max_stars_repo_path stringlengths 4 286 | max_stars_repo_name stringlengths 5 119 | max_stars_count int64 0 191k | id stringlengths 1 7 | content stringlengths 6 1.03M | content_cleaned stringlengths 6 1.03M | language stringclasses 111 values | language_score float64 0.03 1 | comments stringlengths 0 556k | edu_score float64 0.32 5.03 | edu_int_score int64 0 5 |
|---|---|---|---|---|---|---|---|---|---|---|
email_service.py | t04glovern/UGATIT | 28 | 6613651 | from os import environ
from os.path import join, dirname
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
import re
import boto3
import urllib.parse
class EmailService(object):
EMAIL_REGEX = re.compile(
r"(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$)")
def __init__(self):
self.ses = boto3.client('ses')
def is_email(self, candidate):
is_email = False
if self.EMAIL_REGEX.match(candidate):
is_email = True
return is_email
def send_email(self, email_addr, image_url):
email = self.build_email(email_addr, image_url)
self.ses.send_raw_email(
RawMessage={'Data': email.as_string()},
Source=email['From'],
Destinations=[email['To']]
)
def build_email(self, email_addr, image_url):
email = MIMEMultipart()
email['Subject'] = 'Your Anime Selfie is ready!'
email['From'] = environ.get('SENDER_EMAIL')
email['To'] = email_addr
email.preamble = 'Multipart message.\n'
email_body = self.build_email_body(image_url)
part = MIMEText(email_body, 'html')
email.attach(part)
return email
@staticmethod
def build_email_body(image_url):
image_url_escaped = urllib.parse.quote(image_url)
html_file = join(dirname(__file__),
'templates', 'template.html')
html_file = open(html_file, 'r')
email = html_file.read()
email = email.replace('{{image_url}}', image_url)
email = email.replace('{{image_url_escaped}}', image_url_escaped)
return email | from os import environ
from os.path import join, dirname
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
import re
import boto3
import urllib.parse
class EmailService(object):
EMAIL_REGEX = re.compile(
r"(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$)")
def __init__(self):
self.ses = boto3.client('ses')
def is_email(self, candidate):
is_email = False
if self.EMAIL_REGEX.match(candidate):
is_email = True
return is_email
def send_email(self, email_addr, image_url):
email = self.build_email(email_addr, image_url)
self.ses.send_raw_email(
RawMessage={'Data': email.as_string()},
Source=email['From'],
Destinations=[email['To']]
)
def build_email(self, email_addr, image_url):
email = MIMEMultipart()
email['Subject'] = 'Your Anime Selfie is ready!'
email['From'] = environ.get('SENDER_EMAIL')
email['To'] = email_addr
email.preamble = 'Multipart message.\n'
email_body = self.build_email_body(image_url)
part = MIMEText(email_body, 'html')
email.attach(part)
return email
@staticmethod
def build_email_body(image_url):
image_url_escaped = urllib.parse.quote(image_url)
html_file = join(dirname(__file__),
'templates', 'template.html')
html_file = open(html_file, 'r')
email = html_file.read()
email = email.replace('{{image_url}}', image_url)
email = email.replace('{{image_url_escaped}}', image_url_escaped)
return email | none | 1 | 2.294703 | 2 | |
lukcid2.py | omdahshaabi/LuckCid | 1 | 6613652 | import random
import string
import tkMessageBox
from Tkinter import *
# Returns a combination of letter & digits
def gen():
return random.choice(list('abcdef' + string.digits))
# prints the first digits until %, and starts printing random generation from the gen function and converts to upper case
# and then opens up a file and appends every click as a new line.
def printme():
combo1 = '00000001008%s111%s1%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s' % (gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper())
with open("cid_file1.txt", 'a') as outfile:
outfile.write(combo1 + '\n')
entryText.set(combo1)
def printme2():
combo2 = '00000001008%s000%s1%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s' % (gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper())
with open("cid_file2.txt", 'a') as outfile:
outfile.write(combo2 + '\n')
entryText2.set(combo2)
# Main Window
window = Tk()
# Size of the window
window.geometry("500x300")
# Tittle
window.title("Lukcid v2")
# Disable resizing
window.resizable(width=FALSE, height=FALSE)
# *** Status Bar ***
status = Label(window, text="Twitter: 0Katz Youtube: Simple Dev ", bd=1, relief=SUNKEN, anchor=W)
status.pack(side=BOTTOM)
# Variable holding the image
photo = PhotoImage(file="pirate.png")
# Invoke the image and displays it on the main window
label = Label(window, image=photo)
label.pack()
# Prints an info warning before program loads and whishes you good luck :)
tkMessageBox.showinfo("Made by 0Katz", 'May the force be with you!')
# Entry Text
entryText = StringVar(window)
entryText2 = StringVar(window)
# Text Box Button which generates the console ids with 1's
textbox = Entry(window, textvariable=entryText).place(x=260, y=100, width=227)
botton = Button(window, text="Generate", command=printme).place(x=356, y=126)
# Text Box and Button which generates console ids with 0's
textbox2 = Entry(window, textvariable=entryText2).place(x=260, y=160, width=227)
botton = Button(window, text="Generate", command=printme2).place(x=356, y=186)
# Doesn't work yet, just there for an idea until
# We can figure a way to use .Net framework with python
tmApi = Radiobutton(window, text="TMAPI", value=1).place(x=17, y=140)
ccApi = Radiobutton(window, text="CCAPI", value=1).place(x=85, y=140)
apiButton = Button(window, text="Stablish Connection").place(x=26, y=180)
ipPs3 = Entry(window).place(x=20, y=100)
ipLabel = Label(window, text="IP ADDRESS").place(x=37, y=70)
genLabel = Label(window, text="CONSOLE ID").place(x=350, y=70)
window.mainloop()
| import random
import string
import tkMessageBox
from Tkinter import *
# Returns a combination of letter & digits
def gen():
return random.choice(list('abcdef' + string.digits))
# prints the first digits until %, and starts printing random generation from the gen function and converts to upper case
# and then opens up a file and appends every click as a new line.
def printme():
combo1 = '00000001008%s111%s1%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s' % (gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper())
with open("cid_file1.txt", 'a') as outfile:
outfile.write(combo1 + '\n')
entryText.set(combo1)
def printme2():
combo2 = '00000001008%s000%s1%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s' % (gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper(), gen().upper(),
gen().upper(), gen().upper())
with open("cid_file2.txt", 'a') as outfile:
outfile.write(combo2 + '\n')
entryText2.set(combo2)
# Main Window
window = Tk()
# Size of the window
window.geometry("500x300")
# Tittle
window.title("Lukcid v2")
# Disable resizing
window.resizable(width=FALSE, height=FALSE)
# *** Status Bar ***
status = Label(window, text="Twitter: 0Katz Youtube: Simple Dev ", bd=1, relief=SUNKEN, anchor=W)
status.pack(side=BOTTOM)
# Variable holding the image
photo = PhotoImage(file="pirate.png")
# Invoke the image and displays it on the main window
label = Label(window, image=photo)
label.pack()
# Prints an info warning before program loads and whishes you good luck :)
tkMessageBox.showinfo("Made by 0Katz", 'May the force be with you!')
# Entry Text
entryText = StringVar(window)
entryText2 = StringVar(window)
# Text Box Button which generates the console ids with 1's
textbox = Entry(window, textvariable=entryText).place(x=260, y=100, width=227)
botton = Button(window, text="Generate", command=printme).place(x=356, y=126)
# Text Box and Button which generates console ids with 0's
textbox2 = Entry(window, textvariable=entryText2).place(x=260, y=160, width=227)
botton = Button(window, text="Generate", command=printme2).place(x=356, y=186)
# Doesn't work yet, just there for an idea until
# We can figure a way to use .Net framework with python
tmApi = Radiobutton(window, text="TMAPI", value=1).place(x=17, y=140)
ccApi = Radiobutton(window, text="CCAPI", value=1).place(x=85, y=140)
apiButton = Button(window, text="Stablish Connection").place(x=26, y=180)
ipPs3 = Entry(window).place(x=20, y=100)
ipLabel = Label(window, text="IP ADDRESS").place(x=37, y=70)
genLabel = Label(window, text="CONSOLE ID").place(x=350, y=70)
window.mainloop()
| en | 0.845537 | # Returns a combination of letter & digits # prints the first digits until %, and starts printing random generation from the gen function and converts to upper case # and then opens up a file and appends every click as a new line. # Main Window # Size of the window # Tittle # Disable resizing # *** Status Bar *** # Variable holding the image # Invoke the image and displays it on the main window # Prints an info warning before program loads and whishes you good luck :) # Entry Text # Text Box Button which generates the console ids with 1's # Text Box and Button which generates console ids with 0's # Doesn't work yet, just there for an idea until # We can figure a way to use .Net framework with python | 3.646261 | 4 |
Scripts/lab4b/aSurname4b.py | PepperBurst/DSP | 1 | 6613653 | <reponame>PepperBurst/DSP<filename>Scripts/lab4b/aSurname4b.py
def coeff422():
b = []
a = []
b.append([0.16, -0.48, 0.48, -0.16])
b.append([0.634, 0, -0.634])
b.append([0.634, 0, 0.634])
b.append([1, -5, 10])
a.append([1, 0.13, 0.52, 0.3])
a.append([1, 0, -0.268])
a.append([1, 0, 0.268])
a.append([10, -5, 1])
return b, a
| def coeff422():
b = []
a = []
b.append([0.16, -0.48, 0.48, -0.16])
b.append([0.634, 0, -0.634])
b.append([0.634, 0, 0.634])
b.append([1, -5, 10])
a.append([1, 0.13, 0.52, 0.3])
a.append([1, 0, -0.268])
a.append([1, 0, 0.268])
a.append([10, -5, 1])
return b, a | none | 1 | 2.708161 | 3 | |
export_model.py | isamu-isozaki/toxic-joke-generator | 0 | 6613654 | #!/usr/bin/env python3
import fire
import json
import os
import numpy as np
import tensorflow as tf
import model, sample, encoder
from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import signature_def_utils
from tensorflow.python.saved_model import tag_constants
from tensorflow.python.saved_model import utils
from tensorflow.python.util import compat
def export_model(
model_name='117M',
seed=None,
nsamples=0,
batch_size=1,
length=None,
top_k=0,
version=1,
folder_id=None,
):
"""
Run the sample_model
:model_name=117M : String, which model to use
:seed=None : Integer seed for random number generators, fix seed to
reproduce results
:nsamples=0 : Number of samples to return, if 0, continues to
generate samples indefinately.
