(b","))
ty = data["type"]
if ty == "item":
categories = set()
claims = data["claims"]
if "P31" not in claims: # If this isn't an instance of anything, ignore it
return
for cat in claims["P31"]: # Find all the things this is an instance of
snak = cat["mainsnak"]
categories.add(snak['datavalue']['value']['id'])
if "Q5" in categories: # A human!
parse_person(data)
def main():
parser = argparse.ArgumentParser(
description="Extract data from uncompressed JSON file"
)
parser.add_argument(
"data_file_name",
type=str,
help="JSON file with one entry per line",
)
args = parser.parse_args()
with BZ2File(args.data_file_name) as f:
for line_num, line in enumerate(f):
try:
parse(line)
except Exception as e:
print("Error parsing line", f"{line_num} - {type(e).__name__}: {e}")
if line_num >= 200:
break
if __name__ == "__main__":
main()
|
nilq/baby-python
|
python
|
# ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# ----------------------------------------------------------------------------
import abc
import copy
import pandas as pd
from skbio.util._decorator import stable, experimental
from skbio.metadata import IntervalMetadata
class MetadataMixin(metaclass=abc.ABCMeta):
@property
@stable(as_of="0.4.0")
def metadata(self):
"""``dict`` containing metadata which applies to the entire object.
Notes
-----
This property can be set and deleted. When setting new metadata a
shallow copy of the dictionary is made.
Examples
--------
.. note:: scikit-bio objects with metadata share a common interface for
accessing and manipulating their metadata. The following examples
use scikit-bio's ``Sequence`` class to demonstrate metadata
behavior. These examples apply to all other scikit-bio objects
storing metadata.
Create a sequence with metadata:
>>> from pprint import pprint
>>> from skbio import Sequence
>>> seq = Sequence('ACGT', metadata={'id': 'seq-id',
... 'description': 'seq description'})
Retrieve metadata:
>>> pprint(seq.metadata) # using pprint to display dict in sorted order
{'description': 'seq description', 'id': 'seq-id'}
Update metadata:
>>> seq.metadata['id'] = 'new-id'
>>> seq.metadata['pubmed'] = 12345
>>> pprint(seq.metadata)
{'description': 'seq description', 'id': 'new-id', 'pubmed': 12345}
Set metadata:
>>> seq.metadata = {'abc': 123}
>>> seq.metadata
{'abc': 123}
Delete metadata:
>>> seq.has_metadata()
True
>>> del seq.metadata
>>> seq.metadata
{}
>>> seq.has_metadata()
False
"""
if self._metadata is None:
# Not using setter to avoid copy.
self._metadata = {}
return self._metadata
@metadata.setter
def metadata(self, metadata):
if not isinstance(metadata, dict):
raise TypeError("metadata must be a dict, not type %r" %
type(metadata).__name__)
# Shallow copy.
self._metadata = metadata.copy()
@metadata.deleter
def metadata(self):
self._metadata = None
@abc.abstractmethod
def __init__(self, metadata=None):
raise NotImplementedError
def _init_(self, metadata=None):
if metadata is None:
# Could use deleter but this is less overhead and needs to be fast.
self._metadata = None
else:
# Use setter for validation and copy.
self.metadata = metadata
@abc.abstractmethod
def __eq__(self, other):
raise NotImplementedError
def _eq_(self, other):
# We're not simply comparing self.metadata to other.metadata in order
# to avoid creating "empty" metadata representations on the objects if
# they don't have metadata.
if self.has_metadata() and other.has_metadata():
return self.metadata == other.metadata
elif not (self.has_metadata() or other.has_metadata()):
# Both don't have metadata.
return True
else:
# One has metadata while the other does not.
return False
@abc.abstractmethod
def __ne__(self, other):
raise NotImplementedError
def _ne_(self, other):
return not (self == other)
@abc.abstractmethod
def __copy__(self):
raise NotImplementedError
def _copy_(self):
if self.has_metadata():
return self.metadata.copy()
else:
return None
@abc.abstractmethod
def __deepcopy__(self, memo):
raise NotImplementedError
def _deepcopy_(self, memo):
if self.has_metadata():
return copy.deepcopy(self.metadata, memo)
else:
return None
@stable(as_of="0.4.0")
def has_metadata(self):
"""Determine if the object has metadata.
An object has metadata if its ``metadata`` dictionary is not empty
(i.e., has at least one key-value pair).
Returns
-------
bool
Indicates whether the object has metadata.
Examples
--------
.. note:: scikit-bio objects with metadata share a common interface for
accessing and manipulating their metadata. The following examples
use scikit-bio's ``Sequence`` class to demonstrate metadata
behavior. These examples apply to all other scikit-bio objects
storing metadata.
>>> from skbio import Sequence
>>> seq = Sequence('ACGT')
>>> seq.has_metadata()
False
>>> seq = Sequence('ACGT', metadata={})
>>> seq.has_metadata()
False
>>> seq = Sequence('ACGT', metadata={'id': 'seq-id'})
>>> seq.has_metadata()
True
"""
return self._metadata is not None and bool(self.metadata)
class PositionalMetadataMixin(metaclass=abc.ABCMeta):
@abc.abstractmethod
def _positional_metadata_axis_len_(self):
"""Return length of axis that positional metadata applies to.
Returns
-------
int
Positional metadata axis length.
"""
raise NotImplementedError
@property
@stable(as_of="0.4.0")
def positional_metadata(self):
"""``pd.DataFrame`` containing metadata along an axis.
Notes
-----
This property can be set and deleted. When setting new positional
metadata, a shallow copy is made and the ``pd.DataFrame`` index is set
to ``pd.RangeIndex(start=0, stop=axis_len, step=1)``.
Examples
--------
.. note:: scikit-bio objects with positional metadata share a common
interface for accessing and manipulating their positional metadata.
The following examples use scikit-bio's ``DNA`` class to demonstrate
positional metadata behavior. These examples apply to all other
scikit-bio objects storing positional metadata.
Create a DNA sequence with positional metadata:
>>> from skbio import DNA
>>> seq = DNA(
... 'ACGT',
... positional_metadata={'quality': [3, 3, 20, 11],
... 'exons': [True, True, False, True]})
>>> seq
DNA
-----------------------------
Positional metadata:
'exons': <dtype: bool>
'quality': <dtype: int64>
Stats:
length: 4
has gaps: False
has degenerates: False
has definites: True
GC-content: 50.00%
-----------------------------
0 ACGT
Retrieve positional metadata:
>>> seq.positional_metadata
exons quality
0 True 3
1 True 3
2 False 20
3 True 11
Update positional metadata:
>>> seq.positional_metadata['gaps'] = seq.gaps()
>>> seq.positional_metadata
exons quality gaps
0 True 3 False
1 True 3 False
2 False 20 False
3 True 11 False
Set positional metadata:
>>> seq.positional_metadata = {'degenerates': seq.degenerates()}
>>> seq.positional_metadata # doctest: +NORMALIZE_WHITESPACE
degenerates
0 False
1 False
2 False
3 False
Delete positional metadata:
>>> seq.has_positional_metadata()
True
>>> del seq.positional_metadata
>>> seq.positional_metadata
Empty DataFrame
Columns: []
Index: [0, 1, 2, 3]
>>> seq.has_positional_metadata()
False
"""
if self._positional_metadata is None:
# Not using setter to avoid copy.
self._positional_metadata = pd.DataFrame(
index=self._get_positional_metadata_index())
return self._positional_metadata
@positional_metadata.setter
def positional_metadata(self, positional_metadata):
try:
# Pass copy=True to copy underlying data buffer.
positional_metadata = pd.DataFrame(positional_metadata, copy=True)
# Different versions of pandas will raise different error types. We
# don't really care what the type of the error is, just its message, so
# a blanket Exception will do.
except Exception as e:
raise TypeError(
"Invalid positional metadata. Must be consumable by "
"`pd.DataFrame` constructor. Original pandas error message: "
"\"%s\"" % e)
num_rows = len(positional_metadata.index)
axis_len = self._positional_metadata_axis_len_()
if num_rows != axis_len:
raise ValueError(
"Number of positional metadata values (%d) must match the "
"positional metadata axis length (%d)."
% (num_rows, axis_len))
positional_metadata.index = self._get_positional_metadata_index()
self._positional_metadata = positional_metadata
@positional_metadata.deleter
def positional_metadata(self):
self._positional_metadata = None
def _get_positional_metadata_index(self):
"""Create a memory-efficient integer index for positional metadata."""
return pd.RangeIndex(start=0,
stop=self._positional_metadata_axis_len_(),
step=1)
@abc.abstractmethod
def __init__(self, positional_metadata=None):
raise NotImplementedError
def _init_(self, positional_metadata=None):
if positional_metadata is None:
# Could use deleter but this is less overhead and needs to be fast.
self._positional_metadata = None
else:
# Use setter for validation and copy.
self.positional_metadata = positional_metadata
@abc.abstractmethod
def __eq__(self, other):
raise NotImplementedError
def _eq_(self, other):
# We're not simply comparing self.positional_metadata to
# other.positional_metadata in order to avoid creating "empty"
# positional metadata representations on the objects if they don't have
# positional metadata.
if self.has_positional_metadata() and other.has_positional_metadata():
return self.positional_metadata.equals(other.positional_metadata)
elif not (self.has_positional_metadata() or
other.has_positional_metadata()):
# Both don't have positional metadata.
return (self._positional_metadata_axis_len_() ==
other._positional_metadata_axis_len_())
else:
# One has positional metadata while the other does not.
return False
@abc.abstractmethod
def __ne__(self, other):
raise NotImplementedError
def _ne_(self, other):
return not (self == other)
@abc.abstractmethod
def __copy__(self):
raise NotImplementedError
def _copy_(self):
if self.has_positional_metadata():
# deep=True makes a shallow copy of the underlying data buffer.
return self.positional_metadata.copy(deep=True)
else:
return None
@abc.abstractmethod
def __deepcopy__(self, memo):
raise NotImplementedError
def _deepcopy_(self, memo):
if self.has_positional_metadata():
# `copy.deepcopy` no longer recursively copies contents of the
# DataFrame, so we must handle the deep copy ourselves.
# Reference: https://github.com/pandas-dev/pandas/issues/17406
df = self.positional_metadata
data_cp = copy.deepcopy(df.values.tolist(), memo)
return pd.DataFrame(data_cp,
index=df.index.copy(deep=True),
columns=df.columns.copy(deep=True),
copy=False)
else:
return None
@stable(as_of="0.4.0")
def has_positional_metadata(self):
"""Determine if the object has positional metadata.
An object has positional metadata if its ``positional_metadata``
``pd.DataFrame`` has at least one column.
Returns
-------
bool
Indicates whether the object has positional metadata.
Examples
--------
.. note:: scikit-bio objects with positional metadata share a common
interface for accessing and manipulating their positional metadata.
The following examples use scikit-bio's ``DNA`` class to demonstrate
positional metadata behavior. These examples apply to all other
scikit-bio objects storing positional metadata.
>>> import pandas as pd
>>> from skbio import DNA
>>> seq = DNA('ACGT')
>>> seq.has_positional_metadata()
False
>>> seq = DNA('ACGT', positional_metadata=pd.DataFrame(index=range(4)))
>>> seq.has_positional_metadata()
False
>>> seq = DNA('ACGT', positional_metadata={'quality': range(4)})
>>> seq.has_positional_metadata()
True
"""
return (self._positional_metadata is not None and
len(self.positional_metadata.columns) > 0)
class IntervalMetadataMixin(metaclass=abc.ABCMeta):
@abc.abstractmethod
def _interval_metadata_axis_len_(self):
'''Return length of axis that interval metadata applies to.
Returns
-------
int
Interval metadata axis length.
'''
raise NotImplementedError
@abc.abstractmethod
def __init__(self, interval_metadata=None):
raise NotImplementedError
def _init_(self, interval_metadata=None):
if interval_metadata is None:
# Could use deleter but this is less overhead and needs to be fast.
self._interval_metadata = None
else:
# Use setter for validation and copy.
self.interval_metadata = interval_metadata
@property
@experimental(as_of="0.5.1")
def interval_metadata(self):
'''``IntervalMetadata`` object containing info about interval features.
Notes
-----
This property can be set and deleted. When setting new
interval metadata, a shallow copy of the ``IntervalMetadata``
object is made.
'''
if self._interval_metadata is None:
# Not using setter to avoid copy.
self._interval_metadata = IntervalMetadata(
self._interval_metadata_axis_len_())
return self._interval_metadata
@interval_metadata.setter
def interval_metadata(self, interval_metadata):
if isinstance(interval_metadata, IntervalMetadata):
upper_bound = interval_metadata.upper_bound
lower_bound = interval_metadata.lower_bound
axis_len = self._interval_metadata_axis_len_()
if lower_bound != 0:
raise ValueError(
'The lower bound for the interval features (%d) '
'must be zero.' % lower_bound)
if upper_bound is not None and upper_bound != axis_len:
raise ValueError(
'The upper bound for the interval features (%d) '
'must match the interval metadata axis length (%d)'
% (upper_bound, axis_len))
# copy all the data to the mixin
self._interval_metadata = IntervalMetadata(
axis_len, copy_from=interval_metadata)
else:
raise TypeError('You must provide `IntervalMetadata` object, '
'not type %s.' % type(interval_metadata).__name__)
@interval_metadata.deleter
def interval_metadata(self):
self._interval_metadata = None
@experimental(as_of="0.5.1")
def has_interval_metadata(self):
"""Determine if the object has interval metadata.
An object has interval metadata if its ``interval_metadata``
has at least one ```Interval`` objects.
Returns
-------
bool
Indicates whether the object has interval metadata.
"""
return (self._interval_metadata is not None and
self.interval_metadata.num_interval_features > 0)
@abc.abstractmethod
def __eq__(self, other):
raise NotImplementedError
def _eq_(self, other):
# We're not simply comparing self.interval_metadata to
# other.interval_metadata in order to avoid creating "empty"
# interval metadata representations on the objects if they don't have
# interval metadata.
if self.has_interval_metadata() and other.has_interval_metadata():
return self.interval_metadata == other.interval_metadata
elif not (self.has_interval_metadata() or
other.has_interval_metadata()):
# Both don't have interval metadata.
return (self._interval_metadata_axis_len_() ==
other._interval_metadata_axis_len_())
else:
# One has interval metadata while the other does not.
return False
@abc.abstractmethod
def __ne__(self, other):
raise NotImplementedError
def _ne_(self, other):
return not (self == other)
@abc.abstractmethod
def __copy__(self):
raise NotImplementedError
def _copy_(self):
if self.has_interval_metadata():
return copy.copy(self.interval_metadata)
else:
return None
@abc.abstractmethod
def __deepcopy__(self, memo):
raise NotImplementedError
def _deepcopy_(self, memo):
if self.has_interval_metadata():
return copy.deepcopy(self.interval_metadata, memo)
else:
return None
|
nilq/baby-python
|
python
|
from rest_framework.test import APITestCase
from rest_framework import status
from rest_framework.reverse import reverse as api_reverse
from survivor.models import Survivor, FlagAsInfected, Reports
class ReportsAPITestCase(APITestCase):
def setUp(self):
Survivor.objects.create(
name='New Name', age=20, gender='M', latitude='11', longitude='22',
items='Fiji Water:13;Campbell Soup:17;First Aid Pouch:18;AK47:652'
)
Survivor.objects.create(
name='New Name', age=20, gender='M', latitude='11', longitude='22',
items='Fiji Water:13;Campbell Soup:17;First Aid Pouch:18;AK47:652'
)
def test_report_get(self):
url = api_reverse("api-survivor:reports-retrieve-update")
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
Survivor.objects.create(
name='New Name FHEUHF', age=20, gender='M', latitude='11', longitude='22',
items='Fiji Water:27;Campbell Soup:40;First Aid Pouch:18;AK47:652'
)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
Survivor.objects.create(
name='New Name FHEUHFddwdw', age=20, gender='M', latitude='11', longitude='22',
items='Fiji Water:0;Campbell Soup:0;First Aid Pouch:0;AK47:300', infected=True
)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
|
nilq/baby-python
|
python
|
import pathlib
from setuptools import setup
current_location = pathlib.Path(__file__).parent
README = (current_location / "README.md").read_text()
setup(
name="wiktionary-parser-ru",
version="0.0.1",
packages=["wiktionaryparserru", "tests"],
url="https://github.com/ShatteredMind/wiktionaryparserru",
license="MIT",
author="internethero",
author_email="sashalekoncev@gmail.com",
description="Basic parser for russian wiktionary",
long_description_content_type='text/markdown',
install_requires=["beautifulsoup4", "requests"],
classifiers=[
"Development Status :: 2 - Pre-Alpha",
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License"
],
)
|
nilq/baby-python
|
python
|
def convert(s):
s_split = s.split(' ')
return s_split
def niceprint(s):
for i, elm in enumerate(s):
print('Element #', i + 1, ' = ', elm, sep='')
return None
c1 = 10
c2 = 's'
|
nilq/baby-python
|
python
|
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
"""
LightGBM/Python training script
"""
import os
import sys
import argparse
import logging
import traceback
import json
from distutils.util import strtobool
import lightgbm
from collections import namedtuple
# Add the right path to PYTHONPATH
# so that you can import from common.*
COMMON_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
if COMMON_ROOT not in sys.path:
print(f"Adding {COMMON_ROOT} to PYTHONPATH")
sys.path.append(str(COMMON_ROOT))
# useful imports from common
from common.components import RunnableScript
from common.io import get_all_files
from common.lightgbm_utils import LightGBMCallbackHandler
from common.distributed import MultiNodeScript
class LightGBMPythonMpiTrainingScript(MultiNodeScript):
def __init__(self):
super().__init__(
task = "train",
framework = "lightgbm",
framework_version = lightgbm.__version__
)
@classmethod
def get_arg_parser(cls, parser=None):
"""Adds component/module arguments to a given argument parser.
Args:
parser (argparse.ArgumentParser): an argument parser instance
Returns:
ArgumentParser: the argument parser instance
Notes:
if parser is None, creates a new parser instance
"""
# add generic arguments
parser = RunnableScript.get_arg_parser(parser)
group_i = parser.add_argument_group("Input Data")
group_i.add_argument("--train",
required=True, type=str, help="Training data location (file or dir path)")
group_i.add_argument("--test",
required=True, type=str, help="Testing data location (file path)")
group_i.add_argument("--construct",
required=False, default=True, type=strtobool, help="use lazy initialization during data loading phase")
group_i.add_argument("--header", required=False, default=False, type=strtobool)
group_i.add_argument("--label_column", required=False, default="0", type=str)
group_i.add_argument("--group_column", required=False, default=None, type=str)
group_o = parser.add_argument_group("Outputs")
group_o.add_argument("--export_model",
required=False, type=str, help="Export the model in this location (file path)")
# learner params
group_lgbm = parser.add_argument_group("LightGBM learning parameters")
group_lgbm.add_argument("--objective", required=True, type=str)
group_lgbm.add_argument("--metric", required=True, type=str)
group_lgbm.add_argument("--boosting_type", required=True, type=str)
group_lgbm.add_argument("--tree_learner", required=True, type=str)
group_lgbm.add_argument("--label_gain", required=False, type=str, default=None)
group_lgbm.add_argument("--num_trees", required=True, type=int)
group_lgbm.add_argument("--num_leaves", required=True, type=int)
group_lgbm.add_argument("--min_data_in_leaf", required=True, type=int)
group_lgbm.add_argument("--learning_rate", required=True, type=float)
group_lgbm.add_argument("--max_bin", required=True, type=int)
group_lgbm.add_argument("--feature_fraction", required=True, type=float)
group_lgbm.add_argument("--device_type", required=False, type=str, default="cpu")
group_lgbm.add_argument("--custom_params", required=False, type=str, default=None)
return parser
def load_lgbm_params_from_cli(self, args, mpi_config):
"""Gets the right LightGBM parameters from argparse + mpi config
Args:
args (argparse.Namespace)
mpi_config (namedtuple): as returned from detect_mpi_config()
Returns:
lgbm_params (dict)
"""
# copy all parameters from argparse
cli_params = dict(vars(args))
# removing arguments that are purely CLI
for key in ['verbose', 'custom_properties', 'export_model', 'test', 'train', 'custom_params', 'construct', 'disable_perf_metrics']:
del cli_params[key]
# doing some fixes and hardcoded values
lgbm_params = cli_params
lgbm_params['feature_pre_filter'] = False
lgbm_params['verbose'] = 2
lgbm_params['header'] = bool(args.header) # strtobool returns 0 or 1, lightgbm needs actual bool
lgbm_params['is_provide_training_metric'] = True
# add mpi parameters if relevant
if mpi_config.mpi_available:
lgbm_params['num_machines'] = mpi_config.world_size
lgbm_params['machines'] = ":"
# process custom params
if args.custom_params:
custom_params = json.loads(args.custom_params)
lgbm_params.update(custom_params)
return lgbm_params
def assign_train_data(self, args, mpi_config):
""" Identifies which training file to load on this node.
Checks for consistency between number of files and mpi config.
Args:
args (argparse.Namespace)
mpi_config (namedtuple): as returned from detect_mpi_config()
Returns:
str: path to the data file for this node
"""
train_file_paths = get_all_files(args.train)
if mpi_config.mpi_available:
# depending on mode, we'll require different number of training files
if args.tree_learner == "data" or args.tree_learner == "voting":
if len(train_file_paths) == mpi_config.world_size:
train_data = train_file_paths[mpi_config.world_rank]
else:
raise Exception(f"To use MPI with tree_learner={args.tree_learner} and node count {mpi_config.world_rank}, you need to partition the input data into {mpi_config.world_rank} files (currently found {len(train_file_paths)})")
elif args.tree_learner == "feature":
if len(train_file_paths) == 1:
train_data = train_file_paths[0]
else:
raise Exception(f"To use MPI with tree_learner=parallel you need to provide only 1 input file, but {len(train_file_paths)} were found")
elif args.tree_learner == "serial":
if len(train_file_paths) == 1:
train_data = train_file_paths[0]
else:
raise Exception(f"To use single node training, you need to provide only 1 input file, but {len(train_file_paths)} were found")
else:
NotImplementedError(f"tree_learner mode {args.tree_learner} does not exist or is not implemented.")
else:
# if not using mpi, let's just use serial mode with one unique input file
if args.tree_learner != "serial":
logging.getLogger().warning(f"Using tree_learner={args.tree_learner} on single node does not make sense, switching back to tree_learner=serial")
args.tree_learner = "serial"
if len(train_file_paths) == 1:
train_data = train_file_paths[0]
else:
raise Exception(f"To use single node training, you need to provide only 1 input file, but {len(train_file_paths)} were found")
return train_data
def run(self, args, logger, metrics_logger, unknown_args):
"""Run script with arguments (the core of the component)
Args:
args (argparse.namespace): command line arguments provided to script
logger (logging.getLogger() for this script)
metrics_logger (common.metrics.MetricLogger)
unknown_args (list[str]): list of arguments not recognized during argparse
"""
# get mpi config as a namedtuple
mpi_config = self.mpi_config()
# figure out the lgbm params from cli args + mpi config
lgbm_params = self.load_lgbm_params_from_cli(args, mpi_config)
# create a handler for the metrics callbacks
callbacks_handler = LightGBMCallbackHandler(
metrics_logger=metrics_logger,
metrics_prefix=f"node_{mpi_config.world_rank}/"
)
# make sure the output argument exists
if args.export_model and mpi_config.main_node:
os.makedirs(args.export_model, exist_ok=True)
args.export_model = os.path.join(args.export_model, "model.txt")
# log params only once by doing it only on main node (node 0)
if mpi_config.main_node:
# log lgbm parameters
logger.info(f"LGBM Params: {lgbm_params}")
metrics_logger.log_parameters(**lgbm_params)
# register logger for lightgbm logs
lightgbm.register_logger(logger)
logger.info(f"Loading data for training")
with metrics_logger.log_time_block("time_data_loading", step=mpi_config.world_rank):
# obtain the path to the train data for this node
train_data_path = self.assign_train_data(args, mpi_config)
test_data_paths = get_all_files(args.test)
logger.info(f"Running with 1 train file and {len(test_data_paths)} test files.")
# construct datasets
if args.construct:
train_data = lightgbm.Dataset(train_data_path, params=lgbm_params).construct()
val_datasets = [
train_data.create_valid(test_data_path).construct() for test_data_path in test_data_paths
]
# capture data shape in metrics
metrics_logger.log_metric(key="train_data.length", value=train_data.num_data(), step=mpi_config.world_rank)
metrics_logger.log_metric(key="train_data.width", value=train_data.num_feature(), step=mpi_config.world_rank)
else:
train_data = lightgbm.Dataset(train_data_path, params=lgbm_params)
val_datasets = [
train_data.create_valid(test_data_path) for test_data_path in test_data_paths
]
# can't count rows if dataset is not constructed
# mlflow can only log float.
# metrics_logger.log_metric(key="train_data.length", value="n/a")
# metrics_logger.log_metric(key="train_data.width", value="n/a")
logger.info(f"Training LightGBM with parameters: {lgbm_params}")
with metrics_logger.log_time_block("time_training", step=mpi_config.world_rank):
booster = lightgbm.train(
lgbm_params,
train_data,
valid_sets = val_datasets,
callbacks=[callbacks_handler.callback]
)
if args.export_model and mpi_config.main_node:
logger.info(f"Writing model in {args.export_model}")
booster.save_model(args.export_model)
def get_arg_parser(parser=None):
""" To ensure compatibility with shrike unit tests """
return LightGBMPythonMpiTrainingScript.get_arg_parser(parser)
def main(cli_args=None):
""" To ensure compatibility with shrike unit tests """
LightGBMPythonMpiTrainingScript.main(cli_args)
if __name__ == "__main__":
main()
|
nilq/baby-python
|
python
|
"""
Lark parser and AST definition for Domain Relational Calculus.
"""
from typing import Dict, NamedTuple, Set, Tuple
from lark import Lark, Token, Tree
from relcal import config
from relcal.helpers.primitives import Singleton
####################
# Type definitions #
####################
Fields = Tuple[Token, ...]
class Table(NamedTuple):
name: Token
fields: Fields
class Query(NamedTuple):
tuple_vars: Fields
predicate: object
class DRCParsedObject(NamedTuple):
table_defs: Dict[Token, Fields]
query: Query
########################
# Language definitions #
########################
class DRCQueryLanguage(metaclass=Singleton):
"""
A collection of methods for domain relational calculus query language.
The actual Lark parser is stored within 'parser' class attribute.
"""
parser = Lark(r'''
start: table_defs query
table_defs: (table ";")*
table: TABLE_NAME "(" fields? ")"
fields: FIELD_NAME ("," FIELD_NAME)* ","?
query: "{" fields ":" iff_test "}"
?iff_test: implies_test (_IFF_OP implies_test)?
?implies_test: or_test (_IMPLIES_OP or_test)?
?or_test: and_test (_OR_OP and_test)*
?and_test: not_test (_AND_OP not_test)*
?not_test: _NOT_OP atom_test -> not
| atom_test
?atom_test: "(" iff_test ")"
| table
| FIELD_NAME COMP_OP FIELD_NAME -> compare_op
| _FOR_ALL_OP "[" FIELD_NAME "]" "(" iff_test ")" -> for_all
| _THERE_EXISTS_OP "[" FIELD_NAME "]" "(" iff_test ")" -> there_exists
TABLE_NAME: /[A-Z][A-Za-z0-9_]*/
FIELD_NAME: /[a-z][A-Za-z0-9_]*/
QUERY_IDENTIFIER: /\$[A-Za-z0-9_]+/
_IFF_OP.10: "<=>" | "⇔" | "↔" | "IFF"
_IMPLIES_OP.10: "=>" | "⇒" | "→" | "IMPLIES"
_OR_OP.10: "∨" | "|" | "OR"
_AND_OP.10: "∧" | "&" | "AND"
_NOT_OP.10: "~" | "¬" | "NOT"
_FOR_ALL_OP.10: "∀" | "ALL"
_THERE_EXISTS_OP.10: "∃" | "EXISTS"
COMP_OP: "==" | "!=" | ">=" | ">" | "<=" | "<"
%import common.WS
%ignore WS
''', parser="lalr", debug=config.DEBUG_MODE)
def parse(self, text: str) -> Tree:
"""
Use the parser to parse the given domain relational calculus query
into parsed tree.
"""
return self.parser.parse(text)
def transform(self, node: Tree) -> DRCParsedObject:
"""
Transforms the entire parsed tree into the abstract syntax tree.
The given parsed tree node must be of type 'start'.
"""
assert node.data == 'start' and len(node.children) == 2
table_defs_node: Tree = node.children[0]
query_node: Tree = node.children[1]
table_defs = self.transform_table_defs(table_defs_node)
query = self.transform_query(query_node)
return DRCParsedObject(table_defs, query)
def transform_table_defs(self, node: Tree) -> Dict[Token, Fields]:
"""
Transforms the node with type 'table_defs' into
a dictionary which maps table name string to field names.
"""
assert node.data == 'table_defs'
table_defs = {}
for table_node in node.children:
name, fields = self.transform_single_table_def(table_node)
# Check that all table names are unique
if name in table_defs:
raise SyntaxError(
f"duplicated table name {str(name)!r} "
f"at line {name.line} column {name.column}",
)
table_defs[name] = fields
return table_defs
def transform_single_table_def(self, node: Tree) -> Table:
"""
Transforms the node with type 'table' into
a tuple of table name strings and field names.
"""
assert node.data == 'table' and len(node.children) == 2
table_name: Token = node.children[0]
fields_node: Tree = node.children[1]
# Check that all field names are unique
collected = set()
for field_name in fields_node.children:
if field_name in collected:
raise SyntaxError(
f"duplicated field name {str(field_name)!r} "
f"at line {field_name.line} column {field_name.column}",
)
collected.add(field_name)
return Table(table_name, tuple(fields_node.children))
def transform_query(self, node: Tree) -> Query:
"""
Transforms the node with type 'query' into the Query tuple object.
"""
assert node.data == 'query' and len(node.children) == 2
tuple_vars_node: Tree = node.children[0]
predicate_node: Tree = node.children[1]
tuple_vars = tuple(tuple_vars_node.children)
self.validate_scope(predicate_node, set(tuple_vars))
return Query(tuple(tuple_vars_node.children), predicate_node)
def validate_scope(self, node: Tree, scope: Set[str]):
"""
Recursively checks that
1. There is not variable shadowing of variables from within
the given scope under the tree node.
2. There is no free variable not in scope.
"""
visitor = getattr(self, f"validate_scope_{node.data}", None)
if visitor:
return visitor(node, scope)
for child_node in node.children:
if isinstance(child_node, Tree):
self.validate_scope(child_node, scope)
elif isinstance(child_node, Token) and child_node.type == 'FIELD_NAME':
self.validate_free_variable(child_node, scope)
def validate_scope_there_exists(self, node: Tree, scope: Set[str]):
assert node.data == 'there_exists'
return self.validate_scope_quantifier(node, scope)
def validate_scope_for_all(self, node: Tree, scope: Set[str]):
assert node.data == 'for_all'
return self.validate_scope_quantifier(node, scope)
def validate_scope_quantifier(self, node: Tree, scope: Set[str]):
assert len(node.children) == 2
variable: Token = node.children[0]
expr_node: Tree = node.children[1]
# Check that new variable is not overshadowed
if variable in scope:
raise SyntaxError(
f"variable name {str(variable)!r} overshadowed "
f"at line {variable.line} column {variable.column}",
)
# Recursively check sub-expression node
self.validate_scope(expr_node, scope | {variable})
def validate_free_variable(self, variable: Token, scope: Set[str]):
if variable not in scope:
raise SyntaxError(
f"variable name {str(variable)!r} is a free variable "
f"at line {variable.line} column {variable.column}",
)
|
nilq/baby-python
|
python
|
import logging
import logging.config
import os
import signal
import sys
import click
import gevent
import gevent.pool
from eth_keys.datatypes import PrivateKey
from eth_utils import to_checksum_address
from gevent.queue import Queue
from marshmallow.exceptions import ValidationError
from toml.decoder import TomlDecodeError
from web3 import HTTPProvider, Web3
import bridge.node_status
import bridge.version
from bridge.config import load_config
from bridge.confirmation_sender import (
ConfirmationSender,
ConfirmationWatcher,
make_sanity_check_transfer,
)
from bridge.confirmation_task_planner import ConfirmationTaskPlanner
from bridge.constants import (
APPLICATION_CLEANUP_TIMEOUT,
COMPLETION_EVENT_NAME,
CONFIRMATION_EVENT_NAME,
HOME_CHAIN_STEP_DURATION,
TRANSFER_EVENT_NAME,
)
from bridge.contract_abis import HOME_BRIDGE_ABI, MINIMAL_ERC20_TOKEN_ABI
from bridge.contract_validation import (
get_validator_proxy_contract,
validate_contract_existence,
)
from bridge.event_fetcher import EventFetcher
from bridge.events import ChainRole
from bridge.service import Service, start_services
from bridge.transfer_recorder import TransferRecorder
from bridge.utils import get_validator_private_key
from bridge.validator_balance_watcher import ValidatorBalanceWatcher
from bridge.validator_status_watcher import ValidatorStatusWatcher
from bridge.webservice import InternalState, Webservice
logger = logging.getLogger(__name__)
class SetupError(Exception):
pass
def configure_logging(config):
"""configure the logging subsystem via the 'logging' key in the TOML config"""
try:
logging.config.dictConfig(config["logging"])
except (ValueError, TypeError, AttributeError, ImportError) as err:
click.echo(
f"Error configuring logging: {err}\n"
"Please check your configuration file and the LOGLEVEL environment variable"
)
raise click.Abort()
logger.debug(
"Initialized logging system with the following config: %r", config["logging"]
)
def make_w3(config, chain: ChainRole):
chaincfg = config[chain.configuration_key]
return Web3(
HTTPProvider(
chaincfg["rpc_url"], request_kwargs={"timeout": chaincfg["rpc_timeout"]}
)
)
def make_w3_home(config):
return make_w3(config, ChainRole.home)
def make_w3_foreign(config):
return make_w3(config, ChainRole.foreign)
def get_max_pending_transactions(config):
return min(
(config["home_chain"]["max_reorg_depth"] + 1)
* config["home_chain"]["max_pending_transactions_per_block"],
512,
)
def make_validator_address(config):
private_key_bytes = get_validator_private_key(config)
return PrivateKey(private_key_bytes).public_key.to_canonical_address()
def sanity_check_home_bridge_contracts(home_bridge_contract):
validate_contract_existence(home_bridge_contract)
validator_proxy_contract = get_validator_proxy_contract(home_bridge_contract)
try:
validate_contract_existence(validator_proxy_contract)
except ValueError as error:
raise SetupError(
"Serious bridge setup error. The validator proxy contract at the address the home "
"bridge property points to does not exist or is not intact!"
) from error
balance = home_bridge_contract.web3.eth.getBalance(home_bridge_contract.address)
if balance == 0:
raise SetupError("Serious bridge setup error. The bridge has no funds.")
def make_transfer_event_fetcher(config, transfer_event_queue):
w3_foreign = make_w3_foreign(config)
token_contract = w3_foreign.eth.contract(
address=config["foreign_chain"]["token_contract_address"],
abi=MINIMAL_ERC20_TOKEN_ABI,
)
return EventFetcher(
web3=w3_foreign,
contract=token_contract,
filter_definition={
TRANSFER_EVENT_NAME: {
"to": config["foreign_chain"]["bridge_contract_address"]
}
},
event_queue=transfer_event_queue,
max_reorg_depth=config["foreign_chain"]["max_reorg_depth"],
start_block_number=config["foreign_chain"]["event_fetch_start_block_number"],
chain_role=ChainRole.foreign,
)
def make_home_bridge_event_fetcher(config, home_bridge_event_queue):
w3_home = make_w3_home(config)
home_bridge_contract = w3_home.eth.contract(
address=config["home_chain"]["bridge_contract_address"], abi=HOME_BRIDGE_ABI
)
validator_address = make_validator_address(config)
return EventFetcher(
web3=w3_home,
contract=home_bridge_contract,
filter_definition={
CONFIRMATION_EVENT_NAME: {"validator": validator_address},
COMPLETION_EVENT_NAME: {},
},
event_queue=home_bridge_event_queue,
max_reorg_depth=config["home_chain"]["max_reorg_depth"],
start_block_number=config["home_chain"]["event_fetch_start_block_number"],
chain_role=ChainRole.home,
)
def make_recorder(config):
minimum_balance = config["home_chain"]["minimum_validator_balance"]
return TransferRecorder(minimum_balance)
def make_confirmation_task_planner(
config,
recorder,
control_queue,
transfer_event_queue,
home_bridge_event_queue,
confirmation_task_queue,
):
return ConfirmationTaskPlanner(
sync_persistence_time=HOME_CHAIN_STEP_DURATION,
recorder=recorder,
control_queue=control_queue,
transfer_event_queue=transfer_event_queue,
home_bridge_event_queue=home_bridge_event_queue,
confirmation_task_queue=confirmation_task_queue,
)
def make_confirmation_sender(
*, config, pending_transaction_queue, confirmation_task_queue
):
w3_home = make_w3_home(config)
home_bridge_contract = w3_home.eth.contract(
address=config["home_chain"]["bridge_contract_address"], abi=HOME_BRIDGE_ABI
)
sanity_check_home_bridge_contracts(home_bridge_contract)
return ConfirmationSender(
transfer_event_queue=confirmation_task_queue,
home_bridge_contract=home_bridge_contract,
private_key=get_validator_private_key(config),
gas_price=config["home_chain"]["gas_price"],
max_reorg_depth=config["home_chain"]["max_reorg_depth"],
pending_transaction_queue=pending_transaction_queue,
sanity_check_transfer=make_sanity_check_transfer(
foreign_bridge_contract_address=to_checksum_address(
config["foreign_chain"]["bridge_contract_address"]
)
),
)
def make_confirmation_watcher(*, config, pending_transaction_queue):
w3_home = make_w3_home(config)
max_reorg_depth = config["home_chain"]["max_reorg_depth"]
return ConfirmationWatcher(
w3=w3_home,
pending_transaction_queue=pending_transaction_queue,
max_reorg_depth=max_reorg_depth,
)
def make_validator_status_watcher(config, control_queue):
w3_home = make_w3_home(config)
home_bridge_contract = w3_home.eth.contract(
address=config["home_chain"]["bridge_contract_address"], abi=HOME_BRIDGE_ABI
)
sanity_check_home_bridge_contracts(home_bridge_contract)
validator_proxy_contract = get_validator_proxy_contract(home_bridge_contract)
validator_address = make_validator_address(config)
return ValidatorStatusWatcher(
validator_proxy_contract,
validator_address,
poll_interval=HOME_CHAIN_STEP_DURATION,
control_queue=control_queue,
stop_validating_callback=shutdown,
)
def make_validator_balance_watcher(config, control_queue):
w3 = make_w3_home(config)
validator_address = make_validator_address(config)
poll_interval = config["home_chain"]["balance_warn_poll_interval"]
return ValidatorBalanceWatcher(
w3=w3,
validator_address=validator_address,
poll_interval=poll_interval,
control_queue=control_queue,
)
public_config_keys = ()
def make_webservice(*, config, recorder):
d = config["webservice"]
if d and d["enabled"]:
ws = Webservice(host=d["host"], port=d["port"])
else:
return None
def encode_address(v):
if isinstance(v, bytes):
return to_checksum_address(v)
else:
return v
public_config = {k: encode_address(config[k]) for k in public_config_keys}
ws.enable_internal_state(InternalState(recorder=recorder, config=public_config))
return ws
def make_main_services(config, recorder):
control_queue = Queue()
transfer_event_queue = Queue()
home_bridge_event_queue = Queue()
confirmation_task_queue = Queue()
transfer_event_fetcher = make_transfer_event_fetcher(config, transfer_event_queue)
home_bridge_event_fetcher = make_home_bridge_event_fetcher(
config, home_bridge_event_queue
)
confirmation_task_planner = make_confirmation_task_planner(
config,
recorder=recorder,
control_queue=control_queue,
transfer_event_queue=transfer_event_queue,
home_bridge_event_queue=home_bridge_event_queue,
confirmation_task_queue=confirmation_task_queue,
)
validator_status_watcher = make_validator_status_watcher(config, control_queue)
max_pending_transactions = get_max_pending_transactions(config)
logger.info("maximum number of pending transactions: %s", max_pending_transactions)
pending_transaction_queue = Queue(max_pending_transactions)
sender = make_confirmation_sender(
config=config,
pending_transaction_queue=pending_transaction_queue,
confirmation_task_queue=confirmation_task_queue,
)
watcher = make_confirmation_watcher(
config=config, pending_transaction_queue=pending_transaction_queue
)
validator_balance_watcher = make_validator_balance_watcher(config, control_queue)
return (
[
Service(
"fetch-foreign-bridge-events",
transfer_event_fetcher.fetch_events,
config["foreign_chain"]["event_poll_interval"],
),
Service(
"fetch-home-bridge-events",
home_bridge_event_fetcher.fetch_events,
config["home_chain"]["event_poll_interval"],
),
Service("validator-status-watcher", validator_status_watcher.run),
Service("validator_balance_watcher", validator_balance_watcher.run),
Service("log-internal-state", log_internal_state, recorder),
]
+ sender.services
+ watcher.services
+ confirmation_task_planner.services
)
def reload_logging_config(config_path):
logger.info(f"Trying to reload the logging configuration from {config_path}")
try:
config = load_config(config_path)
configure_logging(config)
logger.info("Logging has been reconfigured")
except Exception as err:
# this function is being called as signal handler. make sure
# we don't die as this would raise the error in the main
# greenlet.
logger.critical(
f"Error while trying to reload the logging configuration from {config_path}: {err}"
)
def install_signal_handler(signum, name, f, *args, **kwargs):
def handler():
gevent.getcurrent().name = name
logger.info(f"Received {signal.Signals(signum).name} signal.")
f(*args, **kwargs)
gevent.signal_handler(signum, handler)
def log_internal_state(recorder):
while True:
gevent.sleep(60.0)
recorder.log_current_state()
main_pool = gevent.pool.Pool()
def shutdown_raw(timeout=APPLICATION_CLEANUP_TIMEOUT, exitcode=0):
"""gracefully shut down the application"""
logger.info("Stopping with exitcode %s", exitcode)
timeout = gevent.Timeout(timeout)
timeout.start()
try:
main_pool.kill()
main_pool.join()
except gevent.Timeout as handled_timeout:
if handled_timeout is not timeout:
logger.error("Catched wrong timeout exception, exciting anyway")
else:
logger.error("Bridge didn't clean up in time, doing a hard exit")
sys.stderr.flush()
sys.stdout.flush()
os._exit(os.EX_SOFTWARE)
os._exit(exitcode)
def shutdown(timeout=APPLICATION_CLEANUP_TIMEOUT, exitcode=0):
"""call shutdown_raw in a new greenlet. we need to call that one,
if the calling greenlet is running inside the main_pool"""
gevent.spawn(shutdown_raw, timeout=timeout, exitcode=exitcode)
def handle_greenlet_exception(gr):
logger.exception(
f"Application Error: {gr.name} unexpectedly died. Shutting down.",
exc_info=gr.exception,
)
shutdown_raw(exitcode=os.EX_SOFTWARE)
def start_services_in_main_pool(services):
return start_services(
services,
start=main_pool.start,
link_exception_callback=handle_greenlet_exception,
)
def wait_for_node_fully_synced(config, chain):
w3 = make_w3(config, chain)
start_block = config[chain.configuration_key]["event_fetch_start_block_number"]
def is_synced(node_status):
syncmsg = "still syncing" if node_status.is_syncing else "fully synced"
start_block_reached = node_status.latest_synced_block >= start_block
start_block_msg = "reached" if start_block_reached else "not reached"
logger.info(
f"{chain.name} node {syncmsg}, start block {start_block} {start_block_msg}: {node_status}"
)
return not node_status.is_syncing and start_block_reached
bridge.node_status.wait_for_node_status(w3, is_synced)
logger.info(f"{chain.name} node is fully synced")
def wait_until_home_node_is_ready(config):
wait_for_node_fully_synced(config, ChainRole.home)
w3_home = make_w3_home(config)
home_bridge_contract = w3_home.eth.contract(
address=config["home_chain"]["bridge_contract_address"], abi=HOME_BRIDGE_ABI
)
sanity_check_home_bridge_contracts(home_bridge_contract)
logger.info("home node has passed the sanity checks")
def wait_until_foreign_node_is_ready(config):
wait_for_node_fully_synced(config, ChainRole.foreign)
w3_foreign = make_w3_foreign(config)
token_contract = w3_foreign.eth.contract(
address=config["foreign_chain"]["token_contract_address"],
abi=MINIMAL_ERC20_TOKEN_ABI,
)
validate_contract_existence(token_contract)
logger.info("foreign node has passed the sanity checks")
def start_system(config):
recorder = make_recorder(config)
install_signal_handler(
signal.SIGUSR1, "report-internal-state", recorder.log_current_state
)
webservice = make_webservice(config=config, recorder=recorder)
if webservice is not None:
start_services_in_main_pool(webservice.services)
wait_node_ready_services = [
Service("home_wait_ready", wait_until_home_node_is_ready, config),
Service("foreign_wait_ready", wait_until_foreign_node_is_ready, config),
]
gevent.joinall(
start_services_in_main_pool(wait_node_ready_services), raise_error=True
)
main_services = make_main_services(config, recorder)
start_services_in_main_pool(main_services)
@click.command()
@click.version_option(version=bridge.version.version)
@click.option(
"-c",
"--config",
"config_path",
type=click.Path(exists=True),
required=True,
envvar="BRIDGE_CONFIG",
help="Path to a config file",
)
@click.pass_context
def main(ctx, config_path: str) -> None:
"""The Trustlines Bridge Validation Server
Configuration can be made using a TOML file.
See config.py for valid configuration options and defaults.
"""
try:
logger.info(f"Loading configuration file from {config_path}")
config = load_config(config_path)
except TomlDecodeError as decode_error:
raise click.UsageError(f"Invalid config file: {decode_error}") from decode_error
except ValidationError as validation_error:
raise click.UsageError(
f"Invalid config file: {validation_error}"
) from validation_error
configure_logging(config)
validator_address = make_validator_address(config)
logger.info(
f"Starting Trustlines Bridge Validation Server for address {to_checksum_address(validator_address)}"
)
install_signal_handler(
signal.SIGHUP, "reload-logging-config", reload_logging_config, config_path
)
for signum in [signal.SIGINT, signal.SIGTERM]:
install_signal_handler(signum, "terminator", shutdown_raw, exitcode=0)
try:
start_services_in_main_pool([Service("start_system", start_system, config)])
except Exception as exception:
logger.exception("Application error", exc_info=exception)
os._exit(os.EX_SOFTWARE)
finally:
gevent.hub.get_hub().join()
|
nilq/baby-python
|
python
|
# -*- coding: utf-8 -*-
"""
Test cases related to direct loading of external libxml2 documents
"""
from __future__ import absolute_import
import sys
import unittest
from .common_imports import HelperTestCase, etree
DOC_NAME = b"libxml2:xmlDoc"
DESTRUCTOR_NAME = b"destructor:xmlFreeDoc"
class ExternalDocumentTestCase(HelperTestCase):
def setUp(self):
try:
import ctypes
from ctypes import pythonapi
from ctypes.util import find_library
except ImportError:
raise unittest.SkipTest("ctypes support missing")
def wrap(func, restype, *argtypes):
func.restype = restype
func.argtypes = list(argtypes)
return func
self.get_capsule_name = wrap(
pythonapi.PyCapsule_GetName, ctypes.c_char_p, ctypes.py_object
)
self.capsule_is_valid = wrap(
pythonapi.PyCapsule_IsValid,
ctypes.c_int,
ctypes.py_object,
ctypes.c_char_p,
)
self.new_capsule = wrap(
pythonapi.PyCapsule_New,
ctypes.py_object,
ctypes.c_void_p,
ctypes.c_char_p,
ctypes.c_void_p,
)
self.set_capsule_name = wrap(
pythonapi.PyCapsule_SetName,
ctypes.c_int,
ctypes.py_object,
ctypes.c_char_p,
)
self.set_capsule_context = wrap(
pythonapi.PyCapsule_SetContext,
ctypes.c_int,
ctypes.py_object,
ctypes.c_char_p,
)
self.get_capsule_context = wrap(
pythonapi.PyCapsule_GetContext, ctypes.c_char_p, ctypes.py_object
)
self.get_capsule_pointer = wrap(
pythonapi.PyCapsule_GetPointer,
ctypes.c_void_p,
ctypes.py_object,
ctypes.c_char_p,
)
self.set_capsule_pointer = wrap(
pythonapi.PyCapsule_SetPointer,
ctypes.c_int,
ctypes.py_object,
ctypes.c_void_p,
)
self.set_capsule_destructor = wrap(
pythonapi.PyCapsule_SetDestructor,
ctypes.c_int,
ctypes.py_object,
ctypes.c_void_p,
)
self.PyCapsule_Destructor = ctypes.CFUNCTYPE(None, ctypes.py_object)
libxml2 = ctypes.CDLL(find_library("xml2"))
self.create_doc = wrap(
libxml2.xmlReadMemory,
ctypes.c_void_p,
ctypes.c_char_p,
ctypes.c_int,
ctypes.c_char_p,
ctypes.c_char_p,
ctypes.c_int,
)
self.free_doc = wrap(libxml2.xmlFreeDoc, None, ctypes.c_void_p)
def as_capsule(self, text, capsule_name=DOC_NAME):
if not isinstance(text, bytes):
text = text.encode("utf-8")
doc = self.create_doc(text, len(text), b"base.xml", b"utf-8", 0)
ans = self.new_capsule(doc, capsule_name, None)
self.set_capsule_context(ans, DESTRUCTOR_NAME)
return ans
def test_external_document_adoption(self):
xml = '<r a="1">t</r>'
self.assertRaises(TypeError, etree.adopt_external_document, None)
capsule = self.as_capsule(xml)
self.assertTrue(self.capsule_is_valid(capsule, DOC_NAME))
self.assertEqual(DOC_NAME, self.get_capsule_name(capsule))
# Create an lxml tree from the capsule (this is a move not a copy)
root = etree.adopt_external_document(capsule).getroot()
self.assertIsNone(self.get_capsule_name(capsule))
self.assertEqual(root.text, "t")
root.text = "new text"
# Now reset the capsule so we can copy it
self.assertEqual(0, self.set_capsule_name(capsule, DOC_NAME))
self.assertEqual(0, self.set_capsule_context(capsule, b"invalid"))
# Create an lxml tree from the capsule (this is a copy not a move)
root2 = etree.adopt_external_document(capsule).getroot()
self.assertEqual(self.get_capsule_context(capsule), b"invalid")
# Check that the modification to the tree using the transferred
# document was successful
self.assertEqual(root.text, root2.text)
# Check that further modifications do not show up in the copy (they are
# disjoint)
root.text = "other text"
self.assertNotEqual(root.text, root2.text)
# delete root and ensure root2 survives
del root
self.assertEqual(root2.text, "new text")
def test_suite():
suite = unittest.TestSuite()
if sys.platform != "win32":
suite.addTests([unittest.makeSuite(ExternalDocumentTestCase)])
return suite
if __name__ == "__main__":
print("to test use test.py %s" % __file__)
|
nilq/baby-python
|
python
|
import math
from tkinter import Tk, Canvas, W, E, NW
from tkinter.filedialog import askopenfilename
from tkinter import messagebox
from scipy.interpolate import interp1d
import time
import numpy as np
# Definición recursiva, requiere numpy
# def B(t,P):
# if len(P)==1:
# return np.array(P[0])
# else:
# return (1-t)*B(t,P[:-1])+t*B(t,P[1:])
def getCubic(L, disp):
K = np.array([[3*L**2, 2*L], [L**3, L**2]])
F = np.array([[np.arctan2(disp, L)], [disp]])
return np.linalg.solve(K, F)[:, 0]
def graficarBola():
my_canvas.delete("all")
graphAcel()
ponerTextos()
global width, height, mult, u, L, m, k, multu
up = u*mult*multu
Lp = L*mult
xi, yi = Lp, 0
centrox, centroy = width/2, height-100
r = m*2
my_canvas.create_line(centrox, centroy, centrox,
centroy-Lp+r, fill='gray', width=1, dash=[3, 3])
my_canvas.create_oval(yi-r+centrox, centroy-(xi-r), yi +
r+centrox, centroy-(xi+r), fill="")
theta = np.arctan2(up, Lp)
Lp = Lp*np.cos(theta)
up = up*np.cos(theta)
a, b = getCubic(Lp, up)
x, y = 0, 0
for i in range(51):
equis = Lp/50*i
xi, yi = equis, a*equis**3+b*equis**2
my_canvas.create_line(y+centrox, centroy-x, yi+centrox,
centroy-xi, fill='red', width=max(1, int(k/100)))
x, y = xi, yi
my_canvas.create_oval(y-r+centrox, centroy-(x-r), y +
r+centrox, centroy-(x+r), fill="blue")
def editPoint(event):
global editando
editando = not editando
if not editando:
drawBezier()
def drawBezier():
global editando, u, v, L, z, f, m, k, dt, t, height, width, T, U, V
U = []
T = []
c = 2*z*m*np.sqrt(k/m)
while not editando:
a = -m*f(t)*9.81
k1u = v
k1v = -1/m*(c*v+k*u+a)
ui0 = u + k1u*dt*0.5
vi0 = v + k1v*dt*0.5
k2u = vi0
k2v = -1/m*(c*vi0+k*ui0+a)
ui1 = u + k2u*dt*0.5
vi1 = v + k2v*dt*0.5
k3u = vi1
k3v = -1/m*(c*vi1+k*ui1+a)
ui2 = u + k3u*dt
vi2 = v + k3v*dt
k4u = vi2
k4v = -1/m*(c*vi2+k*ui2+a)
phiu = (k1u+2*k2u+2*k3u+k4u)/6
phiv = (k1v+2*k2v+2*k3v+k4v)/6
u = u + phiu*dt
v = v + phiv*dt
t += dt
U += [u]
V += [v]
T += [t]
x0 = 100
y0 = height-90
b = 300
h = 100
# time.sleep(dt/10)
graficarBola()
createGraph(width-b-x0, y0-130, b, h, T, U, title='u [m]', alert=True)
createGraph(width-b-x0, y0-2*130, b, h, T,
V, title='v [m/s]', alert=True)
my_canvas.update()
def movePoint(event):
global editando, u, L, width, height
centrox, centroy = width/2, height-100
if editando:
my_canvas.delete("all")
x = centrox-event.x
y = centroy-event.y
P[0] = x
P[1] = y
u, L = -x/mult/multu, y/mult
graficarBola()
def ponerTextos():
global z, k, m, dt, height, multu, strt
my_canvas.create_text(100, height-90, fill="black",
font='20', text=f"omega={format(np.sqrt(k/m),'.2f')}", anchor=W)
my_canvas.create_text(200, height-90, fill="black",
font='20', text=f"T={format(2*np.pi/np.sqrt(k/m),'.2f')}", anchor=W)
my_canvas.create_text(100, height-110, fill="black",
font='20', text=f"multu={format(multu,'.2f')}", anchor=W)
my_canvas.create_text(100, height-130, fill="black",
font='20', text=f"z={format(z,'.2f')}", anchor=W)
my_canvas.create_text(100, height-150, fill="black",
font='20', text=f"k={format(k,'.2f')}", anchor=W)
my_canvas.create_text(100, height-170, fill="black",
font='20', text=f"m={format(m,'.2f')}", anchor=W)
my_canvas.create_text(100, height-190, fill="black",
font='20', text=f"dt={format(dt,'.2f')}", anchor=W)
my_canvas.create_text(100, 50, fill="black",
font='20', text=strt, anchor=NW)
def createGraph(x0, y0, b, h, X, Y, maxs=None, color='red', title='', alert=False):
XC = []
YC = []
np = 70
if alert:
if len(X) > np+1:
for i in range(0, len(X), int(len(X)/np)):
XC += [X[i]]
YC += [Y[i]]
XC += [X[-1]]
YC += [Y[-1]]
else:
XC = X
YC = Y
else:
XC = X
YC = Y
X = XC
Y = YC
xf = x0+b
yf = y0-h
ym = y0-h/2
xmax = max(X)
xmin = min(X)
if maxs:
ymax, ymin = maxs
else:
ymax = max(Y)
ymin = min(Y)
ymax = max(abs(ymax), abs(ymin))
if ymax == 0:
ymax = 1
dx = xmax-xmin
if dx == 0:
dx = 1
def z(x): return (x)/ymax
X = [(i-xmin)/dx*b for i in X]
Y = [z(i) for i in Y]
my_canvas.create_line(x0, y0, x0, yf, fill='gray', width=1)
my_canvas.create_line(x0, ym, xf, ym, fill='gray', width=1)
my_canvas.create_text(x0-20, yf-20, fill="black",
font='20', text=f"{format(t,'.2f')}", anchor=W)
my_canvas.create_text(x0-5, ym, fill="black",
font='20', text=title, anchor=E)
for i in range(len(X)-1):
my_canvas.create_line(x0+X[i], ym-Y[i]*h/2, x0 +
X[i+1], ym-Y[i+1]*h/2, fill=color, width=2)
def graphAcel():
global f, dt, height, t, data, width
n = 20
x0 = 100
y0 = height-90
b = 300
h = 100
dx = b/n
maxs = None
X = []
Y = []
for i in range(n+1):
X += [i*dx]
Y += [f(t+i*dt)]
try:
eq = data[:, 0]
ey = data[:, 1]
if t < np.max(eq):
maxs = [np.max(ey), np.min(ey)]
except:
pass
createGraph(width-b-x0, y0, b, h, X, Y, maxs, color='blue', title='a [g]')
def importarArchivo():
global ARCHIVO
ARCHIVO = askopenfilename()
parseArchivo()
def parseArchivo():
global f, u, v, editando, data
data = np.loadtxt(ARCHIVO, skiprows=1, delimiter=',')
f = interp1d(data[:, 0], data[:, 1], kind='linear',
fill_value=(0, 0), bounds_error=False)
u = 0
v = 0
graficarBola()
drawBezier()
def kpup(e):
global editando, actual, f, u, v, t, U, T
if e.char.lower() == 'a':
u, v, t = 0, 0, 0
def f(x): return 0
importarArchivo()
if e.char.lower() == 'r':
u, v, t = 0, 0, 0
def f(x): return 0
U, T = [], []
if e.char.lower() == 't':
u, v, t = 0, 0, 0
U, T = [], []
else:
actual = e.char.lower()
def wheel(event):
global z, k, m, dt, height, actual, multu, editando
editando = True
delta = event.delta
if actual == 'z':
z += 0.05*np.sign(delta)
z = max(z, 0)
elif actual == 'k':
k += 10*np.sign(delta)
k = max(k, 0)
elif actual == 'm':
m += np.sign(delta)
m = max(m, 0)
elif actual == 'd':
dt += 0.01*np.sign(delta)
dt = max(dt, 0)
elif actual == 'u':
multu += 5*np.sign(delta)
multu = max(multu, 1)
graficarBola()
editando = False
drawBezier()
my_window = Tk()
ARCHIVO = ''
def f(t): return 0
actual = 'z'
mult = 500
t = 0
u = 0
v = 0
acel = 0
L = 1
z = 0.05
m = 20
k = 1500
dt = 0.01
multu = 100
data = None
U = []
V = []
T = []
ACEL = []
P = [u*mult/multu, L*mult]
strt = "Controles:\nClick: Mover la masa\nA: Seleccionar archivo de aceleración\n\nPara cambiar las propiedades, use una de las siguientes letras\ny cambielas usando la rueda del mouse:\n\nK: Rigidez\nM: Masa\nZ: Amortiguamiento\nd: Paso en el tiempo\nu: Multiplicador de desplazamientos (solo para graficar)\n\nR: Reiniciar todo\nT: Reiniciar tiempo"
width = my_window.winfo_screenwidth()
height = my_window.winfo_screenheight()
my_canvas = Canvas(my_window, width=width, height=height,
background='white')
my_canvas.grid(row=0, column=0)
my_canvas.bind('<Button-1>', editPoint)
my_canvas.bind('<Motion>', movePoint)
my_canvas.bind('<MouseWheel>', wheel)
my_window.bind('<KeyRelease>', kpup)
editando = False
my_window.title('Amortiguada')
my_window.state('zoomed')
drawBezier()
my_window.mainloop()
|
nilq/baby-python
|
python
|
from gensim.models.keyedvectors import KeyedVectors
import numpy as np
import pandas as pd
__author__ = "Sreejith Sreekumar"
__email__ = "sreekumar.s@husky.neu.edu"
__version__ = "0.0.1"
model = KeyedVectors.load_word2vec_format('/media/sree/venus/pre-trained-models/GoogleNews-vectors-negative300.bin', binary=True)
def tovector(words):
vector_array = []
for w in words:
try:
vector_array.append(model[w])
except:
continue
vector_array = np.array(vector_array)
v = vector_array.sum(axis=0)
return v / np.sqrt((v ** 2).sum())
def get_vectorizer_model():
"""
"""
return model
|
nilq/baby-python
|
python
|
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Filename: results_to_latex.py
import os, argparse, json, math
import logging
TEMPLATE_CE_RESULTS = r"""\begin{table}[tb]
\scriptsize
\centering
\caption{Chaos Engineering Experiment Results on %s}\label{tab:ce-experiment-results-%s}
\begin{tabularx}{\columnwidth}{lrrrXXXX}
\toprule
\textbf{System Call}& \textbf{Error Code}& \textbf{E. R.}& \textbf{Inj.}& \textbf{H\textsubscript{C}}& \textbf{H\textsubscript{L}}& \textbf{H\textsubscript{P}}& \textbf{H\textsubscript{R}} \\
\midrule
""" + "%s" + r"""
\bottomrule
\multicolumn{8}{p{8.5cm}}{
H\textsubscript{C}: Marked if the injected errors crash the client.\newline
H\textsubscript{L}: Marked if the injected errors can be found in the client's log.\newline
H\textsubscript{P}: Marked if the injected errors have side effects on the number of connected peers.\newline
H\textsubscript{R}: Marked if the client can recover to its steady state after the error injection stops.}
\end{tabularx}
\end{table}
"""
def get_args():
parser = argparse.ArgumentParser(
description="Chaos engineering experiments .json to a table in latex")
parser.add_argument("-f", "--file", required=True, help="the experiment result file (.json)")
parser.add_argument("-t", "--template", default="ce", choices=['ce', 'benchmark'], help="the template to be used")
parser.add_argument("-c", "--client", default="XXX", choices=['geth', 'openethereum'], help="the client's name")
args = parser.parse_args()
return args
def round_number(x, sig = 3):
return round(x, sig - int(math.floor(math.log10(abs(x)))) - 1)
def main(args):
with open(args.file, 'rt') as file:
data = json.load(file)
body = ""
for experiment in data["experiments"]:
if experiment["result"]["injection_count"] == 0: continue
body += "%s& %s& %s& %d& %s& %s& %s& %s\\\\\n"%(
experiment["syscall_name"],
experiment["error_code"][1:], # remove the "-" before the error code
round_number(experiment["failure_rate"]),
experiment["result"]["injection_count"],
"X" if experiment["result"]["client_crashed"] else "",
"?",
"?",
"?"
)
body = body[:-1] # remove the very last line break
latex = TEMPLATE_CE_RESULTS%(args.client, args.client, body)
latex = latex.replace("_", "\\_")
print(latex)
if __name__ == "__main__":
logger_format = '%(asctime)-15s %(levelname)-8s %(message)s'
logging.basicConfig(level=logging.INFO, format=logger_format)
args = get_args()
main(args)
|
nilq/baby-python
|
python
|
import yfinance as yf
import pandas as pd
import numpy as np
import quandl
API_KEY = '8ohVvwCgmzgRpFeza8FD'
quandl.ApiConfig.api_key = API_KEY
# Download APPL Stock price
df = yf.download('AAPL', start='1999-12-31', end='2010-12-31', progress=False)
df.rename(columns = {'Adj Close': 'adj_close'},inplace=True)
df = df.loc[:,['adj_close']]
#
'''
Calculate the simple return:
Percent Change/Simple Return = (CurrentValue-PrevValue)/CurrentValue
'''
df['simple_rtn'] = df.adj_close.pct_change()
'''
Calculate the log return:
Log Return = ln(CurrentValue/PrevValue)
'''
df['log_rtn'] = np.log(df.adj_close/df.adj_close.shift(1))
# print(df.head(5))
# Download Consumer Price Index from Quandl
df_cpi = quandl.get(dataset='RATEINF/CPI_USA', start_date='1999-12-31', end_date='2010-12-31')
df_cpi.rename(columns= {'Value' : 'cpi'}, inplace=True)
df_dates = pd.DataFrame(index=pd.date_range(start='1999-12-31', end='2010-12-31'))
print(df_dates.asfreq('M').head(20))
# print('Reached here successfully')
|
nilq/baby-python
|
python
|
#!/usr/bin/env python
#############################################################################
##
## This file is part of Taurus, a Tango User Interface Library
##
## http://www.tango-controls.org/static/taurus/latest/doc/html/index.html
##
## Copyright 2011 CELLS / ALBA Synchrotron, Bellaterra, Spain
##
## Taurus is free software: you can redistribute it and/or modify
## it under the terms of the GNU Lesser General Public License as published by
## the Free Software Foundation, either version 3 of the License, or
## (at your option) any later version.
##
## Taurus is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU Lesser General Public License for more details.
##
## You should have received a copy of the GNU Lesser General Public License
## along with Taurus. If not, see <http://www.gnu.org/licenses/>.
##
#############################################################################
__doc__ = """ABBA, Archiving Best Browser for Alba"""
import re,sys,os,time,traceback,threading
import PyTango
import fandango as fn
from fandango.log import tracer
#############################################################################
import taurus
QT_API=os.getenv('QT_API')
USE_PYQTGRAPH=os.getenv('USE_PYQTGRAPH')
USE_QWT=str(QT_API).lower() in ('pyqt','pyqt4') and not USE_PYQTGRAPH
if USE_QWT:
print('Loading taurus pyqwt')
try:
from fandango.qt import Qwt5
from taurus.qt.qtgui.qwt5 import TaurusTrend,TaurusPlot
except:
USE_QWT=False
if not USE_QWT:
print('Loading taurus pyqtgraph')
#from taurus.qt.qtgui.plot import TaurusTrend,TaurusPlot
from taurus.qt.qtgui.tpg import TaurusTrend,TaurusPlot
print('QT_API: %s' % QT_API)
print('USE_PYQTGRAPH: %s' % USE_PYQTGRAPH)
print('TaurusTrend: %s' % TaurusTrend)
print('TaurusPlot: %s' % TaurusPlot)
from taurus.qt.qtgui.panel import TaurusDevicePanel
from taurus.qt.qtgui.panel import TaurusValue
from taurus.qt.qtcore.util.emitter import SingletonWorker
try:
# taurus 4
from taurus.core.tango.util import tangoFormatter
from taurus.qt.qtgui.display import TaurusLabel
TaurusLabel.FORMAT=tangoFormatter
except:
try:
# Tau / Taurus < 3.4
from taurus.qt.qtgui.display import TaurusValueLabel as TaurusLabel
except:
# Taurus > 3.4
from taurus.qt.qtgui.display import TaurusLabel
#############################################################################
from fandango.qt import Qt,getColorsForValue
from fandango.qt import QGridTable, QDictToolBar
try:
from PyTangoArchiving.widget.tree import TaurusModelChooser
except:
traceback.print_exc()
TaurusModelChooser = None
#############################################################################
def get_distinct_domains(l):
return sorted(set(str(s).upper().split('/')[0] for s in l))
def launch(script,args=[]):
import os
f = '%s %s &'%(script,' '.join(args))
print 'launch(%s)'%f
os.system(f)
###############################################################################
class MyScrollArea(Qt.QScrollArea):
def setChildrenPanel(self,child):
self._childrenPanel = child
def childrenPanel(self):
return getattr(self,'_childrenPanel',None)
def resizeEvent(self,event):
Qt.QScrollArea.resizeEvent(self,event)
if self.childrenPanel():
w,h = self.width()-15,self.childrenPanel().height()
#print 'AttributesPanel.ScrollArea.resize(%s,%s)'%(w,h)
self.childrenPanel().resize(w,h)
PARENT_KLASS = QGridTable #Qt.QFrame #Qt.QWidget
class AttributesPanel(PARENT_KLASS):
_domains = ['ALL EPS']+['LI','LT']+['LT%02d'%i for i in range(1,3)]+['SR%02d'%i for i in range(1,17)]
_fes = [f for f in get_distinct_domains(fn.get_database().get_device_exported('fe*')) if fn.matchCl('fe[0-9]',f)]
LABELS = 'Label/Value Device Attribute Alias Archiving Check'.split()
SIZES = [500, 150, 90, 90, 120, 40]
STRETCH = [8, 4, 4, 4, 2, 1]
def __init__(self,parent=None,devices=None):
#print '~'*80
tracer('In AttributesPanel()')
PARENT_KLASS.__init__(self,parent)
self.setSizePolicy(Qt.QSizePolicy(Qt.QSizePolicy.Ignored,Qt.QSizePolicy.Ignored))
self.worker = SingletonWorker(parent=self,cursor=True,sleep=50.,start=True)
#self.worker.log.setLogLevel(self.worker.log.Debug)
self.filters=('','','') #server/device/attribute
self.devices=devices or []
self.setValues(None)
self.models = []
self.current_item = None
#self.connect(self, Qt.SIGNAL('customContextMenuRequested(const QPoint&)'), self.onContextMenu)
self.popMenu = Qt.QMenu(self)
self.actions = {
'TestDevice': self.popMenu.addAction(Qt.QIcon(),
"Test Device",self.onTestDevice),
'ShowDeviceInfo': self.popMenu.addAction(Qt.QIcon(),
"Show Device Info",self.onShowInfo),
#'ShowDevicePanel': self.popMenu.addAction(Qt.QIcon(),"Show Info",self.onShowPanel),
'ShowArchivingInfo': self.popMenu.addAction(Qt.QIcon(),
"Show Archiving Info",self.onShowArchivingModes),
'AddToTrend': self.popMenu.addAction(Qt.QIcon(),
"Add attribute to Trend", self.addAttributeToTrend),
'AddSelected': self.popMenu.addAction(Qt.QIcon(),
"Add selected attributes to Trend", self.addSelectedToTrend),
'CheckAll': self.popMenu.addAction(Qt.QIcon(),
"Select all attributes", self.checkAll),
'UncheckAll': self.popMenu.addAction(Qt.QIcon(),
"Deselect all attributes", self.uncheckAll),
#'Test Device': self.popMenu.addAction(Qt.QIcon(),"Test Device",self.onTestDevice)
}
#if hasattr(self,'setFrameStyle'):
#self.setFrameStyle(self.Box)
try:
import PyTangoArchiving
self.reader = PyTangoArchiving.Reader('*')
except:
traceback.print_exc()
def __del__(self):
print 'AttributesPanel.__del__'
QGridTable.__del__(self)
def setItem(self,x,y,item,spanx=1,spany=1,align=None,model=None):
align = align or Qt.Qt.AlignLeft
try:
if model:
item._model = model
except: pass
self.layout().addWidget(item,x,y,spany,spanx,Qt.Qt.AlignCenter)
if item not in self._widgets: self._widgets.append(item)
def mousePressEvent(self, event):
point = event.pos()
widget = Qt.QApplication.instance().widgetAt(self.mapToGlobal(point))
if hasattr(widget,'_model'):
print('onMouseEvent(%s)'%(getattr(widget,'text',lambda:widget)()))
self.current_item = widget
if event.button()==Qt.Qt.RightButton:
self.onContextMenu(point)
getattr(super(type(self),self),'mousePressEvent',lambda e:None)(event)
def onContextMenu(self, point):
print('onContextMenu()')
try:
self.actions['TestDevice'].setEnabled('/' in self.current_item._model)
self.actions['ShowDeviceInfo'].setEnabled('/' in self.current_item._model)
self.actions['ShowArchivingInfo'].setEnabled('/' in self.current_item._model)
self.actions['AddToTrend'].setEnabled(hasattr(self,'trend'))
self.actions['AddSelected'].setEnabled(hasattr(self,'trend'))
self.popMenu.exec_(self.mapToGlobal(point))
except:
traceback.print_exc()
def getCurrentModel(self):
return '/'.join(str(self.current_item._model).split('/')[-4:])
def getCurrentDevice(self):
return str(self.current_item._model.rsplit('/',1)[0])
def onTestDevice(self,device=None):
from PyTangoArchiving.widget.panel import showTestDevice
showTestDevice(device or self.getCurrentDevice())
def onShowInfo(self,device=None):
from PyTangoArchiving.widget.panel import showDeviceInfo
showDeviceInfo(device=device or self.getCurrentDevice(),parent=self)
def onShowArchivingModes(self,model=None):
try:
from PyTangoArchiving.widget.panel import showArchivingModes
model = model or self.getCurrentModel()
showArchivingModes(model,parent=self)
except:
Qt.QMessageBox.warning(self,"ups!",traceback.format_exc())
def addAttributeToTrend(self,model=None):
try:
model = model or self.getCurrentModel()
self.trend.addModels([model])
except:
Qt.QMessageBox.warning(self,"ups!",traceback.format_exc())
def addSelectedToTrend(self):
try:
y = self.columnCount()-1
models = []
for x in range(self.rowCount()):
item = self.itemAt(x,y).widget()
m = getattr(item,'_model','')
if m and item.isChecked():
models.append(m)
if len(models) > 20:
Qt.QMessageBox.warning(self,"warning",
"To avoid performance issues, dynamic scale will be disabled")
self.trend.setXDynScale(False)
self.trend.addModels(models)
except:
Qt.QMessageBox.warning(self,"ups!",traceback.format_exc())
def checkAll(self):
y = self.columnCount()-1
for x in range(self.rowCount()):
self.itemAt(x,y).widget().setChecked(True)
def uncheckAll(self):
y = self.columnCount()-1
for x in range(self.rowCount()):
self.itemAt(x,y).widget().setChecked(False)
def setValues(self,values,filters=None):
""" filters will be a tuple containing several regular expressions to match """
#print('In AttributesPanel.setValues([%s])'%len(values or []))
if values is None:
self.generateTable([])
elif True: #filters is None:
self.generateTable(values)
#print 'In AttributesPanel.setValues(...): done'
return
def generateTable(self,values):
#thermocouples = thermocouples if thermocouples is not None else self.thermocouples
self.setRowCount(len(values))
self.setColumnCount(5)
#self.vheaders = []
self.offset = 0
self.widgetbuffer = []
for i,tc in enumerate(sorted(values)):
#print 'setTableRow(%s,%s)'%(i,tc)
model,device,attribute,alias,archived,label = tc
model,device,attribute,alias = map(str.upper,(model,device,attribute,alias))
#self.vheaders.append(model)
def ITEM(m,model='',size=0):
q = fn.qt.Draggable(Qt.QLabel)(m)
if size is not 0:
q.setMinimumWidth(size) #(.7*950/5.)
q._model = model or m
q._archived = archived
q.setDragEventCallback(lambda s=q:s._model)
return q
###################################################################
qf = Qt.QFrame()
qf.setLayout(Qt.QGridLayout())
qf.setMinimumWidth(self.SIZES[0])
qf.setSizePolicy(Qt.QSizePolicy.Expanding,Qt.QSizePolicy.Fixed)
#Order changed, it is not clear if it has to be done before or after adding TaurusValue selfect
self.setCellWidget(i+self.offset,0,qf)
#print('Adding item: %s, %s, %s, %s, %s' % (model,device,attribute,alias,archived))
ok = fn.check_attribute(model,brief=False,timeout=500)# is not None
print(ok)
ok = ok if not isinstance(ok,Exception) else None
if False:
tv = TaurusValue() #TaurusValueLabel()
qf.layout().addWidget(tv,0,0)
tv.setParent(qf)
elif ok:
import PyTangoArchiving.widget.panel as ptawp
tv = ptawp.TaurusSingleValue()
tv.setModel(model)
self.setItem(i+self.offset,0,tv)
else:
tv = ITEM(label+'(-)',model)
self.setItem(i+self.offset,0,tv)
devlabel = ITEM(device,model,self.SIZES[1])
self.setItem(i+self.offset,1,devlabel)
self.setItem(i+self.offset,2,ITEM(attribute,model,self.SIZES[2]))
self.setItem(i+self.offset,3,ITEM(alias,model,self.SIZES[3]))
from PyTangoArchiving.widget.panel import showArchivingModes,show_history
if archived:
active = self.reader.is_attribute_archived(model,active=True)
txt = '/'.join(a.upper() if a in active else a for a in archived)
else:
txt = '...'
q = Qt.QPushButton(txt)
q.setFixedWidth(self.SIZES[-2])
q.setToolTip("""%s<br><br><pre>
'HDB' : Archived and updated, push to export values
'hdb' : Archiving stopped, push to export values
'...' : Not archived
</pre>"""%txt)
cb = (lambda a=self.reader.get_attribute_alias(model),o=q:
setattr(q,'w',show_history(a))) #showArchivingModes(a,parent=self))))
try:
self.connect(q, Qt.SIGNAL("pressed ()"), cb)
except:
self.q.pressed.connect(cb)
self.setItem(i+self.offset,4,q)
qc = Qt.QCheckBox()
qc.setFixedWidth(self.SIZES[-1])
self.setItem(i+self.offset,5,qc,1,1,Qt.Qt.AlignCenter,model)
if isinstance(tv,TaurusValue): #ok:
#print('Setting Model %s'%model)
#ADDING WIDGETS IN BACKGROUND DIDN'T WORKED, I JUST CAN SET MODELS FROM THE WORKER
try:
if self.worker:
self.worker.put([(lambda w=tv,m=model:w.setModel(m))])
#print 'worker,put,%s'%str(model)
else:
tv.setModel(model)
except:
print traceback.format_exc()
self.models.append(tv)
#self.widgetbuffer.extend([qf,self.itemAt(i+self.offset,1),self.itemAt(i+self.offset,2),self.itemAt(i+self.offset,3),self.itemAt(i+self.offset,4)])
fn.threads.Event().wait(.02)
if len(values):
def setup(o=self):
[o.setRowHeight(i,20) for i in range(o.rowCount())]
#o.setColumnWidth(0,350)
o.update()
o.repaint()
#print o.rowCount()
o.show()
setup(self)
if self.worker:
print( '%s.next()' % (self.worker) )
self.worker.next()
#threading.Event().wait(10.)
tracer('Out of generateTable()')
def clear(self):
try:
#print('In AttributesPanel.clear()')
for m in self.models:
m.setModel(None)
self.models = []
self.setValues(None)
#QGridTable.clear(self)
def deleteItems(layout):
if layout is not None:
while layout.count():
item = layout.takeAt(0)
widget = item.widget()
if widget is not None:
widget.deleteLater()
else:
deleteItems(item.layout())
deleteItems(self.layout())
#l = self.layout()
#l.deleteLater()
#self.setLayout(Qt.QGridLayout())
except:
traceback.print_exc()
class ArchivingBrowser(Qt.QWidget):
_persistent_ = None #It prevents the instances to be destroyed if not called explicitly
MAX_DEVICES = 500
MAX_ATTRIBUTES = 1500
LABELS = AttributesPanel.LABELS
SIZES = AttributesPanel.SIZES
STRETCH = AttributesPanel.STRETCH
def __init__(self,parent=None,domains=None,regexp='*pnv-*',USE_SCROLL=True,USE_TREND=False):
print('%s: ArchivingBrowser()' % fn.time2str())
Qt.QWidget.__init__(self,parent)
self.setupUi(USE_SCROLL=USE_SCROLL, USE_TREND=USE_TREND, SHOW_OPTIONS=False)
self.load_all_devices()
try:
import PyTangoArchiving
self.reader = PyTangoArchiving.Reader('*')
self.archattrs = sorted(set(self.reader.get_attributes()))
self.archdevs = list(set(a.rsplit('/',1)[0] for a in self.archattrs))
except:
traceback.print_exc()
self.extras = []
#self.domains = domains if domains else ['MAX','ANY','LI/LT','BO/BT']+['SR%02d'%i for i in range(1,17)]+['FE%02d'%i for i in (1,2,4,9,11,13,22,24,29,34)]
#self.combo.addItems((['Choose...']+self.domains) if len(self.domains)>1 else self.domains)
self.connectSignals()
print('%s: ArchivingBrowser(): done' % fn.time2str())
def load_all_devices(self,filters='*'):
import fandango as fn #needed by subprocess
self.tango = fn.get_database()
self.alias_devs = fn.defaultdict_fromkey(
lambda k,s=self: str(s.tango.get_device_alias(k)))
self.archattrs = []
self.archdevs = []
#print('In load_all_devices(%s)...'%str(filters))
devs = fn.tango.get_all_devices()
if filters!='*':
devs = [d for d in devs if fn.matchCl(
filters.replace(' ','*'),d,extend=True)]
self.all_devices = devs
self.all_domains = sorted(set(a.split('/')[0] for a in devs))
self.all_families = sorted(set(a.split('/')[1] for a in devs))
members = []
for a in devs:
try:
members.append(a.split('/')[2])
except:
# Wrong names in DB? yes, they are
pass #print '%s is an invalid name!'%a
members = sorted(set(members))
self.all_members = sorted(set(e for m in members
for e in re.split('[-_0-9]',m)
if not fn.matchCl('^[0-9]+([ABCDE][0-9]+)?$',e)))
#print 'Loading alias list ...'
self.all_alias = self.tango.get_device_alias_list('*')
#self.alias_devs = dict((str(self.tango.get_device_alias(a)).lower(),a) for a in self.all_alias)
tracer('Loading (%s) finished.'%(filters))
def load_attributes(self,servfilter,devfilter,attrfilter,warn=True,
exclude = ('dserver','tango*admin','sys*database','tmp','archiving')):
servfilter = servfilter.replace(' ','*').strip()
attrfilter = (attrfilter or 'state').replace(' ','*')
devfilter = (devfilter or attrfilter).replace(' ','*')
#Solve fqdn issues
devfilter = devfilter.replace('tango://','')
if ':' in devfilter:
tracer('ArchivingBrowser ignores tango host filters')
devfilter = fn.clsub(fn.tango.rehost,'',devfilter)
tracer('In load_attributes(%s,%s,%s)'%(servfilter,devfilter,attrfilter))
archive = self.dbcheck.isChecked()
all_devs = self.all_devices if not archive else self.archdevs
all_devs = [d for d in all_devs if not
any(d.startswith(e) for e in exclude)
or any(d.startswith(e)
and fn.matchCl(e,devfilter) for e in exclude)]
if servfilter.strip('.*'):
sdevs = map(str.lower,fn.Astor(servfilter).get_all_devices())
all_devs = [d for d in all_devs if d in sdevs]
#print('In load_attributes(%s,%s,%s): Searching through %d %s names'
#%(servfilter,devfilter,attrfilter,len(all_devs),
#'server' if servfilter else 'device'))
if devfilter.strip().strip('.*'):
devs = [d for d in all_devs if
(fn.searchCl(devfilter,d,extend=True))]
print('\tFound %d devs, Checking alias ...'%(len(devs)))
alias,alias_devs = [],[]
if '&' in devfilter:
alias = self.all_alias
else:
for df in devfilter.split('|'):
alias.extend(self.tango.get_device_alias_list('*%s*'%df.strip()))
if alias:
print('\t%d alias found'%len(alias))
alias_devs.extend(self.alias_devs[a] for a in alias if fn.searchCl(devfilter,a,extend=True))
print('\t%d alias_devs found'%len(alias_devs))
#if not self.alias_devs:
#self.alias_devs = dict((str(self.tango.get_device_alias(a)).lower(),a) for a in self.all_alias)
#devs.extend(d for d,a in self.alias_devs.items() if fn.searchCl(devfilter,a) and (not servfilter or d in all_devs))
devs.extend(d for d in alias_devs if not servfilter.strip('.*') or d in all_devs)
else:
devs = all_devs
devs = sorted(set(devs))
self.matching_devs = devs
print('In load_attributes(%s,%s,%s): %d devices found'%(servfilter,devfilter,attrfilter,len(devs)))
if not len(devs) and not archive:
#Devices do not actually exist, but may exist in archiving ...
#Option disabled, was mostly useless
self.dbcheck.setChecked(True)
return self.load_attributes(servfilter,devfilter,attrfilter,warn=False)
if len(devs)>self.MAX_DEVICES and warn:
Qt.QMessageBox.warning(self, "Warning" , "Your search (%s,%s) matches too many devices!!! (%d); please refine your search\n\n%s\n..."%(devfilter,attrfilter,len(devs),'\n'.join(devs[:30])))
return {}
elif warn and len(devs)>15:
r = Qt.QMessageBox.warning(self, "Message" , "Your search (%s,%s) matches %d devices."%(devfilter,attrfilter,len(devs)),Qt.QMessageBox.Ok|Qt.QMessageBox.Cancel)
if r==Qt.QMessageBox.Cancel:
return {}
self.matching_attributes = {} #{attribute: (device,alias,attribute,label)}
failed_devs = []
for d in sorted(devs):
try:
dp = taurus.Device(d)
if not archive:
dp.ping()
tcs = [t for t in dp.get_attribute_list()]
else:
tcs = [a.split('/')[-1] for a in self.archattrs if a.startswith(d+'/')]
matches = [t for t in tcs if fn.searchCl(attrfilter,t,extend=True)]
for t in sorted(tcs):
if not self.dbcheck.isChecked() or not matches:
label = dp.get_attribute_config(t).label
else:
label = t
if t in matches or fn.searchCl(attrfilter,label,extend=True):
if self.archivecheck.isChecked() \
and not self.reader.is_attribute_archived(d+'/'+t):
continue
if d in self.alias_devs:
alias = self.alias_devs[d]
else:
try: alias = str(self.tango.get_alias(d))
except: alias = ''
self.matching_attributes['%s/%s'%(d,t)] = (d,alias,t,label)
if warn and len(self.matching_attributes)>self.MAX_ATTRIBUTES:
Qt.QMessageBox.warning(self, "Warning" ,
"Your search (%s,%s) matches too many attributes!!! (%d); please refine your search\n\n%s\n..."%(
devfilter,attrfilter,len(self.matching_attributes),'\n'.join(sorted(self.matching_attributes.keys())[:30])))
return {}
except:
print('load_attributes(%s,%s,%s => %s) failed!'%(servfilter,devfilter,attrfilter,d))
failed_devs.append(d)
if attrfilter in ('state','','*','**'):
self.matching_attributes[d+'/state'] = (d,d,'state',None) #A None label means device-not-readable
if warn and len(self.matching_attributes)>30:
r = Qt.QMessageBox.warning(self, "Message" , "(%s) matches %d attributes."%(attrfilter,len(self.matching_attributes)),Qt.QMessageBox.Ok|Qt.QMessageBox.Cancel)
if r==Qt.QMessageBox.Cancel:
return {}
if not len(self.matching_attributes):
Qt.QMessageBox.warning(self, "Warning", "No matching attribute has been found in %s." % ('Archiving DB' if archive else 'Tango DB (try DB Cache option)'))
if failed_devs:
print('\t%d failed devs!!!: %s'%(len(failed_devs),failed_devs))
if warn:
Qt.QMessageBox.warning(self, "Warning" ,
"%d devices were not running:\n"%len(failed_devs) +'\n'.join(failed_devs[:10]+(['...'] if len(failed_devs)>10 else []) ))
tracer('\t%d attributes found'%len(self.matching_attributes))
return self.matching_attributes
def setupUi(self,USE_SCROLL=False, SHOW_OPTIONS=False, USE_TREND=False):
self.setWindowTitle('Tango Finder : Search Attributes and Archiving')
self.setLayout(Qt.QVBoxLayout())
self.setMinimumWidth(950)#550)
#self.setMinimumHeight(700)
self.layout().setAlignment(Qt.Qt.AlignTop)
self.browser = Qt.QFrame()
self.browser.setLayout(Qt.QVBoxLayout())
self.chooser = Qt.QTabWidget()
self.chooser.setTabPosition(self.chooser.West if SHOW_OPTIONS else self.chooser.North)
#self.combo = Qt.QComboBox() # Combo used for domains, currently disabled
self.searchbar = Qt.QFrame()
self.searchbar.setLayout(Qt.QGridLayout())
#self.label = Qt.QLabel('Type a part of device name and a part of attribute name, use "*" or " " as wildcards:')
#self.layout().addWidget(self.label)
self.ServerFilter = Qt.QLineEdit()
self.ServerFilter.setMaximumWidth(250)
self.DeviceFilter = fn.qt.Dropable(Qt.QLineEdit)()
self.DeviceFilter.setSupportedMimeTypes(fn.qt.TAURUS_DEV_MIME_TYPE)
self.AttributeFilter = fn.qt.Dropable(Qt.QLineEdit)()
self.AttributeFilter.setSupportedMimeTypes([fn.qt.TAURUS_ATTR_MIME_TYPE,fn.qt.TEXT_MIME_TYPE])
self.update = Qt.QPushButton('Update')
self.archivecheck = Qt.QCheckBox("Only archived")
self.archivecheck.setChecked(False)
self.dbcheck = Qt.QCheckBox("DB cache")
self.dbcheck.setChecked(True)
self.searchbar.layout().addWidget(Qt.QLabel(
'Enter Device and Attribute filters using wildcards '
'(e.g. li/ct/plc[0-9]+ / ^stat*$ & !status ) and push Update'),0,0,3,13)
[self.searchbar.layout().addWidget(o,x,y,h,w) for o,x,y,h,w in (
(Qt.QLabel("Device or Alias:"),4,0,1,1),(self.DeviceFilter,4,1,1,4),
(Qt.QLabel("Attribute:"),4,5,1,1),(self.AttributeFilter,4,6,1,4),
(self.update,4,10,1,1),(self.archivecheck,4,11,1,1),
(self.dbcheck,4,12,1,1),
)]
if SHOW_OPTIONS:
self.options = Qt.QWidget() #self.searchbar
self.options.setLayout(Qt.QGridLayout())
separator = lambda x:Qt.QLabel(' '*x)
row = 1
[self.options.layout().addWidget(o,x,y,h,w) for o,x,y,h,w in (
#separator(120),Qt.QLabel("Options: "),separator(5),
(Qt.QLabel("Server: "),row,0,1,1),(self.ServerFilter,row,1,1,4),(Qt.QLabel(''),row,2,1,11)
)]
#self.panel = generate_table(load_all_thermocouples('SR14')[-1])
self.optiontab = Qt.QTabWidget()
self.optiontab.addTab(self.searchbar,'Filters')
self.optiontab.addTab(self.options,'Options')
self.optiontab.setMaximumHeight(100)
self.optiontab.setTabPosition(self.optiontab.North)
self.browser.layout().addWidget(self.optiontab)
else:
self.browser.layout().addWidget(self.searchbar)
self.toppan = Qt.QWidget(self)
self.toppan.setLayout(Qt.QVBoxLayout())
if USE_SCROLL:
print '*'*30 + ' USE_SCROLL=True '+'*'*30
## TO USE SCROLL, HEADER HAS BEEN SET AS A SEPARATE WIDGET
#self.header = QGridTable(self.toppan)
#self.header.setHorizontalHeaderLabels(self.LABELS)
#self.header.setColumnWidth(0,350)
self.headers = []
self.header = Qt.QWidget(self.toppan)
self.header.setLayout(Qt.QHBoxLayout())
for l,s in zip(self.LABELS,self.SIZES):
ql = Qt.QLabel(l)
self.headers.append(ql)
#if s is not None:
#ql.setFixedWidth(s)
#else:
#ql.setSizePolicy(Qt.QSizePolicy.MinimumExpanding,Qt.QSizePolicy.Fixed)
self.header.layout().addWidget(ql)
self.toppan.layout().addWidget(self.header)
self._scroll = MyScrollArea(self.toppan)#Qt.QScrollArea(self)
self._background = AttributesPanel(self._scroll) #At least a panel should be kept (never deleted) in background to not crash the worker!
self.panel = None
self._scroll.setChildrenPanel(self.panel)
self._scroll.setWidget(self.panel)
self._scroll.setMaximumHeight(700)
self.toppan.layout().addWidget(self._scroll)
self.attrpanel = self._background
else:
self.panel = AttributesPanel(self.toppan)
self.toppan.layout().addWidget(self.panel)
self.attrpanel = self.panel
self.toppan.layout().addWidget(Qt.QLabel('If drag&drop fails, PLEASE USE RIGHT-CLICK ON THE NAME OF THE ATTRIBUTE OR CHECKBOX!!'))
self.browser.layout().addWidget(self.toppan)
self.chooser.addTab(self.browser,'Search ...')
if USE_TREND:
self.split = Qt.QSplitter(Qt.Qt.Vertical)
self.split.setHandleWidth(25)
self.split.addWidget(self.chooser)
if "qwt" in str(TaurusPlot.__bases__).lower():
from PyTangoArchiving.widget.trend import ArchivingTrend,ArchivingTrendWidget
self.trend = ArchivingTrendWidget() #TaurusArchivingTrend()
self.trend.setUseArchiving(True)
self.trend.showLegend(True)
else: #PyQtGraph
try:
from taurus_tangoarchiving.widget.tpgarchivingwidget import \
ArchivingWidget
except:
from PyTangoArchiving.widget.tpgarchivingwidget import \
ArchivingWidget
self.trend = ArchivingWidget()
self.attrpanel.trend = self.trend
if TaurusModelChooser is not None:
self.treemodel = TaurusModelChooser(parent=self.chooser)
self.chooser.addTab(self.treemodel,'Tree')
try:
self.treemodel.updateModels.connect(self.trend.addModels)
except:
traceback.print_exc()
#self.treemodel.connect(self.treemodel,Qt.SIGNAL('updateModels'),self.trend.addModels)
else:
tracer('TaurusModelChooser not available!')
self.split.addWidget(self.trend)
self.layout().addWidget(self.split)
else:
self.layout().addWidget(self.chooser)
type(self)._persistent_ = self
def connectSignals(self):
#self.combo.connect(self.combo, Qt.SIGNAL("currentIndexChanged (const QString&)"), self.comboIndexChanged)
#self.connect(self.combo, Qt.SIGNAL("currentIndexChanged (const QString&)"), self.comboIndexChanged)
try:
self.connect(self.update, Qt.SIGNAL("pressed ()"), self.updateSearch)
except:
self.update.pressed.connect(self.updateSearch)
#if len(self.domains)==1: self.emit(Qt.SIGNAL("currentIndexChanged (const QString&)"),Qt.QString(self.domains[0]))
def open_new_trend(self):
from taurus.qt.qtgui.plot import TaurusTrend
tt = TaurusTrend()
tt.show()
self.extras.append(tt)
tt.setUseArchiving(True)
tt.showLegend(True)
return tt
def resizeEvent(self,evt):
try:
Qt.QWidget.resizeEvent(self,evt)
self.adjustColumns()
#type(self)._persistent_ = None
except:
traceback.print_exc()
def adjustColumns(self):
try:
if not getattr(self,'panel',None):
return
w = int(max((self.panel.width()+20,self.width()*0.9)))
self.header.setMaximumWidth(w)
for j in range(self.panel.columnCount()):
m = 0
for i in range(self.panel.rowCount()):
try:
w = self.panel.layout().itemAtPosition(i,j).geometry().width()
if w > m: m = w
except:
m = self.SIZES[j]
#print(j,self.LABELS[j],self.SIZES[j],m)
self.headers[j].setFixedWidth(max((m,self.SIZES[j])))
except:
traceback.print_exc()
def closeEvent(self,evt):
Qt.QWidget.closeEvent(self,evt)
type(self)._persistent_ = None
#def __del__(self):
#print 'In ValvesChooser.del()'
##try: Qt.QWidget.__del__(self)
##except: pass
#type(self)._persistent_ = None
def comboIndexChanged(self,text=None):
#print 'In comboIndexChanged(...)'
pass
def splitFilters(self,filters):
if filters.count(',')>1: filters.replace(',',' ')
if ',' in filters: filters = filters.split(',')
elif ';' in filters: filters = filters.split(';')
elif filters.count('/') in (1,3): filters = filters.rsplit('/',1)
elif ' ' in filters: filters = filters.rsplit(' ',1)
else: filters = [filters,'^state$'] #'*']
return filters
def setModel(self,model):
model = str(model).strip()
if model: self.updateSearch(model)
def updateSearch(self,*filters):
#Text argument applies only to device/attribute filter; not servers
try:
#print('In updateSearch(%s[%d])'%(filters,len(filters)))
if len(filters)>2:
filters = [' '.join(filters[:-1]),filters[-1]]
if len(filters)==1:
filters = ['']+self.splitFilters(filters[0])
elif len(filters)==2:
filters = ['']+list(filters)
elif len(filters)==3:
filters = list(filters)
else:
filters = (self.ServerFilter,self.DeviceFilter,self.AttributeFilter)
filters = [str(f.text()).strip() for f in filters]
#Texts are rewritten to show format as it is really used
self.ServerFilter.setText(filters[0])
self.DeviceFilter.setText(filters[1])
self.AttributeFilter.setText(filters[2])
if not any(filters):
Qt.QMessageBox.warning(self, "Warning" , "you must type a text to search")
return
if not any (f.strip('.*') for f in filters): #Empty or too wide filters not allowed
Qt.QMessageBox.warning(self, "Warning" , "you must reduce your filtering!")