:batch_size=1 : Number of batches (only affects speed/memory).
:length=None : Number of tokens in generated text, if None (default), is
determined by model hyperparameters
:top_k=0 : Integer value controlling diversity. 1 means only 1 word is
considered for each step (token), resulting in deterministic completions,
while 40 means 40 words are considered at each step. 0 (default) is a
special setting meaning no restrictions. 40 generally is a good value.
:version=1 : Integer value giving the version the model is exported as.
:folder_id=None : If the google drive is being used, specify the folder to upload here. Otherwise, keep as None
:
"""
enc = encoder.get_encoder(model_name)
hparams = model.default_hparams()
with open(os.path.join('models', model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if length is None:
length = hparams.n_ctx
elif length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" % hparams.n_ctx)
with tf.Session(graph=tf.Graph()) as sess:
np.random.seed(seed)
tf.set_random_seed(seed)
temperature = tf.placeholder("float", [1])
output_tensor = sample.sample_sequence(
hparams=hparams, length=length,
start_token=enc.encoder['<|endoftext|>'],
batch_size=batch_size,
temperature=temperature, top_k=top_k
)[:, 1:]
saver = tf.train.Saver()
ckpt = tf.train.latest_checkpoint(os.path.join('models', model_name))
saver.restore(sess, ckpt)
def export_model(path):#Thanks Siraj! Couldn't have done it without you!
#Link is https://github.com/llSourcell/How-to-Deploy-a-Tensorflow-Model-in-Production/blob/master/custom_model.py
print("Exporting trained model to ", path)
builder = saved_model_builder.SavedModelBuilder(path)
input_temperature = utils.build_tensor_info(temperature)
output = utils.build_tensor_info(output_tensor)
prediction_signature = signature_def_utils.build_signature_def(
inputs={'temperature': input_temperature},
outputs={'output': output},
method_name=signature_constants.PREDICT_METHOD_NAME)
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={
'predict':
prediction_signature
},
main_op=tf.tables_initializer())
builder.save()
base_directory = "./export_model"
export_path = f"./export_model/{version}"
export_model(export_path)
if(folder_id != None):
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials
# 1. Authenticate and create the PyDrive client.
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)
for content in os.listdir(export_path):
f = drive.CreateFile({"parents": [{"kind": "drive#fileLink", "id": folder_id}]})
f.SetContentFile(f"{export_path}"+"/"+content)
f.Upload()
if __name__ == '__main__':
fire.Fire(export_model)
| #!/usr/bin/env python3
import fire
import json
import os
import numpy as np
import tensorflow as tf
import model, sample, encoder
from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import signature_def_utils
from tensorflow.python.saved_model import tag_constants
from tensorflow.python.saved_model import utils
from tensorflow.python.util import compat
def export_model(
model_name='117M',
seed=None,
nsamples=0,
batch_size=1,
length=None,
top_k=0,
version=1,
folder_id=None,
):
"""
Run the sample_model
:model_name=117M : String, which model to use
:seed=None : Integer seed for random number generators, fix seed to
reproduce results
:nsamples=0 : Number of samples to return, if 0, continues to
generate samples indefinately.
:batch_size=1 : Number of batches (only affects speed/memory).
:length=None : Number of tokens in generated text, if None (default), is
determined by model hyperparameters
:top_k=0 : Integer value controlling diversity. 1 means only 1 word is
considered for each step (token), resulting in deterministic completions,
while 40 means 40 words are considered at each step. 0 (default) is a
special setting meaning no restrictions. 40 generally is a good value.
:version=1 : Integer value giving the version the model is exported as.
:folder_id=None : If the google drive is being used, specify the folder to upload here. Otherwise, keep as None
:
"""
enc = encoder.get_encoder(model_name)
hparams = model.default_hparams()
with open(os.path.join('models', model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if length is None:
length = hparams.n_ctx
elif length > hparams.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" % hparams.n_ctx)
with tf.Session(graph=tf.Graph()) as sess:
np.random.seed(seed)
tf.set_random_seed(seed)
temperature = tf.placeholder("float", [1])
output_tensor = sample.sample_sequence(
hparams=hparams, length=length,
start_token=enc.encoder['<|endoftext|>'],
batch_size=batch_size,
temperature=temperature, top_k=top_k
)[:, 1:]
saver = tf.train.Saver()
ckpt = tf.train.latest_checkpoint(os.path.join('models', model_name))
saver.restore(sess, ckpt)
def export_model(path):#Thanks Siraj! Couldn't have done it without you!
#Link is https://github.com/llSourcell/How-to-Deploy-a-Tensorflow-Model-in-Production/blob/master/custom_model.py
print("Exporting trained model to ", path)
builder = saved_model_builder.SavedModelBuilder(path)
input_temperature = utils.build_tensor_info(temperature)
output = utils.build_tensor_info(output_tensor)
prediction_signature = signature_def_utils.build_signature_def(
inputs={'temperature': input_temperature},
outputs={'output': output},
method_name=signature_constants.PREDICT_METHOD_NAME)
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={
'predict':
prediction_signature
},
main_op=tf.tables_initializer())
builder.save()
base_directory = "./export_model"
export_path = f"./export_model/{version}"
export_model(export_path)
if(folder_id != None):
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials
# 1. Authenticate and create the PyDrive client.
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)
for content in os.listdir(export_path):
f = drive.CreateFile({"parents": [{"kind": "drive#fileLink", "id": folder_id}]})
f.SetContentFile(f"{export_path}"+"/"+content)
f.Upload()
if __name__ == '__main__':
fire.Fire(export_model)
| en | 0.78722 | #!/usr/bin/env python3 Run the sample_model :model_name=117M : String, which model to use :seed=None : Integer seed for random number generators, fix seed to reproduce results :nsamples=0 : Number of samples to return, if 0, continues to generate samples indefinately. :batch_size=1 : Number of batches (only affects speed/memory). :length=None : Number of tokens in generated text, if None (default), is determined by model hyperparameters :top_k=0 : Integer value controlling diversity. 1 means only 1 word is considered for each step (token), resulting in deterministic completions, while 40 means 40 words are considered at each step. 0 (default) is a special setting meaning no restrictions. 40 generally is a good value. :version=1 : Integer value giving the version the model is exported as. :folder_id=None : If the google drive is being used, specify the folder to upload here. Otherwise, keep as None : #Thanks Siraj! Couldn't have done it without you! #Link is https://github.com/llSourcell/How-to-Deploy-a-Tensorflow-Model-in-Production/blob/master/custom_model.py # 1. Authenticate and create the PyDrive client. #fileLink", "id": folder_id}]}) | 2.323997 | 2 |
plot_vlc_compare_runs.py | serl/hls-bba-testbed | 3 | 6613655 | <gh_stars>1-10
import sys, os
from pylibs.log import Session
from pylibs.plot import plotCompareVLCRuns
from pylibs.parallelize import Parallelize
if __name__ == "__main__":
filenames = sys.argv[1:]
assert len(filenames)
export = len(filenames) > 1
if filenames[0] == 'export':
export = True
filenames = sys.argv[2:]
for filename in filenames:
filename = filename.rstrip(os.sep)
sessions = []
run = 1
funcs = []
while True:
runpath = os.path.join(filename, str(run))
if not os.path.isdir(runpath):
runpath += '.tar.gz'
if not os.path.isfile(runpath):
break
print "Reading {0} run {1}...".format(filename, run)
funcs.append({'args': (runpath,)})
run += 1
p = Parallelize(funcs, fn=Session.read)
p.run()
sessions = p.results
if len(sessions) == 0:
print "No runs in {0}".format(filename)
continue
print "Plotting {0}...".format(filename)
plotCompareVLCRuns(sessions, os.path.join('tests', sessions[0].collection, 'compare_runs_' + sessions[0].name + '.png') if export else False, thickness_factor=2, size=(12,9))
#plotCompareVLCRuns(sessions, sessions[0].collection + '_compare_runs_' + sessions[0].name + '_paper.svg' if export else False, thickness_factor=2, size=(9,12))
| import sys, os
from pylibs.log import Session
from pylibs.plot import plotCompareVLCRuns
from pylibs.parallelize import Parallelize
if __name__ == "__main__":
filenames = sys.argv[1:]
assert len(filenames)
export = len(filenames) > 1
if filenames[0] == 'export':
export = True
filenames = sys.argv[2:]
for filename in filenames:
filename = filename.rstrip(os.sep)
sessions = []
run = 1
funcs = []
while True:
runpath = os.path.join(filename, str(run))
if not os.path.isdir(runpath):
runpath += '.tar.gz'
if not os.path.isfile(runpath):
break
print "Reading {0} run {1}...".format(filename, run)
funcs.append({'args': (runpath,)})
run += 1
p = Parallelize(funcs, fn=Session.read)
p.run()
sessions = p.results
if len(sessions) == 0:
print "No runs in {0}".format(filename)
continue
print "Plotting {0}...".format(filename)
plotCompareVLCRuns(sessions, os.path.join('tests', sessions[0].collection, 'compare_runs_' + sessions[0].name + '.png') if export else False, thickness_factor=2, size=(12,9))
#plotCompareVLCRuns(sessions, sessions[0].collection + '_compare_runs_' + sessions[0].name + '_paper.svg' if export else False, thickness_factor=2, size=(9,12)) | en | 0.20316 | #plotCompareVLCRuns(sessions, sessions[0].collection + '_compare_runs_' + sessions[0].name + '_paper.svg' if export else False, thickness_factor=2, size=(9,12)) | 2.140019 | 2 |
project/service/serve.py | hnliu-git/decepticon | 0 | 6613656 | <reponame>hnliu-git/decepticon<gh_stars>0
import os
from flask import Flask
from flask import render_template, request
from t5_inf import RaceInfModule
# Flask App
app = Flask(__name__)
app.config["TEMPLATES_AUTO_RELOAD"] = True
app.config["APPLICATION_ROOT"] = os.environ.get("APP_ROOT","/service")
# Model
q_model = RaceInfModule.load_from_checkpoint(app.config["APPLICATION_ROOT"]+"/ckpts/t5_que.ckpt")
q_model.eval()
d_model = RaceInfModule.load_from_checkpoint(app.config["APPLICATION_ROOT"]+"/ckpts/t5_dis.ckpt")
d_model.eval()
@app.route("/")
def index():
return render_template("index.html",app_root=app.config["APPLICATION_ROOT"])
@app.route("/predict",methods=["POST"])
def predict():
global q_model, d_model
article=request.json["article"]
answer = request.json["answer"]
print("Start generating..")