return
wildcard = '*' if not '.*' in str(filters) else '.*'
for i,f in enumerate(filters):
if not (f.startswith('*') or f.startswith('.')):
filters[i] = '^state$' if (i==2 and not f) else f #'%s%s%s'%(wildcard,f,wildcard)
if self.panel and filters==self.panel.filters:
return
else:
old = self.panel
if self.panel:
if hasattr(self,'_scroll'): self._scroll.setWidget(None)
self.panel.setParent(None)
self.panel = None
if not self.panel:
self.panel = AttributesPanel(
self._scroll,devices=self.all_devices)
self.attrpanel = self.panel
if hasattr(self,'trend'):
self.attrpanel.trend = self.trend
else:
self.panel.clear()
if old:
old.clear()
old.deleteLater() #Must be done after creating the new one!!
table = [] #model,device,attribute,alias,archived,label
#ATTRIBUTES ARE FILTERED HERE!! <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
for k,v in self.load_attributes(*filters).items():
#load_attributes = (d,alias,t,label)
try:
archived = self.reader.is_attribute_archived(k)
except Exception,e:
print('Archiving not available!:\n %s'
%traceback.format_exc())
archived = []
#print(k,v,archived)
#model,device,attribute,alias,archived,label
table.append((k,v[0],v[2],v[1],archived,v[3]))
self.panel.setValues(sorted(table))
if hasattr(self,'_scroll'):
self._scroll.setWidget(self.panel)
#self.panel.setParent(self._scroll) #IT DOESNT WORK
self._scroll.setChildrenPanel(self.panel)
#print('labels/columns: %d,%d' % (len(self.SIZES),self.panel.columnCount()))
for j in range(self.panel.columnCount()):
#print(j)
l, s = self.LABELS[j], self.SIZES[j]
#print('Resizing %s cells to %s' % (l,s))
self.panel.layout().setColumnStretch(j,self.STRETCH[j])
#for i in range(self.panel.rowCount()):
#try:
#w = self.panel.itemAt(i,j).widget()
#if s is not None:
#w.setFixedWidth(s)
#else:
#p = Qt.QSizePolicy(Qt.QSizePolicy.Expanding,Qt.QSizePolicy.Fixed)
#w.setSizePolicy(p)
#except:
#traceback.print_exc()
self.adjustColumns()
except Exception,e:
#traceback.print_exc()
Qt.QMessageBox.warning(self, "Warning" , "There's something wrong in your search (%s), please simplify the string"%traceback.format_exc())
return
ModelSearchWidget = ArchivingBrowser
def main(args=None):
"""
--range=YYYY/MM/DD_HH:mm,XXh
"""
import sys
opts = dict(a.split('=',1) for a in args if a.startswith('-'))
print(opts)
args = [a for a in args if not a.startswith('-')]
print(args)
#from taurus.qt.qtgui.container import TaurusMainWindow
tmw = Qt.QMainWindow() #TaurusMainWindow()
tmw.setWindowTitle('Tango Attribute Search (%s)'%(fn.get_tango_host()))
table = ArchivingBrowser(domains=args,USE_SCROLL=True,USE_TREND=True)
tmw.setCentralWidget(table)
use_toolbar = True
if use_toolbar:
toolbar = QDictToolBar(tmw)
toolbar.set_toolbar([
##('PDFs','icon-all.gif',[
#('Pdf Q1','icon-all.gif',lambda:launch('%s %s'%('kpdf','TC_Q1.pdf'))),
#('Pdf Q2','icon-all.gif',lambda:launch('%s %s'%('kpdf','TC_Q2.pdf'))),
#('Pdf Q3','icon-all.gif',lambda:launch('%s %s'%('kpdf','TC_Q3.pdf'))),
#('Pdf Q4','icon-all.gif',lambda:launch('%s %s'%('kpdf','TC_Q4.pdf'))),
## ]),
#('Archiving Viewer','Mambo-icon.ico', lambda:launch('mambo')),
('Show New Trend','qwtplot.png',table.open_new_trend),
])
toolbar.add_to_main_window(tmw,where=Qt.Qt.BottomToolBarArea)
tmw.show()
if args:
table.updateSearch(*args)
if '--range' in opts:
tracer('Setting trend range to %s' % opts['--range'])
table.trend.applyNewDates(opts['--range'].replace('_',' ').split(','))
return tmw
if __name__ == "__main__":
import sys
if 'qapp' not in locals() and 'qapp' not in globals():
qapp = Qt.QApplication([])
import taurus
taurus.setLogLevel('WARNING')
t = main(args = sys.argv[1:])
sys.exit(qapp.exec_())
|
nilq/baby-python
|
python
|
TEST = 'noe'
|
nilq/baby-python
|
python
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
routines for getting network interface addresses
"""
# by Benjamin C. Wiley Sittler, BSD/OSX support by Greg Hazel
__all__ = [
'getifaddrs',
'getaddrs',
'getnetaddrs',
'getbroadaddrs',
'getdstaddrs',
'main',
'IFF_UP',
'IFF_BROADCAST',
'IFF_DEBUG',
'IFF_LOOPBACK',
'IFF_POINTOPOINT',
'IFF_NOTRAILERS',
'IFF_RUNNING',
'IFF_NOARP',
'IFF_PROMISC',
'IFF_ALLMULTI',
'IFF_MASTER',
'IFF_SLAVE',
'IFF_MULTICAST',
'IFF_PORTSEL',
'IFF_AUTOMEDIA',
'IFF_DYNAMIC',
'IFF_LOWER_UP',
'IFF_DORMANT',
'ARPHRD_NETROM',
'ARPHRD_ETHER',
'ARPHRD_EETHER',
'ARPHRD_AX25',
'ARPHRD_PRONET',
'ARPHRD_CHAOS',
'ARPHRD_IEEE802',
'ARPHRD_ARCNET',
'ARPHRD_APPLETLK',
'ARPHRD_DLCI',
'ARPHRD_ATM',
'ARPHRD_METRICOM',
'ARPHRD_IEEE1394',
'ARPHRD_EUI64',
'ARPHRD_INFINIBAND',
'ARPHRD_SLIP',
'ARPHRD_SLIP6',
'ARPHRD_RSRVD',
'ARPHRD_ADAPT',
'ARPHRD_X25',
'ARPHRD_HWX25',
'ARPHRD_PPP',
'ARPHRD_CISCO',
'ARPHRD_HDLC',
'ARPHRD_DDCMP',
'ARPHRD_RAWHDLC',
'ARPHRD_TUNNEL',
'ARPHRD_TUNNEL6',
'ARPHRD_FRAD',
'ARPHRD_SKIP',
'ARPHRD_LOOPBACK',
'ARPHRD_LOCALTLK',
'ARPHRD_FDDI',
'ARPHRD_BIF',
'ARPHRD_SIT',
'ARPHRD_IPDDP',
'ARPHRD_IPGRE',
'ARPHRD_PIMREG',
'ARPHRD_HIPPI',
'ARPHRD_ASH',
'ARPHRD_ECONET',
'ARPHRD_IRDA',
'ARPHRD_FCPP',
'ARPHRD_FCAL',
'ARPHRD_FCPL',
'ARPHRD_FCFABRIC',
'ARPHRD_IEEE802_TR',
'ARPHRD_IEEE80211',
'ARPHRD_IEEE80211_PRISM',
'ARPHRD_IEEE80211_RADIOTAP',
'ARPHRD_VOID',
'ARPHRD_NONE',
'ETH_P_LOOP',
'ETH_P_PUP',
'ETH_P_PUPAT',
'ETH_P_IP',
'ETH_P_X25',
'ETH_P_ARP',
'ETH_P_BPQ',
'ETH_P_IEEEPUP',
'ETH_P_IEEEPUPAT',
'ETH_P_DEC',
'ETH_P_DNA_DL',
'ETH_P_DNA_RC',
'ETH_P_DNA_RT',
'ETH_P_LAT',
'ETH_P_DIAG',
'ETH_P_CUST',
'ETH_P_SCA',
'ETH_P_RARP',
'ETH_P_ATALK',
'ETH_P_AARP',
'ETH_P_8021Q',
'ETH_P_IPX',
'ETH_P_IPV6',
'ETH_P_SLOW',
'ETH_P_WCCP',
'ETH_P_PPP_DISC',
'ETH_P_PPP_SES',
'ETH_P_MPLS_UC',
'ETH_P_MPLS_MC',
'ETH_P_ATMMPOA',
'ETH_P_ATMFATE',
'ETH_P_AOE',
'ETH_P_TIPC',
'ETH_P_802_3',
'ETH_P_AX25',
'ETH_P_ALL',
'ETH_P_802_2',
'ETH_P_SNAP',
'ETH_P_DDCMP',
'ETH_P_WAN_PPP',
'ETH_P_PPP_MP',
'ETH_P_LOCALTALK',
'ETH_P_PPPTALK',
'ETH_P_TR_802_2',
'ETH_P_MOBITEX',
'ETH_P_CONTROL',
'ETH_P_IRDA',
'ETH_P_ECONET',
'ETH_P_HDLC',
'ETH_P_ARCNET',
]
import sys
import os
import socket
import struct
from ctypes import *
from BTL.obsoletepythonsupport import set
_libc = None
BSD = sys.platform.startswith('darwin') or sys.platform.startswith('freebsd')
def libc():
global _libc
if _libc is None:
uname = os.uname()
assert sizeof(c_ushort) == 2
assert sizeof(c_uint) == 4
if sys.platform.startswith('darwin'):
_libc = CDLL('libc.dylib')
elif sys.platform.startswith('freebsd'):
_libc = CDLL('libc.so.6')
else:
assert uname[0] == 'Linux' and [ int(x) for x in uname[2].split('.')[:2] ] >= [ 2, 2 ]
_libc = CDLL('libc.so.6')
return _libc
def errno():
return cast(addressof(libc().errno), POINTER(POINTER(c_int)))[0][0]
uint16_t = c_ushort
uint32_t = c_uint
uint8_t = c_ubyte
if BSD:
sa_family_t = c_uint8
def SOCKADDR_COMMON(prefix):
"""
Common data: address family and length.
"""
return [ (prefix + 'len', c_uint8),
(prefix + 'family', sa_family_t) ]
else:
sa_family_t = c_ushort
def SOCKADDR_COMMON(prefix):
"""
Common data: address family and length.
"""
return [ (prefix + 'family', sa_family_t) ]
SOCKADDR_COMMON_SIZE = sum([ sizeof(t) for n, t in SOCKADDR_COMMON('') ])
class sockaddr(Structure):
"""
Structure describing a generic socket address.
"""
pass
sockaddr._fields_ = SOCKADDR_COMMON('sa_') + [ # Common data: address family and length.
('sa_data', ARRAY(c_ubyte, 14)), # Address data.
]
class sockaddr_storage(Structure):
"""
Structure large enough to hold any socket address (with the historical exception of AF_UNIX). We reserve 128 bytes.
"""
pass
_SS_SIZE = 128
__ss_aligntype = c_ulong
sockaddr_storage._fields_ = SOCKADDR_COMMON('ss_') + [ # Address family, etc.
('__ss_align', __ss_aligntype), # Force desired alignment.
('__ss_padding', ARRAY(c_byte, _SS_SIZE - 2 * sizeof(__ss_aligntype))),
]
class sockaddr_ll(Structure):
pass
sockaddr_ll._fields_ = SOCKADDR_COMMON('sll_') + [
('sll_protocol', c_ushort),
('sll_ifindex', c_int),
('sll_hatype', c_ushort),
('sll_pkttype', c_ubyte),
('sll_halen', c_ubyte),
('sll_addr', ARRAY(c_ubyte, 8)),
]
in_port_t = uint16_t
in_addr_t = uint32_t
class in_addr(Structure):
"""
Internet address.
"""
pass
in_addr._fields_ = [
('s_addr', in_addr_t)
]
class in6_u(Union):
pass
in6_u._fields_ = [
('u6_addr8', ARRAY(uint8_t, 16)),
('u6_addr16', ARRAY(uint16_t, 8)),
('u6_addr32', ARRAY(uint32_t, 4)),
]
class in6_addr(Structure):
"""
IPv6 address
"""
pass
in6_addr._fields_ = [
('in6_u', in6_u),
]
class sockaddr_in(Structure):
"""
Structure describing an Internet socket address.
"""
pass
sockaddr_in._fields_ = SOCKADDR_COMMON('sin_') + [
('sin_port', in_port_t), # Port number.
('sin_addr', in_addr), # Internet address.
('sin_zero', ARRAY(c_ubyte, sizeof(sockaddr) - SOCKADDR_COMMON_SIZE - sizeof(in_port_t) - sizeof(in_addr))), # Pad to size of `struct sockaddr'.
]
class sockaddr_in6(Structure):
"""
Structure describing an IPv6 socket address.
"""
pass
sockaddr_in6._fields_ = SOCKADDR_COMMON('sin6_') + [
('sin6_port', in_port_t), # Transport layer port #
('sin6_flowinfo', uint32_t), # IPv6 flow information
('sin6_addr', in6_addr), # IPv6 address
('sin6_scope_id', uint32_t), # IPv6 scope-id
]
class net_device_stats(Structure):
"""
Network device statistics.
"""
pass
net_device_stats._fields_ = [
('rx_packets', c_ulong),
('tx_packets', c_ulong),
('rx_bytes', c_ulong),
('tx_bytes', c_ulong),
('rx_errors', c_ulong),
('tx_errors', c_ulong),
('rx_dropped', c_ulong),
('tx_dropped', c_ulong),
('multicast', c_ulong),
('collisions', c_ulong),
('rx_length_errors', c_ulong),
('rx_over_errors', c_ulong),
('rx_crc_errors', c_ulong),
('rx_frame_errors', c_ulong),
('rx_fifo_errors', c_ulong),
('rx_missed_errors', c_ulong),
('tx_aborted_errors', c_ulong),
('tx_carrier_errors', c_ulong),
('tx_fifo_errors', c_ulong),
('tx_heartbeat_errors', c_ulong),
('tx_window_errors', c_ulong),
('rx_compressed', c_ulong),
('tx_compressed', c_ulong),
]
class ifa_ifu(Union):
"""
At most one of the following two is valid. If the IFF_BROADCAST
bit is set in `ifa_flags', then `ifa_broadaddr' is valid. If the
IFF_POINTOPOINT bit is set, then `ifa_dstaddr' is valid.
It is never the case that both these bits are set at once.
"""
pass
ifa_ifu._fields_=[
('ifu_broadaddr', POINTER(sockaddr)), # Broadcast address of this interface.
('ifu_dstaddr', POINTER(sockaddr)), # Point-to-point destination address.
]
class ifaddrs(Structure):
"""
The `getifaddrs' function generates a linked list of these structures.
Each element of the list describes one network interface.
"""
pass
ifaddrs._fields_=[
('ifa_next', POINTER(ifaddrs)), # Pointer to the next structure.
('ifa_name', c_char_p), # Name of this network interface.
('ifa_flags', c_uint), # Flags as from SIOCGIFFLAGS ioctl.
('ifa_addr', POINTER(sockaddr)), # Network address of this interface.
('ifa_netmask', POINTER(sockaddr)), # Netmask of this interface.
('ifa_ifu', ifa_ifu),
('ifa_data', c_void_p), # Address-specific data (may be unused).
]
class NamedLong(long):
def __new__(self, name, value):
self._long = long.__new__(self, value)
self._long._name = name
return self._long
def __repr__(self):
return self._name
pass
class OrSet(set):
def __repr__(self):
return ' | '.join([ repr(x) for x in self ])
IFF_UP = NamedLong(name = 'IFF_UP', value = 0x1)
IFF_BROADCAST = NamedLong(name = 'IFF_BROADCAST', value = 0x2)
IFF_DEBUG = NamedLong(name = 'IFF_DEBUG', value = 0x4)
IFF_LOOPBACK = NamedLong(name = 'IFF_LOOPBACK', value = 0x8)
IFF_POINTOPOINT = NamedLong(name = 'IFF_POINTOPOINT', value = 0x10)
IFF_NOTRAILERS = NamedLong(name = 'IFF_NOTRAILERS', value = 0x20)
IFF_RUNNING = NamedLong(name = 'IFF_RUNNING', value = 0x40)
IFF_NOARP = NamedLong(name = 'IFF_NOARP', value = 0x80)
IFF_PROMISC = NamedLong(name = 'IFF_PROMISC', value = 0x100)
IFF_ALLMULTI = NamedLong(name = 'IFF_ALLMULTI', value = 0x200)
IFF_MASTER = NamedLong(name = 'IFF_MASTER', value = 0x400)
IFF_SLAVE = NamedLong(name = 'IFF_SLAVE', value = 0x800)
IFF_MULTICAST = NamedLong(name = 'IFF_MULTICAST', value = 0x1000)
IFF_PORTSEL = NamedLong(name = 'IFF_PORTSEL', value = 0x2000)
IFF_AUTOMEDIA = NamedLong(name = 'IFF_AUTOMEDIA', value = 0x4000)
IFF_DYNAMIC = NamedLong(name = 'IFF_DYNAMIC', value = 0x8000L)
IFF_LOWER_UP = NamedLong(name = 'IFF_LOWER_UP', value = 0x10000)
IFF_DORMANT = NamedLong(name = 'IFF_DORMANT', value = 0x20000)
ARPHRD_NETROM = NamedLong(name = 'ARPHRD_NETROM', value = 0)
ARPHRD_ETHER = NamedLong(name = 'ARPHRD_ETHER', value = 1)
ARPHRD_EETHER = NamedLong(name = 'ARPHRD_EETHER', value = 2)
ARPHRD_AX25 = NamedLong(name = 'ARPHRD_AX25', value = 3)
ARPHRD_PRONET = NamedLong(name = 'ARPHRD_PRONET', value = 4)
ARPHRD_CHAOS = NamedLong(name = 'ARPHRD_CHAOS', value = 5)
ARPHRD_IEEE802 = NamedLong(name = 'ARPHRD_IEEE802', value = 6)
ARPHRD_ARCNET = NamedLong(name = 'ARPHRD_ARCNET', value = 7)
ARPHRD_APPLETLK = NamedLong(name = 'ARPHRD_APPLETLK', value = 8)
ARPHRD_DLCI = NamedLong(name = 'ARPHRD_DLCI', value = 15)
ARPHRD_ATM = NamedLong(name = 'ARPHRD_ATM', value = 19)
ARPHRD_METRICOM = NamedLong(name = 'ARPHRD_METRICOM', value = 23)
ARPHRD_IEEE1394 = NamedLong(name = 'ARPHRD_IEEE1394', value = 24)
ARPHRD_EUI64 = NamedLong(name = 'ARPHRD_EUI64', value = 27)
ARPHRD_INFINIBAND = NamedLong(name = 'ARPHRD_INFINIBAND', value = 32)
ARPHRD_SLIP = NamedLong(name = 'ARPHRD_SLIP', value = 256)
ARPHRD_SLIP6 = NamedLong(name = 'ARPHRD_SLIP6', value = 258)
ARPHRD_RSRVD = NamedLong(name = 'ARPHRD_RSRVD', value = 260)
ARPHRD_ADAPT = NamedLong(name = 'ARPHRD_ADAPT', value = 264)
ARPHRD_X25 = NamedLong(name = 'ARPHRD_X25', value = 271)
ARPHRD_HWX25 = NamedLong(name = 'ARPHRD_HWX25', value = 272)
ARPHRD_PPP = NamedLong(name = 'ARPHRD_PPP', value = 512)
ARPHRD_CISCO = NamedLong(name = 'ARPHRD_CISCO', value = 513)
ARPHRD_HDLC = NamedLong(name = 'ARPHRD_HDLC', value = ARPHRD_CISCO)
ARPHRD_DDCMP = NamedLong(name = 'ARPHRD_DDCMP', value = 517)
ARPHRD_RAWHDLC = NamedLong(name = 'ARPHRD_RAWHDLC', value = 518)
ARPHRD_TUNNEL = NamedLong(name = 'ARPHRD_TUNNEL', value = 768)
ARPHRD_TUNNEL6 = NamedLong(name = 'ARPHRD_TUNNEL6', value = 769)
ARPHRD_FRAD = NamedLong(name = 'ARPHRD_FRAD', value = 770)
ARPHRD_SKIP = NamedLong(name = 'ARPHRD_SKIP', value = 771)
ARPHRD_LOOPBACK = NamedLong(name = 'ARPHRD_LOOPBACK', value = 772)
ARPHRD_LOCALTLK = NamedLong(name = 'ARPHRD_LOCALTLK', value = 773)
ARPHRD_FDDI = NamedLong(name = 'ARPHRD_FDDI', value = 774)
ARPHRD_BIF = NamedLong(name = 'ARPHRD_BIF', value = 775)
ARPHRD_SIT = NamedLong(name = 'ARPHRD_SIT', value = 776)
ARPHRD_IPDDP = NamedLong(name = 'ARPHRD_IPDDP', value = 777)
ARPHRD_IPGRE = NamedLong(name = 'ARPHRD_IPGRE', value = 778)
ARPHRD_PIMREG = NamedLong(name = 'ARPHRD_PIMREG', value = 779)
ARPHRD_HIPPI = NamedLong(name = 'ARPHRD_HIPPI', value = 780)
ARPHRD_ASH = NamedLong(name = 'ARPHRD_ASH', value = 781)
ARPHRD_ECONET = NamedLong(name = 'ARPHRD_ECONET', value = 782)
ARPHRD_IRDA = NamedLong(name = 'ARPHRD_IRDA', value = 783)
ARPHRD_FCPP = NamedLong(name = 'ARPHRD_FCPP', value = 784)
ARPHRD_FCAL = NamedLong(name = 'ARPHRD_FCAL', value = 785)
ARPHRD_FCPL = NamedLong(name = 'ARPHRD_FCPL', value = 786)
ARPHRD_FCFABRIC = NamedLong(name = 'ARPHRD_FCFABRIC', value = 787)
ARPHRD_IEEE802_TR = NamedLong(name = 'ARPHRD_IEEE802_TR', value = 800)
ARPHRD_IEEE80211 = NamedLong(name = 'ARPHRD_IEEE80211', value = 801)
ARPHRD_IEEE80211_PRISM = NamedLong(name = 'ARPHRD_IEEE80211_PRISM', value = 802)
ARPHRD_IEEE80211_RADIOTAP = NamedLong(name = 'ARPHRD_IEEE80211_RADIOTAP', value = 803)
ARPHRD_VOID = NamedLong(name = 'ARPHRD_VOID', value = 0xFFFF)
ARPHRD_NONE = NamedLong(name = 'ARPHRD_NONE', value = 0xFFFE)
ETH_P_LOOP = NamedLong(name = 'ETH_P_LOOP', value = 0x0060)
ETH_P_PUP = NamedLong(name = 'ETH_P_PUP', value = 0x0200)
ETH_P_PUPAT = NamedLong(name = 'ETH_P_PUPAT', value = 0x0201)
ETH_P_IP = NamedLong(name = 'ETH_P_IP', value = 0x0800)
ETH_P_X25 = NamedLong(name = 'ETH_P_X25', value = 0x0805)
ETH_P_ARP = NamedLong(name = 'ETH_P_ARP', value = 0x0806)
ETH_P_BPQ = NamedLong(name = 'ETH_P_BPQ', value = 0x08FF)
ETH_P_IEEEPUP = NamedLong(name = 'ETH_P_IEEEPUP', value = 0x0a00)
ETH_P_IEEEPUPAT = NamedLong(name = 'ETH_P_IEEEPUPAT', value = 0x0a01)
ETH_P_DEC = NamedLong(name = 'ETH_P_DEC', value = 0x6000)
ETH_P_DNA_DL = NamedLong(name = 'ETH_P_DNA_DL', value = 0x6001)
ETH_P_DNA_RC = NamedLong(name = 'ETH_P_DNA_RC', value = 0x6002)
ETH_P_DNA_RT = NamedLong(name = 'ETH_P_DNA_RT', value = 0x6003)
ETH_P_LAT = NamedLong(name = 'ETH_P_LAT', value = 0x6004)
ETH_P_DIAG = NamedLong(name = 'ETH_P_DIAG', value = 0x6005)
ETH_P_CUST = NamedLong(name = 'ETH_P_CUST', value = 0x6006)
ETH_P_SCA = NamedLong(name = 'ETH_P_SCA', value = 0x6007)
ETH_P_RARP = NamedLong(name = 'ETH_P_RARP', value = 0x8035)
ETH_P_ATALK = NamedLong(name = 'ETH_P_ATALK', value = 0x809B)
ETH_P_AARP = NamedLong(name = 'ETH_P_AARP', value = 0x80F3)
ETH_P_8021Q = NamedLong(name = 'ETH_P_8021Q', value = 0x8100)
ETH_P_IPX = NamedLong(name = 'ETH_P_IPX', value = 0x8137)
ETH_P_IPV6 = NamedLong(name = 'ETH_P_IPV6', value = 0x86DD)
ETH_P_SLOW = NamedLong(name = 'ETH_P_SLOW', value = 0x8809)
ETH_P_WCCP = NamedLong(name = 'ETH_P_WCCP', value = 0x883E)
ETH_P_PPP_DISC = NamedLong(name = 'ETH_P_PPP_DISC', value = 0x8863)
ETH_P_PPP_SES = NamedLong(name = 'ETH_P_PPP_SES', value = 0x8864)
ETH_P_MPLS_UC = NamedLong(name = 'ETH_P_MPLS_UC', value = 0x8847)
ETH_P_MPLS_MC = NamedLong(name = 'ETH_P_MPLS_MC', value = 0x8848)
ETH_P_ATMMPOA = NamedLong(name = 'ETH_P_ATMMPOA', value = 0x884c)
ETH_P_ATMFATE = NamedLong(name = 'ETH_P_ATMFATE', value = 0x8884)
ETH_P_AOE = NamedLong(name = 'ETH_P_AOE', value = 0x88A2)
ETH_P_TIPC = NamedLong(name = 'ETH_P_TIPC', value = 0x88CA)
ETH_P_802_3 = NamedLong(name = 'ETH_P_802_3', value = 0x0001)
ETH_P_AX25 = NamedLong(name = 'ETH_P_AX25', value = 0x0002)
ETH_P_ALL = NamedLong(name = 'ETH_P_ALL', value = 0x0003)
ETH_P_802_2 = NamedLong(name = 'ETH_P_802_2', value = 0x0004)
ETH_P_SNAP = NamedLong(name = 'ETH_P_SNAP', value = 0x0005)
ETH_P_DDCMP = NamedLong(name = 'ETH_P_DDCMP', value = 0x0006)
ETH_P_WAN_PPP = NamedLong(name = 'ETH_P_WAN_PPP', value = 0x0007)
ETH_P_PPP_MP = NamedLong(name = 'ETH_P_PPP_MP', value = 0x0008)
ETH_P_LOCALTALK = NamedLong(name = 'ETH_P_LOCALTALK', value = 0x0009)
ETH_P_PPPTALK = NamedLong(name = 'ETH_P_PPPTALK', value = 0x0010)
ETH_P_TR_802_2 = NamedLong(name = 'ETH_P_TR_802_2', value = 0x0011)
ETH_P_MOBITEX = NamedLong(name = 'ETH_P_MOBITEX', value = 0x0015)
ETH_P_CONTROL = NamedLong(name = 'ETH_P_CONTROL', value = 0x0016)
ETH_P_IRDA = NamedLong(name = 'ETH_P_IRDA', value = 0x0017)
ETH_P_ECONET = NamedLong(name = 'ETH_P_ECONET', value = 0x0018)
ETH_P_HDLC = NamedLong(name = 'ETH_P_HDLC', value = 0x0019)
ETH_P_ARCNET = NamedLong(name = 'ETH_P_ARCNET', value = 0x001A)
def NamedLongs(x, names):
s = OrSet()
for k in names:
if x & k:
s |= OrSet([k])
x ^= k
k = 1L
while x:
if x & k:
s |= OrSet([k])
x ^= k
k+=k
return s
def _getifaddrs(ifap):
"""
Create a linked list of `struct ifaddrs' structures, one for each
network interface on the host machine. If successful, store the
list in *IFAP and return 0. On errors, return -1 and set `errno'.
The storage returned in *IFAP is allocated dynamically and can
only be properly freed by passing it to `freeifaddrs'.
"""
__getifaddrs = libc().getifaddrs
return CFUNCTYPE(c_int, POINTER(POINTER(ifaddrs)))(__getifaddrs)(ifap)
def _freeifaddrs(ifa):
"""
Reclaim the storage allocated by a previous `getifaddrs' call.
"""
__freeifaddrs = libc().freeifaddrs
return CFUNCTYPE(None, POINTER(ifaddrs))(__freeifaddrs)(ifa)
def hardware_type(hatype):
this = sys.modules[__name__]
return ([ x for x in [ NamedLong(n, getattr(this, n)) for n in dir(this) if n[:len('ARPHRD_')] == 'ARPHRD_' ] if x == hatype ] + [ hatype ])[0]
def eth_protocol_type(protocol):
this = sys.modules[__name__]
return ([ x for x in [ NamedLong(n, getattr(this, n)) for n in dir(this) if n[:len('ETH_P_')] == 'ETH_P_' ] if x == protocol ] + [ protocol ])[0]
def packet_type(pkttype):
return ([ x for x in [ NamedLong(n, getattr(socket, n)) for n in dir(socket) if n[:len('PACKET_')] == 'PACKET_' ] if x == pkttype ] + [ pkttype ])[0]
def addrfamily(family):
return ([ x for x in [ NamedLong(n, getattr(socket, n)) for n in dir(socket) if n[:len('AF_')] == 'AF_' ] if x == family ] + [ family ])[0]
def sockaddr2addr(ifname, addr):
"""
Convert a sockaddr pointer (addr) to a descriptive dict or None
for a void pointer.
"""
if addr:
sa = addr[0]
else:
return None
d = { 'family': addrfamily(sa.sa_family) }
if hasattr(socket, 'AF_INET6') and sa.sa_family == socket.AF_INET6:
sin6 = cast(addr, POINTER(sockaddr_in6))[0]
d['port'] = sin6.sin6_port or None
d['addr'] = ':'.join([ '%04.4x' % socket.ntohs(x) for x in sin6.sin6_addr.in6_u.u6_addr16 ])
d['flowinfo'] = sin6.sin6_flowinfo or None
d['scope_id'] = sin6.sin6_scope_id
elif hasattr(socket, 'AF_INET') and sa.sa_family == socket.AF_INET:
sin = cast(addr, POINTER(sockaddr_in))[0]
d['port'] = sin.sin_port or None
d['addr'] = '.'.join([ str(ord(x)) for x in struct.pack('I', sin.sin_addr.s_addr) ])
elif hasattr(socket, 'AF_PACKET') and sa.sa_family == socket.AF_PACKET:
sll = cast(addr, POINTER(sockaddr_ll))[0]
#d['ifindex'] = sll.sll_ifindex
hwaddr = None
if sll.sll_hatype == ARPHRD_ETHER and sll.sll_halen == 6:
hwaddr = ':'.join([ chr(x).encode('hex') for x in sll.sll_addr[:sll.sll_halen] ])
elif sll.sll_hatype == ARPHRD_SIT and sll.sll_halen == 4:
try:
hwaddr = socket.inet_ntop(socket.AF_INET,
''.join([ chr(x) for x in sll.sll_addr[:sll.sll_halen] ]))
except:
pass
d['addr'] = (ifname,
eth_protocol_type(sll.sll_protocol),
packet_type(sll.sll_pkttype),
hardware_type(sll.sll_hatype),
) + ((hwaddr is not None) and (hwaddr,) or ())
else:
pass
try:
if 'addr' in d:
d['addr'] = socket.inet_ntop(sa.sa_family,
socket.inet_pton(sa.sa_family,
d['addr']))
except:
pass
return dict([ (k, v) for k, v in d.items() if v is not None ])
def flagset(flagbits):
if isinstance(flagbits, set):
return flagbits
return NamedLongs(flagbits,
(IFF_UP,
IFF_BROADCAST,
IFF_DEBUG,
IFF_LOOPBACK,
IFF_POINTOPOINT,
IFF_NOTRAILERS,
IFF_RUNNING,
IFF_NOARP,
IFF_PROMISC,
IFF_ALLMULTI,
IFF_MASTER,
IFF_SLAVE,
IFF_MULTICAST,
IFF_PORTSEL,
IFF_AUTOMEDIA,
IFF_DYNAMIC,
IFF_LOWER_UP,
IFF_DORMANT,
))
def getifaddrs(name = None):
"""
Create a list of ifaddrs, one for each network interface on the
host machine. If successful, return the list. On errors, raises
an exception. If the optional name is not None, only entries for
that interface name are returned.
"""
ifa = POINTER(ifaddrs)()
ret = _getifaddrs(byref(ifa))
ifa0 = ifa
if ret == -1:
raise IOError(os.strerror(errno()))
try:
iflist = []
while ifa:
d = {
'name': ifa[0].ifa_name,
'flags': flagset(ifa[0].ifa_flags),
}
d['addr'] = sockaddr2addr(d['name'], ifa[0].ifa_addr)
d['netmask'] = sockaddr2addr(d['name'], ifa[0].ifa_netmask)
if ifa[0].ifa_flags & IFF_BROADCAST:
d['broadaddr'] = sockaddr2addr(d['name'], ifa[0].ifa_ifu.ifu_broadaddr)
elif ifa[0].ifa_flags & IFF_POINTOPOINT:
d['dstaddr'] = sockaddr2addr(d['name'], ifa[0].ifa_ifu.ifu_dstaddr)
#d['data'] = ifa[0].ifa_data or None
if ifa[0].ifa_data:
if (d.get('addr') is not None and
hasattr(socket, 'AF_PACKET') and
d['addr'].get('family') == socket.AF_PACKET):
nds = cast(ifa[0].ifa_data, POINTER(net_device_stats))[0]
d['data'] = {
'rx_packets': nds.rx_packets,
'tx_packets': nds.tx_packets,
'rx_bytes': nds.rx_bytes,
'tx_bytes': nds.tx_bytes,
'rx_errors': nds.rx_errors,
'tx_errors': nds.tx_errors,
'rx_dropped': nds.rx_dropped,
'tx_dropped': nds.tx_dropped,
'multicast': nds.multicast,
'collisions': nds.collisions,
'rx_length_errors': nds.rx_length_errors,
'rx_over_errors': nds.rx_over_errors,
'rx_crc_errors': nds.rx_crc_errors,
'rx_frame_errors': nds.rx_frame_errors,
'rx_fifo_errors': nds.rx_fifo_errors,
'rx_missed_errors': nds.rx_missed_errors,
'tx_aborted_errors': nds.tx_aborted_errors,
'tx_carrier_errors': nds.tx_carrier_errors,
'tx_fifo_errors': nds.tx_fifo_errors,
'tx_heartbeat_errors': nds.tx_heartbeat_errors,
'tx_window_errors': nds.tx_window_errors,
'rx_compressed': nds.rx_compressed,
'tx_compressed': nds.tx_compressed,
}
iflist.append(dict([ (k, v) for k, v in d.items() if v is not None ]))
ifa = ifa[0].ifa_next
return [ iface for iface in iflist if name is None or iface.get('name') == name ]
finally:
_freeifaddrs(ifa0)
def getaddrs(family = None, flags = OrSet([IFF_UP, IFF_RUNNING]), name = None):
flags = flagset(flags)
for a in getifaddrs(name = name):
if family is not None and a.get('addr', {}).get('family') != family:
continue
if flags:
for flag in flags:
if flag not in flagset(a.get('flags', 0)):
continue
if 'addr' in a and 'addr' in a['addr']:
yield a['addr']['addr']
def getnetaddrs(family = None, flags = OrSet([IFF_UP, IFF_RUNNING]), name = None):
flags = flagset(flags)
for a in getifaddrs(name = name):
if family is not None and a.get('addr', {}).get('family') != family:
continue
if 'netmask' in a and a.get('netmask', {}).get('family') != a.get('addr', {}).get('family') != family:
continue
if flags:
for flag in flags:
if flag not in flagset(a.get('flags', 0)):
continue
if 'addr' in a and 'addr' in a['addr']:
yield str(a['addr']['addr']) + (('netmask' in a and 'addr' in a['netmask'] and a['netmask'].get('family') == a['addr']['family']) and '/' + str(a['netmask']['addr']) or '')
def getbroadaddrs(family = None, flags = OrSet([IFF_UP, IFF_RUNNING, IFF_BROADCAST]), name = None):
flags = flagset(flags)
for a in getifaddrs(name = name):
if family is not None and a.get('broadaddr', {}).get('family') != family:
continue
if flags:
for flag in flags:
if flag not in flagset(a.get('flags', 0)):
continue
if 'broadaddr' in a and 'addr' in a['broadaddr']:
yield str(a['broadaddr']['addr']) + (('netmask' in a and 'addr' in a['netmask'] and a['netmask'].get('family') == a['broadaddr']['family']) and '/' + str(a['netmask']['addr']) or '')
def getdstaddrs(family = None, flags = OrSet([IFF_UP, IFF_RUNNING, IFF_POINTOPOINT]), name = None):
flags = flagset(flags)
for a in getifaddrs(name = None):
if family is not None and a.get('dstaddr', {}).get('family') != family:
continue
if flags:
for flag in flags:
if flag not in flagset(a.get('flags', 0)):
continue
if 'dstaddr' in a and 'addr' in a['dstaddr']:
yield a['dstaddr']['addr']
def main():
'''
Print a list of network interfaces.
'''
print 'live interface addresses:'
for a in getaddrs():
print '\t' 'addr', a
for a in getdstaddrs():
print '\t' 'dstaddr', a
for a in getnetaddrs():
print '\t' 'netaddr', a
for a in getbroadaddrs():
print '\t' 'broadaddr', a
print 'all interface details:'
for a in getifaddrs():
print '\t' + a['name'] + ':'
i = a.items()
i.sort()
for k, v in i:
if k not in ('name', 'data'):
print '\t\t' + k, `v`
if 'data' in a:
print '\t\t' 'data' + ':'
i = a['data'].items()
i.sort()
for k, v in i:
print '\t\t\t' + k, `v`
if __name__ == '__main__':
main()
|
nilq/baby-python
|
python
|
from .main import *
from .database import *
from .headless import *
from .bdi import *
from .spq import *
from .oci import *
from .stai import *
from .starter import *
from .two_of_four_ocd_practice import *
from .two_of_four_ocd_test import *
from .extra import *
from .teacher_practice import *
from .teacher_test import *
from .teacher_starter import *
|
nilq/baby-python
|
python
|
# Do not edit this file directly.
# It was auto-generated by: code/programs/reflexivity/reflexive_refresh
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_file")
def imgui():
http_archive(
name = "imgui",
build_file = "//bazel/deps/imgui:build.BUILD",
sha256 = "1514c3b9037137331f57abec14c6ba238f9c6a4d2c0c1f0bab3debe5afdf3854",
strip_prefix = "imgui-ec945f44b5eff1d82129233be5643abbff2845da",
urls = [
"https://github.com/Unilang/imgui/archive/ec945f44b5eff1d82129233be5643abbff2845da.tar.gz",
],
patch_cmds = [
"find . -type f -name '*.h' -exec sed -i 's/typedef unsigned short ImDrawIdx;/typedef unsigned int ImDrawIdx;/g' {} \\;",
"sed -i '1s/^/#include <cfloat>\\n/' imgui_internal.h",
"sed -i '1s/^/#include <float.h>\\n/' imgui_internal.h",
"sed -i '1s/^/#include <cfloat>\\n/' imgui.h",
"sed -i '1s/^/#include <float.h>\\n/' imgui.h",
],
)
|
nilq/baby-python
|
python
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Perform a functional test of the status command."""
import os
import orion.core.cli
def test_no_experiments(clean_db, monkeypatch, capsys):
"""Test status with no experiments."""
monkeypatch.chdir(os.path.dirname(os.path.abspath(__file__)))
orion.core.cli.main(['status'])
captured = capsys.readouterr().out
assert captured == ""
def test_experiment_without_trials_wout_ac(clean_db, one_experiment, capsys):
"""Test status with only one experiment and no trials."""
orion.core.cli.main(['status'])
captured = capsys.readouterr().out
expected = """\
test_single_exp
===============
empty
"""
assert captured == expected
def test_experiment_wout_success_wout_ac(clean_db, single_without_success, capsys):
"""Test status with only one experiment and no successful trial."""
orion.core.cli.main(['status'])
captured = capsys.readouterr().out
expected = """\
test_single_exp
===============
status quantity
----------- ----------
broken 1
interrupted 1
new 1
reserved 1
suspended 1
"""
assert captured == expected
def test_experiment_w_trials_wout_ac(clean_db, single_with_trials, capsys):
"""Test status with only one experiment and all trials."""
orion.core.cli.main(['status'])
captured = capsys.readouterr().out
expected = """\
test_single_exp
===============
status quantity min obj
----------- ---------- ---------
broken 1
completed 1 0
interrupted 1
new 1
reserved 1
suspended 1
"""
assert captured == expected
def test_two_unrelated_w_trials_wout_ac(clean_db, unrelated_with_trials, capsys):
"""Test two unrelated experiments, with all types of trials."""
orion.core.cli.main(['status'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
status quantity
----------- ----------
broken 1
completed 1
interrupted 1
new 1
reserved 1
suspended 1
test_single_exp
===============
status quantity min obj
----------- ---------- ---------
broken 1
completed 1 0
interrupted 1
new 1
reserved 1
suspended 1
"""
assert captured == expected
def test_two_related_w_trials_wout_ac(clean_db, family_with_trials, capsys):
"""Test two related experiments, with all types of trials."""
orion.core.cli.main(['status'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
status quantity
----------- ----------
broken 1
completed 1
interrupted 1
new 1
reserved 1
suspended 1
test_double_exp_child
=====================
status quantity
----------- ----------
broken 1
completed 1
interrupted 1
new 1
reserved 1
suspended 1
"""
assert captured == expected
def test_three_unrelated_wout_ac(clean_db, three_experiments_with_trials, capsys):
"""Test three unrelated experiments with all types of trials."""
orion.core.cli.main(['status'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
status quantity
----------- ----------
broken 1
completed 1
interrupted 1
new 1
reserved 1
suspended 1
test_double_exp_child
=====================
status quantity
----------- ----------
broken 1
completed 1
interrupted 1
new 1
reserved 1
suspended 1
test_single_exp
===============
status quantity min obj
----------- ---------- ---------
broken 1
completed 1 0
interrupted 1
new 1
reserved 1
suspended 1
"""
assert captured == expected
def test_three_related_wout_ac(clean_db, three_family_with_trials, capsys):
"""Test three related experiments with all types of trials."""
orion.core.cli.main(['status'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
status quantity
----------- ----------
broken 1
completed 1
interrupted 1
new 1
reserved 1
suspended 1
test_double_exp_child
=====================
status quantity
----------- ----------
broken 1
completed 1
interrupted 1
new 1
reserved 1
suspended 1
test_double_exp_child2
======================
status quantity
----------- ----------
broken 1
completed 1
interrupted 1
new 1
reserved 1
suspended 1
"""
assert captured == expected
def test_three_related_branch_wout_ac(clean_db, three_family_branch_with_trials, capsys):
"""Test three related experiments with all types of trials."""
orion.core.cli.main(['status'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
status quantity
----------- ----------
broken 1
completed 1
interrupted 1
new 1
reserved 1
suspended 1
test_double_exp_child
=====================
status quantity
----------- ----------
broken 1
completed 1
interrupted 1
new 1
reserved 1
suspended 1
test_double_exp_grand_child
===========================
status quantity
----------- ----------
broken 1
completed 1
interrupted 1
new 1
reserved 1
suspended 1
"""
assert captured == expected
def test_one_wout_trials_w_a_wout_c(clean_db, one_experiment, capsys):
"""Test experiments, without trials, with --all."""
orion.core.cli.main(['status', '--all'])
captured = capsys.readouterr().out
expected = """\
test_single_exp
===============
id status best objective
---- -------- ----------------
"""
assert captured == expected
def test_one_w_trials_w_a_wout_c(clean_db, single_with_trials, capsys):
"""Test experiment, with all trials, with --all."""
orion.core.cli.main(['status', '--all'])
captured = capsys.readouterr().out
expected = """\
test_single_exp
===============
id status min obj
-------------------------------- ----------- ---------
ec6ee7892275400a9acbf4f4d5cd530d broken
c4c44cb46d075546824e2a32f800fece completed 0
2b5059fa8fdcdc01f769c31e63d93f24 interrupted
7e8eade99d5fb1aa59a1985e614732bc new
507496236ff94d0f3ad332949dfea484 reserved
caf6afc856536f6d061676e63d14c948 suspended
"""
assert captured == expected
def test_one_wout_success_w_a_wout_c(clean_db, single_without_success, capsys):
"""Test experiment, without success, with --all."""
orion.core.cli.main(['status', '--all'])
captured = capsys.readouterr().out
expected = """\
test_single_exp
===============
id status
-------------------------------- -----------
ec6ee7892275400a9acbf4f4d5cd530d broken
2b5059fa8fdcdc01f769c31e63d93f24 interrupted
7e8eade99d5fb1aa59a1985e614732bc new
507496236ff94d0f3ad332949dfea484 reserved
caf6afc856536f6d061676e63d14c948 suspended
"""
assert captured == expected
def test_two_unrelated_w_a_wout_c(clean_db, unrelated_with_trials, capsys):
"""Test two unrelated experiments with --all."""
orion.core.cli.main(['status', '--all'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
id status
-------------------------------- -----------
a8f8122af9e5162e1e2328fdd5dd75db broken
ab82b1fa316de5accb4306656caa07d0 completed
c187684f7c7d9832ba953f246900462d interrupted
1497d4f27622520439c4bc132c6046b1 new
bd0999e1a3b00bf8658303b14867b30e reserved
b9f1506db880645a25ad9b5d2cfa0f37 suspended
test_single_exp
===============
id status min obj
-------------------------------- ----------- ---------
ec6ee7892275400a9acbf4f4d5cd530d broken
c4c44cb46d075546824e2a32f800fece completed 0
2b5059fa8fdcdc01f769c31e63d93f24 interrupted
7e8eade99d5fb1aa59a1985e614732bc new
507496236ff94d0f3ad332949dfea484 reserved
caf6afc856536f6d061676e63d14c948 suspended
"""
assert captured == expected
def test_two_related_w_a_wout_c(clean_db, family_with_trials, capsys):
"""Test two related experiments with --all."""
orion.core.cli.main(['status', '--all'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
id status
-------------------------------- -----------
a8f8122af9e5162e1e2328fdd5dd75db broken
ab82b1fa316de5accb4306656caa07d0 completed
c187684f7c7d9832ba953f246900462d interrupted
1497d4f27622520439c4bc132c6046b1 new
bd0999e1a3b00bf8658303b14867b30e reserved
b9f1506db880645a25ad9b5d2cfa0f37 suspended
test_double_exp_child
=====================
id status
-------------------------------- -----------
45c359f1c753a10f2cfeca4073a3a7ef broken
e79761fe3fc24dcbb7850939ede84b68 completed
69928939792d67f6fe30e9b8459be1ec interrupted
5f4a9c92b8f7c26654b5b37ecd3d5d32 new
58c4019fb2f92da88a0e63fafb36b3da reserved
82f340cb9d90cbf024169926b60aeef2 suspended
"""
assert captured == expected
def test_three_unrelated_w_a_wout_c(clean_db, three_experiments_with_trials, capsys):
"""Test three unrelated experiments with --all."""
orion.core.cli.main(['status', '--all'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
id status
-------------------------------- -----------
a8f8122af9e5162e1e2328fdd5dd75db broken
ab82b1fa316de5accb4306656caa07d0 completed
c187684f7c7d9832ba953f246900462d interrupted
1497d4f27622520439c4bc132c6046b1 new
bd0999e1a3b00bf8658303b14867b30e reserved
b9f1506db880645a25ad9b5d2cfa0f37 suspended
test_double_exp_child
=====================
id status
-------------------------------- -----------
45c359f1c753a10f2cfeca4073a3a7ef broken
e79761fe3fc24dcbb7850939ede84b68 completed
69928939792d67f6fe30e9b8459be1ec interrupted
5f4a9c92b8f7c26654b5b37ecd3d5d32 new
58c4019fb2f92da88a0e63fafb36b3da reserved
82f340cb9d90cbf024169926b60aeef2 suspended
test_single_exp
===============
id status min obj
-------------------------------- ----------- ---------
ec6ee7892275400a9acbf4f4d5cd530d broken
c4c44cb46d075546824e2a32f800fece completed 0
2b5059fa8fdcdc01f769c31e63d93f24 interrupted
7e8eade99d5fb1aa59a1985e614732bc new
507496236ff94d0f3ad332949dfea484 reserved
caf6afc856536f6d061676e63d14c948 suspended
"""
assert captured == expected
def test_three_related_w_a_wout_c(clean_db, three_family_with_trials, capsys):
"""Test three related experiments with --all."""