question = q_model.generate_sentence(article, answer)
print("Question generated!")
distractor = d_model.generate_sentence(article, answer, question)
print("Distractor generated!")
generation = {'question': question, 'distractor': distractor}
generation_html=render_template("result.html",generation=generation)
return {"generation_html":generation_html}
@app.route("/predict_json",methods=["POST"])
def predict_json():
global q_model, d_model
article = request.json["article"]
answer = request.json["answer"]
question = q_model.generate_sentence(article, answer)
distractor = d_model.generate_sentence(article, answer, question)
generation = {'question': question, 'distractor': distractor}
return {"generation":generation}
| import os
from flask import Flask
from flask import render_template, request
from t5_inf import RaceInfModule
# Flask App
app = Flask(__name__)
app.config["TEMPLATES_AUTO_RELOAD"] = True
app.config["APPLICATION_ROOT"] = os.environ.get("APP_ROOT","/service")
# Model
q_model = RaceInfModule.load_from_checkpoint(app.config["APPLICATION_ROOT"]+"/ckpts/t5_que.ckpt")
q_model.eval()
d_model = RaceInfModule.load_from_checkpoint(app.config["APPLICATION_ROOT"]+"/ckpts/t5_dis.ckpt")
d_model.eval()
@app.route("/")
def index():
return render_template("index.html",app_root=app.config["APPLICATION_ROOT"])
@app.route("/predict",methods=["POST"])
def predict():
global q_model, d_model
article=request.json["article"]
answer = request.json["answer"]
print("Start generating..")
question = q_model.generate_sentence(article, answer)
print("Question generated!")
distractor = d_model.generate_sentence(article, answer, question)
print("Distractor generated!")
generation = {'question': question, 'distractor': distractor}
generation_html=render_template("result.html",generation=generation)
return {"generation_html":generation_html}
@app.route("/predict_json",methods=["POST"])
def predict_json():
global q_model, d_model
article = request.json["article"]
answer = request.json["answer"]
question = q_model.generate_sentence(article, answer)
distractor = d_model.generate_sentence(article, answer, question)
generation = {'question': question, 'distractor': distractor}
return {"generation":generation} | en | 0.647125 | # Flask App # Model | 2.281823 | 2 |
_unittests/ut_pycode/test_pip_helper.py | Pandinosaurus/pyquickhelper | 18 | 6613657 | """
@brief test tree node (time=2s)
"""
import unittest
import pandas
from pyquickhelper.pycode import ExtTestCase
from pyquickhelper.pycode.pip_helper import (
get_packages_list, package2dict, get_package_info,
PQPipError)
class TestPipHelper(ExtTestCase):
def test_exc(self):
exc = PQPipError('cmd', 'out', 'err')
msg = str(exc)
self.assertEqual([msg.replace('\n', '')], [
'CMD:cmdOUT:out[piperror]err'])
def test_pip_list(self):
li = get_packages_list()
dt = package2dict(li[0])
avoid = {'py_version'}
empty = []
for k, v in dt.items():
if k not in avoid:
if k is None:
empty.append(k)
self.assertEmpty(empty)
self.assertNotEmpty(li)
def test_pip_show(self):
info = get_package_info("pandas")
if "version" not in str(info):
raise AssertionError(str(info))
info = get_package_info("sphinx")
if "version" not in str(info):
raise Exception(str(info))
def test_pip_show_all(self):
info = get_package_info(start=0, end=2)
df = pandas.DataFrame(info)
self.assertNotEmpty(info)
if __name__ == "__main__":
info = get_package_info()
df = pandas.DataFrame(info)
df.to_excel("out_packages.xlsx")
if __name__ == "__main__":
unittest.main()
| """
@brief test tree node (time=2s)
"""
import unittest
import pandas
from pyquickhelper.pycode import ExtTestCase
from pyquickhelper.pycode.pip_helper import (
get_packages_list, package2dict, get_package_info,
PQPipError)
class TestPipHelper(ExtTestCase):
def test_exc(self):
exc = PQPipError('cmd', 'out', 'err')
msg = str(exc)
self.assertEqual([msg.replace('\n', '')], [
'CMD:cmdOUT:out[piperror]err'])
def test_pip_list(self):
li = get_packages_list()
dt = package2dict(li[0])
avoid = {'py_version'}
empty = []
for k, v in dt.items():
if k not in avoid:
if k is None:
empty.append(k)
self.assertEmpty(empty)
self.assertNotEmpty(li)
def test_pip_show(self):
info = get_package_info("pandas")
if "version" not in str(info):
raise AssertionError(str(info))
info = get_package_info("sphinx")
if "version" not in str(info):
raise Exception(str(info))
def test_pip_show_all(self):
info = get_package_info(start=0, end=2)
df = pandas.DataFrame(info)
self.assertNotEmpty(info)
if __name__ == "__main__":
info = get_package_info()
df = pandas.DataFrame(info)
df.to_excel("out_packages.xlsx")
if __name__ == "__main__":
unittest.main()
| en | 0.402504 | @brief test tree node (time=2s) | 2.640693 | 3 |
CSES/Introductory_Problems/Permutations.py | kancharlaraju21/Competitive_Programming | 0 | 6613658 | n=int(input())
if n==2 or n==3:
print("NO SOLUTION")
else:
if n % 2==0:
for i in range(n-1,0,-2):
print(i,end=' ')
print(n,end=' ')
for i in range(2,n,2):
print(i,end=' ')
else:
for i in range(n-1,0,-2):
print(i,end=' ')
print(n,end=' ')
for i in range(n-2,0,-2):
print(i,end=' ') | n=int(input())
if n==2 or n==3:
print("NO SOLUTION")
else:
if n % 2==0:
for i in range(n-1,0,-2):
print(i,end=' ')
print(n,end=' ')
for i in range(2,n,2):
print(i,end=' ')
else:
for i in range(n-1,0,-2):
print(i,end=' ')
print(n,end=' ')
for i in range(n-2,0,-2):
print(i,end=' ') | none | 1 | 3.984146 | 4 | |
opticalFlow/deepvel/testing/imax/analyze_output.py | aasensio/DeepLearning | 0 | 6613659 | <gh_stars>0
import numpy as np
import matplotlib.pyplot as pl
import h5py
import platform
import os
from ipdb import set_trace as stop
from astropy.io import fits
import scipy.io as io
import time
import matplotlib.animation as manimation
os.environ["KERAS_BACKEND"] = "tensorflow"
if (platform.node() != 'vena'):
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from keras.layers import Input, Convolution2D, merge, Activation, Lambda, BatchNormalization
from keras.callbacks import ModelCheckpoint, Callback
from keras.models import Model, model_from_json
import tensorflow as tf
import keras.backend.tensorflow_backend as ktf
def running_mean(x, N):
cumsum = np.cumsum(np.insert(x, 0, 0))
return (cumsum[N:] - cumsum[:-N]) / N
class trainDNNFull(object):
def __init__(self, root, observations, output, name_of_variable):
# Only allocate needed memory
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
session = tf.Session(config=config)
ktf.set_session(session)
self.root = root
self.nx = 800
self.ny = 800
self.n_times = 2
self.n_filters = 64
self.batch_size = 1
self.n_conv_layers = 20
self.stride = 1
self.skip_frequency = 2
self.n_frames = 1
self.observations = observations
self.output = output
self.name_of_variable = name_of_variable
def residual(self, inputs):
x = Convolution2D(self.n_filters, 3, 3, border_mode='same', init='he_normal')(inputs)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Convolution2D(self.n_filters, 3, 3, border_mode='same', init='he_normal')(x)
x = BatchNormalization()(x)
x = merge([x, inputs], 'sum')
return x
def defineNetwork(self):
print("Setting up network...")