orion.core.cli.main(['status', '--all'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
id status
-------------------------------- -----------
a8f8122af9e5162e1e2328fdd5dd75db broken
ab82b1fa316de5accb4306656caa07d0 completed
c187684f7c7d9832ba953f246900462d interrupted
1497d4f27622520439c4bc132c6046b1 new
bd0999e1a3b00bf8658303b14867b30e reserved
b9f1506db880645a25ad9b5d2cfa0f37 suspended
test_double_exp_child
=====================
id status
-------------------------------- -----------
45c359f1c753a10f2cfeca4073a3a7ef broken
e79761fe3fc24dcbb7850939ede84b68 completed
69928939792d67f6fe30e9b8459be1ec interrupted
5f4a9c92b8f7c26654b5b37ecd3d5d32 new
58c4019fb2f92da88a0e63fafb36b3da reserved
82f340cb9d90cbf024169926b60aeef2 suspended
test_double_exp_child2
======================
id status
-------------------------------- -----------
d0f4aa931345bfd864201b7dd93ae667 broken
5005c35be98025a24731d7dfdf4423de completed
c9fa9f0682a370396c8c4265c4e775dd interrupted
3d8163138be100e37f1656b7b591179e new
790d3c4c965e0d91ada9cbdaebe220cf reserved
6efdb99952d5f80f55adbba9c61dc288 suspended
"""
assert captured == expected
def test_three_related_branch_w_a_wout_c(clean_db, three_family_branch_with_trials, capsys):
"""Test three related experiments in a branch with --all."""
orion.core.cli.main(['status', '--all'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
id status
-------------------------------- -----------
a8f8122af9e5162e1e2328fdd5dd75db broken
ab82b1fa316de5accb4306656caa07d0 completed
c187684f7c7d9832ba953f246900462d interrupted
1497d4f27622520439c4bc132c6046b1 new
bd0999e1a3b00bf8658303b14867b30e reserved
b9f1506db880645a25ad9b5d2cfa0f37 suspended
test_double_exp_child
=====================
id status
-------------------------------- -----------
45c359f1c753a10f2cfeca4073a3a7ef broken
e79761fe3fc24dcbb7850939ede84b68 completed
69928939792d67f6fe30e9b8459be1ec interrupted
5f4a9c92b8f7c26654b5b37ecd3d5d32 new
58c4019fb2f92da88a0e63fafb36b3da reserved
82f340cb9d90cbf024169926b60aeef2 suspended
test_double_exp_grand_child
===========================
id status
-------------------------------- -----------
994602c021c470989d6f392b06cb37dd broken
24c228352de31010d8d3bf253604a82d completed
a3c8a1f4c80c094754c7217a83aae5e2 interrupted
d667f5d719ddaa4e1da2fbe568e11e46 new
a40748e487605df3ed04a5ac7154d4f6 reserved
229622a6d7132c311b7d4c57a08ecf08 suspended
"""
assert captured == expected
def test_two_unrelated_w_c_wout_a(clean_db, unrelated_with_trials, capsys):
"""Test two unrelated experiments with --collapse."""
orion.core.cli.main(['status', '--collapse'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
status quantity
----------- ----------
broken 1
completed 1
interrupted 1
new 1
reserved 1
suspended 1
test_single_exp
===============
status quantity min obj
----------- ---------- ---------
broken 1
completed 1 0
interrupted 1
new 1
reserved 1
suspended 1
"""
assert captured == expected
def test_two_related_w_c_wout_a(clean_db, family_with_trials, capsys):
"""Test two related experiments with --collapse."""
orion.core.cli.main(['status', '--collapse'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
status quantity
----------- ----------
broken 1
completed 1
interrupted 1
new 2
reserved 1
suspended 1
"""
assert captured == expected
def test_three_unrelated_w_c_wout_a(clean_db, three_experiments_with_trials, capsys):
"""Test three unrelated experiments with --collapse."""
orion.core.cli.main(['status', '--collapse'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
status quantity
----------- ----------
broken 1
completed 1
interrupted 1
new 2
reserved 1
suspended 1
test_single_exp
===============
status quantity min obj
----------- ---------- ---------
broken 1
completed 1 0
interrupted 1
new 1
reserved 1
suspended 1
"""
assert captured == expected
def test_three_related_w_c_wout_a(clean_db, three_family_with_trials, capsys):
"""Test three related experiments with --collapse."""
orion.core.cli.main(['status', '--collapse'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
status quantity
----------- ----------
broken 1
completed 1
interrupted 1
new 3
reserved 1
suspended 1
"""
assert captured == expected
def test_three_related_branch_w_c_wout_a(clean_db, three_family_branch_with_trials, capsys):
"""Test three related experiments with --collapse."""
orion.core.cli.main(['status', '--collapse'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
status quantity
----------- ----------
broken 1
completed 1
interrupted 1
new 3
reserved 1
suspended 1
"""
assert captured == expected
def test_two_unrelated_w_ac(clean_db, unrelated_with_trials, capsys):
"""Test two unrelated experiments with --collapse and --all."""
orion.core.cli.main(['status', '--collapse', '--all'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
id status
-------------------------------- -----------
a8f8122af9e5162e1e2328fdd5dd75db broken
ab82b1fa316de5accb4306656caa07d0 completed
c187684f7c7d9832ba953f246900462d interrupted
1497d4f27622520439c4bc132c6046b1 new
bd0999e1a3b00bf8658303b14867b30e reserved
b9f1506db880645a25ad9b5d2cfa0f37 suspended
test_single_exp
===============
id status min obj
-------------------------------- ----------- ---------
ec6ee7892275400a9acbf4f4d5cd530d broken
c4c44cb46d075546824e2a32f800fece completed 0
2b5059fa8fdcdc01f769c31e63d93f24 interrupted
7e8eade99d5fb1aa59a1985e614732bc new
507496236ff94d0f3ad332949dfea484 reserved
caf6afc856536f6d061676e63d14c948 suspended
"""
assert captured == expected
def test_two_related_w_ac(clean_db, family_with_trials, capsys):
"""Test two related experiments with --collapse and --all."""
orion.core.cli.main(['status', '--collapse', '--all'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
id status
-------------------------------- -----------
a8f8122af9e5162e1e2328fdd5dd75db broken
ab82b1fa316de5accb4306656caa07d0 completed
c187684f7c7d9832ba953f246900462d interrupted
1497d4f27622520439c4bc132c6046b1 new
ad6ea2decff2f298594b948fdaea03b2 new
bd0999e1a3b00bf8658303b14867b30e reserved
b9f1506db880645a25ad9b5d2cfa0f37 suspended
"""
assert captured == expected
def test_three_unrelated_w_ac(clean_db, three_experiments_with_trials, capsys):
"""Test three unrelated experiments with --collapse and --all."""
orion.core.cli.main(['status', '--collapse', '--all'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
id status
-------------------------------- -----------
a8f8122af9e5162e1e2328fdd5dd75db broken
ab82b1fa316de5accb4306656caa07d0 completed
c187684f7c7d9832ba953f246900462d interrupted
1497d4f27622520439c4bc132c6046b1 new
ad6ea2decff2f298594b948fdaea03b2 new
bd0999e1a3b00bf8658303b14867b30e reserved
b9f1506db880645a25ad9b5d2cfa0f37 suspended
test_single_exp
===============
id status min obj
-------------------------------- ----------- ---------
ec6ee7892275400a9acbf4f4d5cd530d broken
c4c44cb46d075546824e2a32f800fece completed 0
2b5059fa8fdcdc01f769c31e63d93f24 interrupted
7e8eade99d5fb1aa59a1985e614732bc new
507496236ff94d0f3ad332949dfea484 reserved
caf6afc856536f6d061676e63d14c948 suspended
"""
assert captured == expected
def test_three_related_w_ac(clean_db, three_family_with_trials, capsys):
"""Test three related experiments with --collapse and --all."""
orion.core.cli.main(['status', '--collapse', '--all'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
id status
-------------------------------- -----------
a8f8122af9e5162e1e2328fdd5dd75db broken
ab82b1fa316de5accb4306656caa07d0 completed
c187684f7c7d9832ba953f246900462d interrupted
1497d4f27622520439c4bc132c6046b1 new
ad6ea2decff2f298594b948fdaea03b2 new
f357f8c185ccab3037c65dcf721b9e71 new
bd0999e1a3b00bf8658303b14867b30e reserved
b9f1506db880645a25ad9b5d2cfa0f37 suspended
"""
assert captured == expected
def test_three_related_branch_w_ac(clean_db, three_family_branch_with_trials, capsys):
"""Test three related experiments in a branch with --collapse and --all."""
orion.core.cli.main(['status', '--collapse', '--all'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
id status
-------------------------------- -----------
a8f8122af9e5162e1e2328fdd5dd75db broken
ab82b1fa316de5accb4306656caa07d0 completed
c187684f7c7d9832ba953f246900462d interrupted
1497d4f27622520439c4bc132c6046b1 new
ad6ea2decff2f298594b948fdaea03b2 new
8f763d441db41d0f56e4e6aa40cc2321 new
bd0999e1a3b00bf8658303b14867b30e reserved
b9f1506db880645a25ad9b5d2cfa0f37 suspended
"""
assert captured == expected
def test_experiment_wout_child_w_name(clean_db, unrelated_with_trials, capsys):
"""Test status with the name argument and no child."""
orion.core.cli.main(['status', '--name', 'test_single_exp'])
captured = capsys.readouterr().out
expected = """test_single_exp
===============
status quantity min obj
----------- ---------- ---------
broken 1
completed 1 0
interrupted 1
new 1
reserved 1
suspended 1
"""
assert captured == expected
def test_experiment_w_child_w_name(clean_db, three_experiments_with_trials, capsys):
"""Test status with the name argument and one child."""
orion.core.cli.main(['status', '--name', 'test_double_exp'])
captured = capsys.readouterr().out
expected = """\
test_double_exp
===============
status quantity
----------- ----------
broken 1
completed 1
interrupted 1
new 1
reserved 1
suspended 1
"""
assert captured == expected
|
nilq/baby-python
|
python
|
import json
import sqlite3
with open('../data_retrieval/authors/author_info.json') as data_file:
data = json.load(data_file)
connection = sqlite3.connect('scholarDB.db')
with connection:
cursor = connection.cursor()
for row in data:
name = row['name']
website = row['website']
email = row['email']
photo = row['photo']
affiliations = ''
for a in row['university']:
affiliations += a
if a != row['university'][-1]:
affiliations += '|'
citation_count = row['citation count']
publication_count = row['publication count']
publication_years = row['publication years']
total_downloads = row['total downloads']
cursor.execute('insert into authors values(?,?,?,?,?,?,?,?,?)', [name, website, email, photo, affiliations, citation_count, publication_count, publication_years, total_downloads])
|
nilq/baby-python
|
python
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""
Custom Jupyterhub Authenticator to use Facebook OAuth with business manager check.
"""
import json
import os
import urllib
from tornado.web import HTTPError
from .authenticator import FBAuthenticator
class FBBusinessAuthenticator(FBAuthenticator):
scope = ["business_management", "email"]
BUSINESS_ID = os.environ.get("BUSINESS_ID")
PAGE_THRESHOLD = 100
async def authorize(self, access_token, user_id):
proof = self._get_app_secret_proof(access_token)
# check if the user has business management permission
if not await self._check_permission(access_token, "business_management", proof):
self.log.warning(
"User %s doesn't have business management permission", user_id
)
raise HTTPError(
403, "Your access token doesn't have the required permission"
)
self.log.info("User %s passed business management permission check", user_id)
# check if the user is in the business
if not await self._check_in_business(access_token, proof):
self.log.warning(
"User %s is not in the business %s", user_id, self.BUSINESS_ID
)
raise HTTPError(403, "Your are not in the business yet")
self.log.info("User %s passed business check", user_id)
return {
"name": user_id,
"auth_state": {
"access_token": access_token,
"fb_user": {"username": user_id},
},
}
async def _check_permission(self, access_token, permission, proof):
"""
Return true if the user has the given permission, false if not.
Throw a HTTP 500 error otherwise.
"""
try:
url = f"{FBAuthenticator.FB_GRAPH_EP}/me/permissions/?permission={permission}&access_token={access_token}&appsecret_proof={proof}"
with urllib.request.urlopen(url) as response:
body = response.read()
permission = json.loads(body).get("data")
return permission and permission[0]["status"] == "granted"
except Exception:
raise HTTPError(500, "Failed to check permission")
async def _check_in_business(self, access_token, proof):
"""
Return true if the user is in the given business, false if not.
Throw a HTTP 500 error otherwise.
"""
try:
url = f"{FBAuthenticator.FB_GRAPH_EP}/me/business_users?access_token={access_token}&appsecret_proof={proof}"
with urllib.request.urlopen(url) as response:
body = response.read()
body_json = json.loads(body)
return await self._check_in_page(body_json, 1)
except Exception:
raise HTTPError(500, "Authorization failed")
async def _check_in_page(self, body_json, current_page):
"""
Return false if the current page is larger thatn the threshold.
Return true if the user is in the given page.
Then recursively check the next page if it exists, return false if not.
Throw a HTTP 500 error otherwise.
"""
if current_page > self.PAGE_THRESHOLD:
return False
if self._has_business(body_json["data"]):
return True
paging = body_json.get("paging", {})
if "next" not in paging:
return False
try:
next_page_url = paging["next"]
with urllib.request.urlopen(next_page_url) as response:
body = response.read()
return await self._check_in_page(json.loads(body), current_page + 1)
except Exception:
raise HTTPError(500, "Authorization failed")
def _has_business(self, data):
"""
Given the data of one page of business users, check if the user is in the business.
Return true if the user is in the business, false otherwise.
"""
return any(
"business" in entry
and "id" in entry["business"]
and entry["business"]["id"] == self.BUSINESS_ID
for entry in data
)
|
nilq/baby-python
|
python
|
import numpy as np
import pandas as pd
from sklearn import *
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from matplotlib import pyplot
import time
import os
showPlot=True
#prepare data
data_file_name = "../FinalCost.csv"
data_csv = pd.read_csv(data_file_name, delimiter = ';',header=None, usecols=[3,4,5,6,7,8,9,10,11,12,16,17])
#Lire ligne par ligne
data = data_csv[1:]
#Renommer les colonne
data.columns = ['ConsommationHier','MSemaineDernier','MSemaine7','ConsoMmJrAnP','ConsoMmJrMP','ConsoMMJrSmDer',
'MoyenneMoisPrec','MoyenneMMSAnPrec','MoyenneMMmAnPrec','ConsommationMaxMDer', 'PoidTot', 'SumRetrait']
# print (data.head(10))
# pd.options.display.float_format = '{:,.0f}'.format
#supprimer les lignes dont la valeur est null ( au moins une valeur null)
data = data.dropna ()
#Output Y avec son type
y=data['SumRetrait'].astype(float)
cols=['ConsommationHier','MSemaineDernier','MSemaine7','ConsoMmJrAnP','ConsoMmJrMP','ConsoMMJrSmDer',
'MoyenneMoisPrec','MoyenneMMSAnPrec','MoyenneMMmAnPrec','ConsommationMaxMDer', 'PoidTot']
x=data[cols].astype(float)
print(data.head())
x_train ,x_test ,y_train ,y_test = train_test_split( x,y, test_size=0.2 , random_state=1116)
print(type(y_test))
#print(y_test)
print(x.shape)
#Design the Regression Model
regressor =LinearRegression()
##training
regressor.fit(x_train,y_train)
#Make prediction
y_pred =regressor.predict(x_test)
# print (y_pred)
# print("---- test----")
#print(y_test)
YArray = y_test.as_matrix()
testData = pd.DataFrame(YArray)
preddData = pd.DataFrame(y_pred)
meanError = np.abs((YArray - y_pred)/YArray)*100
meanError2 = np.abs((YArray - y_pred))
print("Mean: ", meanError.mean()," - ", meanError2.mean())
dataF = pd.concat([testData,preddData], axis=1)
dataF.columns =['Real demand','predicted Demand']
dataF.to_csv('Predictions.csv')
print(">>> Test values saved into amina.csv file ")
#vendredi;2018-03-16;116700;179,10370,;753,685,127100,119800,145500,760,721,768,4000;GAB_02
Xnew = [[179,10370,753,685,127100,119800,145500,760,721,768,4000]]
# make a prediction
ynew = regressor.predict(Xnew)
# show the inputs and predicted outputs
print("X= 116700 , Predicted=%s" % ynew[0])
if showPlot:
pyplot.plot(y_pred,'r-', label='forecast')
pyplot.plot(YArray,'b-',label='actual')
pyplot.legend()
pyplot.show()
|
nilq/baby-python
|
python
|
import numpy as np
class ZeroNoisePrng:
"""
A dummy PRNG returning zeros always.
"""
def laplace(self, *args, size=1, **kwargs):
return np.zeros(shape=size)
def exponential(self, *args, size=1, **kwargs):
return np.zeros(shape=size)
def binomial(self, *args, size=1, **kwargs):
return np.zeros(shape=size)
|
nilq/baby-python
|
python
|
'''
@author: Sana Dev Team
Created on May 24, 2011
'''
from __future__ import with_statement
import sys, traceback
from django.conf import settings
from django.core import urlresolvers
from piston.utils import decorator
from sana import api
from sana.api.fields import REQUEST, DISPATCHABLE
CRUD = {'GET':'read', 'POST':'create','PUT':'update', 'DELETE':'delete'}
class DispatchConf(object):
''' configures and manages the dispatchables <--> dispatcher mappings '''
def __init__(self, dispatchables={}):
self.dispatchables = dispatchables
self.handlers = {}
for dispatchable, dispatcher in dispatchables.items():
self.handlers[dispatchable] = '{0}.handlers'.format(dispatcher)
self.ctx = None
self.dispatcher = None
def reload(self, dispatcher):
self.dispatcher = dispatcher
mod = __import__('{0}'.format(dispatcher), fromlist=['contexts'])
self.ctx = getattr(mod, 'paths')
def get_context(self, dispatcher, dispatchable, method='GET', format='all'):
if not self.dispatcher or self.dispatcher != dispatcher:
self.reload(dispatcher)
p = self.ctx.get(dispatchable,{})
m = p.get(method, {})
return m.get(format,None)
def get_dispatcher(self, dispatchable):
return self.dispatchables.get(dispatchable, None)
dispatchconf = DispatchConf(dispatchables=settings.DISPATCHABLES)
def dispatch(operation='POST'):
''' Adds form attr to a request and is intended to handle all CRUD requests.
Note: Only 'POST' requests will be validated via django's
Form.is_valid(). All other requests will treat the request data as the
initial parameter for the Form.__init__ method in essence parsing any
query strings.
'''
@decorator
def wrap(f, handler, request, *a, **kwa):
# gets the dispatchable form we will validate
klass = handler.__class__
if hasattr(klass, 'v_form'):
v_form = getattr(klass, 'v_form')
else:
return api.ERROR(u'No valid dispatchable form')
form = v_form(data=getattr(request, REQUEST.CONTENT))
if operation == 'POST':
if not form.is_valid():
errs = dict((key, [unicode(v) for v in values]) for key,values in form.errors.items())
return api.FAIL(errs)
# set attributes
setattr(request, 'dispatch_form', form)
setattr(request, DISPATCHABLE.DATA, form.dispatch_data)
return f(handler, request, *a, **kwa)
return wrap
def dispatcher(klass):
''' Decorator indicating a class method will dispatch a dispatchable object.
klass => A class that extends piston.handler.BaseHandler
Looks first the 'dispatchable'. If not set, an attempt will be made to
look up the dispatchable based on the klass 'model' attribute
'''
def wrap(klass):
''' Verifies that a dispatchable attribute is set and sets the callback
to use for dispatching requests upstreams.
The callback may be a NoneType if not set in settings.py
'''
if not hasattr(klass, 'dispatchable'):
if hasattr(klass, 'model'):
setattr(klass,'dispatchable', klass.model.__name__.lower())
else:
setattr(klass,'dispatchable', None)
# get the crud handler callback which will dipatch upstream
callback = mdispatch_handler(klass.dispatchable)
setattr(klass, '_mdispatcher', callback)
wrap(klass)
return klass
def dispatch_reverse(namespace, dispatchable, method='read',
dconf='dispatch_urls', format=None, suffix=None):
''' Looks up a middleware handler CRUD method
namespace => the namespace of the handler
dispatchable => the type of dispatchable that will be sent
method => the CRUD method name
dconf => a module name containing the name/url mappings formatted as
per the standard django urls.py
format => use if multiple formats are supported; i.e json, xml
suffix => an additional flag; implementation dependent
'''
if method not in CRUD.values():
raise Exception
urlconf = '.'.join((namespace,dconf))
parts = [dispatchable,method,]
name = '-'.join(parts)
if format:
name+= '-' + format
if suffix:
name+= '-' + suffix
resolver = urlresolvers.get_resolver(urlconf)
try:
return resolver.reverse(name)
except Exception as e:
tb = sys.exc_info()[2]
for item in traceback.format_tb(tb):
print 'dispatch_reverse:::' , item
return ''
def mdispatch_handler(dispatchable):
''' Gets the middleware handler which will send the dispatchables upstream
or None if not available.
'''
try:
module = dispatchconf.get_dispatcher(dispatchable)
uconf = '{0}.{1}'.format(module, 'urls')
match = urlresolvers.reverse(dispatchable, urlconf=uconf)
resource, _, _ = urlresolvers.get_resolver(uconf).resolve(match)
return resource.handler
except Exception as e:
return None
|
nilq/baby-python
|
python
|
"""You are given an integer n, the number of teams in a tournament that has strange rules:
If the current number of teams is even, each team gets paired with another team. A total of n / 2 matches are played, and n / 2 teams advance to the next round.
If the current number of teams is odd, one team randomly advances in the tournament, and the rest gets paired. A total of (n - 1) / 2 matches are played, and (n - 1) / 2 + 1 teams advance to the next round.
Return the number of matches played in the tournament until a winner is decided.
Example 1:
Input: n = 7
Output: 6
Explanation: Details of the tournament:
- 1st Round: Teams = 7, Matches = 3, and 4 teams advance.
- 2nd Round: Teams = 4, Matches = 2, and 2 teams advance.
- 3rd Round: Teams = 2, Matches = 1, and 1 team is declared the winner.
Total number of matches = 3 + 2 + 1 = 6."""
n = 14
# number of matchs played until winnner is decided
matches = []
while n != 1:
if n % 2 == 0:
matches.append(n // 2)
n = n // 2
else:
matches.append((n - 1) // 2)
n = ((n - 1) // 2) + 1
print(matches)
print(sum(matches))
|
nilq/baby-python
|
python
|
from django.contrib import admin
from publicmarkup.legislation.models import Resource, Legislation, Title, Section
class LegislationAdmin(admin.ModelAdmin):
prepopulated_fields = {"slug": ("name",)}
class SectionInline(admin.TabularInline):
model = Section
extra = 5
class TitleAdmin(admin.ModelAdmin):
inlines = [SectionInline,]
list_filter = ('legislation',)
admin.site.register(Resource)
admin.site.register(Legislation, LegislationAdmin)
admin.site.register(Title, TitleAdmin)
|
nilq/baby-python
|
python
|
from pyscenario import *
from util.arg import *
from util.scene import RoadSideGen
class Scenario(PyScenario):
def get_description(self):
return 'Test random boxes at the border of roads'
def get_map(self):
return arg_get('-map', 'shapes-1')
def init(self):
gen = RoadSideGen(self.get_all_road_curves(), self.coord)
gen.place_scene_boxes(self)
|
nilq/baby-python
|
python
|
def two(x, y, *args):
print(x, y, args)
if __name__ == '__main__':
two('a', 'b', 'c')
|
nilq/baby-python
|
python
|
#!/usr/bin/env python3
from _common import StreamContext
import sys
from argparse import ArgumentParser
import itertools
from shelltools.ressample import ReservoirSampler
from typing import Sequence
def _has_dupes(items: Sequence):
if len(items) <= 1:
return False
if len(items) == 2:
return items[0] == items[1]
# would len(set(items)) < len(items) be faster?
for i in range(len(items)):
for j in range(i + 1, len(items)):
if items[i] == items[j]:
return True
return False
class Generator(object):
avoid_dupes = False
# noinspection PyMethodMayBeStatic
def _uniques(self, iterator):
for combo in iterator:
if not _has_dupes(combo):
yield combo
def generate(self, input_args):
iterables = []
for input_arg in input_args:
with StreamContext(input_arg, 'r') as ifile:
iterables.append([line.rstrip("\r\n") for line in ifile])
all_combos = itertools.product(*iterables)
if self.avoid_dupes:
return self._uniques(all_combos)
else:
return all_combos
def render(selection, delimiter, ofile=sys.stdout):
print(*selection, sep=delimiter, file=ofile)
def main():
parser = ArgumentParser(description="Print combinations of items from multiple streams.", epilog="Note that all input content is stored in memory.")
parser.add_argument("input", nargs='+', metavar="FILE", help="multiple files from which product will be printed")
parser.add_argument("-k", "--sample", type=int, metavar="K", help="sample size")
parser.add_argument("-d", "--delimiter", default=' ', metavar="STR", help="set delimiter between items on each line")
parser.add_argument("-u", "--unique", action='store_true', help="only print combinations with unique items")
args = parser.parse_args()
if len(args.input) == 1:
print("streamproduct: n > 1 arguments required", file=sys.stderr)
return 1
# TODO remove this restriction by caching stdin if present more than once
if len(list(filter(lambda x: x == '-' or x == '/dev/stdin', args.input))) > 1:
print("streamproduct: at most one argument may specify standard input", file=sys.stderr)
return 1
g = Generator()
g.avoid_dupes = args.unique or False
if args.sample is not None:
sampler = ReservoirSampler()
combos = sampler.collect(g.generate(args.input), args.sample)
else:
combos = g.generate(args.input)
for selection in combos:
render(selection, args.delimiter)
return 0
|
nilq/baby-python
|
python
|
import sys
import argparse
import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
from sklearn.utils import shuffle
def main(_):
# read data
df = pd.read_csv('../../data/boston/boston_train.csv', header=0)
print(df.describe())
f, ax1 = plt.subplots()
for i in range(1, 8):
number = 420 + i
ax1.locator_params(nbins=3)
ax1 = plt.subplot(number)
plt.title(list(df)[i])
ax1.scatter(df[df.columns[i]], df['MEDV'])
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
plt.show()
x_ph = tf.placeholder(tf.float32, name='X')
y_ph = tf.placeholder(tf.float32, name='Y')
with tf.name_scope('Model'):
w = tf.get_variable('W', shape=[2], initializer=tf.truncated_normal_initializer())
b = tf.get_variable('b', shape=[2], initializer=tf.truncated_normal_initializer())
y_model = tf.multiply(x_ph, w) + b
with tf.name_scope('CostFunction'):
cost = tf.reduce_mean(tf.pow(y_ph - y_model, 2))
train_op = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate).minimize(cost)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
def print_params(w_op, b_op):
b_val = sess.run(b_op)
w_val = sess.run(w_op)
print('w: {} b: {}'.format(w_val, b_val))
def plot_params(w_op, b_op, x_data, y_data):
b_val = sess.run(b_op)
w_val = sess.run(w_op)
plt.scatter(x_data[:, 0], y_data, marker='o')
plt.scatter(x_data[:, 1], y_data, marker='x')
plt.plot(x_data, w_val * x_data + b_val)
plt.show()
# x=[INDUS, AGE] y=[MEDV]
x_values = df[['INDUS', 'AGE']].values.astype(float)
y_values = df['MEDV'].values.astype(float)
print_params(w, b)
plot_params(w, b, x_values, y_values)
for a in range(1, FLAGS.train_steps + 1):
cost_sum = 0.0
for i, j in zip(x_values, y_values):
_, cost_val = sess.run([train_op, cost], feed_dict={x_ph: i, y_ph: j})
cost_sum += cost_val
x_values, y_values = shuffle(x_values, y_values)
if a % 5 == 0:
print('@{:-3d}: {:.3f}'.format(a, cost_sum / x_values.shape[0]))
print_params(w, b)
plot_params(w, b, x_values, y_values)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--learning_rate', type=float, default=0.005,
help='The initial learning rate')
parser.add_argument('--train_steps', type=int, default=100,
help='The number of training steps')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
|
nilq/baby-python
|
python
|
# Copyright 2021 Michal Krassowski
from collections import defaultdict
from time import time
from jedi.api.classes import Completion
from .logger import log
class LabelResolver:
def __init__(self, format_label, time_to_live=60 * 30):
self.format_label = format_label
self._cache = {}
self._time_to_live = time_to_live
self._cache_ttl = defaultdict(set)
self._clear_every = 2
# see https://github.com/davidhalter/jedi/blob/master/jedi/inference/helpers.py#L194-L202
self._cached_modules = {'pandas', 'numpy', 'tensorflow', 'matplotlib'}
def clear_outdated(self):
now = self.time_key()
to_clear = [
timestamp
for timestamp in self._cache_ttl
if timestamp < now
]
for time_key in to_clear:
for key in self._cache_ttl[time_key]:
del self._cache[key]
del self._cache_ttl[time_key]
def time_key(self):
return int(time() / self._time_to_live)
def get_or_create(self, completion: Completion):
if not completion.full_name:
use_cache = False
else:
module_parts = completion.full_name.split('.')
use_cache = module_parts and module_parts[0] in self._cached_modules
if use_cache:
key = self._create_completion_id(completion)
if key not in self._cache:
if self.time_key() % self._clear_every == 0:
self.clear_outdated()
self._cache[key] = self.resolve_label(completion)
self._cache_ttl[self.time_key()].add(key)
return self._cache[key]
return self.resolve_label(completion)
def _create_completion_id(self, completion: Completion):
return (
completion.full_name, completion.module_path,
completion.line, completion.column,
self.time_key()
)
def resolve_label(self, completion):
try:
sig = completion.get_signatures()
return self.format_label(completion, sig)
except Exception as e: # pylint: disable=broad-except
log.warning(
'Something went wrong when resolving label for {completion}: {e}',
completion=completion, e=e
)
|
nilq/baby-python
|
python
|
import pandas as pd
import numpy as np
#seri olusturma
#s = pd.Series(data, index=index) ile seri olusturulur
# s = pd.Series(np.random.randn(5)) #index --> 0,1,2,3,4
# s = pd.Series(np.random.randn(5),index=['a','b','c','d','e'])
# print(s)
# print('-'*50)
# print(s.index)
# print('*'*50)
# data = {'a':23,'b':24,'c':25}
# s = pd.Series(data)
# s = pd.Series(data,index=['b','c','a'])
# s = pd.Series(data,index=['e','c','a','d'])
# print(s)
# print('*'*50)
#serilerin ndarrray ile benzerligi
# s = pd.Series(np.random.randn(5))
# print(s)
# print('-'*50)
# print(s[2])
# print('-'*50)
# print(s[:2])
# print('-'*50)
# print(s[2:])
# print('-'*50)
# print(s[s > s.median()])
# print('-'*50)
# print(s[[3,2]])
# print('-'*50)
# print(s.dtype)
# print('-'*50)
# print(s.array)
# print('-'*50)
# print(s.to_numpy)
# print('*'*50)
#serilerin dict yapısı ile benzerligi
# s = pd.Series(np.random.randn(5),index=['a','b','c','d','e'])
# print(s)
# print('-'*50)
# print(s['c'])
# print('-'*50)
# s['f'] = 2
# print(s)
# print('-'*50)
# print('a' in s)
#serilerde matematikler islemler
# s = pd.Series(np.random.randn(5),index=['a','b','c','d','e'])
# print(s)
# print('-'*50)
# print(s + s)
# print('-'*50)
# print(s * 3)
# print('-'*50)
# print(s[2:] + s[:-1]) # NaN degerlerini s.dropna metodu ile silebiliriz
#Name degeri
# s = pd.Series(np.random.randn(5),index=['a','b','c','d','e'],name='Tutorial')
# print(s)
# print('-'*50)
# print(s.name)
# print('-'*50)
# s = s.rename('Yeni Tutorial')
# print(s.name)
|
nilq/baby-python
|
python
|
import numpy as np
def fieldFromCurrentLoop(current, radius, R, Z):
"""
Checks inputs to fieldFromCurrentLoop() for TypeErrors etc.
"""
if type(current) != type(0.):
raise TypeError("Current should be a float, "+str(type(current))+" detected.")
if type(radius) != type(0.):
raise TypeError("Radius should be a float, "+str(type(radius))+" detected.")
if R.ndim != 2:
raise IndexError("R should be a 2D gridded array, "+str(R.ndim)+" dimensions detected.")
if Z.ndim != 2:
raise IndexError("Z should be a 2D gridded array, "+str(Z.ndim)+" dimensions detected.")
def makeCurrentLayer(numLoops, separation, startLoopPosition, current, radius, R, Z):
"""
Checks inputs to makeCurrentLayer() for TypeErrors etc.
"""
if type(numLoops) != type(0):
raise TypeError("numLoops should be an int, "+str(type(numLoops))+" detected.")
if type(separation) != type (0.):
raise TypeError("separation should be a float, "+str(type(separation))+" detected.")
if type(startLoopPosition) != type (0.):
raise TypeError("startLoopPosition should be a float, "+str(type(startLoopPosition))+" detected.")
if type(current) != type(0.):
raise TypeError("current should be a double, "+str(type(current))+" detected.")
if type(radius) != type(0.):
raise TypeError("radius should be a double, "+str(type(radius))+" detected.")
if R.ndim != 2:
raise IndexError("R should be a 2D gridded array, "+str(R.ndim)+" dimensions detected.")
if Z.ndim != 2:
raise IndexError("Z should be a 2D gridded array, "+str(Z.ndim)+" dimensions detected.")
def makeCoil(numLayers, numLoopsPerLayer, layerSeparation, loopSeparation, startPosition, current, minRadius, R, Z):
"""
Checks inputs to makeCoil() for TypeErrors etc.
"""
if type(numLayers) != type(0):
raise TypeError("numLayers should be an int, "+str(type(numLayers))+" detected.")
if type(numLoopsPerLayer) != type(0):
raise TypeError("numLoopsPerLayer should be an int, "+str(type(numLoops))+" detected.")
if type(layerSeparation) != type (0.):
raise TypeError("layerSeparation should be a float, "+str(type(separation))+" detected.")
if type(loopSeparation) != type (0.):
raise TypeError("loopSeparation should be a float, "+str(type(separation))+" detected.")
if type(startPosition) != type (0.):
raise TypeError("startPosition should be a float, "+str(type(startPosition))+" detected.")
if type(current) != type(0.):
raise TypeError("current should be a double, "+str(type(current))+" detected.")
if type(minRadius) != type(0.):
raise TypeError("minRadius should be a double, "+str(type(radius))+" detected.")
if R.ndim != 2:
raise IndexError("R should be a 2D gridded array, "+str(R.ndim)+" dimensions detected.")
if Z.ndim != 2:
raise IndexError("Z should be a 2D gridded array, "+str(Z.ndim)+" dimensions detected.")
def makeMagnet(numCoils, numLayers, numLoopsPerLayer, layerSeparation, loopSeparation, startPosition, current, minRadius, R, Z):
"""
Checks inputs to makeMagnet() for TypeErrors etc.
"""
if type(numCoils) != type(0):
raise TypeError("numLayers should be an int, "+str(type(numCoils))+" detected.")
if type(numLayers[0]) != type(0):
raise TypeError("numLayers should be a list of ints, "+str(type(numLayers[0]))+" detected.")
if len(numLayers) != numCoils:
raise IndexError("numLayers should be a list of length numCoils ("+str(numCoils)+"), but numLayers has length "+str(len(numLayers))+".")
if type(numLoopsPerLayer[0]) != type(0):
raise TypeError("numLoopsPerLayer should be a list of ints, "+str(type(numLoopsPerLayer[0]))+" detected.")
if len(numLoopsPerLayer) != numCoils:
raise IndexError("numLoopsPerLayer should be a list of length numCoils ("+str(numCoils)+"), but numLoopsPerLayer has length "+str(len(numLoopsPerLayer))+".")
if type(layerSeparation[0]) != type (0.):
raise TypeError("layerSeparation should be a list of floats, "+str(type(layerSeparation[0]))+" detected.")
if len(layerSeparation) != numCoils:
raise IndexError("layerSeparation should be a list of length numCoils ("+str(numCoils)+"), but layerSeparation has length "+str(len(layerSeparation))+".")
if type(loopSeparation[0]) != type (0.):
raise TypeError("loopSeparation should be a list of floats, "+str(type(loopSeparation[0]))+" detected.")
if len(loopSeparation) != numCoils:
raise IndexError("loopSeparation should be a list of length numCoils ("+str(numCoils)+"), but loopSeparation has length "+str(len(loopSeparation))+".")
if type(startPosition[0]) != type (0.):
raise TypeError("startPosition should be a list of floats, "+str(type(startPosition[0]))+" detected.")
if len(startPosition) != numCoils:
raise IndexError("startPosition should be a list of length numCoils ("+str(numCoils)+"), but startPosition has length "+str(len(startPosition))+".")
if type(current[0]) != type(0.):
raise TypeError("current should be a list of floats, "+str(type(current[0]))+" detected.")
if len(current) != numCoils:
raise IndexError("current should be a list of length numCoils ("+str(numCoils)+"), but current has length "+str(len(current))+".")
if type(minRadius[0]) != type(0.):
raise TypeError("minRadius should be a list of floats, "+str(type(radius[0]))+" detected.")
if len(minRadius) != numCoils:
raise IndexError("minRadius should be a list of length numCoils ("+str(numCoils)+"), but minRadius has length "+str(len(minRadius))+".")
if R.ndim != 2:
raise IndexError("R should be a 2D gridded array, "+str(R.ndim)+" dimensions detected.")
if Z.ndim != 2:
raise IndexError("Z should be a 2D gridded array, "+str(Z.ndim)+" dimensions detected.")
def calcFieldOnAxis(numLayers, numLoopsPerLayer, layerSeparation, loopSeparation, startPosition, current, minRadius, z):
"""
Checks inputs to calcFieldOnAxis() for TypeErrors etc.
"""
if type(numLayers) != type(0):
raise TypeError("numLayers should be an int, "+str(type(numLayers))+" detected.")
if type(numLoopsPerLayer) != type(0):
raise TypeError("numLoopsPerLayer should be an int, "+str(type(numLoops))+" detected.")
if type(layerSeparation) != type (0.):
raise TypeError("layerSeparation should be a float, "+str(type(separation))+" detected.")
if type(loopSeparation) != type (0.):
raise TypeError("loopSeparation should be a float, "+str(type(separation))+" detected.")
if type(startPosition) != type (0.):
raise TypeError("startPosition should be a float, "+str(type(startPosition))+" detected.")
if type(current) != type(0.):
raise TypeError("current should be a double, "+str(type(current))+" detected.")
if type(minRadius) != type(0.):
raise TypeError("minRadius should be a double, "+str(type(radius))+" detected.")
if z.ndim != 1:
raise IndexError("z should be a 1D array (i.e. not gridded with r), "+str(z.ndim)+" dimensions detected.")
def calcMagnetFieldOnAxis(numCoils, numLayers, numLoopsPerLayer, layerSeparation, loopSeparation, startPosition, current, minRadius, z):
"""
Checks inputs to calcMagnetFieldOnAxis() for TypeErrors etc.
"""
if type(numCoils) != type(0):
raise TypeError("numLayers should be an int, "+str(type(numCoils))+" detected.")
if type(numLayers[0]) != type(0):
raise TypeError("numLayers should be an int, "+str(type(numLayers[0]))+" detected.")
if len(numLayers) != numCoils:
raise IndexError("numLayers should be a list of length numCoils ("+str(numCoils)+"), but numLayers has length "+str(len(numLayers))+".")
if type(numLoopsPerLayer[0]) != type(0):
raise TypeError("numLoopsPerLayer should be an int, "+str(type(numLoops[0]))+" detected.")
if len(numLoopsPerLayer) != numCoils:
raise IndexError("numLoopsPerLayer should be a list of length numCoils ("+str(numCoils)+"), but numLoopsPerLayer has length "+str(len(numLoopsPerLayer))+".")
if type(layerSeparation[0]) != type (0.):
raise TypeError("layerSeparation should be a float, "+str(type(layerSeparation[0]))+" detected.")
if len(layerSeparation) != numCoils:
raise IndexError("layerSeparation should be a list of length numCoils ("+str(numCoils)+"), but layerSeparation has length "+str(len(layerSeparation))+".")
if type(loopSeparation[0]) != type (0.):
raise TypeError("loopSeparation should be a float, "+str(type(loopSeparation[0]))+" detected.")
if len(loopSeparation) != numCoils:
raise IndexError("loopSeparation should be a list of length numCoils ("+str(numCoils)+"), but loopSeparation has length "+str(len(loopSeparation))+".")
if type(startPosition[0]) != type (0.):
raise TypeError("startPosition should be a float, "+str(type(startPosition[0]))+" detected.")
if len(startPosition) != numCoils:
raise IndexError("startPosition should be a list of length numCoils ("+str(numCoils)+"), but startPosition has length "+str(len(startPosition))+".")
if type(current[0]) != type(0.):
raise TypeError("current should be a float, "+str(type(current[0]))+" detected.")
if len(current) != numCoils:
raise IndexError("current should be a list of length numCoils ("+str(numCoils)+"), but current has length "+str(len(current))+".")
if type(minRadius[0]) != type(0.):
raise TypeError("minRadius should be a float, "+str(type(minRadius[0]))+" detected.")
if len(minRadius) != numCoils:
raise IndexError("minRadius should be a list of length numCoils ("+str(numCoils)+"), but minRadius has length "+str(len(minRadius))+".")
if z.ndim != 1:
raise IndexError("z should be a 1D array (i.e. not gridded with r), "+str(z.ndim)+" dimensions detected.")
def printField(R, Z, Br, Bz, saveName, description):
"""
Checks inputs to printField() for TypeErrors etc.
"""
if R.ndim != 2:
raise IndexError("R should be a 2D gridded array, "+str(R.ndim)+" dimensions detected.")
if Z.ndim != 2:
raise IndexError("Z should be a 2D gridded array, "+str(Z.ndim)+" dimensions detected.")
if Br.ndim != 2:
raise IndexError("Br should be a 2D gridded array, "+str(Br.ndim)+" dimensions detected.")
if Bz.ndim != 2:
raise IndexError("Bz should be a 2D gridded array, "+str(Bz.ndim)+" dimensions detected.")
if type(saveName) != type("I am a string"):
raise TypeError("saveName should be a string, "+str(type(saveName))+" detected.")
if description != None:
if type(description) != type("I am a string"):
raise TypeError("description should be a string, "+str(type(description))+"detected.")
def readOriginalFiles(fileList, sensorList=None, surveyedOffsets=None, surveyedAngles=None):
if type(fileList[0]) != type("I am a string"):
raise TypeError("fileList should be a python-like list of strings, "+str(type(fileList[0]))+" detected.")
def readFile(fileName):
if type(fileName) != type("I am a string"):
raise TypeError("fileName should be a string, "+str(type(fileName))+" detected.")
def setSensorPosition(sensorNumber, xPosition, yPosition, phiRotation):
if type(sensorNumber) != type(0):
raise TypeException("sensorNumber must be an integer between 0 and 6, type "+str(type(sensorNumber))+" detected with value "+str(sensorNumber)+".")
if type(xPosition) != type(0.):
raise TypeException("xPosition should be a float, "+str(type(xPosition))+" detected.")
if type(yPosition) != type(0.):
raise TypeException("yPosition should be a float, "+str(type(yPosition))+" detected.")
if type(phiRotation) != type(0.):
raise TypeException("phiRotation should be a float, "+str(type(phiRotation))+" detected.")
def getSensorPosition(sensorNumber):
if type(sensorNumber) != type(0):
raise TypeError("sensorNumber should be an integer between 0 and 6, type "+string(type(sensorNumber))+" detected.")
def rotateMapperCoordinates(rotationAngle, sensorNumber, x_local, B_local):
if type(rotationAngle) != type(0.0):
raise TypeError("rotationAngle should be a float, "+str(type(rotationAngle))+" detected.")
if type(sensorNumber) != type(0):
raise TypeError("x_local should be an int between 0 and 6, "+str(type(x_local))+" detected.")
if type(x_local) != type(0.0):
raise TypeError("x_local should be a float, "+str(type(x_local))+" detected.")
if type(B_local[0]) != type(0.0):
raise TypeError("B_local should be a list of floats, "+str(type(B_local[0]))+" detected.")
if type(B_local[1]) != type(0.0):
raise TypeError("B_local should be a list of floats, "+str(type(B_local[1]))+" detected.")
if type(B_local[2]) != type(0.0):
raise TypeError("B_local should be a list of floats, "+str(type(B_local[2]))+" detected.")
if len(B_local) != 3:
raise TypeError("B_local should be a list of length 3, length "+str(len(B_local))+" detected.")
def rotateToSurveyCoordinates(x_mapper, B_mapper, offsets, angles):
test = np.array([0.0, 0.0, 0.0])
if type(x_mapper.dtype) != type(test.dtype):
raise TypeError("x_mapper should have type numpy.float64, "+str(type(x_mapper.dtype))+" detected")
if x_mapper.size != 3:
raise TypeError("x_mapper should have three components, "+str(x_mapper.size)+" components detected")
if type(B_mapper.dtype) != type(test.dtype):
raise TypeError("B_mapper should have type numpy.float64, "+str(type(B_mapper.dtype))+" detected")
if B_mapper.size != 3:
raise TypeError("B_mapper should have three components, "+str(B_mapper.size)+" components detected")
if type(offsets.dtype) != type(test.dtype):
raise TypeError("offsets should have type numpy.float64, "+str(type(offsets.dtype))+" detected")
if offsets.size != 3:
raise TypeError("offsets should have three components, "+str(offsets.size)+" components detected")
if type(angles.dtype) != type(test.dtype):
raise TypeError("angles should have type numpy.float64, "+str(type(angles.dtype))+" detected")
if angles.size != 3:
raise TypeError("angles should have three components, "+str(angles.size)+" components detected")
def plotVariables(data, xAxisVariable, yAxisVariable, zAxisVariable, cutVariable, HallProbeList, xRange, yRange, zRange, cutRange):
polarVariables = ['r', 'phi', 'z', 'Br', 'Bphi', 'Bz', 'B', 'probe', 't', 'date']
cartesianVariables = ['x', 'y', 'z', 'Bx', 'By', 'Bz', 'B', 'probe', 't', 'date']
# 1. Make sure axis variables are strings:
if type(xAxisVariable) != type('string'):
raise TypeError('x-axis variable should be a string, e.g. \'x\' or \'r\'. Type '+str(type(xAxisVariable))+' detected.')
if type(yAxisVariable) != type('string') and yAxisVariable != None:
raise TypeError('y-axis variable should be a string, e.g. \'x\' or \'r\'. Type '+str(type(yAxisVariable))+' detected.')
if type(zAxisVariable) != type('string') and zAxisVariable != None:
raise TypeError('z-axis variable should be a string, e.g. \'x\' or \'r\'. Type '+str(type(zAxisVariable))+' detected.')
if type(cutVariable) != type('string') and cutVariable != None:
raise TypeError('cut-variable should be a string, e.g. \'x\' or \'r\'. Type '+str(type(cutVariable))+' detected.')
# 2. Make sure they're the *right strings for the data type*:
if data[0].identifier() == 'Polar Data':
if xAxisVariable not in polarVariables:
raise TypeError("x-axis variable must be a valid Polar co-ordinate or field component: "+xAxisVariable+" was requested.")
if yAxisVariable not in polarVariables and yAxisVariable != None:
raise TypeError("y-axis variable must be a valid Polar co-ordinate or field component: "+yAxisVariable+" was requested.")
if zAxisVariable not in polarVariables and zAxisVariable != None:
raise TypeError("z-axis variable must be a valid Polar co-ordinate or field component: "+zAxisVariable+" was requested.")
if cutVariable not in polarVariables and cutVariable != None:
raise TypeError("cut-variable must be a valid Polar co-ordinate or field component: "+cutVariable+" was requested.")
if data[0].identifier() == 'Cartesian Data':
if xAxisVariable not in cartesianVariables:
raise TypeError("x-axis variable must be a valid Cartesian co-ordinate or field component: "+xAxisVariable+" was requested.")
if yAxisVariable not in cartesianVariables and yAxisVariable != None:
raise TypeError("y-axis variable must be a valid Cartesian co-ordinate or field component: "+yAxisVariable+" was requested.")
if zAxisVariable not in cartesianVariables and zAxisVariable != None:
raise TypeError("z-axis variable must be a valid Cartesian co-ordinate or field component: "+zAxisVariable+" was requested.")
if cutVariable not in cartesianVariables and cutVariable != None:
raise TypeError("cut-variable must be a valid Cartesian co-ordinate or field component: "+cutVariable+" was requested.")
# 3. Make sure we don't have a spurious number of hall probes being requested, that they're in the correct range and are all ints:
if HallProbeList != None:
if len(HallProbeList) > 7:
raise TypeError("Too many entries in list of Hall probes. Maximum considered = 7, "+str(len(HallProbeList))+" requested.")
for probe in range(0, len(HallProbeList)):
if type(HallProbeList[probe]) != type(0):
raise TypeError("Hall probe identifiers should be ints, probe "+str(probe)+" in list is of type "+str(type(HallProbeList[probe]))+".")
if probe > 6 or probe < 0:
raise TypeError("Hall probe "+str(probe)+" in list is out of range. Valid probe ID's are 0..6, but "+str(HallProbeList[probe])+" was given.")
# 4. Make sure xRange, yRange, zRange are all sensible:
if xRange != None:
if len(xRange) != 2:
raise TypeError("xRange is specified as [min, max], list of length "+str(len(xRange))+" detected.")
for x in range(0, len(xRange)):
if xAxisVariable != 'probe' and xAxisVariable != 't' and xAxisVariable != 'date':
# should be using floats:
if type(xRange[x]) != type(0.0):
raise TypeError("xRange should be a list of floats, xRange["+str(x)+"] is of type "+str(type(xRange[x]))+".")
else:
# should be using ints:
if type(xRange[x]) != type(0):
raise TypeError("xRange should be a list of ints, xRange["+str(x)+"] is of type "+str(type(xRange[x]))+".")
# Finally, check that min < max:
if xRange[0] > xRange[1]:
raise TypeError("xRange should be specified as [min, max], but xRange[0] > xRange[1]: ("+str(xRange[0])+", "+str(xRange[1])+") given.")
if yRange != None:
if len(yRange) != 2:
raise TypeError("yRange is specified as [min, max], list of length "+str(len(yRange))+" detected.")
for y in range(0, len(yRange)):
if yAxisVariable != 'probe' and xAxisVariable != 't' and xAxisVariable != 'date':
# should be using floats:
if type(xRange[y]) != type(0.0):
raise TypeError("yRange should be a list of floats, yRange["+str(y)+"] is of type "+str(type(yRange[y]))+".")
else:
# should be using ints:
if type(yRange[y]) != type(0):
raise TypeError("yRange should be a list of ints, yRange["+str(y)+"] is of type "+str(type(yRange[y]))+".")
# Finally, check that min < max:
if yRange[0] > yRange[1]:
raise TypeError("yRange should be specified as [min, max], but yRange[0] > yRange[1]: ("+str(yRange[0])+", "+str(yRange[1])+") given.")
if zRange != None:
if len(zRange) != 2:
raise TypeError("zRange is specified as [min, max], list of length "+str(len(zRange))+" detected.")
for z in range(0, len(zRange)):
if zAxisVariable != 'probe' and zAxisVariable != 't' and zAxisVariable != 'date':
# should be using floats:
if type(zRange[z]) != type(0.0):
raise TypeError("zRange should be a list of floats, zRange["+str(z)+"] is of type "+str(type(zRange[z]))+".")
else:
# should be using ints:
if type(zRange[z]) != type(0):
raise TypeError("zRange should be a list of ints, zRange["+str(z)+"] is of type "+str(type(zRange[z]))+".")
# Finally, check that min < max:
if zRange[0] > zRange[1]:
raise TypeError("zRange should be specified as [min, max], but zRange[0] > zRange[1]: ("+str(zRange[0])+", "+str(zRange[1])+") given.")
if cutRange != None:
if len(cutRange) != 2:
raise TypeError("cutRange is specified as [min, max], list of length "+str(len(cutRange))+" detected.")
for z in range(0, len(cutRange)):
if cutVariable != 'probe' and cutVariable != 't' and cutVariable != 'date':
# should be using floats:
if type(cutRange[z]) != type(0.0):
raise TypeError("cutRange should be a list of floats, cutRange["+str(z)+"] is of type "+str(type(cutRange[z]))+".")
else:
# should be using ints:
if type(cutRange[z]) != type(0):
raise TypeError("cutRange should be a list of ints, cutRange["+str(z)+"] is of type "+str(type(cutRange[z]))+".")
# Finally, check that min < max:
if cutRange[0] > cutRange[1]:
raise TypeError("cutRange should be specified as [min, max], but cutRange[0] > cutRange[1]: ("+str(cutRange[0])+", "+str(cutRange[1])+") given.")
|
nilq/baby-python
|
python
|
n = int(input('number: '))
count = 0
for i in range(1, n+1):
if n % i == 0:
count += 1
print(i, end=' ')
print()
print('count:', count)
|
nilq/baby-python
|
python
|
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Copyright © 2014, 2015, 2017 Kevin Thibedeau
# (kevin 'period' thibedeau 'at' gmail 'punto' com)
#
# 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.
from __future__ import print_function, division, unicode_literals, absolute_import
from opbasm.color import *
class Optimizer(object):
name = ''
requires = []
removes_code = False
def __init__(self):
self.priority = 10
def apply(self, asm, assembled_code):
return []
def summary(self, printf):
pass
def register(self, asm):
# Register this optimizer
asm.optimizers[self.name] = self
# Register any other required optimizers recursively
for opt_class in self.requires:
opt = opt_class()
if opt.name not in asm.optimizers:
opt.register(asm)
class StaticAnalyzer(Optimizer):
'''Analyzes code for reachability by statically tracing execution paths'''
name = 'static'
def __init__(self):
self.priority = 50
self.dead_instructions = None
self.entry_points = None
def apply(self, asm, assembled_code):
self.keep_instructions(asm, assembled_code)
self.dead_instructions = None
self.entry_points = None
# Run static analysis
asm._print(_(' Static analysis: searching for dead code... '), end='')
self.entry_points = set((asm.default_jump & 0xFFF, 0))
self.entry_points |= set(asm.config.entry_point)
self.find_dead_code(assembled_code, self.entry_points)
asm._print(success(_('COMPLETE')))
if not asm.config.quiet:
# Summarize analysis
asm._print(_(' Entry points:'), ', '.join(['0x{:03X}'.format(e) for e in \
sorted(self.entry_points)]))
self.dead_instructions = len([s for s in assembled_code if s.is_removable()])
asm._print(_(' {} dead instructions found').format(self.dead_instructions))
return assembled_code
def keep_instructions(self, asm, assembled_code):
'''Mark instructions we want to automatically keep'''
# Find continuous blocks of labeled load&return instructions
if asm.config.target_arch.has_string_table_support: # PB6 load&return depends on string/table
cur_label = None
prev_jump = None
for s in assembled_code:
if s.label is not None:
cur_label = s.xlabel
prev_jump = None
unconditional_jump = s.command == 'jump' and s.arg2 is None
# Mark l&r for preservation if its associated label is referenced by other code
# Mark two or more consecutive unconditional jumps for preservation as part of a jump table
if s.command == 'load&return' or (unconditional_jump and prev_jump):
if cur_label is not None and asm.labels[cur_label].in_use:
if 'keep' not in s.tags:
s.tags['keep_auto'] = (True,)
# Mark this as the (possible) end of a jump table and flag it for preservation
if unconditional_jump: # and s.arg1 in self.labels:
s.tags['jump_table_end'] = (True,)
s.tags['keep'] = (True,) # For jump table instructions we need to tag with 'keep'
del s.tags['keep_auto']
# Mark previous jump as part of a jump table and flag it for preservation
if 'jump_table_end' in prev_jump.tags: del prev_jump.tags['jump_table_end']
prev_jump.tags['jump_table'] = (True,)
prev_jump.tags['keep'] = (True,) # For jump table instructions we need to tag with 'keep'
elif s.is_instruction() and not unconditional_jump: cur_label = None
# Remember previous jump instruction to identify jump tables
if unconditional_jump:
prev_jump = s
elif s.is_instruction():
prev_jump = None
# Apply keep_auto to INST directives
for s in assembled_code:
if s.command == 'inst' and 'keep' not in s.tags:
s.tags['keep_auto'] = (True,)
def find_dead_code(self, assembled_code, entry_points):
'''Perform dead code analysis'''
itable = self.build_instruction_table(assembled_code)
self.analyze_code_reachability(assembled_code, itable, entry_points)
self.analyze_recursive_keeps(assembled_code, itable)
def build_instruction_table(self, slist):
'''Build index of all instruction statements by address'''
itable = {}
for s in slist:
if s.is_instruction():
itable[s.address] = s
return itable
def analyze_code_reachability(self, slist, itable, entry_points):
'''Scan assembled statements for reachability'''
addresses = set(entry_points)
addresses.add(0)
self.find_reachability(addresses, itable)
def analyze_recursive_keeps(self, slist, itable):
'''Scan assembled statements for reachability'''
for s in slist:
if s.is_instruction() and 'keep' in s.tags:
self.find_reachability((s.address,), itable, follow_keeps=True)
def find_reachability(self, addresses, itable, follow_keeps=False):
'''Recursive function that follows graph of executable statements to determine
reachability'''
for a in addresses:
while a in itable:
s = itable[a]
if s.reachable: break # Skip statements already visited
if follow_keeps and 'keep_auto' in s.tags: break
if s.is_instruction():
if not follow_keeps:
s.reachable = True
elif 'keep' not in s.tags:
s.tags['keep_auto'] = (True,)
# Stop on unconditional return, returni, load&return, and jump@ instructions
if s.command in ('returni', 'load&return', 'jump@') or \
(s.command == 'return' and s.arg1 is None):
break
# Follow branch address for jump and call
if s.command in ('jump', 'call'):
if not follow_keeps or (s.immediate in itable and 'keep' not in itable[s.immediate].tags):
self.find_reachability((s.immediate,), itable, follow_keeps)
# Stop on unconditional jump
# Only 1 argument -> unconditional
if s.command == 'jump' and s.arg2 is None and 'jump_table' not in s.tags:
break
# Continue with next instruction if it exists
a += 1
def summary(self, printf):
printf(_(' Static analysis:\n Dead instructions {}: {}').format( \
_('found'), self.dead_instructions))
printf(_(' Analyzed entry points:'), ', '.join(['0x{:03X}'.format(e) for e in \
sorted(self.entry_points)]))
class DeadCodeRemover(Optimizer):
'''Removes instructions marked as dead'''
name = 'dead_code'
requires = [StaticAnalyzer]
removes_code = True
def __init__(self):
self.priority = 60
self.removed = 0
def apply(self, asm, assembled_code):
self.removed = 0
self.remove_dead_code(asm, assembled_code)
if self.removed > 0:
# Reinitialize registers to default names
asm._init_registers()
asm._print(_(' Dead code removal: '), end='')
# Reassemble code with dead code removed
assembled_code = asm._raw_assemble(assembled_code)
asm._print(success(_('COMPLETE')))
return assembled_code
def remove_dead_code(self, asm, assembled_code):
'''Mark unreachable code for removal'''
for s in assembled_code:
if s.is_removable():
# Convert the old instruction into a comment
s.comment_out()
self.removed += 1
# Track any removed labels
if s.label is not None:
if s.xlabel in asm.labels:
del asm.labels[s.xlabel]
asm.removed_labels.add(s.xlabel)
s.label = None
s.xlabel = None
def summary(self, printf):
printf(_(' Dead code removal: {}'.format(_('Applied') if self.removed > 0 else _('None'))))
_all_optimizers = set([StaticAnalyzer, DeadCodeRemover])
|
nilq/baby-python
|
python
|
import unittest
from sqlalchemy.orm import sessionmaker
from nesta.core.orms.nomis_orm import Base
from nesta.core.orms.orm_utils import get_mysql_engine
class TestNomis(unittest.TestCase):
'''Check that the WiktionaryNgram ORM works as expected'''
engine = get_mysql_engine("MYSQLDBCONF", "mysqldb")
Session = sessionmaker(engine)
def setUp(self):
'''Create the temporary table'''
Base.metadata.create_all(self.engine)
def tearDown(self):
'''Drop the temporary table'''
Base.metadata.drop_all(self.engine)
def test_build(self):
pass
if __name__ == "__main__":
unittest.main()
|
nilq/baby-python
|
python
|
from django.apps import AppConfig
class DeptConfig(AppConfig):
name = 'dept'
|
nilq/baby-python
|
python
|
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Copyright (c) 2015 Amir Mofasser <amir.mofasser@gmail.com> (@amimof)
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)
DOCUMENTATION = """
module: ibmim_installer
version_added: "1.9.4"
short_description: Install/Uninstall IBM Installation Manager
description:
- Install/Uninstall IBM Installation Manager
options:
src:
required: false
description: Path to installation files for Installation Manager
dest:
required: false
default: "/opt/IBM/InstallationManager"
description: Path to desired installation directory of Installation Manager
accessRights:
required: false
default: "admin"
description: admin (root) or nonAdmin installation?
logdir:
required: false
default: "/tmp/"
description: Path and file name of installation log file
state:
required: false
choices: [ present, absent ]
default: "present"
description: Whether Installation Manager should be installed or removed
author: "Amir Mofasser (@amofasser)"
"""
EXAMPLES = """
- name: Install
ibmim:
state: present
src: /some/dir/install/
logdir: /tmp/im_install.log
- name: Uninstall
ibmim:
state: absent
dest: /opt/IBM/InstallationManager
"""
import os
import subprocess
import platform
import datetime
import socket
class InstallationManagerInstaller():
module = None
module_facts = dict(
im_version = None,
im_internal_version = None,
im_arch = None,
im_header = None
)
def __init__(self):
# Read arguments
self.module = AnsibleModule(
argument_spec = dict(
state = dict(default='present', choices=['present', 'absent']),
src = dict(required=False),
dest = dict(default="/opt/IBM/InstallationManager/"),
accessRights = dict(default="admin", choices=['admin', 'nonAdmin']),
logdir = dict(default="/tmp/")
),
supports_check_mode=True
)
def getItem(self, str):
return self.module_facts[str]
def isProvisioned(self, dest):
"""
Checks if Installation Manager is already installed at dest
:param dest: Installation directory of Installation Manager
:return: True if already provisioned. False if not provisioned
"""
# If destination dir does not exists then its safe to assume that IM is not installed
if not os.path.exists(dest):
print ("Path does not exist: '%s'" % (dest))
return False
else:
resultDict = self.getVersion(dest)
print ("ResultDict is: '%s'" % (resultDict))
if "installed" in resultDict["im_header"]:
return True
print ("installed not found in ReturnDict")
return False
def getVersion(self, dest):
"""
Runs imcl with the version parameter and stores the output in a dict
:param dest: Installation directory of Installation Manager
:return: dict
"""
imclCmd = "{0}/eclipse/tools/imcl version".format(dest)
print ("imclCmd is: '%s'" % (imclCmd))
child = subprocess.Popen(
[ imclCmd ],
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
stdout_value, stderr_value = child.communicate()
stdout_value = repr(stdout_value)
stderr_value = repr(stderr_value)
try:
self.module_facts["im_version"] = re.search("Version: ([0-9].*)", stdout_value).group(1)
self.module_facts["im_internal_version"] = re.search("Internal Version: ([0-9].*)", stdout_value).group(1)
self.module_facts["im_arch"] = re.search("Architecture: ([0-9].*-bit)", stdout_value).group(1)
self.module_facts["im_header"] = re.search("Installation Manager.*", stdout_value).group(0)
except AttributeError:
self.module_facts["im_header"] = "**AttributeError**"
##### pass
return self.module_facts
def main(self):
state = self.module.params['state']
src = self.module.params['src']
dest = self.module.params['dest']
logdir = self.module.params['logdir']
accessRights = self.module.params['accessRights']
##
## If we have a nonAdmin Installation we might need to expand "~" for the
## users home directory
dest = os.path.expanduser(dest)
if state == 'present':
if self.module.check_mode:
self.module.exit_json(changed=False, msg="IBM IM where to be installed at {0}".format(dest))
# Check if IM is already installed
if not self.isProvisioned(dest):
# Check if paths are valid
if not os.path.exists(src+"/install"):
self.module.fail_json(msg=src+"/install not found")
if not os.path.exists(logdir):
if not os.listdir(logdir):
os.makedirs(logdir)
logfile = "{0}_ibmim_{1}.xml".format(platform.node(), datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
installCmd = "{0}/tools/imcl install com.ibm.cic.agent -repositories {0}/repository.config -accessRights {1} -acceptLicense -log {2}/{3} -installationDirectory {4} -properties com.ibm.cic.common.core.preferences.preserveDownloadedArtifacts=true".format(src, accessRights, logdir, logfile, dest)
print ("installCmd is: '%s'" % (installCmd))
child = subprocess.Popen(
[ installCmd ],
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
stdout_value, stderr_value = child.communicate()
stdout_value = repr(stdout_value)
stderr_value = repr(stderr_value)
if child.returncode != 0:
self.module.fail_json(
msg="IBM IM installation failed",
stderr=stderr_value,
stdout=stdout_value,
module_facts=self.module_facts
)
# Module finished. Get version of IM after installation so that we can print it to the user
self.getVersion(dest)
self.module.exit_json(
msg="IBM IM installed successfully",
changed=True,
stdout=stdout_value,
stderr=stderr_value,
module_facts=self.module_facts
)
else:
self.module.exit_json(
changed=False,
msg="IBM IM is already installed",
module_facts=self.module_facts
)
if state == 'absent':
if self.module.check_mode:
self.module.exit_json(
changed=False,
msg="IBM IM where to be uninstalled from {0}".format(dest),
module_facts=self.module_facts
)
# Check if IM is already installed
if self.isProvisioned(dest):
if (accessRights == 'admin'):
uninstall_dir = "/var/ibm/InstallationManager/uninstall/uninstallc"
else:
uninstall_dir = os.path.expanduser("~/var/ibm/InstallationManager/uninstall/uninstallc")
if not os.path.exists(uninstall_dir):
self.module.fail_json(msg=uninstall_dir + " does not exist")
child = subprocess.Popen(
[uninstall_dir],
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
stdout_value, stderr_value = child.communicate()
stdout_value = repr(stdout_value)
stderr_value = repr(stderr_value)
if child.returncode != 0:
self.module.fail_json(
msg="IBM IM uninstall failed",
stderr=stderr_value,
stdout=stdout_value,
module_facts=self.module_facts
)
# Module finished
self.module.exit_json(
changed=True,
msg="IBM IM uninstalled successfully",
stdout=stdout_value,
module_facts=self.module_facts
)
else:
self.module.exit_json(
changed=False,
msg="IBM IM is not installed",
module_facts=self.module_facts
)
# import module snippets
from ansible.module_utils.basic import *
if __name__ == '__main__':
imi = InstallationManagerInstaller()
imi.main()
|
nilq/baby-python
|
python
|
"""
This file defines and implements the basic goal descriptors for PDDL2.2.
The implementation of comparison goals is implemented in DomainInequality.
"""
from typing import Union, List
from enum import Enum
from pddl.domain_formula import DomainFormula, TypedParameter
from pddl.domain_time_spec import TIME_SPEC
class GoalType(Enum):
EMPTY = "empty"
SIMPLE = "conjunction"
CONJUNCTION = "inequality"
DISJUNCTION = "timed"
NEGATIVE = "negative"
IMPLICATION = "implication"
EXISTENTIAL = "existential"
UNIVERSAL = "universal"
COMPARISON = "comparison"
TIMED = "timed"
class GoalDescriptor:
"""
This superclass describes a goal for action or goal spec.
"""
def __init__(self, goal_type : GoalType = GoalType.EMPTY) -> None:
self.goal_type = goal_type
def __repr__(self) -> str:
return "()"
class GoalSimple(GoalDescriptor):
def __init__(self, atomic_formula : DomainFormula) -> None:
super().__init__(goal_type=GoalType.SIMPLE)
self.atomic_formula = atomic_formula
def __repr__(self) -> str:
return self.atomic_formula.print_pddl(include_types=False)
class GoalConjunction(GoalDescriptor):
def __init__(self, goals : List[GoalDescriptor]) -> None:
super().__init__(goal_type=GoalType.CONJUNCTION)
self.goals = goals
def __repr__(self) -> str:
return "(and " + " ".join([repr(g) for g in self.goals]) + ")"
class GoalDisjunction(GoalDescriptor):
def __init__(self, goals : List[GoalDescriptor]) -> None:
super().__init__(goal_type=GoalType.DISJUNCTION)
self.goals = goals
def __repr__(self) -> str:
return "(or " + " ".join([repr(g) for g in self.goals]) + ")"
class GoalNegative(GoalDescriptor):
def __init__(self, goal : GoalDescriptor) -> None:
super().__init__(goal_type=GoalType.NEGATIVE)
self.goal = goal
def __repr__(self) -> str:
return "(not " + repr(self.goal) + ")"
class GoalImplication(GoalDescriptor):
def __init__(self, antecedent : GoalDescriptor, consequent : GoalDescriptor) -> None:
super().__init__(goal_type=GoalType.IMPLICATION)
self.antecedent = antecedent
self.consequent = consequent
def __repr__(self) -> str:
return "(imples " + repr(self.antecedent) + " " + repr(self.consequent) + ")"
class GoalQuantified(GoalDescriptor):
def __init__(self,
typed_parameters : List[TypedParameter],
goal : GoalDescriptor,
quantification : GoalType
) -> None:
super().__init__(goal_type=quantification)
assert(quantification==GoalType.EXISTENTIAL or self.goal_type==GoalType.UNIVERSAL)
self.typed_parameters = typed_parameters
self.goal = goal
def __repr__(self) -> str:
return ("(forall (" if self.goal_type==GoalType.UNIVERSAL else "(exists (") \
+ ' '.join([p.label + " - " + p.type for p in self.typed_parameters]) \
+ ") " + repr(self.goal) + ")"
class TimedGoal(GoalDescriptor):
"""
This class describes a simple add or delete effect with time specifier for durative action.
"""
def __init__(self, time_spec : TIME_SPEC, goal : GoalDescriptor) -> None:
super().__init__(goal_type=GoalType.TIMED)
self.time_spec = time_spec
self.goal = goal
def __repr__(self) -> str:
return "(" + self.time_spec.value + " " + str(self.goal) + ")"
|
nilq/baby-python
|
python
|
# pylint # {{{
# vim: tw=100 foldmethod=indent
# pylint: disable=bad-continuation, invalid-name, superfluous-parens
# pylint: disable=bad-whitespace, mixed-indentation
# pylint: disable=redefined-outer-name
# pylint: disable=missing-docstring, trailing-whitespace, trailing-newlines, too-few-public-methods
# }}}
import os
import sys
import logging
import configargparse
logger = logging.getLogger(__name__)
def parseOptions():
'''Parse the commandline options'''
logger.info("reading config")
path_of_executable = os.path.realpath(sys.argv[0])
folder_of_executable = os.path.split(path_of_executable)[0]
full_name_of_executable = os.path.split(path_of_executable)[1]
name_of_executable = full_name_of_executable.rstrip('.py')
config_in_home = ''
try:
config_in_home = os.environ['HOME']+'/.config/%s.conf' % name_of_executable
except KeyError:
pass
config_files = [config_in_home,
folder_of_executable +'/%s.conf' % name_of_executable,
'/etc/mqtt-to-influx/mqtt-to-influx.conf']
parser = configargparse.ArgumentParser(
default_config_files = config_files,
description=name_of_executable, ignore_unknown_config_file_keys=True)
parser.add('-c', '--my-config', is_config_file=True, help='config file path')
parser.add_argument('--verbose', '-v', action="count", default=0, help='Verbosity')
parser.add_argument('--debug', '-d', action="count", default=0, help='Debug level')
parser.add_argument('--influx_db_name', default="")
parser.add_argument('--influx_db_user', default="")
parser.add_argument('--influx_db_password', default="")
parser.add_argument('--influx_db_host', default="")
parser.add_argument('--influx_db_port', default=8086)
parser.add_argument('--mqtt_user', default="")
parser.add_argument('--mqtt_password', default="")
parser.add_argument('--mqtt_host', default="")
parser.add_argument('--mqtt_port', default=1883)
parser.add_argument('--quiet', '-q' , default=False, action="store_true")
# parser.add_argument(dest='access_token' )
return parser
# reparse args on import
args = parseOptions().parse_args()
|
nilq/baby-python
|
python
|
import mock
import unittest
import mongomock
from core.seen_manager import canonize, SeenManager
from core.metadata import DocumentMetadata
from tests.test_base import BaseTestClass
class TestSeenManager(BaseTestClass):
def test_canonize(self):
input_url = ["http://www.google.com",
"http://www.google.com/",
"https://www.google.com",
"https://www.google.it/",
"https://www.google.it?test=10&q=1",
"https://www.google.it/test/test/",
"https://www.google.it/test/test",
]
output = ["www.google.com",
"www.google.com",
"www.google.com",
"www.google.it",
"www.google.it%3Ftest%3D10%26q%3D1",
"www.google.it/test/test",
"www.google.it/test/test",
]
for i, u in enumerate(input_url):
self.assertEqual(canonize(u), output[i])
@mock.patch('pymongo.MongoClient')
def test_add_and_delete(self, mc):
mc.return_value = mongomock.MongoClient()
sm = SeenManager("test", "host", 0, "db")
dmeta = DocumentMetadata("http://www.google.com")
dmeta.alternatives = [
"http://www.google.com",
"http://www.google2.com/",
"https://www.google3.com",
]
dmeta.dhash = 2413242
other_urls = [
"www.prova.com",
"www.other.com",
]
# adding urls
sm.add(dmeta)
# tring removing not present urls
for o in other_urls:
sm.delete(o)
# checking presence
for i in dmeta.alternatives:
self.assertTrue(canonize(i) in sm.store)
self.assertEqual(len(dmeta.alternatives), len(sm.store))
# checking not precence
for u in other_urls:
self.assertFalse(canonize(u) in sm.store)
# checking correctness
for i in dmeta.alternatives:
data = sm.store.get(canonize(i))
self.assertEqual(data["count"], 1)
self.assertEqual(data["page_hash"], dmeta.dhash)
# deleting alternatives
sm.delete(dmeta.alternatives[0])
# checking empty db
for i in dmeta.alternatives:
self.assertFalse(canonize(i) in sm.store)
self.assertEqual(0, len(sm.store))
@mock.patch('pymongo.MongoClient')
def test_update(self, mc):
mc.return_value = mongomock.MongoClient()
sm = SeenManager("test", "host", 0, "db")
dmeta = DocumentMetadata("http://www.google.com?q=test")
dmeta.alternatives = [
"http://www.google.com?q=test",
"http://www.google2.com/",
"https://www.google3.com",
]
dmeta.dhash = 2413242
dmeta2 = DocumentMetadata("http://www.google2.com")
dmeta2.alternatives = [
"http://www.google2.com",
"https://www.google3.com",
]
dmeta2.dhash = 12121212
# adding urls
sm.add(dmeta)
sm.add(dmeta2)
output_alternatives = dmeta.alternatives + dmeta2.alternatives
output_alternatives = list(set(canonize(i) for i in output_alternatives))
# checking presence and checking not double anonization
for i in output_alternatives:
self.assertTrue(i in sm.store)
for i, v in enumerate(sm.store.get(i)['alternatives']):
self.assertEqual(v, output_alternatives[i])
@mock.patch('pymongo.MongoClient')
def test_is_new(self, mc):
mc.return_value = mongomock.MongoClient()
sm = SeenManager("test", "host", 0, "db")
dmeta = DocumentMetadata("http://www.google.com")
dmeta.alternatives = ["http://www.google.com",
"http://www.google2.com/",
"https://www.google3.com",
]
dmeta.dhash = 2413242
other_urls = [
"www.test.com",
"www.other.com",
]
# adding urls
sm.add(dmeta)
for u in dmeta.alternatives:
self.assertFalse(sm.is_new(canonize(u)))
for u in other_urls:
self.assertTrue(sm.is_new(canonize(u)))
@mock.patch('pymongo.MongoClient')
def test_incr_n(self, mc):
mc.return_value = mongomock.MongoClient()
sm = SeenManager("test", "host", 0, "db")
dmeta = DocumentMetadata("http://www.google.com")
dmeta.alternatives = ["http://www.google.com",
"http://www.google2.com/",
"https://www.google3.com",
]
dmeta.dhash = 2413242
# adding urls
sm.add(dmeta)
# increase counters
sm.incr_n(dmeta.alternatives[0])
for i in dmeta.alternatives:
data = sm.store.get(canonize(i))
self.assertEqual(data["count"], 2)
@mock.patch('pymongo.MongoClient')
def test_is_changed(self, mc):
mc.return_value = mongomock.MongoClient()
sm = SeenManager("test", "host", 0, "db")
dmeta = DocumentMetadata("http://www.google.com")
dmeta.alternatives = ["http://www.google.com",
"http://www.google2.com/",
"https://www.google3.com",
]
dmeta.dhash = 2413242
# adding urls
sm.add(dmeta)
for u in dmeta.alternatives:
self.assertFalse(sm.is_changed(u, dmeta.dhash))
for u in dmeta.alternatives:
self.assertFalse(sm.is_changed(u, dmeta.dhash+2))
for u in dmeta.alternatives:
self.assertTrue(sm.is_changed(u, dmeta.dhash+3))
if __name__ == "__main__":
unittest.main()
|
nilq/baby-python
|
python
|
""" Python generator utilities for DMT """
import shutil
from pathlib import Path
from setuptools import setup, find_packages
here = Path(__file__).parent.resolve()
# Remove build and dist folders
shutil.rmtree(Path("build"), ignore_errors=True)
shutil.rmtree(Path("dist"), ignore_errors=True)
# Get the long description from the README file
long_description = (here / 'README.md').read_text(encoding='utf-8')
with open('requirements.txt',encoding='utf8') as f:
required = f.read().splitlines()
setup(
name='dmtgen',
version='0.2.1',
author="SINTEF Ocean",
description="Python generator utilities for DMT",
long_description=long_description,
long_description_content_type="text/markdown",
package_dir={"": "src"},
packages= find_packages(where="src"),
package_data={'dmt': ['data/system/SIMOS/*.json']},
install_requires=required,
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
python_requires='>=3.8',
)
|
nilq/baby-python
|
python
|
#!/usr/bin/env python
# coding: utf-8
# <!--BOOK_INFORMATION-->
# <img align="left" style="padding-right:10px;" src="images/book_cover.jpg" width="120">
#
# *This notebook contains an excerpt from the [Python Programming and Numerical Methods - A Guide for Engineers and Scientists](https://www.elsevier.com/books/python-programming-and-numerical-methods/kong/978-0-12-819549-9), the content is also available at [Berkeley Python Numerical Methods](https://pythonnumericalmethods.berkeley.edu/notebooks/Index.html).*
#
# *The copyright of the book belongs to Elsevier. We also have this interactive book online for a better learning experience. The code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work on [Elsevier](https://www.elsevier.com/books/python-programming-and-numerical-methods/kong/978-0-12-819549-9) or [Amazon](https://www.amazon.com/Python-Programming-Numerical-Methods-Scientists/dp/0128195495/ref=sr_1_1?dchild=1&keywords=Python+Programming+and+Numerical+Methods+-+A+Guide+for+Engineers+and+Scientists&qid=1604761352&sr=8-1)!*
# <!--NAVIGATION-->
# < [14.2 Linear Transformations](chapter14.02-Linear-Transformations.ipynb) | [Contents](Index.ipynb) | [14.4 Solutions to Systems of Linear Equations](chapter14.04-Solutions-to-Systems-of-Linear-Equations.ipynb) >
# # Systems of Linear Equations
# A $\textbf{linear equation}$ is an equality of the form
# $$
# \sum_{i = 1}^{n} (a_i x_i) = y,
# $$
# where $a_i$ are scalars, $x_i$ are unknown variables in $\mathbb{R}$, and $y$ is a scalar.
#
# **TRY IT!** Determine which of the following equations is linear and which is not. For the ones that are not linear, can you manipulate them so that they are?
#
# 1. $3x_1 + 4x_2 - 3 = -5x_3$
# 2. $\frac{-x_1 + x_2}{x_3} = 2$
# 3. $x_1x_2 + x_3 = 5$
#
# Equation 1 can be rearranged to be $3x_1 + 4x_2 + 5x_3= 3$, which
# clearly has the form of a linear equation. Equation 2 is not linear
# but can be rearranged to be $-x_1 + x_2 - 2x_3 = 0$, which is
# linear. Equation 3 is not linear.
#
# A $\textbf{system of linear equations}$ is a set of linear equations that share the same variables. Consider the following system of linear equations:
#
# \begin{eqnarray*}
# \begin{array}{rcrcccccrcc}
# a_{1,1} x_1 &+& a_{1,2} x_2 &+& {\ldots}& +& a_{1,n-1} x_{n-1} &+&a_{1,n} x_n &=& y_1,\\
# a_{2,1} x_1 &+& a_{2,2} x_2 &+&{\ldots}& +& a_{2,n-1} x_{n-1} &+& a_{2,n} x_n &=& y_2, \\
# &&&&{\ldots} &&{\ldots}&&&& \\
# a_{m-1,1}x_1 &+& a_{m-1,2}x_2&+ &{\ldots}& +& a_{m-1,n-1} x_{n-1} &+& a_{m-1,n} x_n &=& y_{m-1},\\
# a_{m,1} x_1 &+& a_{m,2}x_2 &+ &{\ldots}& +& a_{m,n-1} x_{n-1} &+& a_{m,n} x_n &=& y_{m}.
# \end{array}
# \end{eqnarray*}
#
# where $a_{i,j}$ and $y_i$ are real numbers. The $\textbf{matrix form}$ of a system of linear equations is $\textbf{$Ax = y$}$ where $A$ is a ${m} \times {n}$ matrix, $A(i,j) = a_{i,j}, y$ is a vector in ${\mathbb{R}}^m$, and $x$ is an unknown vector in ${\mathbb{R}}^n$. The matrix form is showing as below:
#
# $$\begin{bmatrix}
# a_{1,1} & a_{1,2} & ... & a_{1,n}\\
# a_{2,1} & a_{2,2} & ... & a_{2,n}\\
# ... & ... & ... & ... \\
# a_{m,1} & a_{m,2} & ... & a_{m,n}
# \end{bmatrix}\left[\begin{array}{c} x_1 \\x_2 \\ ... \\x_n \end{array}\right] =
# \left[\begin{array}{c} y_1 \\y_2 \\ ... \\y_m \end{array}\right]$$
#
# If you carry out the matrix multiplication, you will see that you arrive back at the original system of equations.
#
# **TRY IT!** Put the following system of equations into matrix form.
# \begin{eqnarray*}
# 4x + 3y - 5z &=& 2 \\
# -2x - 4y + 5z &=& 5 \\
# 7x + 8y &=& -3 \\
# x + 2z &=& 1 \\
# 9 + y - 6z &=& 6 \\
# \end{eqnarray*}
#
# $$\begin{bmatrix}
# 4 & 3 & -5\\
# -2 & -4 & 5\\
# 7 & 8 & 0\\
# 1 & 0 & 2\\
# 9 & 1 & -6
# \end{bmatrix}\left[\begin{array}{c} x \\y \\z \end{array}\right] =
# \left[\begin{array}{c} 2 \\5 \\-3 \\1 \\6 \end{array}\right]$$
# <!--NAVIGATION-->
# < [14.2 Linear Transformations](chapter14.02-Linear-Transformations.ipynb) | [Contents](Index.ipynb) | [14.4 Solutions to Systems of Linear Equations](chapter14.04-Solutions-to-Systems-of-Linear-Equations.ipynb) >
|
nilq/baby-python
|
python
|
import markdown
from django.template.loader import render_to_string
from modules.polygon import models
CONTEST_TAG_RE = r'(?i)\[polygon_contest\s+id:(?P<contest_id>\d+)\]'
class ContestExtension(markdown.Extension):
"""Contest plugin markdown extension for SIStema wiki."""
def extendMarkdown(self, md):
md.inlinePatterns.add(
'sistema-polygon-contest',
ContestPattern(CONTEST_TAG_RE, md),
'>link')
class ContestPattern(markdown.inlinepatterns.Pattern):
"""
SIStema wiki polygon tag preprocessor. Searches text for
[polygon_contest id:xxxx] tag and replaces it with the list of problems.
"""
def handleMatch(self, m):
contest_id_str = m.group('contest_id')
contest_id = int(contest_id_str)
contest = models.Contest.objects.filter(polygon_id=contest_id).first()
if contest is None:
return 'Контест с ID {} не существует'.format(contest_id)
html = render_to_string(
"polygon/wiki/problem_list.html",
context={
'problems': contest.get_problems(),
})
return self.markdown.htmlStash.store(html)
def makeExtension(*args, **kwargs):
"""Return an instance of the extension."""
return ContestExtension(*args, **kwargs)
|
nilq/baby-python
|
python
|
########
# PART 1
def get_numbers():
numbers = None
with open("event2019/day16/input.txt", "r") as file:
for line in file:
numbers = list(map(int, line[:-1]))
#numbers = [int(x) for x in line[:-1]]
return numbers
def fft(inp):
''' TODO: optimize with part 2 '''
pattern = [0, 1, 0, -1]
out = inp[:]
for offset in range(len(inp)):
out[offset] = abs(sum([digit * pattern[(1 + inner_offset) // (offset + 1) % 4] for inner_offset, digit in enumerate(inp)])) % 10
return out
def repeat_fft(inp, count):
for _ in range(count):
inp = fft(inp)
return inp
def get_answer(inp):
return ''.join([str(x) for x in inp])[:8]
inp = [int(x) for x in "12345678"]
inp = fft(inp)
assert get_answer(inp) == "48226158"
inp = fft(inp)
assert get_answer(inp) == "34040438"
inp = fft(inp)
assert get_answer(inp) == "03415518"
inp = fft(inp)
assert get_answer(inp) == "01029498"
assert get_answer(repeat_fft(list(map(int, "80871224585914546619083218645595")), 100)) == "24176176"
assert get_answer(repeat_fft(list(map(int, "19617804207202209144916044189917")), 100)) == "73745418"
assert get_answer(repeat_fft(list(map(int, "69317163492948606335995924319873")), 100)) == "52432133"
numbers = get_numbers()
answer = get_answer(repeat_fft(numbers, 100)[:8])
print("Part 1 =", answer)
assert answer == "42945143" # check with accepted answer
########
# PART 2
def repeat_fft_p2(inp, count):
offset = int(get_answer(inp)[:7])
inp = inp * 10000
inp_len = len(inp)
for _ in range(count):
acc = 0
for j in range(inp_len - 1, offset - 1, -1):
acc += inp[j]
inp[j] = acc % 10
return inp[offset : offset + 8]
assert get_answer(repeat_fft_p2(list(map(int, "03036732577212944063491565474664")), 100)) == "84462026"
assert get_answer(repeat_fft_p2(list(map(int, "02935109699940807407585447034323")), 100)) == "78725270"
assert get_answer(repeat_fft_p2(list(map(int, "03081770884921959731165446850517")), 100)) == "53553731"
answer = get_answer(repeat_fft_p2(numbers, 100))
print("Part 2 =", answer)
assert get_answer(answer) == "99974970" # check with accepted answer
|
nilq/baby-python
|
python
|
import streamlit as st
def app():
st.write("# About")
col1, col2, col3 = st.columns([5,2,5])
with col1:
st.image("davide.jpg")
st.write("### Davide Torlo")
st.write("Ricercatore PostDoc all'Università SISSA di Trieste")
st.write("Ideatore principale di concept, metriche e grafici")
st.write("[Website](https://davidetorlo.it/), [Twitter](https://twitter.com/accdavlo)")
with col3:
st.image("fede_new.jpeg")
st.write("### Federico Bianchi")
st.write("Ricercatore PostDoc all'Università Bocconi di Milano")
st.write("Support alla realizzazione della webapp e deploy")
st.write("[Website](https://federicobianchi.io/), [Twitter](https://twitter.com/federicobianchy)")
|
nilq/baby-python
|
python
|
keywords = {
".5" : "FOR[ITERATION]",
"wrap" : "KEYWORD",
"as" : "KEYWORD[ASSIGNMENT]",
"let" : "KEYWORD[RELOP]",
"pp" : "INCRE[RELOP]",
".2" : "IF[CONDITIONAL]",
"vomit" : "KEYWORD[PRINT]",
"|" : "PARANTHESIS",
"--" : "OPEN CONDITION",
"---" : "CLOSE CONDITION",
".3" : "ELSE-IF",
"eq" : "EQ_OPERATOR",
"neq" : "NOT_EQ_OPERATOR",
"let" : "REL_OPERATORS",
"lete" : "REL_OPERATORS",
"get" : "REL_OPERATORS",
"gete" : "REL_OPERATORS",
"goto" : "JUMP_STATEMENTS",
"continue" : "JUMP_STATEMENTS",
"break" : "JUMP_STATEMENTS",
"return" : "JUMP_STATEMENTS",
"$$" : "OR OPERATOR",
"&&" : "AND OPERATOR",
".4" : "ELSE[SELECTION]",
".6" : "WHILE",
".7" : "DO[CONDITION]",
":" : "SEPERATOR",
"True" : "BOOL TRUE",
"False" : "BOOL FALSE",
"exit()" : "EXIT LOOP"
}
'''
FOR[ITERATION] = ".5"
KEYWORD = "wrap"
KEYWORD[ASSIGNMENT] = "as"
KEYWORD[RELOP] = "let"
INCRE[RELOP] = "pp"
IF[CONDITIONAL] = ".2"
KEYWORD[PRINT] = "vomit"
PARANTHESIS = "|"
OPEN_CONDITION = "--"
CLOSE_CONDITION = "---"
ELSE_IF = ".3"
EQ_OPERATOR = "eq"
NOT_EQ_OPERATOR = "neq"
REL_OPERATORS = "let"
REL_OPERATORS = "lete"
REL_OPERATORS = "get"
REL_OPERATORS = "gete"
JUMP_STATEMENTS = "goto"
JUMP_STATEMENTS = "continue"
JUMP_STATEMENTS = "break"
JUMP_STATEMENTS = "return"
OR_OPERATOR = "$$"
AND_OPERATOR = "&&"
ELSE[SELECTION] = ".4"
WHILE = ".6"
DO[CONDITION] = ".7"
SEPERATOR = ":"
'''
DIGITS = "0123456789"
|
nilq/baby-python
|
python
|
"""
JAX DSP utility functions
"""
from functools import partial
import jax
import jax.numpy as jnp
import librosa
from jax.numpy import ndarray
def rolling_window(a: ndarray, window: int, hop_length: int):
"""return a stack of overlap subsequence of an array.
``return jnp.stack( [a[0:10], a[5:15], a[10:20],...], axis=0)``
Source: https://github.com/google/jax/issues/3171
Args:
a (ndarray): input array of shape `[L, ...]`
window (int): length of each subarray (window).
hop_length (int): distance between neighbouring windows.
"""
idx = (
jnp.arange(window)[:, None]
+ jnp.arange((len(a) - window) // hop_length + 1)[None, :] * hop_length
)
return a[idx]
@partial(jax.jit, static_argnums=[1, 2, 3, 4])
def batched_stft(
y: ndarray,
n_fft: int,
hop_length: int,
win_length: int,
window: str,
):
"""Batched version of ``stft`` function.
TN => FTN
"""
assert len(y.shape) >= 2
if window == "hann":
fft_window = jnp.hanning(win_length + 1)[:-1]
else:
raise RuntimeError(f"'{window}' window function is not supported!")
pad_len = (n_fft - win_length) // 2
if pad_len > 0:
fft_window = jnp.pad(fft_window, (pad_len, pad_len), mode="constant")
win_length = n_fft
# center padding
p = n_fft // 2
y = jnp.pad(y, [(p, p), (0, 0)], mode="constant")
# jax does not support ``np.lib.stride_tricks.as_strided`` function
# see https://github.com/google/jax/issues/3171 for comments.
y_frames = rolling_window(y, n_fft, hop_length)
fft_window = jnp.reshape(fft_window, (-1,) + (1,) * (len(y.shape)))
y_frames = y_frames * fft_window
stft_matrix = jnp.fft.fft(y_frames, axis=0)
d = int(1 + n_fft // 2)
return stft_matrix[:d]
class MelFilter:
"""Convert waveform to mel spectrogram."""
def __init__(
self,
sample_rate: int,
n_fft: int,
window_length: int,
hop_length: int,
n_mels: int,
fmin=0.0,
fmax=8000,
mel_min=1e-5,
):
self.melfb = librosa.filters.mel(
sr=sample_rate,
n_fft=n_fft,
n_mels=n_mels,
fmin=fmin,
fmax=fmax,
)
self.n_fft = n_fft
self.window_length = window_length
self.hop_length = hop_length
self.mel_min = mel_min
def __call__(self, y: ndarray) -> ndarray:
hop_length = self.hop_length
window_length = self.window_length
assert len(y.shape) == 2
spec = batched_stft(y.T, self.n_fft, hop_length, window_length, "hann")
mag = jnp.sqrt(jnp.square(spec.real) + jnp.square(spec.imag) + 1e-9)
mel = jnp.einsum("ms,sfn->nfm", self.melfb, mag)
cond = jnp.log(jnp.clip(mel, a_min=self.mel_min, a_max=None))
return cond
|
nilq/baby-python
|
python
|
# -*- encoding: utf-8 -*-
'''
Created on 2012-3-23
@author: Neil
'''
from django.shortcuts import render_to_response
from grnglow.glow.views import people
from grnglow.glow.models.photo import Photo
def base(request):
return render_to_response('base.html')
def index(request):
if request.user.is_authenticated():
# 默认情况下，people.home(request,user_id)的user_id参数应该为字符串
return people.home(request, str(request.user.id)) # 如果已登录，跳转到我的个人页
# return render_to_response('index.html', {'request':request})
else:
photos = Photo.objects.all().order_by('-score')[0:12] # 按得分倒序，最大的排在前面
p_len = len(photos)
p_items = []
for i in range(0, p_len, 6):
p_items.extend([photos[i:i + 6]]) # 在末端添加列表元素
return render_to_response('index.html', {'request': request, 'p_items': p_items})
|
nilq/baby-python
|
python
|
# -*- coding: utf-8 -*-
"""
Created on Fri May 24 15:17:13 2019
@author: DaniJ
"""
'''
It is like the four_layer_model_2try_withFixSpeciesOption_Scaling.py, but with two surfaces. There is not Poisson-Boltzman interaction between the two surfaces.
'''
import numpy as np
from scipy import linalg
def four_layer_two_surface_speciation ( T, X_guess, A, Z, log_k, idx_Aq, pos_psi_S1_vec, pos_psi_S2_vec, temp, sS1, aS1, sS2, aS2, e, CapacitancesS1, CapacitancesS2, idx_fix_species = None, zel=1, tolerance = 1e-6, max_iterations = 100,scalingRC = True):
"""
-The implementation of these algorithm is based on Westall (1980), but slightly modified in order to allow a 4th electrostatic layer and 2 surface which its diffuse layers does not interact
Arguments:
- T A vector needed for creating the residual function for the Newthon-Raphson. The vector has the same size of X_guess and contains values like the total number of moles or mol/L of an aquoeus component
- X_guess A vector containing the initial guesses of the primary aqueous species, primary sorption species, electrostatic species
- A A matrix containing the stoichiometric values of the mass balance parameters
- Z The vector of charge of the different ion. The order is determine by the rows of "A" for aqueous species. That means that it is link to idx_Aq somehow.
- log_k A vector of log(Konstant equilibrium). Primary species of aquoues and sorption have a log_k=0
- idx_Aq An index vector with the different aqueous species position. It must coincide with the rows of "A".
- pos_psi_S1_vec Is a vector that contains the position of the boltzmann factor of each plane for surface 1 such as [pos_boltz0, pos_boltzalpha, pos_boltzbeta, pos_boltzgamma](gamma == diffusive of S1)
- pos_psi_S2_vec It is like pos_psi_S2_vec but for the surface 2
- temp Temperature of the chemical system in Kelvins.