inputs = Input(shape=(self.nx, self.ny, self.n_times))
conv = Convolution2D(self.n_filters, 3, 3, activation='relu', border_mode='same', init='he_normal')(inputs)
x = self.residual(conv)
for i in range(self.n_conv_layers):
x = self.residual(x)
x = Convolution2D(self.n_filters, 3, 3, border_mode='same', init='he_normal')(x)
x = BatchNormalization()(x)
x = merge([x, conv], 'sum')
final = Convolution2D(6, 1, 1, activation='linear', border_mode='same', init='he_normal')(x)
self.model = Model(input=inputs, output=final)
print("Loading weights...")
self.model.load_weights("{0}_weights.hdf5".format(self.root))
def validation_generator(self):
f = io.readsav(self.observations)
out = f[self.name_of_variable]
self.median_i = np.median(out[:,100:-100,100:-100])
input_validation = np.zeros((self.batch_size,self.nx,self.ny,2), dtype='float32')
while 1:
for i in range(self.n_frames):
print('{0}/{1}'.format(i,self.n_frames))
input_validation[:,:,:,0] = out[i*self.batch_size:(i+1)*self.batch_size,100:100+self.nx,100:100+self.ny] / self.median_i
input_validation[:,:,:,1] = out[i*self.batch_size+1:(i+1)*self.batch_size+1,100:100+self.nx,100:100+self.ny] / self.median_i
yield input_validation
f.close()
def predict_validation(self):
print("Predicting validation data...")
tmp = np.load('/net/duna/scratch1/aasensio/deepLearning/opticalFlow/database/normalization.npz')
min_i, max_i, min_v, max_v = tmp['arr_0'], tmp['arr_1'], tmp['arr_2'], tmp['arr_3']
f = io.readsav(self.observations)
out = f[self.name_of_variable]
self.median_i = np.median(out[:,100:-100,100:-100])
input_validation = np.zeros((1,self.nx,self.ny,2), dtype='float32')
input_validation[0,:,:,0] = out[0:1,100:100+self.nx,100:100+self.ny] / self.median_i
input_validation[0,:,:,1] = out[1:2,100:100+self.nx,100:100+self.ny] / self.median_i
# ff = io.readsav(self.observations)
# im = ff['cont']
# x = np.arange(self.nx)
# y = np.arange(self.ny)
start = time.time()
out = self.model.predict_generator(self.validation_generator(), self.n_frames, max_q_size=1)
end = time.time()
print("Prediction took {0} seconds...".format(end-start))
fun = ktf.function([self.model.layers[0].input],[self.model.layers[1].output])
output = np.squeeze(fun([input_validation])[0][0,200:300,200:300,:]).reshape((100,100,8,8))
f, ax = pl.subplots(nrows=2, ncols=2, figsize=(12,12))
ax[0,0].imshow(output[:,:,0,0] / np.median(output[:,:,0,0]))
ax[0,1].imshow(output[:,:,4,0] / np.median(output[:,:,4,0]))
ax[1,0].imshow(output[:,:,3,4] / np.median(output[:,:,3,4]))
ax[1,1].imshow(output[:,:,2,2] / np.median(output[:,:,2,2]))
pl.show()
#
stop()
if (__name__ == '__main__'):
# out = trainDNNFull('../training/cnns/resnet', 'cont.idl', 'imax_velocity.h5', 'cont')
out = trainDNNFull('../../training/cnns/resnet2', '/net/vena/scratch1/deepLearning/opticalFlow/database/sf_Icon_307-364.sav', 'imax_velocity_noPmodes.h5', 'mov')
out.defineNetwork()
out.predict_validation()
| import numpy as np
import matplotlib.pyplot as pl
import h5py
import platform
import os
from ipdb import set_trace as stop
from astropy.io import fits
import scipy.io as io
import time
import matplotlib.animation as manimation
os.environ["KERAS_BACKEND"] = "tensorflow"
if (platform.node() != 'vena'):
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from keras.layers import Input, Convolution2D, merge, Activation, Lambda, BatchNormalization
from keras.callbacks import ModelCheckpoint, Callback
from keras.models import Model, model_from_json
import tensorflow as tf
import keras.backend.tensorflow_backend as ktf
def running_mean(x, N):
cumsum = np.cumsum(np.insert(x, 0, 0))
return (cumsum[N:] - cumsum[:-N]) / N
class trainDNNFull(object):
def __init__(self, root, observations, output, name_of_variable):
# Only allocate needed memory
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
session = tf.Session(config=config)
ktf.set_session(session)
self.root = root
self.nx = 800
self.ny = 800
self.n_times = 2
self.n_filters = 64
self.batch_size = 1
self.n_conv_layers = 20
self.stride = 1
self.skip_frequency = 2
self.n_frames = 1
self.observations = observations
self.output = output
self.name_of_variable = name_of_variable
def residual(self, inputs):
x = Convolution2D(self.n_filters, 3, 3, border_mode='same', init='he_normal')(inputs)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Convolution2D(self.n_filters, 3, 3, border_mode='same', init='he_normal')(x)
x = BatchNormalization()(x)
x = merge([x, inputs], 'sum')
return x
def defineNetwork(self):
print("Setting up network...")
inputs = Input(shape=(self.nx, self.ny, self.n_times))
conv = Convolution2D(self.n_filters, 3, 3, activation='relu', border_mode='same', init='he_normal')(inputs)
x = self.residual(conv)
for i in range(self.n_conv_layers):
x = self.residual(x)
x = Convolution2D(self.n_filters, 3, 3, border_mode='same', init='he_normal')(x)
x = BatchNormalization()(x)
x = merge([x, conv], 'sum')
final = Convolution2D(6, 1, 1, activation='linear', border_mode='same', init='he_normal')(x)
self.model = Model(input=inputs, output=final)
print("Loading weights...")
self.model.load_weights("{0}_weights.hdf5".format(self.root))
def validation_generator(self):
f = io.readsav(self.observations)
out = f[self.name_of_variable]
self.median_i = np.median(out[:,100:-100,100:-100])
input_validation = np.zeros((self.batch_size,self.nx,self.ny,2), dtype='float32')
while 1:
for i in range(self.n_frames):
print('{0}/{1}'.format(i,self.n_frames))
input_validation[:,:,:,0] = out[i*self.batch_size:(i+1)*self.batch_size,100:100+self.nx,100:100+self.ny] / self.median_i
input_validation[:,:,:,1] = out[i*self.batch_size+1:(i+1)*self.batch_size+1,100:100+self.nx,100:100+self.ny] / self.median_i
yield input_validation
f.close()
def predict_validation(self):
print("Predicting validation data...")