- sS1 is the specific surface area for the surface 1
- aS1 concentration of suspended solid for the surface 1
- sS2 is the specific surface area for the surface 2
- aS2 concentration of suspended solid for the surface 2
- e relative permittivity
- CapacitancesS1 [C1, C2, C3] for surface 1
- CapacitancesS2 [C1, C2, C3] for surface 2
- scalingRC If true a scaling stp will be done if false not scaling step is done (based on Marinoni et al. 2017) [Default = true]
- idx_fix_species Index of the primary species that have a fixed value, it must coincide with X_guess.
Outputs: the outputs right now are:
- C the vector of species concentrations (aqueous and surface species, not electrostatic). The order of the species will depend on the given matrix A, so it is user dependent.
- The vector X of primary unknowns. The value of the primary species of aqueous and surface species should be equivalent to the C vector. Here we can find the values
of the boltzman factors, which are related to psi values.
Preconditions:
1) The order of the rows of matrix "A" must agree with the order of the unknowns in the vector X_guess.
Namely, if the first row correspond to the species "H+", the first unknow in X_guess must be "H+"
This also implies that number of rows of A equals the length of the vector of unknows.
2) Since the order of the species is not known, the positions in the "X_guess" of the electrostatic species
is needed to update vector "T", and also the Jacobian matrix.
3) It is also assumed that T has the same order than X_guess. Namely, if the first components is "H+" in "T",
it should also be in "H+" in "X_guess".
4) log_k is the vector of the logarithm (equilibrium constant). For each species a logK is given, if the species
is a primary species, the value would be zero. The K must be coherent with matrix A.
5) The vectors, and matrix are suppossed to be in a numpy 'format', due to the fact that we are using its libraries, it should be like that.
6) The plane gamma is place at the "same lcation" that the diffusion plane. SO, basically is the same.
"""
counter_iterations = 0
abs_err = tolerance + 1
if idx_fix_species != None:
X_guess [idx_fix_species] = T [idx_fix_species]
while abs_err>tolerance and counter_iterations < max_iterations:
# Calculate Y
[Y, T] = func_NR_FLM (X_guess, A, log_k, temp, idx_Aq, sS1, aS1, sS2, aS2, e, CapacitancesS1, CapacitancesS2, T, Z, zel, pos_psi_S1_vec, pos_psi_S2_vec, idx_fix_species)
# Calculate Z
J = Jacobian_NR_FLM (X_guess, A, log_k, temp, idx_Aq, sS1, aS1, sS2, aS2, e, CapacitancesS1, CapacitancesS2, T, Z, zel, pos_psi_S1_vec, pos_psi_S2_vec, idx_fix_species)
# Calculating the diff, Delta_X
# Scaling technique is the RC technique from "Thermodynamic Equilibrium Solutions Through a Modified Newton Raphson Method"-Marianna Marinoni, Jer^ome Carrayrou, Yann Lucas, and Philippe Ackerer (2016)
if scalingRC == True:
D1 = diagonal_row(J)
D2 = diagonal_col(J)
J_new = np.matmul(D1,np.matmul(J, D2))
Y_new = np.matmul(D1, Y)
delta_X_new = linalg.solve(J_new,-Y_new)
delta_X = np.matmul(D2, delta_X_new)
else:
# Calculating the diff, Delta_X
delta_X = linalg.solve(J,-Y)
#print(delta_X))
# Relaxation factor borrow from Craig M.Bethke to avoid negative values
max_1 = 1
max_2 =np.amax(-2*np.multiply(delta_X, 1/X_guess))
Max_f = np.amax([max_1, max_2])
Del_mul = 1/Max_f
X_guess=X_guess + Del_mul*delta_X
log_C = log_k + np.matmul(A,np.log10(X_guess))
# transf
C = 10**(log_C)
u = np.matmul(A.transpose(),C)
# Vector_error
d = u-T
#print(d)
if idx_fix_species != None:
d[idx_fix_species] =0
abs_err = max(abs(d))
counter_iterations += 1
if counter_iterations >= max_iterations:
raise ValueError('Max number of iterations surpassed.')
# Speciation - mass action law
log_C = log_k + np.matmul(A,np.log10(X_guess))
# transf
C = 10**(log_C)
return X_guess, C
def func_NR_FLM (X, A, log_k, temp, idx_Aq, sS1, aS1, sS2, aS2, e, CapacitancesS1, CapacitancesS2, T, Z, zel, pos_psi_S1_vec, pos_psi_S2_vec, idx_fix_species=None):
"""
This function is supossed to be linked to the four_layer_two_surface_speciation function.
It just gave the evaluated vector of Y, and T for the Newton-raphson procedure.
The formulation of Westall (1980) is followed.
FLM = four layer model
"""
# Speciation - mass action law
log_C = log_k + np.matmul(A,np.log10(X))
# transf
C = 10**(log_C)
# Update T - "Electrostatic parameters"
psi_S1_v = [Boltzman_factor_2_psi(X[pos_psi_S1_vec[0]], temp), Boltzman_factor_2_psi(X[pos_psi_S1_vec[1]], temp), Boltzman_factor_2_psi(X[pos_psi_S1_vec[2]], temp), Boltzman_factor_2_psi(X[pos_psi_S1_vec[3]], temp)]
psi_S2_v = [Boltzman_factor_2_psi(X[pos_psi_S2_vec[0]], temp), Boltzman_factor_2_psi(X[pos_psi_S2_vec[1]], temp), Boltzman_factor_2_psi(X[pos_psi_S2_vec[2]], temp), Boltzman_factor_2_psi(X[pos_psi_S2_vec[3]], temp)]
C_aq = C[idx_Aq]
I = Calculate_ionic_strength(Z, C_aq)
T = Update_T_FLM(T, sS1, sS2, e, I, temp, aS1, aS2, Z,CapacitancesS1, CapacitancesS2, psi_S1_v, psi_S2_v, zel, pos_psi_S1_vec, pos_psi_S2_vec, C_aq)
# Calculation of Y
Y= np.matmul(A.transpose(),C)-T
# fix?
if idx_fix_species != None:
Y[idx_fix_species]=0
return Y,T
def Boltzman_factor_2_psi (x,temp):
'''
Transforms the equation from Xb = exp(-psi*F/RT) to psi = -ln(Xb)RT/F
from Boltzman factor to electrostatic potential
The units of "temp" (short for temperature) should be Kelvin
'''
R = 8.314472 # J/(K*mol)
F = 96485.3328959 # C/mol
D = R*temp
psi = - np.log(x)*(D/F)
return psi
def Calculate_ionic_strength(Z,C):
'''
It is supossed to be numpy format vector
Z is the vector of charge
'''
# Multiplication must be pointwise for the vector
# multiply function of numpy. Multiplies pointwise according to the documentation and own experience.
I = np.matmul(np.multiply(Z,Z),C)
I = I/2
return I
def Update_T_FLM(T, sS1, sS2, e, I, temp, aS1, aS2, Z,CapacitancesS1, CapacitancesS2, psi_S1_v, psi_S2_v, zel, pos_psi_S1_vec, pos_psi_S2_vec, C_aq):
"""
This equation is linked to func_NR_FLM. It updates the values of T for the electrostatic parameters.
- All the arguments of the function have been stated in four_layer_two_surface_speciation function.
"""
# constant
F = 96485.3328959 # C/mol
R = 8.314472 # J/(K*mol)
eo = 8.854187871e-12 # Farrads = F/m - permittivity in vaccuum
#e = 1.602176620898e-19 # C
kb = 1.38064852e-23 # J/K other units --> kb=8,6173303e-5 eV/K
Na = 6.022140857e23 # 1/mol
########## S1 #####################
sigma_S1_0 = CapacitancesS1[0]*(psi_S1_v[0]-psi_S1_v[1])
sigma_S1_alpha = -sigma_S1_0 + CapacitancesS1[1]*(psi_S1_v[1]-psi_S1_v[2])
sigma_S1_beta = -sigma_S1_0-sigma_S1_alpha+CapacitancesS1[2]*(psi_S1_v[2]-psi_S1_v[3])
sigma_S1_gamma = -sigma_S1_0 - sigma_S1_alpha - sigma_S1_beta
# Now the diffusive layer surface potential (sigma_d) is calculated. Using the formula given by Bethke in his book Geochemical Modeling Reactions
sigma_S1_d = np.sqrt(8*1000*R*temp*eo*e*I)*np.sinh((zel*psi_S1_v[3]*F)/(2*R*temp))
# T
T_S1_0 = ((sS1*aS1)/F)*sigma_S1_0; # units mol/L or mol/kg
T_S1_alpha = ((sS1*aS1)/F)*sigma_S1_alpha; # units mol/L or mol/kg
T_S1_beta = ((sS1*aS1)/F)*sigma_S1_beta; # units mol/L or mol/kg
#!! Important!!
#T_gammad = ((s*a)/F)*(-sigma_gamma+sigma_d) # This part should be equal to C[2]*(psi_beta-psi_dorgamma)+sigma_d
T_S1_gammad = ((sS1*aS1)/F)*(sigma_S1_gamma+sigma_S1_d)
########## S2 #####################
sigma_S2_0 = CapacitancesS2[0]*(psi_S2_v[0]-psi_S2_v[1])
sigma_S2_alpha = -sigma_S2_0 + CapacitancesS2[1]*(psi_S2_v[1]-psi_S2_v[2])
sigma_S2_beta = -sigma_S2_0-sigma_S2_alpha+CapacitancesS2[2]*(psi_S2_v[2]-psi_S2_v[3])
sigma_S2_gamma = -sigma_S2_0 - sigma_S2_alpha - sigma_S2_beta
# Now the diffusive layer surface potential (sigma_d) is calculated. Using the formula given by Bethke in his book Geochemical Modeling Reactions
sigma_S2_d = np.sqrt(8*1000*R*temp*eo*e*I)*np.sinh((zel*psi_S2_v[3]*F)/(2*R*temp))
# T
T_S2_0 = ((sS2*aS2)/F)*sigma_S2_0; # units mol/L or mol/kg
T_S2_alpha = ((sS2*aS2)/F)*sigma_S2_alpha; # units mol/L or mol/kg
T_S2_beta = ((sS2*aS2)/F)*sigma_S2_beta; # units mol/L or mol/kg
#!! Important!!
#T_gammad = ((s*a)/F)*(-sigma_gamma+sigma_d) # This part should be equal to C[2]*(psi_beta-psi_dorgamma)+sigma_d
T_S2_gammad = ((sS2*aS2)/F)*(sigma_S2_gamma+sigma_S2_d)
# Now the values must be put in T
T[pos_psi_S1_vec[0]] = T_S1_0
T[pos_psi_S1_vec[1]] = T_S1_alpha
T[pos_psi_S1_vec[2]] = T_S1_beta
T[pos_psi_S1_vec[3]] = T_S1_gammad
T[pos_psi_S2_vec[0]] = T_S2_0
T[pos_psi_S2_vec[1]] = T_S2_alpha
T[pos_psi_S2_vec[2]] = T_S2_beta
T[pos_psi_S2_vec[3]] = T_S2_gammad
return T
def Jacobian_NR_FLM (X, A, log_k, temp, idx_Aq, sS1, aS1, sS2, aS2, e, CapacitancesS1, CapacitancesS2, T, Z, zel, pos_psi_S1_vec, pos_psi_S2_vec, idx_fix_species=None):
'''
This function should give the Jacobian. Here The jacobian is calculated as Westall (1980), except the electrostatic terms that are slightly different.
The reason is because there seems to be some typos in Westall paper.
Also, if idx_fix_species is given then the rows of the unknown will be 1 for the unknown and 0 for the other points.
'''
# constant
F = 96485.3328959 # C/mol [Faraday constant]
R = 8.314472 # J/(K*mol) [universal constant gas]
eo = 8.854187871e-12 # Farrads = F/m - permittivity in vaccuum
# Speciation - mass action law
#log_C = log_k + A*np.log10(X)
log_C = log_k + np.matmul(A,np.log10(X))
# transf
C = 10**(log_C)
C_aq = C[idx_Aq]
I = Calculate_ionic_strength(Z, C_aq)
# instantiate Jacobian
length_X = X.size
Z = np.zeros((length_X,length_X))
# First part is the common of the Jacbian derivation
for i in range(0, length_X):
for j in range(0, length_X):
Z[i,j]= np.matmul(np.multiply(A[:,i], A[:,j]), (C/X[j]))
# Now the electrostatic part must be modified, one question hang on the air:
# Should we check that the electrostatic part is as we expected?
############S1#######################
sa_F2S1 = (sS1*aS1)/(F*F)
C1_sa_F2_RTS1 = sa_F2S1*CapacitancesS1[0]*R*temp
# Assigning in Jacobian (plane 0)
Z[pos_psi_S1_vec[0],pos_psi_S1_vec[0]]=Z[pos_psi_S1_vec[0],pos_psi_S1_vec[0]] + C1_sa_F2_RTS1/X[pos_psi_S1_vec[0]]
Z[pos_psi_S1_vec[0],pos_psi_S1_vec[1]]=Z[pos_psi_S1_vec[0],pos_psi_S1_vec[1]] - C1_sa_F2_RTS1/X[pos_psi_S1_vec[1]]
#### plane alpha
C1C2_sa_F2_RTS1 = sa_F2S1*R*temp*(CapacitancesS1[0]+CapacitancesS1[1])
C2_sa_F2_RTS1 = sa_F2S1*CapacitancesS1[1]*R*temp
# Assigning in Jacobian (plane alpha)
Z[pos_psi_S1_vec[1],pos_psi_S1_vec[0]]=Z[pos_psi_S1_vec[1],pos_psi_S1_vec[0]] - C1_sa_F2_RTS1/X[pos_psi_S1_vec[0]]
Z[pos_psi_S1_vec[1],pos_psi_S1_vec[1]]=Z[pos_psi_S1_vec[1],pos_psi_S1_vec[1]] + C1C2_sa_F2_RTS1/X[pos_psi_S1_vec[1]]
Z[pos_psi_S1_vec[1],pos_psi_S1_vec[2]]= Z[pos_psi_S1_vec[1],pos_psi_S1_vec[2]] - C2_sa_F2_RTS1/X[pos_psi_S1_vec[2]]
#### plane beta
C3C2_sa_F2_RTS1 = sa_F2S1*R*temp*(CapacitancesS1[1]+CapacitancesS1[2])
C3_sa_F2_RTS1 = sa_F2S1*CapacitancesS1[2]*R*temp
# Assigning in Jacobian (plane beta)
Z[pos_psi_S1_vec[2],pos_psi_S1_vec[1]] = Z[pos_psi_S1_vec[2],pos_psi_S1_vec[1]] - C2_sa_F2_RTS1/X[pos_psi_S1_vec[1]]
Z[pos_psi_S1_vec[2], pos_psi_S1_vec[2]] = Z[pos_psi_S1_vec[2],pos_psi_S1_vec[2]] + C3C2_sa_F2_RTS1/X[pos_psi_S1_vec[2]]
Z[pos_psi_S1_vec[2], pos_psi_S1_vec[3]] = Z[pos_psi_S1_vec[2],pos_psi_S1_vec[3]] - C3_sa_F2_RTS1/X[pos_psi_S1_vec[3]]
#### plane gamma [diffusive plane]
Z[pos_psi_S1_vec[3],pos_psi_S1_vec[2]] = Z[pos_psi_S1_vec[3],pos_psi_S1_vec[2]] - C3_sa_F2_RTS1/X[pos_psi_S1_vec[2]]
# d_d plane
psi_d = Boltzman_factor_2_psi(X[pos_psi_S1_vec[3]], temp)
DY_Dpsid = -np.sqrt(8*1000*R*temp*e*eo*I)*np.cosh((zel*F*psi_d)/(2*R*temp))*((zel*F)/(2*R*temp)) - CapacitancesS1[2]
Dpsid_DpsidB = (-R*temp)/(F*X[pos_psi_S1_vec[3]])
Z[pos_psi_S1_vec[3], pos_psi_S1_vec[3]] = Z[pos_psi_S1_vec[3], pos_psi_S1_vec[3]] + (DY_Dpsid*Dpsid_DpsidB*((sS1*aS1)/F))
#(Problably S1 and S2 can be enclosed in a for loop, reducing lines of code. If I have time and will, I will look at it.)
############S1#######################
sa_F2S2 = (sS2*aS2)/(F*F)
C1_sa_F2_RTS2 = sa_F2S2*CapacitancesS2[0]*R*temp
# Assigning in Jacobian (plane 0)
Z[pos_psi_S2_vec[0],pos_psi_S2_vec[0]]=Z[pos_psi_S2_vec[0],pos_psi_S2_vec[0]] + C1_sa_F2_RTS2/X[pos_psi_S2_vec[0]]
Z[pos_psi_S2_vec[0],pos_psi_S2_vec[1]]=Z[pos_psi_S2_vec[0],pos_psi_S2_vec[1]] - C1_sa_F2_RTS2/X[pos_psi_S2_vec[1]]
#### plane alpha
C1C2_sa_F2_RTS2 = sa_F2S2*R*temp*(CapacitancesS2[0]+CapacitancesS2[1])
C2_sa_F2_RTS2 = sa_F2S2*CapacitancesS2[1]*R*temp
# Assigning in Jacobian (plane alpha)
Z[pos_psi_S2_vec[1],pos_psi_S2_vec[0]]=Z[pos_psi_S2_vec[1],pos_psi_S2_vec[0]] - C1_sa_F2_RTS2/X[pos_psi_S2_vec[0]]
Z[pos_psi_S2_vec[1],pos_psi_S2_vec[1]]=Z[pos_psi_S2_vec[1],pos_psi_S2_vec[1]] + C1C2_sa_F2_RTS2/X[pos_psi_S2_vec[1]]
Z[pos_psi_S2_vec[1],pos_psi_S2_vec[2]]= Z[pos_psi_S2_vec[1],pos_psi_S2_vec[2]] - C2_sa_F2_RTS2/X[pos_psi_S2_vec[2]]
#### plane beta
C3C2_sa_F2_RTS2 = sa_F2S2*R*temp*(CapacitancesS2[1]+CapacitancesS2[2])
C3_sa_F2_RTS2 = sa_F2S2*CapacitancesS2[2]*R*temp
# Assigning in Jacobian (plane beta)
Z[pos_psi_S2_vec[2],pos_psi_S2_vec[1]] = Z[pos_psi_S2_vec[2],pos_psi_S2_vec[1]] - C2_sa_F2_RTS2/X[pos_psi_S2_vec[1]]
Z[pos_psi_S2_vec[2], pos_psi_S2_vec[2]] = Z[pos_psi_S2_vec[2],pos_psi_S2_vec[2]] + C3C2_sa_F2_RTS2/X[pos_psi_S2_vec[2]]
Z[pos_psi_S2_vec[2], pos_psi_S2_vec[3]] = Z[pos_psi_S2_vec[2],pos_psi_S2_vec[3]] - C3_sa_F2_RTS2/X[pos_psi_S2_vec[3]]
#### plane gamma [diffusive plane]
Z[pos_psi_S2_vec[3],pos_psi_S2_vec[2]] = Z[pos_psi_S2_vec[3],pos_psi_S2_vec[2]] - C3_sa_F2_RTS2/X[pos_psi_S2_vec[2]]
# d_d plane
psi_dS2 = Boltzman_factor_2_psi(X[pos_psi_S2_vec[3]], temp)
DY_Dpsid = -np.sqrt(8*1000*R*temp*e*eo*I)*np.cosh((zel*F*psi_dS2)/(2*R*temp))*((zel*F)/(2*R*temp)) - CapacitancesS2[2]
Dpsid_DpsidB = (-R*temp)/(F*X[pos_psi_S2_vec[3]])
Z[pos_psi_S2_vec[3], pos_psi_S2_vec[3]] = Z[pos_psi_S2_vec[3], pos_psi_S2_vec[3]] + (DY_Dpsid*Dpsid_DpsidB*((sS2*aS2)/F))
# finally just return Z
if idx_fix_species != None:
for d in idx_fix_species:
v=np.zeros(length_X)
v[d]=1
Z[d,:] = v
return Z
def diagonal_row(J):
num_rows = J.shape[0]
D = np.zeros((num_rows,num_rows))
for i in range(0,num_rows):
D[i,i]=np.sqrt(linalg.norm(J[i,:], np.inf))
return D
def diagonal_col(J):
num_cols = J.shape[1]
D = np.zeros((num_cols,num_cols))
for i in range(0,num_cols):
D[i,i]=np.sqrt(linalg.norm(J[:,i], np.inf))
return D
|
nilq/baby-python
|
python
|
# Step 1 - Gather Data
import pandas as pd
import datetime
import re
import json
import os
import unittest
import time
import sys
# Own Imports
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from deployment.Control_Enactor import Enactor
from deployment.Data_Retreiver import Data_Retreiver
class Controller:
def __init__(self, data_ret, enact, allocation, reset_time=4):
self.data_ret = data_ret
self.enact = enact
self.allocation = allocation
self.reset_time = reset_time
self.latest = "Initilised"
def stop(self):
self.enact.stop()
self.data_ret.stop()
def sort_plan_for_dev_socket(self, dev, limit, forecast, date_time):
# print("Sorting Plan for Device: ", dev, " With Limit: ", str(limit), " and Estiamte: ", str(forecast))
# get latest session value, add to it and then update the plan
AC_Session = self.data_ret.retreive_AC_Session(dev, date_time)
if AC_Session is None:
AC_Session = self.data_ret.retreive_AC_Energy(dev, date_time)
# print("AC Session:",AC_Session)
change = False
# if 1 then make it generous
if limit >= 1:
change = self.enact.enact_socket_plan(dev, 1200000) # 1,200,000 is equivalent to 20kW
return 1200000, change
elif limit <= 0.0:
change = self.enact.enact_socket_plan(dev, 0)
return 0, change
else:
p_available = forecast * limit * 1.1 / 0.017
# print("Power Availalbe", p_available)
change = self.enact.enact_socket_plan(dev, AC_Session + p_available)
return AC_Session + p_available, change
def sort_plan_for_dev_light(self, dev, limit, forecast, date_time):
# print("Sorting Plan for Device: ", dev, " With Limit: ", str(limit), " and Estiamte: ", str(forecast))
# get latest session value, add to it and then update the plan
# Nightlight and Brightlight
BL_Session, NL_Session = self.data_ret.retreive_Light_Session(dev, date_time)
if NL_Session is None:
_, NL_Session = self.data_ret.retreive_Light_Energy(dev, date_time)
if BL_Session is None:
BL_Session, _ = self.data_ret.retreive_Light_Energy(dev, date_time)
# print("Light Sessions: ", BL_Session, NL_Session)
change = False
# if 1 then make it generous
if limit >= 1:
change = self.enact.enact_light_plan(dev, 4320, 4320)
return 4320, 4320, change
elif limit <= 0.0:
change = self.enact.enact_light_plan(dev, 0, 0)
return 0, 0, change
else:
avg_p_cons = self.data_ret.retreive_average_P_lights(dev, date_time)
if avg_p_cons is None:
# print(" avg_p_cons Not found")
avg_p_cons = 5.0
avg_p_cons = avg_p_cons / 60 / 3 # to get minutely values for dimmed
# print("Average P Cons", avg_p_cons)
# Divided by three as the NL is half as bright than the BL
minutes_available = forecast * limit / avg_p_cons * 1.1 # add 10%
# print("Mins Availalbe: ", minutes_available)
change = self.enact.enact_light_plan(dev, BL_Session + minutes_available / 2,
NL_Session + minutes_available)
return BL_Session + minutes_available / 2, NL_Session + minutes_available, change
def sort_lights(self, dev, remaining_energy, date_time):
# Gather How much Energy It would consume
df_dev_sums = self.data_ret.get_total_energy_for_group(self.allocation[dev], date_time)
if df_dev_sums is None:
self.latest = "---- Mini Fault: Group Energy Returned None, Considering No Values so 0"
total_energy_used_f = 0.0
else:
# display(df_dev_sums)
total_energy_used_f = df_dev_sums.sum(axis=1)[0]
dev_info = {}
if total_energy_used_f * 1.2 > remaining_energy:
# Constrain devs and calculate
const_rate = 1.0 # All available
if total_energy_used_f != 0.0:
const_rate = remaining_energy / total_energy_used_f / 1.2
# print("Constraint Rate: " + str(const_rate))
for d in self.allocation[dev]:
# print("-----------------" + d + "------------------")
dev_info[d] = self.sort_plan_for_dev_light(d, const_rate, df_dev_sums[d.lower()].values[0], date_time)
return 0, {"state": "Constrained", "energy_est_used_total":
total_energy_used_f * 1.2, "constraining_factor": const_rate, "device_const": dev_info}
else:
for d in self.allocation[dev]:
# print("-----------------" + d + "------------------")
dev_info[d] = self.sort_plan_for_dev_light(d, 1.0, df_dev_sums[d.lower()].values[0], date_time)
return remaining_energy - total_energy_used_f * 1.2, {"state": "Unconstrained", "energy_est_used_total":
total_energy_used_f, "constraining_factor": 1.0, "device_const": dev_info}
def sort_sockets(self, dev, remaining_energy, date_time):
# Gather How much Energy It would consume
df_dev_sums = self.data_ret.get_total_energy_for_group(self.allocation[dev], date_time)
if df_dev_sums is None:
self.latest = "---- Mini Fault: Group Energy Returned None, Considering No Values so 0"
total_energy_used_f = 0.0
else:
# display(df_dev_sums)
total_energy_used_f = df_dev_sums.sum(axis=1)[0]
dev_info = {}
if total_energy_used_f * 1.2 > remaining_energy:
# Constrain devs and calculate
const_rate = 1.0 # All available
if total_energy_used_f != 0.0:
const_rate = remaining_energy / total_energy_used_f / 1.2
# print("Constraint Rate: " + str(const_rate))
for d in self.allocation[dev]:
# print("-----------------" + d + "------------------")
dev_info[d] = self.sort_plan_for_dev_socket(d, const_rate, df_dev_sums[d.lower()].values[0], date_time)
return 0, {"state": "Constrained", "energy_est_used_total":
total_energy_used_f * 1.2, "constraining_factor": const_rate, "device_const": dev_info}
else:
for d in self.allocation[dev]:
# print("-----------------" + d + "------------------")
dev_info[d] = self.sort_plan_for_dev_socket(d, 1.0, df_dev_sums[d.lower()].values[0], date_time)
return remaining_energy - total_energy_used_f * 1.2, {"state": "Unconstrained", "energy_est_used_total":
total_energy_used_f, "constraining_factor": 1.0, "device_const": dev_info}
def sort_device(self, dev, remaining_energy, date_time):
if "ights" in dev:
return self.sort_lights(dev, remaining_energy, date_time)
else:
return self.sort_sockets(dev, remaining_energy, date_time)
def revert_to_standard(self, latest_ts):
self.latest = "Checking Time for revert \n"
if latest_ts.hour >= self.reset_time - 1 and latest_ts.hour <= self.reset_time + 1:
self.latest = "Within 1 hour range on reset, don't panic yet\n"
else:
self.latest = "Reverting to standard setup as no data is available\n"
decision_summary = {}
for a in self.allocation:
dev_info = {}
for dev in self.allocation[a]:
if "ights" in a:
dev_info[dev] = (4329, 4320, self.enact.enact_light_plan(dev, 4320, 4320))
else:
dev_info[dev] = (1200000, self.enact.enact_socket_plan(dev, 1200000))
decision = {"state": "Unconstrained", "energy_est_used_total":
0, "constraining_factor": 1.0, "device_const": dev_info, "timestamp": latest_ts}
decision_summary[a] = decision
self.latest += "Decisions: "+str(decision_summary)+"\n"
self.data_ret.save_decision(decision_summary)
def do_step(self, latest_ts = datetime.datetime.now()):
df_priority = self.data_ret.retreive_latest_priority(latest_ts)
df_system = self.data_ret.retreive_latest_raw_system_snapshot(latest_ts)
if df_system is None or df_system.isnull().values.any():
self.latest = "---- Mini Fault: Historic Returned None, Waiting..."
self.revert_to_standard(latest_ts)
return None
# When Deciding
# latest_ts = datetime.datetime.now()
self.latest ="Latest Data From: " + str(latest_ts)+"\n"
df_system_for = self.data_ret.retreive_latest_forecast(latest_ts)
if df_system_for is None or df_system_for.isnull().values.any():
self.latest = "---- Mini Fault: Forecast Returned None, Waiting..."
self.revert_to_standard(latest_ts)
return None
system_load = df_system_for["system_load"][0]
if system_load < 0:
system_load = 0
gen_energy = df_system_for["generated_energy"][0]
battery_soc = df_system[df_system['parameter'] == "VenusGX/Dc/Battery/Soc"]["value"].values[0]
self.latest +="-------Energy State--------\n"
remaining_energy = gen_energy - system_load * 1.2 + (
battery_soc - 40.0) * 21.1 * 1000 / 100 * 0.9 # system load + 20%; remaining battery SOC, with 90% gettable at a 21kw battery
self.latest +="Generated energy: " + str(gen_energy)+"\n"
self.latest +="System Load: " + str(system_load)+"\n"
self.latest +="Battery SoC: " + str(battery_soc)+"\n"
self.latest +="Remaining Energy: " + str(remaining_energy)+"\n"
# display(df_priority)
# Get Value pair from Priority:
if df_priority is None:
self.latest +="Priority Returned None, Considering Standard..."+"\n"
prior_values = {0: 'nursery1_lights', 4: 'nursery1_sockets', 1: 'nursery2_lights', 5: 'nursery2_sockets',
3: 'playground_lights', 6: 'playground_sockets'}
else:
prior_values = {}
for label, content in df_priority.items():
if label not in ['id', 'timestamp']:
prior_values[content[0]] = label
self.latest +=str(prior_values)+"\n"
#remaining_energy = 0 # Overwrite for testing
decision_summary = {}
for key in sorted(prior_values.keys()):
self.latest +="------------------------------------------------\n"
self.latest +="For Device: " + prior_values[key] + " with energy avialable: " + str(remaining_energy)+"\n"
remaining_energy, decision = self.sort_device(prior_values[key], remaining_energy, latest_ts)
decision['timestamp'] = str(latest_ts)
decision_summary[prior_values[key]] = decision
self.latest +="Decisions: "+str(decision)+"\n"
self.latest +="Remaining: "+str(remaining_energy)+"\n"
self.latest +="Decision Summary: \n"
self.latest +=str(decision_summary)+"\n"
self.data_ret.save_decision(decision_summary)
def getLatest(self):
return "Controller : "+self.latest
|
nilq/baby-python
|
python
|
# Generated by Django 2.2.6 on 2020-01-18 06:25
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('blog', '0002_auto_20200116_2309'),
]
operations = [
migrations.AddField(
model_name='enroll',
name='outofmid',
field=models.IntegerField(null=True),
),
migrations.AddField(
model_name='enroll',
name='sub1mark',
field=models.IntegerField(null=True),
),
migrations.AddField(
model_name='enroll',
name='sub2mark',
field=models.IntegerField(null=True),
),
migrations.AddField(
model_name='enroll',
name='sub3mark',
field=models.IntegerField(null=True),
),
migrations.AddField(
model_name='enroll',
name='sub4mark',
field=models.IntegerField(null=True),
),
migrations.AddField(
model_name='enroll',
name='sub5mark',
field=models.IntegerField(null=True),
),
migrations.AddField(
model_name='enroll',
name='subject1',
field=models.TextField(max_length=15, null=True),
),
migrations.AddField(
model_name='enroll',
name='subject2',
field=models.TextField(max_length=15, null=True),
),
migrations.AddField(
model_name='enroll',
name='subject3',
field=models.TextField(max_length=15, null=True),
),
migrations.AddField(
model_name='enroll',
name='subject4',
field=models.TextField(max_length=15, null=True),
),
migrations.AddField(
model_name='enroll',
name='subject5',
field=models.TextField(max_length=15, null=True),
),
]
|
nilq/baby-python
|
python
|
# Copyright (c) 2016, Dennis Meuwissen
# All rights reserved.
#
# 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 COPYRIGHT HOLDERS AND CONTRIBUTORS "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 COPYRIGHT OWNER OR CONTRIBUTORS 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.
from typing import Dict, Optional, Tuple
import wx
from renderlib.surface import Surface
from turrican2.graphics import Graphics
from turrican2.level import Level
from turrican2.world import World
from ui.camera import Camera
class EditMode:
def __init__(self, frame):
self._frame = frame
self._mouse_position: Tuple[int, int] = (0, 0)
self._world: Optional[World] = None
self._level: Optional[Level] = None
def mouse_left_down(self, event: wx.MouseEvent):
pass
def mouse_left_up(self, event: wx.MouseEvent):
pass
def mouse_move(self, event: wx.MouseEvent):
pass
def paint(self, surface: Surface, camera: Camera, graphics: Graphics):
pass
def key_char(self, key_code: int):
pass
def level_changed(self):
pass
def undo_restore_item(self, item: Dict):
pass
def undo_store_item(self) -> Dict:
pass
def set_mouse_position(self, position: Tuple[int, int]):
self._mouse_position = position
def set_level(self, world: World, level: Level):
self._world = world
self._level = level
self.level_changed()
@staticmethod
def get_selection_rectangle(start: Tuple[int, int], end: Tuple[int, int]):
x1, y1 = start
x2, y2 = end
if x2 < x1:
x1, x2 = x2, x1
if y2 < y1:
y1, y2 = y2, y1
width = x2 - x1 + 1
height = y2 - y1 + 1
return x1, y1, width, height
|
nilq/baby-python
|
python
|
# Simple XML to CSV
# e.g. for https://ghr.nlm.nih.gov/download/ghr-summaries.xml
# Silas S. Brown 2017 - public domain - no warranty
# Bugs: may not correctly handle descriptions that mix
# tags with inline text on the same level.
# FOR EXPLORATORY USE ONLY.
# Where to find history:
# on GitHub at https://github.com/ssb22/bits-and-bobs
# and on GitLab at https://gitlab.com/ssb22/bits-and-bobs
# and on BitBucket https://bitbucket.org/ssb22/bits-and-bobs
# and at https://gitlab.developers.cam.ac.uk/ssb22/bits-and-bobs
# and in China: https://gitee.com/ssb22/bits-and-bobs
max_chars_per_cell = 80
# set max_chars_per_cell = None for unlimited,
# but note many spreadsheet programs will have problems
import sys, csv
from xml.parsers import expat
items = {}
cursorStack = [(0,0,0,0,0)] # x,y,curDir,maxX,maxY
def inc(x,y,curDir,xToSet,yToSet):
if curDir: return x,yToSet
else: return xToSet,y
def turn(curDir):
if curDir==1: return 0
else: return 1
def StartElementHandler(name,attrs):
x,y,curDir,maxX,maxY = cursorStack[-1]
items[(y,x)] = name
childDir = turn(curDir)
cursorStack.append(inc(x,y,childDir,x+1,y+1) + (childDir,x,y))
def EndElementHandler(name):
_,_,_,cMaxX,cMaxY = cursorStack.pop()
x,y,curDir,maxX,maxY = cursorStack.pop()
cursorStack.append(inc(x,y,curDir,cMaxX+1,cMaxY+1) + (curDir,max(maxX,cMaxX),max(maxY,cMaxY)))
def CharacterDataHandler(data):
x,y,curDir,maxX,maxY = cursorStack.pop()
while data:
data = items.get((y,x),"") + data.replace("\n"," ").replace("\r","")
if max_chars_per_cell: data, dataRest = data[:max_chars_per_cell],data[max_chars_per_cell:]
else: dataRest = ""
if dataRest and len(data.split())>1 and data[-1].split() and dataRest[0].split(): data,dataRest = data.rsplit(None,1)[0],data.rsplit(None,1)[1]+dataRest # word wrap on spaces
items[(y,x)] = data
data = dataRest
if data: y += 1
cursorStack.append((x,y,curDir,max(x,maxX),max(y,maxY)))
parser = expat.ParserCreate()
parser.StartElementHandler = StartElementHandler
parser.EndElementHandler = EndElementHandler
parser.CharacterDataHandler = CharacterDataHandler
parser.Parse(sys.stdin.read(),1)
curX=curY=0 ; curRow = [""]
o = csv.writer(sys.stdout)
for y,x in sorted(items.keys()):
while y > curY:
o.writerow(curRow)
curRow = [""]
curY += 1 ; curX = 0
while x > curX:
curRow.append("")
curX += 1
curRow[-1] = ' '.join(items[(y,x)].split()).encode('utf-8')
o.writerow(curRow)
|
nilq/baby-python
|
python
|
def seat_spec_to_id(seat_spec):
row = 0
for pos in range(7):
if seat_spec[pos] == 'B':
row = row + pow(2,6-pos)
# print("adding row", pow(2,6-pos))
# print("row", row)
col = 0
for pos in range(3):
if seat_spec[7+pos] == 'R':
col = col + pow(2,2-pos)
# print("adding col", pow(2,2-pos))
# print("col", col)
return row * 8 + col
|
nilq/baby-python
|
python
|
import argparse
import math
import sys
def main() -> int:
parser = argparse.ArgumentParser(
description="Utility for generating the pitch register table C source.",
)
parser.add_argument(
'c',
metavar='C_FILE',
type=str,
help='The C file we should generate.',
)
parser.add_argument(
'table_size',
metavar='SIZE',
type=int,
help='The table jump size (64, 128, 256 or 512).',
)
args = parser.parse_args()
if args.table_size == 64:
TABLE_JUMP_SIZE = 64
INDEX_SHIFT = 6
FRAC_MASK = 0x3F
elif args.table_size == 128:
TABLE_JUMP_SIZE = 128
INDEX_SHIFT = 7
FRAC_MASK = 0x7F
elif args.table_size == 256:
TABLE_JUMP_SIZE = 256
INDEX_SHIFT = 8
FRAC_MASK = 0xFF
elif args.table_size == 512:
TABLE_JUMP_SIZE = 512
INDEX_SHIFT = 9
FRAC_MASK = 0x1FF
else:
print("Invalid table size selection!", file=sys.stderr)
return 1
IMPORTANT_FREQUENCIES = {8000, 11025, 16000, 22050, 32000, 44100, 48000, 88200, 96000}
# Actual cents calculation.
def cents(x: int) -> int:
return int(1200 * math.log2(x / 44100))
# Start with frequency "0", since this is invalid in a log2.
table = [0]
for i in range(TABLE_JUMP_SIZE, 96000 + (2 * TABLE_JUMP_SIZE), TABLE_JUMP_SIZE):
table.append(cents(i))
# Define the approx function.
def centsapprox(x: int) -> int:
index = x >> INDEX_SHIFT
low = table[index]
high = table[index + 1]
return low + (((high - low) * (x & FRAC_MASK)) >> INDEX_SHIFT)
# Now, calculate error
totalerror = 0
worsterror = 0
for i in range(8000, 96001):
error = cents(i) - centsapprox(i)
totalerror += abs(error)
worsterror = max(abs(error), worsterror)
if i in IMPORTANT_FREQUENCIES and error != 0:
print(f"Frequency {i} has error {error}!")
print(f"Total memory is {len(table) * 2} bytes")
print(f"Total error is {totalerror} cents")
print(f"Worst error is {worsterror} cents")
# Now, calculate cent translation table.
fns = [round(((2 ** (i / 1200)) - 1) * 2**10) for i in range(1200)]
# Now, generate a header file for this
print(f"Generating {args.c} with LUT step size {args.table_size}.")
with open(args.c, "w") as fp:
def p(s: str) -> None:
print(s, file=fp)
# Solely for alignment reasons.
def p_(s: str) -> None:
p(s)
p_("#include <stdint.h>")
p_("")
p(f"int16_t centtable[{len(table)}] = {{")
for chunk in [table[x:(x + 16)] for x in range(0, len(table), 16)]:
p_(" " + ", ".join([str(x) for x in chunk]) + ", ")
p_("};")
p_("")
p(f"uint16_t fnstable[{len(fns)}] = {{")
for chunk in [fns[x:(x + 16)] for x in range(0, len(fns), 16)]:
p_(" " + ", ".join([str(x) for x in chunk]) + ", ")
p_("};")
p_("")
p_("uint32_t pitch_reg(unsigned int samplerate)")
p_("{")
p_(" // Calculate cents difference from 44100.")
p(f" unsigned int index = samplerate >> {INDEX_SHIFT};")
p_(" int low = centtable[index];")
p_(" int high = centtable[index + 1];")
p(f" int cents = low + (((high - low) * (samplerate & {FRAC_MASK})) >> {INDEX_SHIFT});")
p_("")
p_(" // Calcualte octaves from cents.")
p_(" int octave = 0;")
p_(" while (cents < 0)")
p_(" {")
p_(" cents += 1200;")
p_(" octave -= 1;")
p_(" }")
p_(" while (cents >= 1200)")
p_(" {")
p_(" cents -= 1200;")
p_(" octave += 1;")
p_(" }")
p_("")
p_(" // Finally, generate the register contents.")