tmp = np.load('/net/duna/scratch1/aasensio/deepLearning/opticalFlow/database/normalization.npz')
min_i, max_i, min_v, max_v = tmp['arr_0'], tmp['arr_1'], tmp['arr_2'], tmp['arr_3']
f = io.readsav(self.observations)
out = f[self.name_of_variable]
self.median_i = np.median(out[:,100:-100,100:-100])
input_validation = np.zeros((1,self.nx,self.ny,2), dtype='float32')
input_validation[0,:,:,0] = out[0:1,100:100+self.nx,100:100+self.ny] / self.median_i
input_validation[0,:,:,1] = out[1:2,100:100+self.nx,100:100+self.ny] / self.median_i
# ff = io.readsav(self.observations)
# im = ff['cont']
# x = np.arange(self.nx)
# y = np.arange(self.ny)
start = time.time()
out = self.model.predict_generator(self.validation_generator(), self.n_frames, max_q_size=1)
end = time.time()
print("Prediction took {0} seconds...".format(end-start))
fun = ktf.function([self.model.layers[0].input],[self.model.layers[1].output])
output = np.squeeze(fun([input_validation])[0][0,200:300,200:300,:]).reshape((100,100,8,8))
f, ax = pl.subplots(nrows=2, ncols=2, figsize=(12,12))
ax[0,0].imshow(output[:,:,0,0] / np.median(output[:,:,0,0]))
ax[0,1].imshow(output[:,:,4,0] / np.median(output[:,:,4,0]))
ax[1,0].imshow(output[:,:,3,4] / np.median(output[:,:,3,4]))
ax[1,1].imshow(output[:,:,2,2] / np.median(output[:,:,2,2]))
pl.show()
#
stop()
if (__name__ == '__main__'):
# out = trainDNNFull('../training/cnns/resnet', 'cont.idl', 'imax_velocity.h5', 'cont')
out = trainDNNFull('../../training/cnns/resnet2', '/net/vena/scratch1/deepLearning/opticalFlow/database/sf_Icon_307-364.sav', 'imax_velocity_noPmodes.h5', 'mov')
out.defineNetwork()
out.predict_validation() | en | 0.269709 | # Only allocate needed memory # ff = io.readsav(self.observations) # im = ff['cont'] # x = np.arange(self.nx) # y = np.arange(self.ny) # # out = trainDNNFull('../training/cnns/resnet', 'cont.idl', 'imax_velocity.h5', 'cont') | 2.226343 | 2 |
tests/helpers.py | seba-ban/env-var | 0 | 6613660 | import os
from typing import Any, Optional, Sequence
from unittest import TestCase
from env_var.env import _env
from env_var.errors import EnvVarNotDefinedError, EnvVarValidationError
VAR_NAME = "TEST_VAR"
def set_var(val: Optional[str] = None):
if val is None:
if VAR_NAME in os.environ:
del os.environ[VAR_NAME]
return
os.environ[VAR_NAME] = val
def check_validators(
test_case: TestCase,
_env_instance: _env,
valid_values: Sequence[Any],
invalid_values: Sequence[Any],
):
for valid in valid_values:
if isinstance(valid, tuple):
var_value, parsed = valid
else:
var_value = parsed = valid
set_var(var_value)
test_case.assertEqual(_env_instance.required(), parsed)
for invalid in invalid_values:
with test_case.assertRaises(EnvVarValidationError):
set_var(invalid)
_env_instance.required()
with test_case.assertRaises(EnvVarValidationError):
set_var(invalid)
_env_instance.optional()
set_var()
for valid in valid_values:
with test_case.assertRaises(EnvVarNotDefinedError):
_env_instance.required()
for invalid in invalid_values:
test_case.assertIsNone(_env_instance.optional())
for valid in valid_values:
if isinstance(valid, tuple):
_, parsed = valid
else:
parsed = valid
test_case.assertEqual(_env_instance.default(parsed).required(), parsed)
| import os
from typing import Any, Optional, Sequence
from unittest import TestCase
from env_var.env import _env
from env_var.errors import EnvVarNotDefinedError, EnvVarValidationError
VAR_NAME = "TEST_VAR"
def set_var(val: Optional[str] = None):
if val is None:
if VAR_NAME in os.environ:
del os.environ[VAR_NAME]
return
os.environ[VAR_NAME] = val
def check_validators(
test_case: TestCase,
_env_instance: _env,
valid_values: Sequence[Any],
invalid_values: Sequence[Any],
):
for valid in valid_values:
if isinstance(valid, tuple):
var_value, parsed = valid
else:
var_value = parsed = valid
set_var(var_value)
test_case.assertEqual(_env_instance.required(), parsed)
for invalid in invalid_values:
with test_case.assertRaises(EnvVarValidationError):
set_var(invalid)
_env_instance.required()
with test_case.assertRaises(EnvVarValidationError):
set_var(invalid)
_env_instance.optional()
set_var()
for valid in valid_values:
with test_case.assertRaises(EnvVarNotDefinedError):
_env_instance.required()
for invalid in invalid_values:
test_case.assertIsNone(_env_instance.optional())
for valid in valid_values:
if isinstance(valid, tuple):
_, parsed = valid
else:
parsed = valid
test_case.assertEqual(_env_instance.default(parsed).required(), parsed)
| none | 1 | 2.995804 | 3 | |
Funky Sentence Gen/Funky Sentence Generator.py | Software-Cat/Python-Mini-Projects | 0 | 6613661 | <filename>Funky Sentence Gen/Funky Sentence Generator.py
import random
adjectives = ["abandoned", "baffled", "cringy", "dazzling", "eccentric", "fancy", "generous", "happy", "ill", "jocose", "kind", "lazy", "magical", "naked",
"obstinate", "patriotic", "queasy", "raging", "savage", "talented", "unlucky", "vegetarian", "white", "xenophobic", "yawning", "zippy"]
executerNouns = ["apple", "banana", "cat", "dog", "elephatnt", "flamingo", "giraffe", "hippo", "iguana", "jellyfish", "kangaroo", "ladybug", "mammoth", "numbat",
"octopus", "panda", "quail", "rabbit", "snake", "teacher", "umpire", "vocalist", "whale", "xylophone", "yoga instructor", "zoologist"]
adverbs = ["accidentally", "beneficially", "chaotically", "doubtfully", "efficiently", "fearfullly", "gently", "hypocritically", "impulsively", "jealously", "keenly",
"loudly", "mysteriously", "naively", "obediently", "passionately", "quietly", "rationally", "sadly", "telepathically", "uncontrollably", "viciously",
"wildly", "xenophobically", "youthfully", "zealously"]
verbs = ["ate", "bent", "cleaned", "danced", "educated", "fabricated",
"grew", "hacked", "immobilized", "jumbled", "kicked"]
genders = ["his", "hers"]
subjectNouns = ["aquarium", "bandana", "cabbage"]
prepositions = ["with", "without", "in front of",
"behind", "next to", "under", "over"]
objects = ["aeroplane", "broom"]
print("The " + random.choice(adjectives) + " " + random.choice(executerNouns) + " " + random.choice(adverbs) + " " + random.choice(verbs) + " " +
random.choice(genders) + " " + random.choice(subjectNouns) + " " + random.choice(prepositions) + " a/an " + random.choice(objects) + ".")
| <filename>Funky Sentence Gen/Funky Sentence Generator.py
import random
adjectives = ["abandoned", "baffled", "cringy", "dazzling", "eccentric", "fancy", "generous", "happy", "ill", "jocose", "kind", "lazy", "magical", "naked",
"obstinate", "patriotic", "queasy", "raging", "savage", "talented", "unlucky", "vegetarian", "white", "xenophobic", "yawning", "zippy"]
executerNouns = ["apple", "banana", "cat", "dog", "elephatnt", "flamingo", "giraffe", "hippo", "iguana", "jellyfish", "kangaroo", "ladybug", "mammoth", "numbat",
"octopus", "panda", "quail", "rabbit", "snake", "teacher", "umpire", "vocalist", "whale", "xylophone", "yoga instructor", "zoologist"]
adverbs = ["accidentally", "beneficially", "chaotically", "doubtfully", "efficiently", "fearfullly", "gently", "hypocritically", "impulsively", "jealously", "keenly",
"loudly", "mysteriously", "naively", "obediently", "passionately", "quietly", "rationally", "sadly", "telepathically", "uncontrollably", "viciously",
"wildly", "xenophobically", "youthfully", "zealously"]
verbs = ["ate", "bent", "cleaned", "danced", "educated", "fabricated",
"grew", "hacked", "immobilized", "jumbled", "kicked"]
genders = ["his", "hers"]
subjectNouns = ["aquarium", "bandana", "cabbage"]
prepositions = ["with", "without", "in front of",
"behind", "next to", "under", "over"]
objects = ["aeroplane", "broom"]
print("The " + random.choice(adjectives) + " " + random.choice(executerNouns) + " " + random.choice(adverbs) + " " + random.choice(verbs) + " " +
random.choice(genders) + " " + random.choice(subjectNouns) + " " + random.choice(prepositions) + " a/an " + random.choice(objects) + ".")