p_(" return ((octave & 0xF) << 11) | fnstable[cents];")
p_("}")
return 0
if __name__ == "__main__":
sys.exit(main())
|
nilq/baby-python
|
python
|
from llvmlite import ir as lir
import llvmlite.binding as ll
import numba
import hpat
from hpat.utils import debug_prints
from numba import types
from numba.typing.templates import (infer_global, AbstractTemplate, infer,
signature, AttributeTemplate, infer_getattr, bound_function)
from numba.extending import (typeof_impl, type_callable, models, register_model,
make_attribute_wrapper, lower_builtin, box, lower_getattr)
from numba import cgutils, utils
from numba.targets.arrayobj import _empty_nd_impl
from numba.targets.imputils import impl_ret_new_ref, impl_ret_borrowed
class MultinomialNB(object):
def __init__(self, nclasses=-1):
self.n_classes = nclasses
return
class MultinomialNBType(types.Type):
def __init__(self):
super(MultinomialNBType, self).__init__(
name='MultinomialNBType()')
mnb_type = MultinomialNBType()
class MultinomialNBPayloadType(types.Type):
def __init__(self):
super(MultinomialNBPayloadType, self).__init__(
name='MultinomialNBPayloadType()')
@typeof_impl.register(MultinomialNB)
def typeof_mnb_val(val, c):
return mnb_type
# @type_callable(MultinomialNB)
# def type_mnb_call(context):
# def typer(nclasses = None):
# return mnb_type
# return typer
# dummy function providing pysignature for MultinomialNB()
def MultinomialNB_dummy(n_classes=-1):
return 1
@infer_global(MultinomialNB)
class MultinomialNBConstructorInfer(AbstractTemplate):
def generic(self, args, kws):
sig = signature(mnb_type, types.intp)
pysig = utils.pysignature(MultinomialNB_dummy)
sig.pysig = pysig
return sig
@register_model(MultinomialNBType)
class MultinomialNBDataModel(models.StructModel):
def __init__(self, dmm, fe_type):
dtype = MultinomialNBPayloadType()
members = [
('meminfo', types.MemInfoPointer(dtype)),
]
models.StructModel.__init__(self, dmm, fe_type, members)
@register_model(MultinomialNBPayloadType)
class MultinomialNBPayloadDataModel(models.StructModel):
def __init__(self, dmm, fe_type):
members = [
('model', types.Opaque('daal_model')),
('n_classes', types.intp),
]
models.StructModel.__init__(self, dmm, fe_type, members)
@infer_getattr
class MultinomialNBAttribute(AttributeTemplate):
key = MultinomialNBType
@bound_function("mnb.train")
def resolve_train(self, dict, args, kws):
assert not kws
assert len(args) == 2
return signature(types.none, *args)
@bound_function("mnb.predict")
def resolve_predict(self, dict, args, kws):
assert not kws
assert len(args) == 1
return signature(types.Array(types.int32, 1, 'C'), *args)
try:
import daal_wrapper
ll.add_symbol('mnb_train', daal_wrapper.mnb_train)
ll.add_symbol('mnb_predict', daal_wrapper.mnb_predict)
ll.add_symbol('dtor_mnb', daal_wrapper.dtor_mnb)
except ImportError:
if debug_prints(): # pragma: no cover
print("daal import error")
@lower_builtin(MultinomialNB, types.intp)
def impl_mnb_constructor(context, builder, sig, args):
dtype = MultinomialNBPayloadType()
alloc_type = context.get_data_type(dtype)
alloc_size = context.get_abi_sizeof(alloc_type)
llvoidptr = context.get_value_type(types.voidptr)
llsize = context.get_value_type(types.uintp)
dtor_ftype = lir.FunctionType(lir.VoidType(),
[llvoidptr, llsize, llvoidptr])
dtor_fn = builder.module.get_or_insert_function(dtor_ftype, name="dtor_mnb")
meminfo = context.nrt.meminfo_alloc_dtor(
builder,
context.get_constant(types.uintp, alloc_size),
dtor_fn,
)
data_pointer = context.nrt.meminfo_data(builder, meminfo)
data_pointer = builder.bitcast(data_pointer,
alloc_type.as_pointer())
mnb_payload = cgutils.create_struct_proxy(dtype)(context, builder)
mnb_payload.n_classes = args[0]
builder.store(mnb_payload._getvalue(),
data_pointer)
mnb_struct = cgutils.create_struct_proxy(mnb_type)(context, builder)
mnb_struct.meminfo = meminfo
return mnb_struct._getvalue()
@lower_builtin("mnb.train", mnb_type, types.Array, types.Array)
def mnb_train_impl(context, builder, sig, args):
X = context.make_array(sig.args[1])(context, builder, args[1])
y = context.make_array(sig.args[2])(context, builder, args[2])
zero = context.get_constant(types.intp, 0)
one = context.get_constant(types.intp, 1)
# num_features = builder.load(builder.gep(X.shape, [one]))
# num_samples = builder.load(builder.gep(X.shape, [zero]))
num_features = builder.extract_value(X.shape, 1)
num_samples = builder.extract_value(X.shape, 0)
# num_features, num_samples, X, y
arg_typs = [lir.IntType(64), lir.IntType(64),
lir.IntType(32).as_pointer(), lir.IntType(32).as_pointer(),
lir.IntType(64).as_pointer()]
fnty = lir.FunctionType(lir.IntType(8).as_pointer(), arg_typs)
fn = builder.module.get_or_insert_function(fnty, name="mnb_train")
dtype = MultinomialNBPayloadType()
inst_struct = context.make_helper(builder, mnb_type, args[0])
data_pointer = context.nrt.meminfo_data(builder, inst_struct.meminfo)
data_pointer = builder.bitcast(data_pointer,
context.get_data_type(dtype).as_pointer())
mnb_struct = cgutils.create_struct_proxy(dtype)(context, builder, builder.load(data_pointer))
call_args = [num_features, num_samples, X.data, y.data,
mnb_struct._get_ptr_by_name('n_classes')]
model = builder.call(fn, call_args)
mnb_struct.model = model
builder.store(mnb_struct._getvalue(), data_pointer)
return context.get_dummy_value()
@lower_builtin("mnb.predict", mnb_type, types.Array)
def mnb_predict_impl(context, builder, sig, args):
dtype = MultinomialNBPayloadType()
inst_struct = context.make_helper(builder, mnb_type, args[0])
data_pointer = context.nrt.meminfo_data(builder, inst_struct.meminfo)
data_pointer = builder.bitcast(data_pointer,
context.get_data_type(dtype).as_pointer())
mnb_struct = cgutils.create_struct_proxy(dtype)(context, builder, builder.load(data_pointer))
p = context.make_array(sig.args[1])(context, builder, args[1])
num_features = builder.extract_value(p.shape, 1)
num_samples = builder.extract_value(p.shape, 0)
ret_arr = _empty_nd_impl(context, builder, sig.return_type, [num_samples])
call_args = [mnb_struct.model, num_features, num_samples, p.data, ret_arr.data, mnb_struct.n_classes]
# model, num_features, num_samples, p, ret
arg_typs = [lir.IntType(8).as_pointer(), lir.IntType(64), lir.IntType(64),
lir.IntType(32).as_pointer(), lir.IntType(32).as_pointer(),
lir.IntType(64)]
fnty = lir.FunctionType(lir.VoidType(), arg_typs)
fn = builder.module.get_or_insert_function(fnty, name="mnb_predict")
builder.call(fn, call_args)
return impl_ret_new_ref(context, builder, sig.return_type, ret_arr._getvalue())
|
nilq/baby-python
|
python
|
from py.webSocketParser import SurveyTypes
neo = [
{"sigma_tp": 7.2258e-06, "diameter": 16.84, "epoch_mjd": 56800.0, "ad": 1.782556743092633, "producer": "Otto Matic", "rms": 0.49521, "H_sigma": "", "closeness": 3366.5887401966647, "spec_B": "S", "K2": "", "K1": "", "M1": "", "two_body": "", "full_name": "433 Eros (1898 DQ)", "M2": "", "sigma_per": 1.5563e-07, "equinox": "J2000", "DT": "", "diameter_sigma": 0.06, "saved": -49024112093511.164, "albedo": 0.25, "moid_ld": 57.95363972, "pha": "N", "neo": "Y", "sigma_ad": 2.8762e-10, "PC": "", "profit": 1.0778633100953429e-42, "spkid": 2000433.0, "sigma_w": 7.721e-06, "sigma_i": 2.5015e-06, "per": 643.0120278650012, "id": "a0000433", "A1": "", "data_arc": 18507.0, "A3": "", "score": 1.3376292522104002e-53, "per_y": 1.7604709866256, "sigma_n": 1.355e-10, "epoch_cal": 20140523.0, "orbit_id": "JPL 436", "sigma_a": 2.3525e-10, "sigma_om": 5.6736e-06, "A2": "", "sigma_e": 1.0576e-08, "condition_code": 0.0, "rot_per": 5.27, "prov_des": "1898 DQ", "G": 0.46, "last_obs": "2014-03-16", "H": 11.16, "price": 6.688146261052001e-42, "IR": "", "spec_T": "S", "epoch": 2456800.5, "n_obs_used": 5043.0, "moid": 0.148916, "extent": "34.4x11.2x11.2", "dv": 6.112479, "e": 0.2226333844057514, "GM": 0.0004463, "tp_cal": 20131021.652388, "pdes": 433.0, "class": "AMO", "UB": 0.531, "a": 1.457965049726682, "t_jup": 4.583, "om": 304.3352604155472, "ma": 119.4458843601074, "name": "Eros", "i": 10.82897927365984, "tp": 2456587.152387993, "prefix": "", "BV": 0.921, "spec": "S", "q": 1.133373356360731, "w": 178.7833320468003, "n": 0.5598651104479512, "sigma_ma": 4.0456e-06, "first_obs": "1963-07-15", "n_del_obs_used": 1.0, "sigma_q": 1.5477e-08, "n_dop_obs_used": 3.0},
{"sigma_tp": 8.0161e-06, "diameter": "", "sigma_q": 9.4916e-08, "epoch_mjd": 56800.0, "ad": 4.080921984113118, "producer": "Otto Matic", "rms": 0.4505, "H_sigma": "", "closeness": 2749.4040311878002, "spec_B": "S", "K2": "", "K1": "", "M1": "", "two_body": "", "full_name": "719 Albert (1911 MT)", "M2": "", "sigma_per": 5.4956e-06, "equinox": "J2000", "DT": "", "diameter_sigma": "", "saved": -30228733726401.28, "albedo": "", "moid_ld": 72.2533022, "pha": "N", "neo": "Y", "sigma_ad": 9.5983e-09, "PC": "", "profit": 4.3222880172840865e-43, "est_diameter": 2.854166808844959, "sigma_w": 2.0359e-05, "sigma_i": 6.2953e-06, "per": 1557.735319192702, "id": "a0000719", "A1": "", "data_arc": 37161.0, "A3": "", "score": 8.247949175007173e-54, "per_y": 4.26484686979521, "sigma_n": 8.1533e-10, "epoch_cal": 20140523.0, "orbit_id": "JPL 55", "sigma_a": 6.1853e-09, "sigma_om": 1.8824e-05, "A2": "", "sigma_e": 3.5608e-08, "condition_code": 0.0, "rot_per": 5.801, "prov_des": "1911 MT", "G": "", "last_obs": "2013-07-01", "H": 15.4, "price": 4.123974587503586e-42, "IR": "", "spec_T": "", "epoch": 2456800.5, "n_obs_used": 1027.0, "moid": 0.18566, "extent": "", "dv": 7.675843, "e": 0.551772789828196, "GM": "", "tp_cal": 20140617.1343186, "pdes": 719.0, "class": "AMO", "UB": "", "a": 2.629845046171313, "t_jup": 3.14, "om": 184.0620457491692, "ma": 354.1913400828157, "name": "Albert", "i": 11.55289382592962, "tp": 2456825.6343186395, "prefix": "", "BV": "", "spec": "S", "q": 1.178768108229506, "w": 155.7926293702832, "n": 0.2311047297730723, "sigma_ma": 1.8393e-06, "first_obs": "1911-10-04", "n_del_obs_used": "", "spkid": 2000719.0, "n_dop_obs_used": ""},
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{"sigma_tp": 26.89, "diameter": "", "sigma_q": 0.016433, "epoch_mjd": 56800.0, "ad": 49.53596334246939, "producer": "Otto Matic", "rms": 0.30011, "H_sigma": "", "closeness": 2601.26820844047, "spec_B": "", "K2": "", "K1": "", "M1": "", "two_body": "", "full_name": "137294 (1999 RE215)", "M2": "", "sigma_per": 44.913, "equinox": "J2000", "DT": "", "diameter_sigma": "", "saved": -3.7023771749632307e+18, "albedo": "", "moid_ld": 15230.518203, "pha": "N", "neo": "N", "sigma_ad": 0.013522, "PC": "", "profit": 0.0, "est_diameter": 156.84813222680864, "sigma_w": 0.13157, "sigma_i": 0.00082619, "per": 109685.6812772649, "id": "a0137294", "A1": "", "data_arc": 2850.0, "A3": "", "score": 0.0, "per_y": 300.30302882208, "sigma_n": 1.3439e-06, "epoch_cal": 20140523.0, "orbit_id": "JPL 1", "sigma_a": 0.012241, "sigma_om": 0.003769, "A2": "", "sigma_e": 0.00036189, "condition_code": 3.0, "rot_per": "", "prov_des": "1999 RE215", "G": "", "last_obs": "2007-06-27", "H": 6.7, "price": 0.0, "IR": "", "spec_T": "", "epoch": 2456800.5, "n_obs_used": 27.0, "moid": 39.1359, "extent": "", "dv": 12.194585, "e": 0.1046370523516791, "GM": "", "tp_cal": 19530329.2076341, "pdes": 137294.0, "class": "TNO", "UB": "", "a": 44.84365542239553, "t_jup": 5.954, "om": 149.2854499742114, "ma": 73.30514938760786, "name": "", "i": 1.350732937950164, "tp": 2434465.7076341347, "prefix": "", "BV": "", "spec": "?", "q": 40.15134750232167, "w": 112.5831767051867, "n": 0.003282105702475305, "sigma_ma": 0.11372, "first_obs": "1999-09-07", "n_del_obs_used": "", "spkid": 2137294.0, "n_dop_obs_used": ""},
{"sigma_tp": 1.5459, "diameter": "", "sigma_q": 0.0013637, "epoch_mjd": 56800.0, "ad": 61.54003595971932, "producer": "Otto Matic", "rms": 0.63875, "H_sigma": "", "closeness": -1, "spec_B": "", "K2": "", "K1": "", "M1": "", "two_body": "", "full_name": "137295 (1999 RB216)", "M2": "", "sigma_per": 42.356, "equinox": "J2000", "DT": "", "diameter_sigma": "", "saved": -1.6161462537960707e+18, "albedo": "", "moid_ld": 12706.945338, "pha": "N", "neo": "N", "sigma_ad": 0.014491, "PC": "", "profit": -0.0, "est_diameter": 118.98147579246931, "sigma_w": 0.008985, "sigma_i": 0.00011487, "per": 119920.1584043674, "id": "a0137295", "A1": "", "data_arc": 4053.0, "A3": "", "score": 0.0, "per_y": 328.323500080404, "sigma_n": 1.0603e-06, "epoch_cal": 20140523.0, "orbit_id": "JPL 14", "sigma_a": 0.011206, "sigma_om": 0.00013829, "A2": "", "sigma_e": 0.00013894, "condition_code": 3.0, "rot_per": "", "prov_des": "1999 RB216", "G": "", "last_obs": "2010-10-13", "H": 7.3, "price": 0.0, "IR": "", "spec_T": "", "epoch": 2456800.5, "n_obs_used": 87.0, "moid": 32.6514, "extent": "", "dv": 12.07207, "e": 0.2930894485240695, "GM": "", "tp_cal": 20140718.4392828, "pdes": 137295.0, "class": "TNO", "UB": "", "a": 47.59147639009894, "t_jup": 5.751, "om": 175.7553425484363, "ma": 359.8305694216822, "name": "", "i": 12.69875842879368, "tp": 2456856.939282751, "prefix": "", "BV": "", "spec": "?", "q": 33.64291682047857, "w": 209.1118718292492, "n": 0.003001997368833438, "sigma_ma": 0.0046806, "first_obs": "1999-09-08", "n_del_obs_used": "", "spkid": 2137295.0, "n_dop_obs_used": ""},
{"sigma_tp": 48.329, "diameter": "", "sigma_q": 0.011405, "epoch_mjd": 56800.0, "ad": 52.82325846250544, "producer": "Otto Matic", "rms": 0.26192, "H_sigma": "", "closeness": -1, "spec_B": "", "K2": "", "K1": "", "M1": "", "two_body": "", "full_name": "138537 (2000 OK67)", "M2": "", "sigma_per": 54.671, "equinox": "J2000", "DT": "", "diameter_sigma": "", "saved": -1.118100031910736e+19, "albedo": "", "moid_ld": 15180.276356, "pha": "N", "neo": "N", "sigma_ad": 0.016668, "PC": "", "profit": -0.0, "est_diameter": 226.71452828784518, "sigma_w": 0.2076, "sigma_i": 0.00066326, "per": 115506.9993857952, "id": "a0138537", "A1": "", "data_arc": 2249.0, "A3": "", "score": 0.0, "per_y": 316.240929187667, "sigma_n": 1.4752e-06, "epoch_cal": 20140523.0, "orbit_id": "JPL 9", "sigma_a": 0.014646, "sigma_om": 0.0045743, "A2": "", "sigma_e": 0.00020721, "condition_code": 3.0, "rot_per": "", "prov_des": "2000 OK67", "G": "", "last_obs": "2006-09-25", "H": 5.9, "price": 0.0, "IR": "", "spec_T": "", "epoch": 2456800.5, "n_obs_used": 38.0, "moid": 39.0068, "extent": "", "dv": 12.131022, "e": 0.1380253533971713, "GM": "", "tp_cal": 20230720.2689284, "pdes": 138537.0, "class": "TNO", "UB": "", "a": 46.41659195449409, "t_jup": 6.007, "om": 4.354318904116862, "ma": 349.5738195900083, "name": "", "i": 4.894287678832714, "tp": 2460145.7689283695, "prefix": "", "BV": "", "spec": "?", "q": 40.00992544648274, "w": 0.07947902289201168, "n": 0.003116694242896869, "sigma_ma": 0.15367, "first_obs": "2000-07-29", "n_del_obs_used": "", "spkid": 2138537.0, "n_dop_obs_used": ""},
{"sigma_tp": 7.3651, "diameter": "", "sigma_q": 0.0069829, "epoch_mjd": 56800.0, "ad": 56.55774746638873, "producer": "Otto Matic", "rms": 0.62523, "H_sigma": "", "closeness": -1, "spec_B": "", "K2": "", "K1": "", "M1": "", "two_body": "", "full_name": "138628 (2000 QM251)", "M2": "", "sigma_per": 60.339, "equinox": "J2000", "DT": "", "diameter_sigma": "", "saved": -2.1304956895593636e+18, "albedo": "", "moid_ld": 12393.935907, "pha": "N", "neo": "N", "sigma_ad": 0.020842, "PC": "", "profit": -0.0, "est_diameter": 130.4605939513808, "sigma_w": 0.050873, "sigma_i": 0.00049098, "per": 109159.1049910758, "id": "a0138628", "A1": "", "data_arc": 2937.0, "A3": "", "score": 0.0, "per_y": 298.861341522453, "sigma_n": 1.823e-06, "epoch_cal": 20140523.0, "orbit_id": "JPL 10", "sigma_a": 0.016472, "sigma_om": 0.0002433, "A2": "", "sigma_e": 0.00020859, "condition_code": 4.0, "rot_per": "", "prov_des": "2000 QM251", "G": "", "last_obs": "2008-09-09", "H": 7.1, "price": 0.0, "IR": "", "spec_T": "", "epoch": 2456800.5, "n_obs_used": 30.0, "moid": 31.8471, "extent": "", "dv": 12.444162, "e": 0.2652734980145274, "GM": "", "tp_cal": 19780719.5201888, "pdes": 138628.0, "class": "TNO", "UB": "", "a": 44.70001747064124, "t_jup": 5.557, "om": 355.6462318189672, "ma": 43.17489349531562, "name": "", "i": 15.72949867664295, "tp": 2443709.0201887954, "prefix": "", "BV": "", "spec": "?", "q": 32.84228747489374, "w": 313.8483950701307, "n": 0.003297938362809328, "sigma_ma": 0.045359, "first_obs": "2000-08-25", "n_del_obs_used": "", "spkid": 2138628.0, "n_dop_obs_used": ""}
]
def getAsteroidSurvey(survey):
if survey == SurveyTypes.neo:
return neo
elif survey == SurveyTypes.main_belt:
return mainBelt
elif survey == SurveyTypes.kuiper_belt:
return kuiperBelt
|
nilq/baby-python
|
python
|
#
# This script should be sourced after slicerqt.py
#
def tcl(cmd):
global _tpycl
try:
_tpycl
except NameError:
# no tcl yet, so first bring in the adapters, then the actual code
import tpycl
_tpycl = tpycl.tpycl()
packages = ['freesurfer', 'mrml', 'mrmlLogic', 'teem', 'vtk', 'vtkITK']
for p in packages:
_tpycl.py_package(p)
import os
tcl_dir = os.path.dirname(os.path.realpath(__file__)) + '/tcl/'
tcl_dir = tcl_dir.replace('\\','/')
_tpycl.tcl_eval("""
set dir \"%s\"
source $dir/Slicer3Adapters.tcl
::Slicer3Adapters::Initialize
""" % tcl_dir)
return _tpycl.tcl_eval(cmd)
class _sliceWidget(object):
""" an empty class that can be instanced as a place to store
references to sliceWidget components
"""
def __init__(self):
pass
if __name__ == "__main__":
# Initialize global slicer.sliceWidgets dict
# -- it gets populated in qSlicerLayoutManagerPrivate::createSliceView
# and then used by the scripted code that needs to access the slice views
slicer.sliceWidgets = {}
|
nilq/baby-python
|
python
|
import os
import shutil
import sys
from pathlib import Path
from subprocess import Popen
from cmd.Tasks.Task import Task
from cmd.Tasks.Tasks import Tasks
from cmd.Tasks.Build.manifest_config import manifest_config
import json
class Build(Task):
NAME = Tasks.BUILD
def __build_app(self):
print('****')
print('**** BUILD APP : ' + self.package.name())
print('****')
if not self.package.config().has_builder():
raise KeyError('No builder found into `hotballoon-shed` configuration')
production_builder: Path = Path(os.path.dirname(
os.path.realpath(__file__)) + '/../../../build/' + self.package.config().builder() + '/production.js')
production_builder.resolve()
if not production_builder.is_file():
raise FileNotFoundError('No builder file found for this builder : ' + self.package.config().builder())
if not self.package.config().has_build_output():
raise KeyError('No path for build found into `hotballoon-shed` configuration')
verbose: str = '-v' if self.options.debug else ''
inspect: str = '1' if self.options.inspect else '0'
if self.package.config().has_application():
manifest_config.update(self.package.config().application())
html_template: Path = self.__resolve_html_template()
child: Popen = self.exec([
'node',
production_builder.as_posix(),
verbose,
','.join([v.as_posix() for v in self.package.config().build_entries()]),
html_template.as_posix(),
self.package.config().build_output(),
json.dumps(manifest_config),
inspect
])
code = child.returncode
if code != 0:
sys.stderr.write("BUILD APP FAIL" + "\n")
raise ChildProcessError(code)
def __resolve_html_template(self) -> Path:
if self.package.config().has_build_html_template_name():
return self.__tempate_path_for(self.package.config().build_html_template_name())
elif self.package.config().has_build_html_template():
return self.package.config().build_html_template()
else:
return self.__tempate_path_for('minimal')
def __tempate_path_for(self, name: str) -> Path:
template_html: Path = Path(os.path.dirname(
os.path.realpath(__file__)) + '/../../../build/html/' + name + '/index.html')
template_html.resolve()
if not template_html.is_file():
raise FileNotFoundError('No html template found for : ' + name)
return template_html
def __build_bundle(self):
print('****')
print('**** BUILD LIB BUNDLE : ' + self.package.name())
print('****')
lib_builder: Path = Path(os.path.dirname(
os.path.realpath(__file__)) + '/../../../build/' + self.package.config().builder() + '/lib.js')
lib_builder.resolve()
if not lib_builder.is_file():
raise FileNotFoundError('No builder file found for this builder : ' + self.package.config().builder())
if not self.package.config().has_build_output():
raise KeyError('No path for build found into `hotballoon-shed` configuration')
verbose: str = '-v' if self.options.debug else ''
html_template: Path = self.__resolve_html_template()
child2: Popen = self.exec([
'node',
lib_builder.as_posix(),
verbose,
','.join([v.as_posix() for v in self.package.config().build_entries()]),
html_template.as_posix(),
self.package.config().build_output()
])
code = child2.returncode
if code != 0:
sys.stderr.write("BUILD LIB BUNDLE FAIL" + "\n")
raise ChildProcessError(code)
def process(self):
self.__build_app()
if self.options.bundle:
self.__build_bundle()
else:
if self.options.debug:
print('No bundle build required')
|
nilq/baby-python
|
python
|
from django.apps import AppConfig
class Sql3Config(AppConfig):
name = 'SQL3'
|
nilq/baby-python
|
python
|
# Testing environment setting
|
nilq/baby-python
|
python
|
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 3 07:06:58 2019
@author: astar
"""
import random
import math
import matplotlib.pyplot as plt
from time import clock
class Ant:
def __init__(self, map_):
self.map = map_
self.path = []
self.path_length = math.inf
def run(self):
self.path = []
pheromone_map = [self.map.pheromones[row][:] for row in range(len(self.map.pheromones))]
current_node = random.choice(range(len(self.map.nodes)))
self.path.append(current_node)
for row in range(len(pheromone_map)):
pheromone_map[row][current_node] = 0
for i in range(len(self.map.nodes) - 1):
current_node = random.choices(range(len(self.map.nodes)), weights = pheromone_map[self.path[-1]])[0]
self.path.append(current_node)
for row in range(len(pheromone_map)):
pheromone_map[row][current_node] = 0
self.path_length = distance([self.map.nodes[i] for i in self.path])
def get_path_length(self):
return self.path_length
def get_path(self):
return self.path
class TSPMap:
def __init__(self, num_of_ants = 1, size = 10, auto_generate = True, source = "", evaporating_rate = 0):
self.ants = [Ant(self) for i in range(num_of_ants)]
if auto_generate:
self.nodes = TSPMap.generate_nodes(size)
else:
self.nodes = self.read_nodes(source)
self.pheromones = [[1 for i in range(len(self.nodes))] for j in range(len(self.nodes))]
for i in range(len(self.pheromones)):
self.pheromones[i][i] = 0
self.evaporating_rate = evaporating_rate
self.optimal_path = []
self.optimal_length = math.inf
self.optimal_history = []
def generate_nodes(size):
nodes = [(random.uniform(0, size), random.uniform(0, size)) for i in range(size)]
return nodes
def read_nodes(self, source):
with open(source, 'r') as file:
lines = file.readlines()
path = [(float(line.split()[0]), float(line.split()[1])) for line in lines]
return path
def run(self, trials):
for trial in range(trials):
for ant in self.ants:
ant.run()
optimal_ant = min(self.ants, key = Ant.get_path_length)
self.update_pheromones(optimal_ant)
self.optimal_path = optimal_ant.get_path()
self.optimal_length = optimal_ant.get_path_length()
self.optimal_history.append(self.optimal_length)
def update_pheromones(self, optimal_ant):
self.pheromones = [[self.pheromones[i][j] * (1 - self.evaporating_rate)
for j in range(len(self.pheromones))] for i in range(len(self.pheromones))]
path = optimal_ant.get_path()
for i in range(len(path) - 1):
self.pheromones[path[i]][path[i + 1]] += 1 / optimal_ant.get_path_length()
self.pheromones[path[i + 1]][path[i]] += 1 / optimal_ant.get_path_length()
if len(path) > 0 and path[0] != path[-1]:
self.pheromones[path[0]][path[-1]] += 1 / optimal_ant.get_path_length()
self.pheromones[path[-1]][path[0]] += 1 / optimal_ant.get_path_length()
def get_optimal_history(self):
return self.optimal_history
def get_optimal_path(self):
return [self.nodes[i] for i in self.optimal_path]
def get_optimal_distance(self):
return self.optimal_distance
def distance(path):
dist = 0
for i in range(len(path) - 1):
dist += math.sqrt(math.pow(path[i][0] - path[i + 1][0], 2) + math.pow(path[i][1] - path[i + 1][1], 2))
dist += math.sqrt(math.pow(path[0][0] - path[-1][0], 2) + math.pow(path[0][1] - path[-1][1], 2))
return dist
if __name__ == "__main__":
NUM_OF_ANTS = 20
SIZE = 10
NUM_OF_TRIALS = 200
EVAPORATING_RATE = 0.1
SOURCE = f'{SIZE}.txt'
start = clock()
# map_ = TSPMap(num_of_ants=NUM_OF_ANTS, size=SIZE, evaporating_rate=EVAPORATING_RATE)
map_ = TSPMap(num_of_ants=NUM_OF_ANTS, evaporating_rate=EVAPORATING_RATE, auto_generate=False, source=SOURCE)
map_.run(NUM_OF_TRIALS)
end = clock()
history = map_.get_optimal_history()
path = map_.get_optimal_path()
path.append(path[0])
X = [path[i][0] for i in range(len(path))]
Y = [path[i][1] for i in range(len(path))]
plt.figure(1, figsize=(6, 10))
plt.subplot(211)
plt.title('Learning curve\n'
f'Number of ants: {NUM_OF_ANTS}\n'
f'Number of trials: {NUM_OF_TRIALS}\n'
f'Evaporating rate: {EVAPORATING_RATE}\n'
f'Found solution: {history[-1]}\n'
f'Working time: {end - start}')
plt.xlabel('trials')
plt.ylabel('optimal path length')
plt.plot(history)
plt.subplot(212)
plt.title('Optimal path')
plt.plot(X, Y)
|
nilq/baby-python
|
python
|
import unittest
from datetime import datetime
from pyopenrec.comment import Comment
class TestComment(unittest.TestCase):
c = Comment()
def test_get_comment(self):
dt = datetime(2021, 12, 21, 0, 0, 0)
data = self.c.get_comment("n9ze3m2w184", dt)
self.assertEqual(200, data["status"])
self.assertIsNotNone(data["url"])
self.assertIsNotNone(data["data"])
def test_get_recent_comment(self):
data = self.c.get_recent_comment("n9ze3m2w184")
self.assertEqual(200, data["status"])
self.assertIsNotNone(data["url"])
self.assertIsNotNone(data["data"])
def test_get_vod_comment(self):
data = self.c.get_vod_comment("e2zw69jmw8o")
self.assertEqual(200, data["status"])
self.assertIsNotNone(data["url"])
self.assertIsNotNone(data["data"])
if __name__ == "__main__":
unittest.main()
|
nilq/baby-python
|
python
|
from numpy.testing import *
import time
import random
import skimage.graph.heap as heap
def test_heap():
_test_heap(100000, True)
_test_heap(100000, False)
def _test_heap(n, fast_update):
# generate random numbers with duplicates
random.seed(0)
a = [random.uniform(1.0, 100.0) for i in range(n // 2)]
a = a + a
t0 = time.clock()
# insert in heap with random removals
if fast_update:
h = heap.FastUpdateBinaryHeap(128, n)
else:
h = heap.BinaryHeap(128)
for i in range(len(a)):
h.push(a[i], i)
if a[i] < 25:
# double-push same ref sometimes to test fast update codepaths
h.push(2 * a[i], i)
if 25 < a[i] < 50:
# pop some to test random removal
h.pop()
# pop from heap
b = []
while True:
try:
b.append(h.pop()[0])
except IndexError:
break
t1 = time.clock()
# verify
for i in range(1, len(b)):
assert(b[i] >= b[i - 1])
return t1 - t0
if __name__ == "__main__":
run_module_suite()
|
nilq/baby-python
|
python
|
# -*- coding: utf-8 -*-
# Generated by Django 1.11.18 on 2019-01-25 23:37
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('core', '0014_auto_20181116_0716'),
]
operations = [
migrations.AlterField(
model_name='participant',
name='can_receive_invitations',
field=models.BooleanField(default=False, help_text='Check this box to opt-in and receive email invitations for upcoming experiments'),
),
]
|
nilq/baby-python
|
python
|
import discord
from discord.ext import commands
import chickensmoothie as cs
class Pet:
def __init__(self, bot):
self.bot = bot
@commands.command()
@commands.guild_only()
async def pet(self, ctx, link: str = ''): # Pet command
pet = await cs.pet(link) # Get pet data
if pet is None:
embed = discord.Embed(title='Pet', description='An error has occurred while processing pet image.', colour=0xff5252) # Create embed
else:
embed = discord.Embed(title=pet['owner'] + '\'s Pet', colour=0x4ba139) # Create embed
embed.set_image(url=pet['image']) # Set image
initial = True
for key, value in pet.items():
if (key == 'owner' or key == 'pps') and initial:
if key == 'pps':
if not value:
continue
else:
embed.add_field(name='PPS', value='[This pet has "PPS". What\'s that?](http://www.chickensmoothie.com/help/pets#pps)', inline=False)
elif key == 'owner':
value = f'[{pet["owner"]}]({pet["owner_link"]})'
embed.add_field(name=key.capitalize(), value=value, inline=False)
else:
if key == 'image' or key == 'owner_link' or key == 'given_link':
pass
else:
if key == 'id':
key = 'Pet ID'
elif key == 'name':
if value == '':
continue
else:
key = 'Pet\'s name'
elif key == 'age':
key = 'Age'
value = f'{value} days'
elif key == 'given':
if value == '':
continue
else:
key = f'Given to {pet["owner"]} by'
value = f'[{pet["given"]}]({pet["given_link"]})'
else:
key = key.capitalize()
embed.add_field(name=key, value=value, inline=True)
await ctx.send(embed=embed)
def setup(bot):
bot.add_cog(Pet(bot))
|
nilq/baby-python
|
python
|
# ----------------------------------------
# Created on 3rd Apr 2021
# By the Cached Coder
# ----------------------------------------
'''
This script defines the function required
to get a the email ids to send the mail to
from the GForms' responses.
Functions:
getAllResponses():
No Inputs
Returns emails, names and list of whether
they wish to recieve the mail or not
'''
# ----------------------------------------
import gspread
import json
# ----------------------------------------
# Function to open sheet and get all responses
def getAllResponses():
# Gets secrets
with open('secrets.json', 'r') as fh:
secrets = json.load(fh)
# Load spreadsheet
gc = gspread.service_account(filename='secrets.json')
sh = gc.open_by_key(secrets['key'])
# Get all entries
worksheet = sh.sheet1
emails = worksheet.col_values(2)[1:]
names = worksheet.col_values(3)[1:]
sendMail = worksheet.col_values(4)[1:]
# Turn sendMail from strings to bools
sendMail = [True if i == 'Yes' else False for i in sendMail]
# Return email and names
return emails, names, sendMail
if __name__ == '__main__':
emails, names, sendMail = getAllResponses()
print(emails)
print(names)
print(sendMail)
|
nilq/baby-python
|
python
|
import numpy as np
from simulation_api import SimulationAPI
from simulation_control.dm_control.utility import EnvironmentParametrization
from simulation_control.dm_control.utility import SensorsReading
# Check if virtual_arm_environment API works with a given step input
sapi = SimulationAPI()
sapi.step(np.array([0, 0, 0, 0, 0], dtype='float64'))
print(sapi.get_sensors_reading().grip_velp)
print(sapi.export_parameters().object_translate)
# Check if virtual_arm_environment API accepts a manual input
t = {
'object_translate': 6.9,
'object_change_slope': 0.0,
'robot_change_finger_length': 0.0,
'robot_change_joint_stiffness': 0.0,
'robot_change_finger_spring_default': 0.0,
'robot_change_thumb_spring_default': 0.0,
'robot_change_friction': 0.0
}
ep = EnvironmentParametrization(t)
sapi.import_parameters(ep)
print(sapi.export_parameters().object_translate)
# Check if virtual_arm_environment API's run function works
x = np.zeros(shape=(10, 5))
def lmao(last_reward: float, step: int, last_step: bool, readings: SensorsReading) -> float:
return 0.5
sapi.specify_reward_function(lmao)
reward = sapi.run(x)
print(reward)
|
nilq/baby-python
|
python
|
def pg(obs, num_particles=100, num_mcmc_iter=2000):
T = len(obs)
X = np.zeros([num_mcmc_iter, T])
params = [] # list of SV_params
# YOUR CODE
return X, params
|
nilq/baby-python
|
python
|
# -*- coding: utf-8 -*-
import sys
import numpy as np
import scipy.io.wavfile
def main():
try:
if len(sys.argv) != 5:
raise ValueError("Invalid arguement count");
if sys.argv[1] == "towave":
toWave(sys.argv[2], sys.argv[3], float(sys.argv[4]))
elif sys.argv[1] == "totextwave":
toTextWave(sys.argv[2], sys.argv[3], float(sys.argv[4]))
else:
raise ValueError("Invalid first argument");
except Exception as ex:
printUsage()
print(ex)
def toWave(inputFilePath, outputFilePath, gain):
with open(inputFilePath, "r") as inputFile:
lines = [line.rstrip('\n') for line in inputFile]
size = int(lines[0]);
Fs = int(lines[1]);
data = np.zeros((size,), dtype=np.int16);
for i in range(size):
data[i] = int(float(lines[i + 2]) * gain)
scipy.io.wavfile.write(outputFilePath, Fs, data);
def toTextWave(inputFilePath, outputFilePath, gain):
Fs, data = scipy.io.wavfile.read(inputFilePath)
if data.shape != (data.size,):
raise ValueError("Many channel wave are not supported")
data = data * gain;
with open(outputFilePath, "w") as outputFile:
outputFile.write(str(data.size) + "\n")
outputFile.write(str(Fs) + "\n")
for i in range(data.size):
outputFile.write(str(data[i]) + "\n")
def printUsage():
print("Convert a wave file to a text wave file:")
print("\tpython textwav.py totextwave input_file_path output_file_path gain")
print("Convert a text wave file to a wave file:")
print("\tpython textwav.py towave input_file_path output_file_path gain\n\n")
if __name__ == "__main__":
main()
|
nilq/baby-python
|
python
|
#
# BSD 3-Clause License
#
# Copyright (c) 2017 xxxx
# All rights reserved.
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * 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.
#
# * Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "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 COPYRIGHT HOLDER OR CONTRIBUTORS 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.
# ============================================================================
#from PIL import Image
from six.moves import zip
from .utils import download_url, check_integrity
import os
from .vision import VisionDataset
class SBU(VisionDataset):
"""`SBU Captioned Photo <http://www.cs.virginia.edu/~vicente/sbucaptions/>`_ Dataset.
Args:
root (string): Root directory of dataset where tarball
``SBUCaptionedPhotoDataset.tar.gz`` exists.
transform (callable, optional): A function/transform that takes in a PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
download (bool, optional): If True, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.
"""
url = "http://www.cs.virginia.edu/~vicente/sbucaptions/SBUCaptionedPhotoDataset.tar.gz"
filename = "SBUCaptionedPhotoDataset.tar.gz"
md5_checksum = '9aec147b3488753cf758b4d493422285'
def __init__(self, root, transform=None, target_transform=None, download=True):
super(SBU, self).__init__(root, transform=transform,
target_transform=target_transform)
if download:
self.download()
if not self._check_integrity():
raise RuntimeError('Dataset not found or corrupted.' +
' You can use download=True to download it')
# Read the caption for each photo
self.photos = []
self.captions = []
file1 = os.path.join(self.root, 'dataset', 'SBU_captioned_photo_dataset_urls.txt')
file2 = os.path.join(self.root, 'dataset', 'SBU_captioned_photo_dataset_captions.txt')
for line1, line2 in zip(open(file1), open(file2)):
url = line1.rstrip()
photo = os.path.basename(url)
filename = os.path.join(self.root, 'dataset', photo)
if os.path.exists(filename):
caption = line2.rstrip()
self.photos.append(photo)
self.captions.append(caption)
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is a caption for the photo.
"""
filename = os.path.join(self.root, 'dataset', self.photos[index])
img = Image.open(filename).convert('RGB')
if self.transform is not None:
img = self.transform(img)
target = self.captions[index]
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
def __len__(self):
"""The number of photos in the dataset."""
return len(self.photos)
def _check_integrity(self):
"""Check the md5 checksum of the downloaded tarball."""
root = self.root
fpath = os.path.join(root, self.filename)
if not check_integrity(fpath, self.md5_checksum):
return False
return True
def download(self):
"""Download and extract the tarball, and download each individual photo."""
import tarfile
if self._check_integrity():
print('Files already downloaded and verified')
return
download_url(self.url, self.root, self.filename, self.md5_checksum)
# Extract file
with tarfile.open(os.path.join(self.root, self.filename), 'r:gz') as tar:
tar.extractall(path=self.root)
# Download individual photos
with open(os.path.join(self.root, 'dataset', 'SBU_captioned_photo_dataset_urls.txt')) as fh:
for line in fh:
url = line.rstrip()
try:
download_url(url, os.path.join(self.root, 'dataset'))
except OSError:
# The images point to public images on Flickr.
# Note: Images might be removed by users at anytime.
pass
|
nilq/baby-python
|
python
|
# ===- test_floats.py ----------------------------------*- python -*-===//
#
# Copyright (C) 2021 GrammaTech, Inc.
#
# This code is licensed under the MIT license.
# See the LICENSE file in the project root for license terms.
#
# This project is sponsored by the Office of Naval Research, One Liberty
# Center, 875 N. Randolph Street, Arlington, VA 22203 under contract #
# N68335-17-C-0700. The content of the information does not necessarily
# reflect the position or policy of the Government and no official
# endorsement should be inferred.
#
# ===-----------------------------------------------------------------===//
import argparse
import gtirb
def create_floats(filename: str):
ir = gtirb.IR()
ir.aux_data["AFloat"] = gtirb.AuxData(0.5, "float")
ir.aux_data["ADouble"] = gtirb.AuxData(2.0, "double")
ir.save_protobuf(filename)
def check_for_floats(filename: str) -> bool:
ir = gtirb.IR.load_protobuf(filename)
f = ir.aux_data["AFloat"]
float_success = f.type_name == "float" and f.data == 0.5
g = ir.aux_data["ADouble"]
double_success = g.type_name == "double" and g.data == 2.0
return float_success and double_success
parser = argparse.ArgumentParser()
parser.add_argument("-w", required=False, type=str)
parser.add_argument("-r", required=False, type=str)
if __name__ == "__main__":
args = parser.parse_args()
if args.w:
create_floats(args.w)
elif args.r:
if check_for_floats(args.r):
exit(0)
else:
exit(1)
|
nilq/baby-python
|
python
|
# Copyright Tom SF Haines
#
# 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.
import subprocess
from direct.showbase import DirectObject
from panda3d.core import *
class Profile(DirectObject.DirectObject):
"""Connects to pstats, if pstats is not running on the local computer it will set a copy running regardless."""
def __init__(self,manager,xml):
self.pstats = None
def go(self):
if (PStatClient.connect()==0):
# No pstat server - create it, then try and connect again...
self.pstats = subprocess.Popen(['pstats'])
# Need to give pstats some time to warm up - use a do latter task...
def tryAgain(task):
PStatClient.connect()
taskMgr.doMethodLater(0.5,tryAgain,'pstats again')
def reload(self,manager,xml):
pass
def destroy(self):
if self.pstats!=None:
self.pstats.kill()
|
nilq/baby-python
|
python
|
import json
import numpy as np
import boto3
import scipy
import scipy.sparse
from io import BytesIO
import os
ACCESS_KEY = os.environ['ACCESS_KEY']
SECRET_ACCESS_KEY = os.environ['SECRET_ACCESS_KEY']
def getData():
BUCKET = 'personal-bucket-news-ranking'
client = boto3.client('s3',
aws_access_key_id=ACCESS_KEY,
aws_secret_access_key=SECRET_ACCESS_KEY
)
FILE_TO_READ = 'csr_articles.npz'
result = client.get_object(Bucket=BUCKET, Key=FILE_TO_READ)
word_articles = scipy.sparse.load_npz(BytesIO(result["Body"].read()))
FILE_TO_READ = 'word_emb.npy'
result = client.get_object(Bucket=BUCKET, Key=FILE_TO_READ)
word_emb = np.load(BytesIO(result["Body"].read()))
FILE_TO_READ = 'word_bias.npy'
result = client.get_object(Bucket=BUCKET, Key=FILE_TO_READ)
word_bias = np.load(BytesIO(result["Body"].read()))
FILE_TO_READ = 'reversed_word_ids.json'
result = client.get_object(Bucket=BUCKET, Key=FILE_TO_READ)
id_to_word = json.loads(result["Body"].read().decode())
FILE_TO_READ = 'mapped_dataset.json'
result = client.get_object(Bucket=BUCKET, Key=FILE_TO_READ)
real_data = json.loads(result["Body"].read().decode())
return word_articles, word_emb, word_bias, id_to_word, real_data
def lambda_handler(event, context):
print(ACCESS_KEY)
publication_emb = np.asarray([1.0440499, 1.0030843, 1.0340449, 0.992087, 1.0509816,
1.0315005, -1.0493797, -1.0198538, 0.9712321, -1.026394,
-0.9687971, 1.0592866, -1.0200703, -1.0423145, 0.9929519,
1.0220934, 1.021279, -1.0265925, 0.9601833, 0.9763889,
1.0109168, -0.9728226, 0.97199583, -1.0237931, -0.9996001,
0.9932069, 0.97966635, -0.98893607, -0.9876815, -0.98812914,
-0.9625895, 0.99879754, 0.9876508, -0.9581506, -0.95436096,
-0.9601925, -1.0134513, -0.98763955, 0.98665, -1.0140482,
1.004904, 0.9894275, -1.0044671, -0.9839679, -0.97082543,
-0.9798079, 0.9926766, -0.97317344, 0.9797, -0.97642475,
-0.99420726, -0.9972062, -1.0104703, 1.0575777, 0.9957696,
-1.0413874, -1.0056863, -1.0151271, -0.99969465, 0.97463423,
-0.98398715, -1.0211866, -1.0128828, -1.0024365, -0.9800189,
1.0457181, 1.0155835, -1.036794, -1.013707, -1.0498024,
-1.0252678, -1.0388161, -0.97501564, 0.97687274, 0.97906756,
1.0536852, 1.0590494, -0.96917725, 1.0247189, -0.9818878,
-1.0417286, -1.0204054, -1.0285249, -1.0329671, 0.9705739,
0.96375024, 0.9891868, 0.9892464, 1.039075, 1.0042666,
0.9786834, 1.0199072, 0.98080486, 0.9698635, -0.99322844,
-0.95841753, -0.99150276, 0.97394156, 0.9976019, -1.0375009],
dtype=np.float32)
publication_bias = 0.99557
publication_emb[1] = event['a']
publication_emb[5] = event['b']
publication_emb[17] = event['c']
publication_emb[34] = event['d']
publication_emb[67] = event['e']
print(publication_emb)
word_articles, word_emb, word_bias, id_to_word, real_data = getData()
print("Data loaded successfully!")
article_embeddings = word_articles.dot(word_emb)
emb_times_publication = np.dot(article_embeddings, publication_emb.reshape(100,1))
article_bias = word_articles.dot(word_bias)
product_with_bias = emb_times_publication + article_bias
word_counts = word_articles.sum(axis=1).reshape(word_articles.shape[0], 1)
final_logits = np.divide(product_with_bias, word_counts) + float(publication_bias)
indices = final_logits.argsort(axis=0)[-75:].reshape(75)
word_logits = np.dot(word_emb, publication_emb.reshape(100,1)) + word_bias
top_articles = word_articles[indices.tolist()[0]]
broadcasted_words_per_article = top_articles.toarray() * word_logits.T
sorted_word_indices = broadcasted_words_per_article.argsort(axis=1)
return_articles = []
i = 0
for idx in indices.tolist()[0]:
current_article = real_data[int(idx)]
current_article['logit'] = float(final_logits[int(idx)])
current_sorted_words = sorted_word_indices[i]
top_words = []
least_words = []
for top_word in current_sorted_words[-10:]:
word = id_to_word[str(top_word)]
top_words.append(word)
for least_word in current_sorted_words[:10]:
word = id_to_word[str(least_word)]
least_words.append(word)
current_article['top_words'] = top_words
current_article['least_words'] = least_words
return_articles.append(current_article)
i += 1
ordered_return_articles = return_articles[::-1]
response = {
"statusCode": 200,
"body": json.dumps(ordered_return_articles)
}
return response
if __name__ == "__main__":
test_event = {
'a': 5,
'b': 6,
'c': 100,
'd': 12,
'e': -123
}
print(lambda_handler(test_event, ''))
|
nilq/baby-python
|
python
|
B = input().strip()
B1 = ''
for b in B:
if b in ['X', 'L', 'C']:
B1 += b
else:
break
if B1 == 'LX':
B1 = 'XL'
B2 = B[len(B1):]
if B2 == 'VI':
B2 = 'IV'
elif B2 == 'I' and B1.endswith('X'):
B1 = B1[:-1]
B2 = 'IX'
if B1 == 'LX':
B1 = 'XL'
print(B1+B2)
|
nilq/baby-python
|
python
|
from rest_framework import status
from rest_framework.decorators import api_view
from rest_framework.response import Response
from django.conf import settings
import pymongo
from . import permissions
@api_view(['GET'])
def root(request, **kwargs):
permitted_user_types = ['interviewer']
if permissions.check(request, permitted_user_types) != permissions.PASS:
return Response(
{'error': 'Permission denied'},
status.HTTP_403_FORBIDDEN
)
client = pymongo.MongoClient()
db = client[settings.DB_NAME]
token = request.GET.get('token')
cursor = db.users.find({'token': token})
room_cursor = db.rooms.find({'interviewer': cursor[0]['username']})
if room_cursor.count() == 0:
return Response(
{
'error': 'No room found.'
},
status.HTTP_400_BAD_REQUEST
)
room_id = room_cursor[0]['id']
return Response(
{'roomId': room_id},
status.HTTP_200_OK
)
|
nilq/baby-python
|
python
|
import unittest
import sys
import inspect
from unittest.mock import Mock
from io import StringIO
from math import ceil
from damage import Damage
from classes import Paladin
from spells import PaladinSpell
from models.spells.loader import load_paladin_spells_for_level
class PaladinTests(unittest.TestCase):
def setUp(self):
self.name = "Netherblood"
self.level = 3
self.dummy = Paladin(name=self.name, level=self.level, health=100, mana=100, strength=10)
def test_init(self):
""" The __init__ should load/save all the spells for the Paladin"""
spells = [spell for level in range(1,self.level+1) for spell in load_paladin_spells_for_level(level)]
self.assertNotEqual(len(self.dummy.learned_spells), 0)
for spell in spells:
self.assertIn(spell.name, self.dummy.learned_spells)
char_spell = self.dummy.learned_spells[spell.name]
# find the largest rank in our spells list (the char has the highest rank only)
max_rank = list(sorted(filter(lambda x: x.name == spell.name, spells), key=lambda x: x.rank))[-1].rank
self.assertEqual(char_spell.rank, max_rank)
def test_leave_combat(self):
"""
Except the normal behaviour, leave_combat should remove the SOR buff from the pally
and reset his spell cds
"""
self.dummy._in_combat = True
self.dummy.SOR_ACTIVE = True
for spell in self.dummy.learned_spells.values():
spell._cooldown_counter = 100
self.assertTrue(self.dummy.is_in_combat())
self.dummy.leave_combat()
self.assertFalse(self.dummy.is_in_combat())
self.assertFalse(self.dummy.SOR_ACTIVE)
# All cooldowns should be reset
self.assertTrue(all([spell._cooldown_counter == 0 for spell in self.dummy.learned_spells.values()]))
def test_reset_spell_cooldowns(self):
""" The reset_spell_cooldowns goes through every spell and resets its CD"""
for spell in self.dummy.learned_spells.values():
spell._cooldown_counter = 100
self.assertTrue(all([spell._cooldown_counter != 0 for spell in self.dummy.learned_spells.values()]))
self.dummy.reset_spell_cooldowns()
self.assertTrue(all([spell._cooldown_counter == 0 for spell in self.dummy.learned_spells.values()]))
def test_level_up(self):
""" Except the normal behaviour, it should learn new spells for the character """
# empty the learned spells, it's stored as a static variable, which is not good practice but doesn't hurt in the game
Paladin.learned_spells = {}
pl = Paladin(name="fuck a nine to five")
spells_to_learn = [spell.name for spell in load_paladin_spells_for_level(pl.level + 1)]
for spell in spells_to_learn:
self.assertNotIn(spell, pl.learned_spells)
pl._level_up()
for spell in spells_to_learn:
self.assertIn(spell, pl.learned_spells)
def test_level_up_to_level(self):
""" Except the normal behaviour, it should learn new spells for the character """
# empty the learned spells, it's stored as a static variable, which is not good practice but doesn't hurt in the game
Paladin.learned_spells = {}
pl = Paladin(name="fuck a nine to five")
to_level = 4
spells_to_learn = [spell for level in range(2, to_level + 1) for spell in load_paladin_spells_for_level(level)]
for spell in spells_to_learn:
has_not_learned_spell = spell.name not in pl.learned_spells
has_smaller_rank = spell.rank > pl.learned_spells[spell.name].rank if not has_not_learned_spell else False
self.assertTrue(has_not_learned_spell or has_smaller_rank)
pl._level_up(to_level=to_level)
for spell in spells_to_learn:
self.assertIn(spell.name, pl.learned_spells)
def test_lookup_and_handle_new_spells(self):
""" Should look up the available spells for our level and learn them or update our existing ones"""
Paladin.learned_spells = {}
pl = Paladin(name="fuck a nine to five")
print(pl.learned_spells)
pl.level = 3
spells_to_learn = [spell for spell in load_paladin_spells_for_level(pl.level)]
for spell in spells_to_learn:
has_not_learned_spell = spell.name not in pl.learned_spells
has_smaller_rank = spell.rank > pl.learned_spells[spell.name].rank if not has_not_learned_spell else False
self.assertTrue(has_not_learned_spell or has_smaller_rank)
pl._lookup_and_handle_new_spells()
for spell in spells_to_learn:
self.assertIn(spell.name, pl.learned_spells)
def test_learn_new_spell(self):
""" Given a PaladinSpell, add it to the learned_spells dictionary"""
spell = PaladinSpell(name="Too_Alive", rank=5)
expected_message = f'You have learned a new spell - {spell.name}'
self.assertNotIn(spell.name, self.dummy.learned_spells)
try:
output = StringIO()
sys.stdout = output
self.dummy.learn_new_spell(spell)
self.assertIn(expected_message, output.getvalue())
finally:
sys.stdout = sys.__stdout__
self.assertIn(spell.name, self.dummy.learned_spells)
def test_lookup_available_spells_to_learn(self):
""" It's a generator function returning a spell that can be learnt for the level """
lev = 3
expected_spells = load_paladin_spells_for_level(lev)
generator = self.dummy._lookup_available_spells_to_learn(lev)
self.assertTrue(inspect.isgenerator(generator))
for spell in expected_spells:
self.assertEqual(vars(next(generator)), vars(spell))
def test_update_spell(self):
""" The update_spell() function updates a spell we already have learned"""
f_spell = PaladinSpell('Spell', rank=1)
s_spell = PaladinSpell('Spell', rank=2)
expected_message = f'Spell {f_spell.name} has been updated to rank {s_spell.rank}!'
self.dummy.learn_new_spell(f_spell)
try:
output = StringIO()
sys.stdout = output
self.dummy.update_spell(s_spell)
self.assertIn(expected_message, output.getvalue())
finally:
sys.stdout = sys.__stdout__
# assert that it updated the rank
self.assertEqual(self.dummy.learned_spells[s_spell.name].rank, s_spell.rank)
self.assertGreater(self.dummy.learned_spells[s_spell.name].rank, f_spell.rank)
def test_spell_handler_sor(self):
"""
The spell handler takes spell names and casts the appropriate function
It might work in a bad way since it's not too testable
"""
unsuccessful_message = 'Unsuccessful cast'
sor_success_msg = 'SOR_CASTED'
sor_command_name = 'sor'
# Mock the function that should get called
self.dummy.spell_seal_of_righteousness = lambda x: sor_success_msg
try:
output = StringIO()
sys.stdout = output
result = self.dummy.spell_handler(sor_command_name, None)
self.assertNotIn(unsuccessful_message, output.getvalue())
finally:
sys.stdout = sys.__stdout__
# Assert that it called the spell_seal_of_righteousness function
self.assertEqual(result, sor_success_msg)
def test_spell_handler_fol(self):
unsuccessful_message = 'Unsuccessful cast'
fol_success_msg = 'FOL_CASTED'
fol_command_name = 'fol'
# Mock the function that should get called
self.dummy.spell_flash_of_light = lambda x: fol_success_msg
try:
output = StringIO()
sys.stdout = output
result = self.dummy.spell_handler(fol_command_name, None)
self.assertNotIn(unsuccessful_message, output.getvalue())
finally:
sys.stdout = sys.__stdout__
# Assert that it called the spell_seal_of_righteousness function
self.assertEqual(result, fol_success_msg)
def test_spell_handler_ms(self):
unsuccessful_message = 'Unsuccessful cast'
ms_success_msg = 'MS_CASTED'
ms_command_name = 'ms'
# Mock the function that should get called
self.dummy.spell_melting_strike = lambda target=None, spell=None: ms_success_msg
try:
output = StringIO()
sys.stdout = output
result = self.dummy.spell_handler(ms_command_name, None)
self.assertNotIn(unsuccessful_message, output.getvalue())
finally:
sys.stdout = sys.__stdout__
# Assert that it called the spell_seal_of_righteousness function
self.assertEqual(result, ms_success_msg)
def test_spell_handler_invalid_spell(self):
unsuccessful_message = 'Unsuccessful cast'
invalid_command = 'WooHoo'
try:
output = StringIO()
sys.stdout = output
result = self.dummy.spell_handler(invalid_command, None)
self.assertIn(unsuccessful_message, output.getvalue())
finally:
sys.stdout = sys.__stdout__
self.assertFalse(result)
def test_spell_seal_of_righteousness(self):
sor: PaladinSpell = self.dummy.learned_spells[Paladin.KEY_SEAL_OF_RIGHTEOUSNESS]
expected_message = f'{self.dummy.name} activates {Paladin.KEY_SEAL_OF_RIGHTEOUSNESS}!'
expected_mana = self.dummy.mana - sor.mana_cost
self.assertFalse(self.dummy.SOR_ACTIVE)
self.assertEqual(self.dummy.SOR_TURNS, 0)
try:
output = StringIO()
sys.stdout = output
self.dummy.spell_seal_of_righteousness(sor)
self.assertIn(expected_message, output.getvalue())
finally:
sys.stdout = sys.__stdout__
self.assertTrue(self.dummy.SOR_ACTIVE)
self.assertEqual(self.dummy.SOR_TURNS, 3)
self.assertEqual(self.dummy.mana, expected_mana)
def test_spell_seal_of_righteousness_attack(self):
sor: PaladinSpell = self.dummy.learned_spells[Paladin.KEY_SEAL_OF_RIGHTEOUSNESS]
expected_damage = sor.damage1
self.dummy.spell_seal_of_righteousness(sor)
self.assertTrue(self.dummy.SOR_ACTIVE)
self.assertEqual(self.dummy.SOR_TURNS, 3)
result = self.dummy._spell_seal_of_righteousness_attack()
self.assertEqual(result, expected_damage)
self.assertEqual(self.dummy.SOR_TURNS, 2)
def test_spell_seal_of_righteousness_attack_fade(self):
sor: PaladinSpell = self.dummy.learned_spells[Paladin.KEY_SEAL_OF_RIGHTEOUSNESS]
expected_message = f'{Paladin.KEY_SEAL_OF_RIGHTEOUSNESS} has faded from {self.dummy.name}'
self.dummy.spell_seal_of_righteousness(sor)
self.assertTrue(self.dummy.SOR_ACTIVE)
self.dummy.SOR_TURNS = 1
self.dummy._spell_seal_of_righteousness_attack()
self.assertEqual(self.dummy.SOR_TURNS, 0)
self.assertTrue(self.dummy.SOR_ACTIVE)
# Should fade now and not do any damage on turn end
try:
output = StringIO()
sys.stdout = output
self.dummy.end_turn_update()
self.assertIn(expected_message, output.getvalue())
finally:
sys.stdout = sys.__stdout__
self.assertFalse(self.dummy.SOR_ACTIVE)
def test_spell_flash_of_light(self):
import heal
# Nullify the chance to double heal for consistent testing
heal.DOUBLE_HEAL_CHANCE = 0
fol: PaladinSpell = self.dummy.learned_spells[Paladin.KEY_FLASH_OF_LIGHT]
expected_message = f'{self.dummy.name} activates {Paladin.KEY_FLASH_OF_LIGHT}!'
expected_mana = self.dummy.mana - fol.mana_cost
orig_health = 1
self.dummy.health = orig_health
expected_message = f'{fol.name} healed {self.dummy.name} for {fol.heal1}.'
try:
output = StringIO()
sys.stdout = output
self.dummy.spell_flash_of_light(fol)
self.assertIn(expected_message, output.getvalue())
finally:
sys.stdout = sys.__stdout__
self.assertEqual(self.dummy.mana, expected_mana)
self.assertEqual(self.dummy.health, orig_health + fol.heal1)
def test_spell_flash_of_light_overheal(self):
import heal
# Nullify the chance to double heal for consistent testing
heal.DOUBLE_HEAL_CHANCE = 0
fol: PaladinSpell = self.dummy.learned_spells[Paladin.KEY_FLASH_OF_LIGHT]
expected_message = f'{fol.name} healed {self.dummy.name} for 0.00 ({fol.heal1:.2f} Overheal).'
expected_mana = self.dummy.mana - fol.mana_cost
orig_health = self.dummy.health
self.dummy.health = orig_health
try:
output = StringIO()
sys.stdout = output
self.dummy.spell_flash_of_light(fol)
self.assertIn(expected_message, output.getvalue())
finally:
sys.stdout = sys.__stdout__
self.assertEqual(self.dummy.mana, expected_mana)
self.assertEqual(self.dummy.health, orig_health) # should have only overhealed
def test_spell_melting_strike(self):
ms: PaladinSpell = self.dummy.learned_spells[Paladin.KEY_MELTING_STRIKE]
expected_mana = self.dummy.mana - ms.mana_cost
expected_message2 = 'Took attack'
expected_message3 = 'Took buff'
take_attack = lambda x, y: print('Took attack')
add_buff = lambda x: print('Took buff')
target = Mock(name="All",
take_attack=take_attack,
add_buff=add_buff)
expected_message = f'{ms.name} damages {target.name} for {ms.damage1:.2f} physical damage!'
try:
output = StringIO()
sys.stdout = output
result = self.dummy.spell_melting_strike(ms, target)
self.assertIn(expected_message, output.getvalue())
self.assertIn(expected_message2, output.getvalue())
self.assertIn(expected_message3, output.getvalue())
finally:
sys.stdout = sys.__stdout__
self.assertTrue(result)
self.assertEqual(expected_mana, self.dummy.mana)
def test_get_auto_attack_damage(self):
""" Applies damage reduction in regard to level and adds the sor_damage
It attaches the sor_damage to the magic_dmg in the Damage class and
returns the sor_dmg explicitly for easy printing"""
sor: PaladinSpell = self.dummy.learned_spells[Paladin.KEY_SEAL_OF_RIGHTEOUSNESS]
self.dummy.spell_seal_of_righteousness(sor)
received_dmg, sor_dmg = self.dummy.get_auto_attack_damage(self.dummy.level)
self.assertTrue(isinstance(received_dmg, Damage))
self.assertTrue(self.dummy.min_damage <= received_dmg.phys_dmg <= self.dummy.max_damage)
self.assertEqual(received_dmg.magic_dmg, sor.damage1)
self.assertEqual(sor_dmg, sor.damage1)
def test_get_auto_attack_damage_higher_level(self):
""" Applies damage reduction in regard to level and adds the sor_damage
It attaches the sor_damage to the magic_dmg in the Damage class and
returns the sor_dmg explicitly for easy printing"""
sor: PaladinSpell = self.dummy.learned_spells[Paladin.KEY_SEAL_OF_RIGHTEOUSNESS]
level_diff = 2
prc_mod = (level_diff * 0.1)
level = self.dummy.level + level_diff
expected_sor_dg = sor.damage1 - (sor.damage1 * prc_mod)
expected_min_dmg = int(self.dummy.min_damage) - (self.dummy.min_damage * prc_mod)
expected_max_dmg = int(self.dummy.max_damage) - (self.dummy.max_damage * prc_mod)
self.dummy.spell_seal_of_righteousness(sor)
received_dmg, sor_dmg = self.dummy.get_auto_attack_damage(level)
self.assertTrue(isinstance(received_dmg, Damage))
self.assertTrue(expected_min_dmg <= received_dmg.phys_dmg <= expected_max_dmg)
self.assertEqual(received_dmg.magic_dmg, expected_sor_dg)
self.assertEqual(sor_dmg, expected_sor_dg)
def test_attack(self):
expected_message2 = 'Took Attack!'
expected_message3 = 'Get_take_attack_damage_repr called!'
victim = Mock(level=self.dummy.level, take_attack=lambda x, y: print(expected_message2),
get_take_attack_damage_repr=lambda x,y: print(expected_message3))
expected_message = f'{self.dummy.name} attacks {victim.name}'
try:
output = StringIO()
sys.stdout = output
self.dummy.attack(victim)
self.assertIn(expected_message, output.getvalue())
self.assertIn(expected_message2, output.getvalue())
self.assertIn(expected_message3, output.getvalue())
finally:
sys.stdout = sys.__stdout__
def test_get_class(self):
""" get_class() returns the class name as a string in lowercase """
expected_result = 'paladin'
self.assertEqual(self.dummy.get_class(), expected_result)
if __name__ == '__main__':
unittest.main()
|
nilq/baby-python
|
python
|
# -*- coding: UTF-8 -*-
import pika
if __name__ == '__main__':
connection = pika.BlockingConnection(pika.ConnectionParameters("localhost"))
channel = connection.channel()
channel.exchange_declare(exchange="tang",type="fanout")
message = "You are awsome!"
for i in range(0, 100): # 循环100次发送消息
channel.basic_publish(exchange="tang", routing_key='', body=message + " " + str(i),)
print "sending ", message
|
nilq/baby-python
|
python
|
import torch.nn as nn
import config
from utils.manager import PathManager
class BaseModel(nn.Module):
def __init__(self,
model_params: config.ParamsConfig,
path_manager: PathManager,
loss_func,
data_source,
**kwargs):
super(BaseModel, self).__init__()
self.LossFunc = loss_func
self.ModelParams = model_params
self.TaskParams = None
self.ImageW = None
self.TaskType = ""
self.DataSource = data_source
self.FusedFeatureDim = None
self.Fusion = None # buildFusion(self, model_params)
self.SeqEmbedPipeline = []
self.ImgEmbedPipeline = []
def _seqEmbed(self, x, lens=None):
for worker in self.SeqEmbedPipeline:
x = worker(x, lens)
return x
def _imgEmbed(self, x):
for worker in self.ImgEmbedPipeline:
x = worker(x)
return x
def _extractEpisodeTaskStruct(self,
support_seqs, query_seqs,
support_imgs, query_imgs):
assert (support_seqs is None) ^ (query_seqs is not None), \
f"[extractEpisodeTaskStruct] 支持集和查询集的序列数据存在性不一致: support: {support_seqs is None}, query:{query_seqs is None}"
assert (support_imgs is None) ^ (query_imgs is not None), \
f"[extractEpisodeTaskStruct] 支持集和查询集的图像数据存在性不一致: support: {support_imgs is None}, query:{query_imgs is None}"
# TODO: 支持更多task的输入类型来提取任务结构参数
if support_seqs is not None:
k = support_seqs.size(1)
n = support_seqs.size(0)
elif support_imgs is not None:
k = support_imgs.size(1)
n = support_imgs.size(0)
else:
assert False, "[extractEpisodeTaskStruct] 序列和图像的支持集都为None，无法提取n,k"
if query_seqs is not None:
qk = query_seqs.size(0)
elif query_imgs is not None:
qk = query_imgs.size(0)
else:
assert False, "[extractEpisodeTaskStruct] 序列和图像的查询集都为None，无法提取qk"
# support img shape: [n, k, 1, w, w]
# query img shape: [qk, 1, w, w]
if support_imgs is not None:
w = support_imgs.size(3)
elif query_imgs is not None:
w = query_imgs.size(2)
else:
w = None
self.TaskParams = config.EpisodeTaskConfig(k, n, qk)
self.ImageW = w
# 3.20修改：不再对support提供按类的view，直接输出整个support的batch
def embed(self,
support_seqs, query_seqs,
support_lens, query_lens,
support_imgs, query_imgs):
self._extractEpisodeTaskStruct(support_seqs, query_seqs,
support_imgs, query_imgs)
k, n, qk, w = self.TaskParams.k, self.TaskParams.n, self.TaskParams.qk, self.ImageW
# 提取任务结构时，已经判断过支持集和查询集的数据一致性，此处做单侧判断即可
if support_seqs is not None:
support_seqs = support_seqs.view(n * k, -1)
support_seqs = self._seqEmbed(support_seqs, support_lens) # .view(n, k, -1) # embed中不再默认提供整形
query_seqs = self._seqEmbed(query_seqs, query_lens)
assert support_seqs.size(1) == query_seqs.size(1), \
"[BaseProtoModel.Embed] Support/query sequences' feature dimension size must match: (%d,%d)" \
% (support_seqs.size(1), query_seqs.size(1))
# 提取任务结构时，已经判断过支持集和查询集的数据一致性，此处做单侧判断即可
if support_imgs is not None:
support_imgs = support_imgs.view(n*k, 1, w, w) # 默认为单通道图片
support_imgs = self._imgEmbed(support_imgs) # .view(n, k, -1) # embed中不再默认提供整形
query_imgs = self._imgEmbed(query_imgs).squeeze()
assert support_imgs.size(1) == query_imgs.size(1), \
"[BaseProtoModel.Embed] Support/query images' feature dimension size must match: (%d,%d)"\
%(support_imgs.size(1),query_imgs.size(1))
return support_seqs, query_seqs, support_imgs, query_imgs
def forward(self, # forward接受所有可能用到的参数
support_seqs, support_imgs, support_lens, support_labels,
query_seqs, query_imgs, query_lens, query_labels,
loss_func,
**kwargs):
raise NotImplementedError
def name(self):
return "BaseModel"
def test(self, *args, **kwargs):
raise NotImplementedError
def _fuse(self, seq_features, img_features, **kwargs):
return self.Fusion(seq_features, img_features, **kwargs)
def train_state(self, mode=True):
self.TaskType = "Train"
super().train(mode)
def validate_state(self):
self.TaskType = "Validate"
super().eval()
def test_state(self):
self.TaskType = "Test"
super().eval()
|
nilq/baby-python
|
python
|
import pandas as pd
from IPython.display import display_html, Image
import weasyprint as wsp
import matplotlib.pyplot as plt
import os
import math
experiment_pref = 'experiment-log-'
test_file_pref = 'test_file_'
csv_ext = '.csv'
txt_ext = '.txt'
def display_best_values(directory=None):
real_list = []
oracle_list = []
if directory is None:
directory = '/content/CIS-700/results/'
for filename in os.listdir(directory):
if filename.startswith(experiment_pref) and filename.endswith(csv_ext):
fn_split = filename.split(experiment_pref)[1].split(csv_ext)[0].split('-')
if(len(fn_split) == 2):
model = fn_split[0]
training = fn_split[1]
df = pd.read_csv(directory + filename)
best_val_metric_msg = model.capitalize() + '\n\t'
for col in df:
best_val = ''
if col == 'epochs' or col.startswith('Unnamed'):
continue
if col == 'EmbeddingSimilarity':
temp = df.iloc[df[col].argmax()]
best_val = str(round(temp[col], 4))
elif col != 'epochs':
temp = df.iloc[df[col].argmin()]
best_val = str(round(temp[col], 4))
epoch = str(round(temp['epochs']))
if(pd.notna(best_val)):
best_val_metric_msg+= col + ': ' + best_val + ' @epoch ' + epoch +'\t'
if training == 'real':
real_list.append(best_val_metric_msg + '\n')
else:
oracle_list.append(best_val_metric_msg + '\n')
print('********************************')
print('\tOracle Training:')
print('********************************')
print(*sorted(oracle_list), sep = "\n")
print('********************************')
print('\tReal Training:')
print('********************************')
print(*sorted(real_list), sep = "\n")
def display_synth_data(directory=None, rows=None):
container = ''
if directory is None:
directory = '/content/CIS-700/results/'
if rows is None:
rows = 5
else:
rows = int(rows)
real_synth_image_path = directory + "real_synth_data.png"
for filename in os.listdir(directory):
if filename.startswith(test_file_pref) and filename.endswith(txt_ext):
fn_split = filename.split(test_file_pref)[1].split(txt_ext)[0].split('_')
if len(fn_split) == 2:
model = fn_split[0]
training = fn_split[1]
df = pd.read_csv(directory + filename, sep="\n", header=None)
df.columns = [model.capitalize() + " " + training.capitalize() + " Synth Data"]
df_styler = df.head(rows).style.set_table_attributes("style='display:inline-block'")
if container != '':
container += '<hr style="width: 400px; margin-left:0;">'
container += df_styler._repr_html_()
if container != '':
file = open(directory + "real_synth_data.html", "w")
file.write(container)
file.close()
display_html(container, raw=True)
'''
#write html as image
html = wsp.HTML(string=container)
html.write_png(real_synth_image_path, optimize_images=False)
display(Image(filename=real_synth_image_path))
#resize image
from PIL import Image
real_synth_image_path = directory + "real_synth_data.png"
img = Image.open(real_synth_image_path)
resized_image = img.resize((1700,1700))
display(resized_image)
'''
def display_metrics(directory=None):
df_list = []
real_df_list = []
oracle_df_list = []
real_labels = []
oracle_labels = []
if directory is None:
directory = '/content/CIS-700/results/'
for filename in os.listdir(directory):
if filename.startswith(experiment_pref) and filename.endswith(csv_ext):
fn_split = filename.split(experiment_pref)[1].split(csv_ext)[0].split('-')
if(len(fn_split) == 2):
model = fn_split[0]
training = fn_split[1]
df = pd.read_csv(directory + filename)
if training == 'real':
df = df.rename(columns={"EmbeddingSimilarity": "EmbSim_" + model.capitalize(), "nll-test": "Nll-Test_" + model.capitalize()})
real_df_list.append(df.set_index('epochs'))
real_labels.append(model)
elif training == 'oracle':
df = df.rename(columns={"EmbeddingSimilarity": "EmbSim_" + model.capitalize(), "nll-test": "Nll-Test_" + model.capitalize(), "nll-oracle": "Nll-Oracle_" + model.capitalize()})
oracle_df_list.append(df.set_index('epochs'))
oracle_labels.append(model)
#TODO Add CFG Training Logic Here
real_results = pd.concat(real_df_list)
oracle_results = pd.concat(oracle_df_list)
# filter results to get separate lists for each metric type under each training
filter_col = [col for col in real_results if col.startswith('EmbSim_') ]
df_list.append(real_results[filter_col])
filter_col = [col for col in real_results if col.startswith('Nll-Test')]
df_list.append(real_results[filter_col])
filter_col = [col for col in oracle_results if col.startswith('EmbSim_')]
df_list.append(oracle_results[filter_col])
filter_col = [col for col in oracle_results if col.startswith('Nll-Test')]
df_list.append(oracle_results[filter_col])
filter_col = [col for col in oracle_results if col.startswith('Nll-Oracle')]
df_list.append(oracle_results[filter_col])
# define number of rows and columns for subplots
nrow = 3
ncol = math.ceil(len(df_list) / nrow)
# make a list of all dataframes
df_title_list = ['Real EmbeddingSimilarites', 'Real NLL-Test', 'Oracle EmbeddingSimilarites', 'Oracle NLL-Test', 'Oracle NLL-Oracle']
# plot counter
count = 0
#build plot
fig, axes = plt.subplots(nrow, ncol)
plt.subplots_adjust(wspace=0.2, hspace=0.5)
for r in range(nrow):
for c in range(ncol):
if count < len(df_list):
df = df_list[count]
if df.columns.any('EmbSim_'):
df.columns = df.columns.str.replace(r'^EmbSim_', '')
if df.columns.any('Nll-Test_'):
df.columns = df.columns.str.replace(r'^Nll-Test_', '')
if df.columns.any('Nll-Oracle_'):
df.columns = df.columns.str.replace(r'^Nll-Oracle_', '')
df.plot(ax=axes[r, c], y=df.columns, kind='line',
title=df_title_list[count], figsize=(20, 10))
count += 1
# save metrics to .png for later use in pdf report
plt.savefig(directory + 'model_metric_charts.png')
|
nilq/baby-python
|
python
|
'''Collects tweets, embeddings and save to DB'''
from flask_sqlalchemy import SQLAlchemy
from dotenv import load_dotenv
import os
import tweepy
import basilica
from .models import DB, Tweet, User
TWITTER_USERS = ['calebhicks', 'elonmusk', 'rrherr', 'SteveMartinToGo',
'alyankovic', 'nasa', 'sadserver', 'jkhowland', 'austen',
'common_squirrel', 'KenJennings', 'conanobrien',
'big_ben_clock', 'IAM_SHAKESPEARE']
load_dotenv()
API_KEY = os.getenv("API_KEY")
API_SECRET_KEY = os.getenv("API_SECRET_KEY")
BEARER_TOKEN = os.getenv("BEARER_TOKEN")
BASILICA_KEY = os.getenv("BASILICA_KEY")
b = basilica.Connection(BASILICA_KEY)
# Grants authorization
TWITTER_AUTH = tweepy.OAuthHandler(API_KEY, API_SECRET_KEY)
TWITTER = tweepy.API(TWITTER_AUTH)
DB = SQLAlchemy()
user = 'jackblack'
twitter_user = TWITTER.get_user(user)
tweets = twitter_user.timeline(count = 5, exclude_replies=True,
include_rts=False,
tweet_mode = 'extended',)
tweet_text = tweets[0].full_text
embedding = b.embed_sentence(tweet_text, model = 'twitter')
def add_or_update_user(username):
twitter_user = TWITTER.get_user(username)
db_user = (User.query.get(twitter_user.id) or
User(id = twitter_user.id, name = username))
DB.session.add(db_user)
tweets = twitter_user.timeline(count = 3, exclude_replies=True,
include_rts=False,
tweet_mode = 'extended',)
# Get latest tweet ID
if tweets:
db_user.newest_tweet_id = tweets[0].id
for tweet in tweets:
embedding = b.embed_sentence(tweet.full_text, model='twitter')
db_tweet = Tweet(id = tweet.id, text=tweet.full_text[:300], embedding=embedding)
db_user.tweets.append(db_tweet)
DB.session.add(db_tweet)
DB.session.commit()
for name in TWITTER_USERS:
try:
twitter_user = TWITTER.get_user(name)
db_user = (User.query.get(twitter_user.id))
# print(twitter_user.id)
tweets = twitter_user.timeline(count = 3, exclude_replies=True,
include_rts=False, tweet_mode='Extended',
)
# for tweet in tweets:
# print(tweet.text)
except Exception as e:
print(f'Error: {e},\n{username} not found')
else:
DB.session.commit()
def insert_example_user():
for user in TWITTER_USERS[:5]:
add_or_update_user(user)
|
nilq/baby-python
|
python
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities, _tables
from . import outputs
from ._inputs import *
__all__ = ['CodeSigningConfigArgs', 'CodeSigningConfig']
@pulumi.input_type
class CodeSigningConfigArgs:
def __init__(__self__, *,
allowed_publishers: pulumi.Input['CodeSigningConfigAllowedPublishersArgs'],
description: Optional[pulumi.Input[str]] = None,
policies: Optional[pulumi.Input['CodeSigningConfigPoliciesArgs']] = None):
"""
The set of arguments for constructing a CodeSigningConfig resource.
:param pulumi.Input['CodeSigningConfigAllowedPublishersArgs'] allowed_publishers: A configuration block of allowed publishers as signing profiles for this code signing configuration. Detailed below.
:param pulumi.Input[str] description: Descriptive name for this code signing configuration.
:param pulumi.Input['CodeSigningConfigPoliciesArgs'] policies: A configuration block of code signing policies that define the actions to take if the validation checks fail. Detailed below.
"""
pulumi.set(__self__, "allowed_publishers", allowed_publishers)
if description is not None:
pulumi.set(__self__, "description", description)
if policies is not None:
pulumi.set(__self__, "policies", policies)
@property
@pulumi.getter(name="allowedPublishers")
def allowed_publishers(self) -> pulumi.Input['CodeSigningConfigAllowedPublishersArgs']:
"""
A configuration block of allowed publishers as signing profiles for this code signing configuration. Detailed below.
"""
return pulumi.get(self, "allowed_publishers")
@allowed_publishers.setter
def allowed_publishers(self, value: pulumi.Input['CodeSigningConfigAllowedPublishersArgs']):
pulumi.set(self, "allowed_publishers", value)
@property
@pulumi.getter
def description(self) -> Optional[pulumi.Input[str]]:
"""
Descriptive name for this code signing configuration.
"""
return pulumi.get(self, "description")
@description.setter
def description(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "description", value)
@property
@pulumi.getter
def policies(self) -> Optional[pulumi.Input['CodeSigningConfigPoliciesArgs']]:
"""
A configuration block of code signing policies that define the actions to take if the validation checks fail. Detailed below.
"""
return pulumi.get(self, "policies")
@policies.setter
def policies(self, value: Optional[pulumi.Input['CodeSigningConfigPoliciesArgs']]):
pulumi.set(self, "policies", value)
class CodeSigningConfig(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
allowed_publishers: Optional[pulumi.Input[pulumi.InputType['CodeSigningConfigAllowedPublishersArgs']]] = None,
description: Optional[pulumi.Input[str]] = None,
policies: Optional[pulumi.Input[pulumi.InputType['CodeSigningConfigPoliciesArgs']]] = None,
__props__=None,
__name__=None,
__opts__=None):
"""
Provides a Lambda Code Signing Config resource. A code signing configuration defines a list of allowed signing profiles and defines the code-signing validation policy (action to be taken if deployment validation checks fail).
For information about Lambda code signing configurations and how to use them, see [configuring code signing for Lambda functions](https://docs.aws.amazon.com/lambda/latest/dg/configuration-codesigning.html)
## Example Usage
```python
import pulumi
import pulumi_aws as aws
new_csc = aws.lambda_.CodeSigningConfig("newCsc",
allowed_publishers=aws.lambda..CodeSigningConfigAllowedPublishersArgs(
signing_profile_version_arns=[
aws_signer_signing_profile["example1"]["arn"],
aws_signer_signing_profile["example2"]["arn"],
],
),
policies=aws.lambda..CodeSigningConfigPoliciesArgs(
untrusted_artifact_on_deployment="Warn",
),
description="My awesome code signing config.")
```
## Import
Code Signing Configs can be imported using their ARN, e.g.
```sh
$ pulumi import aws:lambda/codeSigningConfig:CodeSigningConfig imported_csc arn:aws:lambda:us-west-2:123456789012:code-signing-config:csc-0f6c334abcdea4d8b
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[pulumi.InputType['CodeSigningConfigAllowedPublishersArgs']] allowed_publishers: A configuration block of allowed publishers as signing profiles for this code signing configuration. Detailed below.
:param pulumi.Input[str] description: Descriptive name for this code signing configuration.
:param pulumi.Input[pulumi.InputType['CodeSigningConfigPoliciesArgs']] policies: A configuration block of code signing policies that define the actions to take if the validation checks fail. Detailed below.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: CodeSigningConfigArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Provides a Lambda Code Signing Config resource. A code signing configuration defines a list of allowed signing profiles and defines the code-signing validation policy (action to be taken if deployment validation checks fail).
For information about Lambda code signing configurations and how to use them, see [configuring code signing for Lambda functions](https://docs.aws.amazon.com/lambda/latest/dg/configuration-codesigning.html)
## Example Usage
```python
import pulumi
import pulumi_aws as aws
new_csc = aws.lambda_.CodeSigningConfig("newCsc",
allowed_publishers=aws.lambda..CodeSigningConfigAllowedPublishersArgs(
signing_profile_version_arns=[
aws_signer_signing_profile["example1"]["arn"],
aws_signer_signing_profile["example2"]["arn"],
],
),
policies=aws.lambda..CodeSigningConfigPoliciesArgs(
untrusted_artifact_on_deployment="Warn",
),
description="My awesome code signing config.")
```
## Import
Code Signing Configs can be imported using their ARN, e.g.
```sh
$ pulumi import aws:lambda/codeSigningConfig:CodeSigningConfig imported_csc arn:aws:lambda:us-west-2:123456789012:code-signing-config:csc-0f6c334abcdea4d8b
```
:param str resource_name: The name of the resource.
:param CodeSigningConfigArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(CodeSigningConfigArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
allowed_publishers: Optional[pulumi.Input[pulumi.InputType['CodeSigningConfigAllowedPublishersArgs']]] = None,
description: Optional[pulumi.Input[str]] = None,
policies: Optional[pulumi.Input[pulumi.InputType['CodeSigningConfigPoliciesArgs']]] = None,
__props__=None,
__name__=None,
__opts__=None):
if __name__ is not None:
warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning)
resource_name = __name__
if __opts__ is not None:
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
if allowed_publishers is None and not opts.urn:
raise TypeError("Missing required property 'allowed_publishers'")
__props__['allowed_publishers'] = allowed_publishers
__props__['description'] = description
__props__['policies'] = policies
__props__['arn'] = None
__props__['config_id'] = None
__props__['last_modified'] = None
super(CodeSigningConfig, __self__).__init__(
'aws:lambda/codeSigningConfig:CodeSigningConfig',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
allowed_publishers: Optional[pulumi.Input[pulumi.InputType['CodeSigningConfigAllowedPublishersArgs']]] = None,
arn: Optional[pulumi.Input[str]] = None,
config_id: Optional[pulumi.Input[str]] = None,
description: Optional[pulumi.Input[str]] = None,
last_modified: Optional[pulumi.Input[str]] = None,
policies: Optional[pulumi.Input[pulumi.InputType['CodeSigningConfigPoliciesArgs']]] = None) -> 'CodeSigningConfig':
"""
Get an existing CodeSigningConfig resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[pulumi.InputType['CodeSigningConfigAllowedPublishersArgs']] allowed_publishers: A configuration block of allowed publishers as signing profiles for this code signing configuration. Detailed below.
:param pulumi.Input[str] arn: The Amazon Resource Name (ARN) of the code signing configuration.
:param pulumi.Input[str] config_id: Unique identifier for the code signing configuration.
:param pulumi.Input[str] description: Descriptive name for this code signing configuration.
:param pulumi.Input[str] last_modified: The date and time that the code signing configuration was last modified.
:param pulumi.Input[pulumi.InputType['CodeSigningConfigPoliciesArgs']] policies: A configuration block of code signing policies that define the actions to take if the validation checks fail. Detailed below.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__["allowed_publishers"] = allowed_publishers
__props__["arn"] = arn
__props__["config_id"] = config_id
__props__["description"] = description
__props__["last_modified"] = last_modified
__props__["policies"] = policies
return CodeSigningConfig(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="allowedPublishers")
def allowed_publishers(self) -> pulumi.Output['outputs.CodeSigningConfigAllowedPublishers']:
"""
A configuration block of allowed publishers as signing profiles for this code signing configuration. Detailed below.
"""
return pulumi.get(self, "allowed_publishers")
@property
@pulumi.getter
def arn(self) -> pulumi.Output[str]:
"""
The Amazon Resource Name (ARN) of the code signing configuration.
"""
return pulumi.get(self, "arn")
@property
@pulumi.getter(name="configId")
def config_id(self) -> pulumi.Output[str]:
"""
Unique identifier for the code signing configuration.
"""
return pulumi.get(self, "config_id")
@property
@pulumi.getter
def description(self) -> pulumi.Output[Optional[str]]:
"""
Descriptive name for this code signing configuration.
"""
return pulumi.get(self, "description")
@property
@pulumi.getter(name="lastModified")
def last_modified(self) -> pulumi.Output[str]:
"""
The date and time that the code signing configuration was last modified.
"""
return pulumi.get(self, "last_modified")
@property
@pulumi.getter
def policies(self) -> pulumi.Output['outputs.CodeSigningConfigPolicies']:
"""
A configuration block of code signing policies that define the actions to take if the validation checks fail. Detailed below.
"""
return pulumi.get(self, "policies")
def translate_output_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
def translate_input_property(self, prop):
return _tables.SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop
|
nilq/baby-python
|
python
|
import logging
from contextlib import contextmanager
from unittest import mock
import pytest
import hedwig.conf
from hedwig.backends.base import HedwigPublisherBaseBackend
from hedwig.backends.import_utils import import_module_attr
from hedwig.testing.config import unconfigure
from tests.models import MessageType
try:
# may not be available
from moto import mock_sqs, mock_sns
except ImportError:
pass
def pytest_configure():
logging.basicConfig()
@pytest.fixture
def settings():
"""
Use this fixture to override settings. Changes are automatically reverted
"""
hedwig.conf.settings._ensure_configured()
original_module = hedwig.conf.settings._user_settings
class Wrapped:
# default to the original module, but allow tests to setattr which would override
def __getattr__(self, name):
return getattr(original_module, name)
unconfigure()
hedwig.conf.settings._user_settings = Wrapped()
try:
yield hedwig.conf.settings._user_settings
finally:
unconfigure()
hedwig.conf.settings._user_settings = original_module
@pytest.fixture(name='message_factory', params=['jsonschema', 'protobuf'])
def _message_factory(request, settings):
if request.param == 'jsonschema':
settings.HEDWIG_DATA_VALIDATOR_CLASS = 'hedwig.validators.jsonschema.JSONSchemaValidator'
try:
import jsonschema # noqa
from hedwig.testing.factories.jsonschema import JSONSchemaMessageFactory # noqa
yield JSONSchemaMessageFactory
except ImportError:
pytest.skip("JSON Schema not importable")
if request.param == 'protobuf':
settings.HEDWIG_DATA_VALIDATOR_CLASS = 'hedwig.validators.protobuf.ProtobufValidator'
try:
from tests.protobuf_factory import ProtobufMessageFactory # noqa
def _encode_proto(msg):
return msg.SerializeToString(deterministic=True)
# make maps deterministically ordered
with mock.patch("hedwig.validators.protobuf.ProtobufValidator._encode_proto", side_effect=_encode_proto):
yield ProtobufMessageFactory
except ImportError:
pytest.skip("Protobuf factory not importable")
@pytest.fixture()
def message_data(message_factory):
return message_factory.build(msg_type=MessageType.trip_created)
@pytest.fixture()
def message(message_factory):
return message_factory(msg_type=MessageType.trip_created)
@pytest.fixture()
def message_with_trace(message_factory):
return message_factory(
msg_type=MessageType.trip_created,
metadata__headers__traceparent="00-aa2ada259e917551e16da4a0ad33db24-662fd261d30ec74c-01",
)
@contextmanager
def _mock_boto3():
settings.AWS_REGION = 'us-west-1'
with mock_sqs(), mock_sns(), mock.patch("hedwig.backends.aws.boto3", autospec=True) as boto3_mock:
yield boto3_mock
@pytest.fixture
def mock_boto3():
with _mock_boto3() as m:
yield m
@pytest.fixture()
def sqs_consumer_backend(mock_boto3):
# may not be available
from hedwig.backends import aws
yield aws.AWSSQSConsumerBackend()
@pytest.fixture
def mock_pubsub_v1():
with mock.patch("hedwig.backends.gcp.pubsub_v1", autospec=True) as pubsub_v1_mock:
yield pubsub_v1_mock
@pytest.fixture(params=['aws', 'google'])
def consumer_backend(request):
if request.param == 'aws':
try:
from hedwig.backends.aws import AWSSQSConsumerBackend # noqa
with _mock_boto3():
yield AWSSQSConsumerBackend()
except ImportError:
pytest.skip("AWS backend not importable")
if request.param == 'google':
try:
from hedwig.backends.gcp import GooglePubSubConsumerBackend # noqa
with mock.patch("hedwig.backends.gcp.pubsub_v1"), mock.patch(
"hedwig.backends.gcp.google_auth_default", return_value=(None, "DUMMY")
):
yield GooglePubSubConsumerBackend()
except ImportError:
pytest.skip("Google backend not importable")
@pytest.fixture(
params=["hedwig.backends.aws.AWSSNSConsumerBackend", "hedwig.backends.gcp.GooglePubSubPublisherBackend"]
)
def publisher_backend(request, mock_boto3):
with mock.patch("hedwig.backends.gcp.pubsub_v1"):
yield import_module_attr(request.param)
@pytest.fixture()
def mock_publisher_backend():
with mock.patch.object(HedwigPublisherBaseBackend, '_publish'):
yield HedwigPublisherBaseBackend()
@pytest.fixture(params=[True, False], ids=["message-attrs", "no-message-attrs"])
def use_transport_message_attrs(request, settings):
settings.HEDWIG_USE_TRANSPORT_MESSAGE_ATTRIBUTES = request.param
yield settings.HEDWIG_USE_TRANSPORT_MESSAGE_ATTRIBUTES
|
nilq/baby-python
|
python
|
# author: Drew Botwinick, Botwinick Innovations
# license: 3-clause BSD
import os
import sys
# region Daemonize (Linux)
# DERIVED FROM: http://code.activestate.com/recipes/66012-fork-a-daemon-process-on-unix/
# This module is used to fork the current process into a daemon.
# Almost none of this is necessary (or advisable) if your daemon
# is being started by inetd. In that case, stdin, stdout and stderr are
# all set up for you to refer to the network connection, and the fork()s
# and session manipulation should not be done (to avoid confusing inetd).
# Only the chdir() and umask() steps remain as useful.
# References:
# UNIX Programming FAQ
# 1.7 How do I get my program to act like a daemon?
# http://www.erlenstar.demon.co.uk/unix/faq_2.html#SEC16
#
# Advanced Programming in the Unix Environment
# W. Richard Stevens, 1992, Addison-Wesley, ISBN 0-201-56317-7.
def daemonize_linux(stdin='/dev/null', stdout='/dev/null', stderr=None, pid_file=None, working_dir=None):
"""
This forks the current process into a daemon.
The stdin, stdout, and stderr arguments are file names that will be opened and be used to replace
the standard file descriptors in sys.stdin, sys.stdout, and sys.stderr.
These arguments are optional and default to /dev/null. Note that stderr is opened unbuffered, so
if it shares a file with stdout then interleaved output may not appear in the order that you expect.
:param stdin:
:param stdout:
:param stderr:
:param pid_file:
:param working_dir:
"""
# Because you're not reaping your dead children, many of these resources are held open longer than they should.
# Your second children are being properly handled by init(8) -- their parent is dead, so they are re-parented
# to init(8), and init(8) will clean up after them (wait(2)) when they die.
#
# However, your program is responsible for cleaning up after the first set of children. C programs typically
# install a signal(7) handler for SIGCHLD that calls wait(2) or waitpid(2) to reap the children's exit status
# and thus remove its entries from the kernel's memory.
#
# But signal handling in a script is a bit annoying. If you can set the SIGCHLD signal disposition to SIG_IGN
# explicitly, the kernel will know that you are not interested in the exit status and will reap the children
# for you_.
import signal
signal.signal(signal.SIGCHLD, signal.SIG_IGN)
# Do first fork.
try:
# sys.stdout.write("attempting first fork, pid=")
pid = os.fork()
if pid > 0:
# sys.stdout.write("%s\n" % pid)
sys.exit(0) # Exit first parent.
except OSError as e:
sys.stderr.write("fork #1 failed: (%d) %s\n" % (e.errno, e.strerror))
sys.exit(1)
# Decouple from parent environment.
# sys.stdout.write("attempting to separate from the parent environment\n")
os.chdir('/')
os.umask(0)
os.setsid()
# Do second fork.
try:
# sys.stdout.write("attempting second fork, pid=")
pid = os.fork()
if pid > 0:
# sys.stdout.write("%s\n" % pid)
sys.exit(0) # Exit second parent.
except OSError as e:
sys.stderr.write("fork #2 failed: (%d) %s\n" % (e.errno, e.strerror))
sys.exit(1)
# sys.stdout.write("\nI am now a daemon -- redirecting stdin, stdout, stderr now -- goodbye terminal\n")
# Redirect standard file descriptors.
if not stderr:
stderr = stdout
si = open(stdin, 'r')
so = open(stdout, 'a+')
se = open(stderr, 'a+', 0)
# this might be a good time to write a PID message to the starting user?
if pid_file:
with open(pid_file, 'w+') as f: # online references don't close this -- is it bad if we do?
f.write('%s\n' % pid)
# flush anything that is in the current stdout/stderr
sys.stdout.flush()
sys.stderr.flush()
# close file descriptors for stdin, stdout, and stderr
os.close(sys.stdin.fileno())
os.close(sys.stdout.fileno())
os.close(sys.stderr.fileno())
# reassign file descriptors for stdin, stdout, and stderr
os.dup2(si.fileno(), sys.stdin.fileno())
os.dup2(so.fileno(), sys.stdout.fileno())
os.dup2(se.fileno(), sys.stderr.fileno())
if working_dir:
os.chdir(working_dir)
# ## Why 2 forks?
# The first fork accomplishes two things - allow the shell to return, and allow you to do a setsid().
#
# The setsid() removes yourself from your controlling terminal.
# You see, before, you were still listed as a job of your previous process, and therefore the user might
# accidentally send you a signal. setsid() gives you a new session, and removes the existing controlling terminal.
#
# The problem is, you are now a session leader. As a session leader, if you open a file descriptor that is a terminal,
# it will become your controlling terminal (oops!). Therefore, the second fork makes you NOT be a session leader.
# Only session leaders can acquire a controlling terminal, so you can open up any file you wish without worrying
# that it will make you a controlling terminal.
#
# So - first fork - allow shell to return, and permit you to call setsid()
#
# Second fork - prevent you from accidentally reacquiring a controlling terminal.
# endregion
|
nilq/baby-python
|
python
|
# -*- coding: utf-8 -*-
# Generated by Django 1.10.6 on 2017-03-30 14:57
from __future__ import unicode_literals
import cms.models.fields
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('cms', '0016_auto_20160608_1535'),
]
operations = [
migrations.CreateModel(
name='MenuItem',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('path', models.CharField(max_length=255, unique=True)),
('depth', models.PositiveIntegerField()),
('numchild', models.PositiveIntegerField(default=0)),
('title', models.CharField(max_length=255, verbose_name='Tytuł')),
('menu_id', models.CharField(max_length=255, verbose_name='Menu ID')),
('url', models.URLField(null=True)),
('target', models.CharField(blank=True, choices=[('_blank', 'Open in new window'), ('_self', 'Open in same window'), ('_parent', 'Delegate to parent'), ('_top', 'Delegate to top')], max_length=255, verbose_name='Target')),
('page', cms.models.fields.PageField(null=True, on_delete=django.db.models.deletion.CASCADE, to='cms.Page', verbose_name='Strona')),
],
options={
'abstract': False,
},
),
]
|
nilq/baby-python
|
python
|
import pandas as pd
#%%
print('hello')
|
nilq/baby-python
|
python
|
import pygeoip
gip = pygeoip.GeoIP("GeoLiteCity.dat")
res = gip.record_by_addr('192.168.29.160')
for key, val in res.items():
print('%s : %s' % (key, val))
|
nilq/baby-python
|
python
|
"""
Simple data container for a observable
"""
from tcvx21 import Quantity
import numpy as np
class MissingDataError(Exception):
"""An error to indicate that the observable is missing data"""
pass
class Observable:
def __init__(self, data, diagnostic, observable, label, color, linestyle):
"""Simple container for individual observables"""
try:
self.name = data.observable_name
self.label = label
self.color = color
self.linestyle = linestyle
self.diagnostic, self.observable = diagnostic, observable
self.dimensionality = data.dimensionality
self.check_dimensionality()
self.experimental_hierarchy = data.experimental_hierarchy
self.simulation_hierarchy = getattr(data, "simulation_hierarchy", None)
self._values = Quantity(data["value"][:], data["value"].units)
try:
self._errors = Quantity(data["error"][:], data["error"].units).to(
self._values.units
)
except IndexError:
self._errors = Quantity(
np.zeros_like(self._values), data["value"].units
).to(self._values.units)
self.mask = np.ones_like(self._values).astype(bool)
except (AttributeError, IndexError):
raise MissingDataError(
f"Missing data for {diagnostic}:{observable}. Data available is {data}"
)
def check_dimensionality(self):
raise NotImplementedError()
@property
def values(self) -> Quantity:
"""Returns the observable values, with a mask applied if applicable"""
return self._values[self.mask]
@property
def errors(self) -> Quantity:
"""Returns the observable errors, with a mask applied if applicable"""
return self._errors[self.mask]
@property
def units(self) -> str:
"""Returns the units of the values and errors, as a string"""
return str(self._values.units)
@property
def is_empty(self):
return False
@property
def has_errors(self):
return bool(np.count_nonzero(self.errors))
@property
def compact_units(self) -> str:
"""Units with compact suffix"""
if self.values.check("[length]^-3"):
# Don't convert 10^19 m^-3 to ~10 1/µm^3
return str(self.values.units)
else:
return str(np.max(np.abs(self.values)).to_compact().units)
@property
def npts(self):
"""Returns the number of unmasked observable points"""
return self.values.size
def nan_mask(self):
"""Returns a mask which will remove NaN values"""
return np.logical_and(~np.isnan(self._values), ~np.isnan(self._errors))
def check_attributes(self, other):
self.mask = np.logical_and(self.mask, other.mask)
assert self.color == other.color
assert self.label == other.label
assert self.dimensionality == other.dimensionality
assert self.linestyle == other.linestyle
if hasattr(self, "_positions_rsep"):
assert np.allclose(
self._positions_rsep, other._positions_rsep, equal_nan=True
)
if hasattr(self, "_positions_zx"):
assert np.allclose(self._positions_zx, other._positions_zx, equal_nan=True)
def fill_attributes(self, result):
"""Fills the attributes when copying to make a new object"""
result.mask = self.mask
result.color = self.color
result.label = self.label
result.dimensionality = self.dimensionality
result.linestyle = self.linestyle
if hasattr(self, "xmin") and hasattr(self, "xmax"):
result.xmin, result.xmax, result.ymin, result.ymax = (
self.xmin,
self.xmax,
None,
None,
)
if hasattr(self, "_positions_rsep"):
result._positions_rsep = self._positions_rsep
if hasattr(self, "_positions_zx"):
result._positions_zx = self._positions_zx
def __add__(self, other):
assert type(self) == type(other)
result = object.__new__(self.__class__)
result._values = self._values + other._values
result._errors = np.sqrt(self._errors ** 2 + other._errors ** 2)
self.fill_attributes(result)
result.check_attributes(other)
return result
def __sub__(self, other):
assert type(self) == type(other)
result = object.__new__(self.__class__)
result._values = self._values - other._values
result._errors = np.sqrt(self._errors ** 2 + other._errors ** 2)
self.fill_attributes(result)
result.check_attributes(other)
return result
def __mul__(self, other):
result = object.__new__(self.__class__)
if isinstance(other, (float, Quantity)):
# Scalar multiplication
result._values = self._values * other
result._errors = self._errors * other
self.fill_attributes(result)
else:
assert type(self) == type(other)
result._values = self._values * other._values
result._errors = result._values * np.sqrt(
(self._errors / self._values) ** 2
+ (other._errors / other._values) ** 2
)
self.fill_attributes(result)
result.check_attributes(other)
return result
def __truediv__(self, other):
assert type(self) == type(other)
assert self._values.size == other._values.size
result = object.__new__(self.__class__)
result._values = self._values / other._values
result._errors = result._values * np.sqrt(
(self._errors / self._values) ** 2 + (other._errors / other._values) ** 2
)
self.fill_attributes(result)
result.check_attributes(other)
return result
def trim_to_mask(self, mask):
result = object.__new__(self.__class__)
result._values = self._values[mask]
result._errors = self._errors[mask]
self.fill_attributes(result)
result.mask = np.ones_like(result._values).astype(bool)
if hasattr(self, "_positions_rsep"):
result._positions_rsep = self._positions_rsep[mask]
if hasattr(self, "_positions_zx"):
result._positions_zx = self._positions_zx[mask]
return result
|
nilq/baby-python
|
python
|
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