| none | 1 | 2.655477 | 3 | |
policy_driven_attack/models/victim/mnist/lr.py | machanic/TangentAttack | 4 | 6613662 | import torch.nn as nn
__all__ = ['lr']
class LR(nn.Module):
def __init__(self, num_classes=10):
super(LR, self).__init__()
self.fc = nn.Linear(784, num_classes)
def forward(self, x):
x = x.view(x.shape[0], -1)
x = self.fc(x)
return x
def lr(**kwargs):
return LR(**kwargs)
| import torch.nn as nn
__all__ = ['lr']
class LR(nn.Module):
def __init__(self, num_classes=10):
super(LR, self).__init__()
self.fc = nn.Linear(784, num_classes)
def forward(self, x):
x = x.view(x.shape[0], -1)
x = self.fc(x)
return x
def lr(**kwargs):
return LR(**kwargs)
| none | 1 | 2.953532 | 3 | |
train_region.py | mguludag/birthplace_region_predict_python_webapp | 2 | 6613663 | from keras.optimizers import SGD
# import h5py
import cv2
from face_network import create_face_network
import numpy as np
from keras.utils.np_utils import to_categorical
from keras.callbacks import ModelCheckpoint
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
train_split = 0.7
# Folder path
PATH = "D:\\Users\\mguludag\\Desktop\\staj_proj\\bolgeler"
FILE_FORMAT = (".png", ".jpg")
# Get first three digits
def getImageId(name):
return name
images = []
imagesResized = []
region = []
for subdir, dirs, files in os.walk(PATH):
for file in files:
if file.endswith(FILE_FORMAT):
name = os.path.join(subdir, file)
im = cv2.imread(name, cv2.IMREAD_COLOR)
im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
im = cv2.cvtColor(im, cv2.COLOR_GRAY2RGB)
# im.show()
images.append(np.array(im))
im = cv2.resize(im, (224, 224))
imagesResized.append(np.array(im))
imageId = getImageId(os.path.basename(subdir))
if(imageId=="akdeniz"):
region.append(0)
if(imageId=="ege"):
region.append(1)
if(imageId=="ic_anadolu"):
region.append(2)
if(imageId=="karadeniz"):
region.append(3)
# cv2.imshow("sfsf",im)
# cv2.waitKey(0)
# Concatenate
# images = np.float64(np.stack(images))
# print(images.shape)
imagesResized = np.float64(np.stack(imagesResized))
region = np.stack(region)
# Normalize data
# images /= 255.0
imagesResized /= 255.0
# f = h5py.File('images.h5', 'r')
X_data = imagesResized
y_data = region
#One-hot
y_data = to_categorical(y_data, 4)
# Split into training and validation sets
num_images = len(y_data)
p = np.random.permutation(num_images)
X_data = X_data[p]
y_data = y_data[p]
X_train = X_data[0:int(round(train_split*num_images))]
y_train = y_data[0:int(round(train_split*num_images))]
X_test = X_data[int(round(train_split*num_images))+1:-1]
y_test = y_data[int(round(train_split*num_images))+1:-1]
# Zero center
means = np.mean(X_train, axis = 0)
X_train -= means
X_test -= means
# Save means (for testing)
np.save('means_region.npy',means)
opt = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
checkpoint = ModelCheckpoint('weights_region.hdf5', monitor='val_acc', verbose=1, save_best_only=False,
save_weights_only=True, mode='max')
model = create_face_network(nb_class=4, hidden_dim=256, shape=(224, 224, 3))
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
model.fit(X_train, y_train,
batch_size=32,
epochs=10,
verbose=1,
callbacks=[checkpoint],
validation_data=(X_test, y_test),
shuffle=True,
class_weight=None,
sample_weight=None,
initial_epoch=0)
| from keras.optimizers import SGD
# import h5py
import cv2
from face_network import create_face_network
import numpy as np
from keras.utils.np_utils import to_categorical
from keras.callbacks import ModelCheckpoint
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
train_split = 0.7
# Folder path
PATH = "D:\\Users\\mguludag\\Desktop\\staj_proj\\bolgeler"
FILE_FORMAT = (".png", ".jpg")
# Get first three digits
def getImageId(name):
return name
images = []
imagesResized = []
region = []
for subdir, dirs, files in os.walk(PATH):
for file in files:
if file.endswith(FILE_FORMAT):
name = os.path.join(subdir, file)
im = cv2.imread(name, cv2.IMREAD_COLOR)
im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
im = cv2.cvtColor(im, cv2.COLOR_GRAY2RGB)
# im.show()
images.append(np.array(im))
im = cv2.resize(im, (224, 224))
imagesResized.append(np.array(im))
imageId = getImageId(os.path.basename(subdir))
if(imageId=="akdeniz"):
region.append(0)
if(imageId=="ege"):
region.append(1)
if(imageId=="ic_anadolu"):
region.append(2)
if(imageId=="karadeniz"):
region.append(3)
# cv2.imshow("sfsf",im)
# cv2.waitKey(0)
# Concatenate
# images = np.float64(np.stack(images))
# print(images.shape)
imagesResized = np.float64(np.stack(imagesResized))
region = np.stack(region)
# Normalize data
# images /= 255.0
imagesResized /= 255.0
# f = h5py.File('images.h5', 'r')
X_data = imagesResized
y_data = region
#One-hot
y_data = to_categorical(y_data, 4)
# Split into training and validation sets
num_images = len(y_data)
p = np.random.permutation(num_images)
X_data = X_data[p]
y_data = y_data[p]
X_train = X_data[0:int(round(train_split*num_images))]
y_train = y_data[0:int(round(train_split*num_images))]
X_test = X_data[int(round(train_split*num_images))+1:-1]
y_test = y_data[int(round(train_split*num_images))+1:-1]
# Zero center
means = np.mean(X_train, axis = 0)
X_train -= means
X_test -= means
# Save means (for testing)
np.save('means_region.npy',means)
opt = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
checkpoint = ModelCheckpoint('weights_region.hdf5', monitor='val_acc', verbose=1, save_best_only=False,
save_weights_only=True, mode='max')
model = create_face_network(nb_class=4, hidden_dim=256, shape=(224, 224, 3))
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
model.fit(X_train, y_train,
batch_size=32,
epochs=10,
verbose=1,
callbacks=[checkpoint],
validation_data=(X_test, y_test),
shuffle=True,
class_weight=None,
sample_weight=None,
initial_epoch=0)
| en | 0.433206 | # import h5py # Folder path # Get first three digits # im.show() # cv2.imshow("sfsf",im) # cv2.waitKey(0) # Concatenate # images = np.float64(np.stack(images)) # print(images.shape) # Normalize data # images /= 255.0 # f = h5py.File('images.h5', 'r') #One-hot # Split into training and validation sets # Zero center # Save means (for testing) | 2.301176 | 2 |
src/foreign_if/python/examples/dt_demo.py | XpressAI/frovedis | 63 | 6613664 | <filename>src/foreign_if/python/examples/dt_demo.py
#!/usr/bin/env python
import sys
import numpy as np
np.set_printoptions(threshold=5)
#from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from frovedis.mllib.tree import DecisionTreeClassifier, DecisionTreeRegressor
# initializing the Frovedis server
argvs = sys.argv
argc = len(argvs)
if (argc < 2):
print ('Please give frovedis_server calling command as the first argument \n(e.g. "mpirun -np 2 /opt/nec/frovedis/ve/bin/frovedis_server")')
quit()
from frovedis.exrpc.server import FrovedisServer
FrovedisServer.initialize(argvs[1])
# classification data
from sklearn.datasets import load_breast_cancer
mat, lbl = load_breast_cancer(return_X_y=True)
# fitting input matrix and label on DecisionTree Classifier object
dtc = DecisionTreeClassifier(criterion='gini', max_depth=5)
dtc.fit(mat,lbl)
#dtc.debug_print()
# predicting on train model
print("predicting on DecisionTree classifier model: ")
print(dtc.predict(mat))
print("predicting probability on DecisionTree classifier model: ")
print (dtc.predict_proba(mat))
print("prediction accuracy: %.4f" % (dtc.score(mat, lbl)))
# regression data
from sklearn.datasets import load_boston
mat, lbl = load_boston(return_X_y=True)
# fitting input matrix and label on DecisionTree Regressor object
dtr = DecisionTreeRegressor(criterion='mse', max_depth=5)
dtr.fit(mat, lbl)
#dtr.debug_print()
# predicting on train model
print("predicting on DecisionTree Regressor model: ")
print(dtr.predict(mat))
print("prediction score: %.4f" % (dtr.score(mat, lbl)))
#clean-up
#dtc.release()
#dtr.release()
FrovedisServer.shut_down()
| <filename>src/foreign_if/python/examples/dt_demo.py
#!/usr/bin/env python
import sys
import numpy as np
np.set_printoptions(threshold=5)
#from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from frovedis.mllib.tree import DecisionTreeClassifier, DecisionTreeRegressor
# initializing the Frovedis server
argvs = sys.argv
argc = len(argvs)
if (argc < 2):
print ('Please give frovedis_server calling command as the first argument \n(e.g. "mpirun -np 2 /opt/nec/frovedis/ve/bin/frovedis_server")')
quit()
from frovedis.exrpc.server import FrovedisServer
FrovedisServer.initialize(argvs[1])
# classification data
from sklearn.datasets import load_breast_cancer
mat, lbl = load_breast_cancer(return_X_y=True)
# fitting input matrix and label on DecisionTree Classifier object
dtc = DecisionTreeClassifier(criterion='gini', max_depth=5)
dtc.fit(mat,lbl)
#dtc.debug_print()
# predicting on train model
print("predicting on DecisionTree classifier model: ")
print(dtc.predict(mat))
print("predicting probability on DecisionTree classifier model: ")
print (dtc.predict_proba(mat))
print("prediction accuracy: %.4f" % (dtc.score(mat, lbl)))
# regression data
from sklearn.datasets import load_boston
mat, lbl = load_boston(return_X_y=True)
# fitting input matrix and label on DecisionTree Regressor object
dtr = DecisionTreeRegressor(criterion='mse', max_depth=5)
dtr.fit(mat, lbl)
#dtr.debug_print()
# predicting on train model
print("predicting on DecisionTree Regressor model: ")
print(dtr.predict(mat))
print("prediction score: %.4f" % (dtr.score(mat, lbl)))
#clean-up
#dtc.release()
#dtr.release()
FrovedisServer.shut_down()
| en | 0.480906 | #!/usr/bin/env python #from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor # initializing the Frovedis server # classification data # fitting input matrix and label on DecisionTree Classifier object #dtc.debug_print() # predicting on train model # regression data # fitting input matrix and label on DecisionTree Regressor object #dtr.debug_print() # predicting on train model #clean-up #dtc.release() #dtr.release() | 2.711921 | 3 |
setting.py | midoks/vms | 1 | 6613665 | # -- coding:utf-8 --
import time
import sys
import os
chdir = os.getcwd()
sys.path.append(chdir + '/class/core')
sys.path.append(chdir + '/class/ctr')
sys.path.append("/usr/local/lib/python2.7/site-packages")
import common
import system_api
cpu_info = system_api.system_api().getCpuInfo()
workers = cpu_info[1]
if not os.path.exists(os.getcwd() + '/logs'):
os.mkdir(os.getcwd() + '/logs')
if not os.path.exists('data/port.pl'):
common.writeFile('data/port.pl', '8000')
app_port = common.readFile('data/port.pl')
bind = []
if os.path.exists('data/ipv6.pl'):
bind.append('[0:0:0:0:0:0:0:0]:%s' % app_port)
else:
bind.append('0.0.0.0:%s' % app_port)
if workers > 2:
workers = 2
threads = workers * 1
backlog = 512
reload = True
daemon = True
worker_class = 'geventwebsocket.gunicorn.workers.GeventWebSocketWorker'
timeout = 7200
keepalive = 60
preload_app = True
capture_output = True
access_log_format = '%(t)s %(p)s %(h)s "%(r)s" %(s)s %(L)s %(b)s %(f)s" "%(a)s"'
loglevel = 'info'
errorlog = chdir + '/logs/error.log'
accesslog = chdir + '/logs/access.log'
pidfile = chdir + '/logs/vms.pid'
| # -- coding:utf-8 --
import time
import sys
import os
chdir = os.getcwd()
sys.path.append(chdir + '/class/core')
sys.path.append(chdir + '/class/ctr')
sys.path.append("/usr/local/lib/python2.7/site-packages")
import common
import system_api
cpu_info = system_api.system_api().getCpuInfo()
workers = cpu_info[1]
if not os.path.exists(os.getcwd() + '/logs'):
os.mkdir(os.getcwd() + '/logs')
if not os.path.exists('data/port.pl'):
common.writeFile('data/port.pl', '8000')
app_port = common.readFile('data/port.pl')
bind = []
if os.path.exists('data/ipv6.pl'):
bind.append('[0:0:0:0:0:0:0:0]:%s' % app_port)
else:
bind.append('0.0.0.0:%s' % app_port)
if workers > 2:
workers = 2
threads = workers * 1
backlog = 512
reload = True
daemon = True
worker_class = 'geventwebsocket.gunicorn.workers.GeventWebSocketWorker'
timeout = 7200
keepalive = 60
preload_app = True
capture_output = True
access_log_format = '%(t)s %(p)s %(h)s "%(r)s" %(s)s %(L)s %(b)s %(f)s" "%(a)s"'
loglevel = 'info'
errorlog = chdir + '/logs/error.log'
accesslog = chdir + '/logs/access.log'
pidfile = chdir + '/logs/vms.pid'
| en | 0.304031 | # -- coding:utf-8 -- | 2.073094 | 2 |
src/sprites/ball.py | LeonGeorgi/ballsurf | 0 | 6613666 | <gh_stars>0
import uuid
from abc import abstractmethod, ABC
import pygame.gfxdraw
from const import Const
from context import Context
from gamerect import GameRect
from image import CachedImage
from sprite import Sprite, Sprites, Type
class Ball(Sprite, ABC):
def __init__(self, x=None):
if x is None:
self.x = 2 * Const.game_height
else:
self.x = x
self.image = CachedImage(self.filename())
self.diameter = self.image.get_width() * Const.pixel_size
self.y = Const.game_height - self.diameter
self.__vspeed = 0
self.bounced = False
self.hit_bottom = False
self.id = uuid.uuid4()
def update(self, context: Context, sprites: Sprites):
self.x -= context.x_delta
if self.bounced:
self.move_by_gravity(context)
def render(self, surface: pygame.Surface, size_factor: float):
rect = self.box.to_pygame(size_factor, False)
if rect.width <= 0 or rect.height <= 0:
return
img = self.image.scale(rect.width, rect.height)
surface.blit(img, (rect.left, rect.top))
def bounce(self, velocity: float):
self.bounced = True
self.__vspeed = -velocity / 2
def move_by_gravity(self, context: Context):
# time in s
t = context.time_factor / Const.fps
# gravity in m/(s**2)
a = context.gravity
self.y = 1 / 2 * a * (t ** 2) + \
self.__vspeed * t + \
self.y
self.__vspeed = a * t + self.__vspeed
if self.y + self.diameter >= Const.game_height + self.diameter * 0.3 and not self.hit_bottom:
self.__vspeed = -self.__vspeed * self.bounciness()
self.hit_bottom = True
elif self.y + self.diameter < Const.game_height + self.diameter * 0.3 and self.hit_bottom:
self.hit_bottom = False
@property
def box(self) -> GameRect:
offset = max(0, (self.y + self.diameter) - Const.game_height)
y_delta = 0
if offset > self.diameter * 0.3:
y_delta = offset - self.diameter * 0.3
offset = self.diameter * 0.3
return GameRect(self.x, self.y - y_delta, self.diameter, self.diameter - offset)
def type(self) -> Type:
return Type.BALL
def can_delete(self) -> bool:
return self.x <= -1
@abstractmethod
def filename(self) -> str:
pass
@abstractmethod
def bounciness(self) -> float:
"""
:return: by how much the velocity of the player is multiplied when he hits the ball
"""
pass
@abstractmethod
def immediate_speed_increase(self) -> float:
pass
@abstractmethod
def desired_speed_increase(self) -> float:
pass
| import uuid
from abc import abstractmethod, ABC
import pygame.gfxdraw
from const import Const
from context import Context
from gamerect import GameRect
from image import CachedImage
from sprite import Sprite, Sprites, Type
class Ball(Sprite, ABC):
def __init__(self, x=None):
if x is None:
self.x = 2 * Const.game_height
else:
self.x = x
self.image = CachedImage(self.filename())
self.diameter = self.image.get_width() * Const.pixel_size
self.y = Const.game_height - self.diameter
self.__vspeed = 0
self.bounced = False
self.hit_bottom = False
self.id = uuid.uuid4()
def update(self, context: Context, sprites: Sprites):
self.x -= context.x_delta
if self.bounced:
self.move_by_gravity(context)
def render(self, surface: pygame.Surface, size_factor: float):
rect = self.box.to_pygame(size_factor, False)
if rect.width <= 0 or rect.height <= 0:
return
img = self.image.scale(rect.width, rect.height)
surface.blit(img, (rect.left, rect.top))
def bounce(self, velocity: float):
self.bounced = True
self.__vspeed = -velocity / 2
def move_by_gravity(self, context: Context):
# time in s
t = context.time_factor / Const.fps
# gravity in m/(s**2)
a = context.gravity
self.y = 1 / 2 * a * (t ** 2) + \
self.__vspeed * t + \
self.y
self.__vspeed = a * t + self.__vspeed
if self.y + self.diameter >= Const.game_height + self.diameter * 0.3 and not self.hit_bottom:
self.__vspeed = -self.__vspeed * self.bounciness()
self.hit_bottom = True
elif self.y + self.diameter < Const.game_height + self.diameter * 0.3 and self.hit_bottom:
self.hit_bottom = False
@property
def box(self) -> GameRect:
offset = max(0, (self.y + self.diameter) - Const.game_height)
y_delta = 0
if offset > self.diameter * 0.3:
y_delta = offset - self.diameter * 0.3
offset = self.diameter * 0.3
return GameRect(self.x, self.y - y_delta, self.diameter, self.diameter - offset)
def type(self) -> Type:
return Type.BALL
def can_delete(self) -> bool:
return self.x <= -1
@abstractmethod
def filename(self) -> str:
pass
@abstractmethod
def bounciness(self) -> float:
"""
:return: by how much the velocity of the player is multiplied when he hits the ball
"""
pass
@abstractmethod
def immediate_speed_increase(self) -> float:
pass
@abstractmethod
def desired_speed_increase(self) -> float:
pass | en | 0.944909 | # time in s # gravity in m/(s**2) :return: by how much the velocity of the player is multiplied when he hits the ball | 2.889262 | 3 |
Test/FunctionalTests/CommonTestScripts/LevelEditorUtil.py | jethac/ATF | 821 | 6613667 | <filename>Test/FunctionalTests/CommonTestScripts/LevelEditorUtil.py
#Copyright (c) 2014 Sony Computer Entertainment America LLC. See License.txt.
import System
import Test
import Sce.Atf.Dom
import LevelEditorSample
from System import Environment
from System.IO import Path
from System.IO import File
def SetSchema(schema):
global Schema
Schema = schema
return
def AddObjectAndVerify(editingContext, domNodeType, vector):
domNode = Sce.Atf.Dom.DomNode(domNodeType)
gameObject = editingContext.Insert(domNode, vector[0], vector[1], vector[2])
Test.Equal(gameObject.DomNode.Type.Name, domNodeType.Name, "Verify the correct type was added")
Test.Equal(System.Decimal(vector[0]), System.Decimal(gameObject.Translation[0]), "Verify new object X pos")
Test.Equal(System.Decimal(vector[1]), System.Decimal(gameObject.Translation[1]), "Verify new object Y pos")
Test.Equal(System.Decimal(vector[2]), System.Decimal(gameObject.Translation[2]), "Verify new object Z pos")
#More generic (and cluttered) way if domNode is returned from insertion
#Test.Equal(domNode.GetAttribute(domNode.Type.GetAttributeInfo("name")), name, "Verify new object name")
#Test.EqualSystem.(Decimal(domNode.GetAttribute(domNode.Type.GetAttributeInfo("translate"))[0]), System.Decimal(x), "Verify new object X pos")
#...
return gameObject
def AddObjectSetPropertiesAndVerify(editingContext, domNodeType, vTranslation, vScale, vRotation, vRotatePivot):
domNode = Sce.Atf.Dom.DomNode(domNodeType)
gameObject = editingContext.Insert(domNode, vTranslation[0], vTranslation[1], vTranslation[2])
editingContext.SetProperty(gameObject.DomNode, Schema.gameObjectType.scaleAttribute, Test.ConstructArray([vScale[0], vScale[1], vScale[2]]))
editingContext.SetProperty(gameObject.DomNode, Schema.gameObjectType.rotatePivotAttribute, Test.ConstructArray([vRotatePivot[0], vRotatePivot[1], vRotatePivot[2]]))
editingContext.SetProperty(gameObject.DomNode, Schema.gameObjectType.rotateAttribute, Test.ConstructArray([vRotation[0], vRotation[1], vRotation[2]]))
Test.Equal(gameObject.DomNode.Type.Name, domNodeType.Name, "Verify the correct type was added")
Test.Equal(System.Decimal(vTranslation[0]), System.Decimal(gameObject.Translation[0]), "Verify new object X pos")
Test.Equal(System.Decimal(vTranslation[1]), System.Decimal(gameObject.Translation[1]), "Verify new object Y pos")
Test.Equal(System.Decimal(vTranslation[2]), System.Decimal(gameObject.Translation[2]), "Verify new object Z pos")
Test.Equal(System.Decimal(vScale[0]), System.Decimal(gameObject.Scale[0]), "Verify new object X scale")
Test.Equal(System.Decimal(vScale[1]), System.Decimal(gameObject.Scale[1]), "Verify new object Y scale")
Test.Equal(System.Decimal(vScale[2]), System.Decimal(gameObject.Scale[2]), "Verify new object Z scale")
VerifyAttributeAngle(gameObject.DomNode, Schema.gameObjectType.rotateAttribute, vRotation)
Test.Equal(System.Decimal(vRotatePivot[0]), System.Decimal(gameObject.RotatePivot[0]), "Verify new object X rotate pivot")
Test.Equal(System.Decimal(vRotatePivot[1]), System.Decimal(gameObject.RotatePivot[1]), "Verify new object Y rotate pivot")
Test.Equal(System.Decimal(vRotatePivot[2]), System.Decimal(gameObject.RotatePivot[2]), "Verify new object Z rotate pivot")
return gameObject
def VerifyAttribute(domNode, attr, expected):
Test.Equal(expected, domNode.GetAttribute(attr), "Verify attribute")
return
def VerifyAttributeVector(domNode, attr, vector):
Test.Equal(System.Decimal(vector[0]), System.Decimal(domNode.GetAttribute(attr)[0]), "Verify X axis")
Test.Equal(System.Decimal(vector[1]), System.Decimal(domNode.GetAttribute(attr)[1]), "Verify Y axis")
Test.Equal(System.Decimal(vector[2]), System.Decimal(domNode.GetAttribute(attr)[2]), "Verify Z axis")
return
#internally, angles are stored as radians, but the api/ui displays degrees
#need to convert the internal radian to degrees, and use a fuzzy compare
#to account for floating point precision differences
def VerifyAttributeAngle(domNode, attr, vector):
Test.FuzzyCompare(System.Decimal(vector[0]), System.Decimal(domNode.GetAttribute(attr)[0]), "Verify X angle")
Test.FuzzyCompare(System.Decimal(vector[1]), System.Decimal(domNode.GetAttribute(attr)[1]), "Verify Y angle")
Test.FuzzyCompare(System.Decimal(vector[2]), System.Decimal(domNode.GetAttribute(attr)[2]), "Verify Z angle")
return
#This function constructs the full path to a resource under the Data folder.
def GetResourceFilePath(relativeFilePath):
#current directory is something like:
#LevelEditor\bin\Debug.vs2010
#Data directory is:
#LevelEditor\Data
#so the relative path to the data directory is:
dataDir = Path.Combine(Environment.CurrentDirectory, "../../Data")
resourcePath = Path.Combine(dataDir, relativeFilePath)
resourcePath = Path.GetFullPath(resourcePath)
return resourcePath
#Helper to convert an array of domNodes, sets each name based on its type, and returns a c# array of objects
def ConstructDomNodeArray(domNodes):
ret = System.Array.CreateInstance(System.Object, domNodes.Count)
for i in range(domNodes.Count):
name = domNodes[i].Type.Name.Replace("gap:", "")
domNodes[i].SetAttribute(LevelEditorSample.Schema.gameObjectType.nameAttribute, name)
ret[i] = domNodes[i]
return ret
| <filename>Test/FunctionalTests/CommonTestScripts/LevelEditorUtil.py
#Copyright (c) 2014 Sony Computer Entertainment America LLC. See License.txt.
import System
import Test
import Sce.Atf.Dom
import LevelEditorSample
from System import Environment
from System.IO import Path
from System.IO import File
def SetSchema(schema):
global Schema
Schema = schema
return
def AddObjectAndVerify(editingContext, domNodeType, vector):
domNode = Sce.Atf.Dom.DomNode(domNodeType)
gameObject = editingContext.Insert(domNode, vector[0], vector[1], vector[2])
Test.Equal(gameObject.DomNode.Type.Name, domNodeType.Name, "Verify the correct type was added")
Test.Equal(System.Decimal(vector[0]), System.Decimal(gameObject.Translation[0]), "Verify new object X pos")
Test.Equal(System.Decimal(vector[1]), System.Decimal(gameObject.Translation[1]), "Verify new object Y pos")
Test.Equal(System.Decimal(vector[2]), System.Decimal(gameObject.Translation[2]), "Verify new object Z pos")
#More generic (and cluttered) way if domNode is returned from insertion
#Test.Equal(domNode.GetAttribute(domNode.Type.GetAttributeInfo("name")), name, "Verify new object name")
#Test.EqualSystem.(Decimal(domNode.GetAttribute(domNode.Type.GetAttributeInfo("translate"))[0]), System.Decimal(x), "Verify new object X pos")
#...
return gameObject
def AddObjectSetPropertiesAndVerify(editingContext, domNodeType, vTranslation, vScale, vRotation, vRotatePivot):
domNode = Sce.Atf.Dom.DomNode(domNodeType)
gameObject = editingContext.Insert(domNode, vTranslation[0], vTranslation[1], vTranslation[2])
editingContext.SetProperty(gameObject.DomNode, Schema.gameObjectType.scaleAttribute, Test.ConstructArray([vScale[0], vScale[1], vScale[2]]))
editingContext.SetProperty(gameObject.DomNode, Schema.gameObjectType.rotatePivotAttribute, Test.ConstructArray([vRotatePivot[0], vRotatePivot[1], vRotatePivot[2]]))
editingContext.SetProperty(gameObject.DomNode, Schema.gameObjectType.rotateAttribute, Test.ConstructArray([vRotation[0], vRotation[1], vRotation[2]]))
Test.Equal(gameObject.DomNode.Type.Name, domNodeType.Name, "Verify the correct type was added")
Test.Equal(System.Decimal(vTranslation[0]), System.Decimal(gameObject.Translation[0]), "Verify new object X pos")
Test.Equal(System.Decimal(vTranslation[1]), System.Decimal(gameObject.Translation[1]), "Verify new object Y pos")
Test.Equal(System.Decimal(vTranslation[2]), System.Decimal(gameObject.Translation[2]), "Verify new object Z pos")
Test.Equal(System.Decimal(vScale[0]), System.Decimal(gameObject.Scale[0]), "Verify new object X scale")
Test.Equal(System.Decimal(vScale[1]), System.Decimal(gameObject.Scale[1]), "Verify new object Y scale")
Test.Equal(System.Decimal(vScale[2]), System.Decimal(gameObject.Scale[2]), "Verify new object Z scale")
VerifyAttributeAngle(gameObject.DomNode, Schema.gameObjectType.rotateAttribute, vRotation)
Test.Equal(System.Decimal(vRotatePivot[0]), System.Decimal(gameObject.RotatePivot[0]), "Verify new object X rotate pivot")
Test.Equal(System.Decimal(vRotatePivot[1]), System.Decimal(gameObject.RotatePivot[1]), "Verify new object Y rotate pivot")
Test.Equal(System.Decimal(vRotatePivot[2]), System.Decimal(gameObject.RotatePivot[2]), "Verify new object Z rotate pivot")
return gameObject
def VerifyAttribute(domNode, attr, expected):
Test.Equal(expected, domNode.GetAttribute(attr), "Verify attribute")
return
def VerifyAttributeVector(domNode, attr, vector):
Test.Equal(System.Decimal(vector[0]), System.Decimal(domNode.GetAttribute(attr)[0]), "Verify X axis")
Test.Equal(System.Decimal(vector[1]), System.Decimal(domNode.GetAttribute(attr)[1]), "Verify Y axis")
Test.Equal(System.Decimal(vector[2]), System.Decimal(domNode.GetAttribute(attr)[2]), "Verify Z axis")
return
#internally, angles are stored as radians, but the api/ui displays degrees
#need to convert the internal radian to degrees, and use a fuzzy compare
#to account for floating point precision differences
def VerifyAttributeAngle(domNode, attr, vector):
Test.FuzzyCompare(System.Decimal(vector[0]), System.Decimal(domNode.GetAttribute(attr)[0]), "Verify X angle")
Test.FuzzyCompare(System.Decimal(vector[1]), System.Decimal(domNode.GetAttribute(attr)[1]), "Verify Y angle")
Test.FuzzyCompare(System.Decimal(vector[2]), System.Decimal(domNode.GetAttribute(attr)[2]), "Verify Z angle")
return
#This function constructs the full path to a resource under the Data folder.
def GetResourceFilePath(relativeFilePath):
#current directory is something like:
#LevelEditor\bin\Debug.vs2010
#Data directory is:
#LevelEditor\Data
#so the relative path to the data directory is:
dataDir = Path.Combine(Environment.CurrentDirectory, "../../Data")
resourcePath = Path.Combine(dataDir, relativeFilePath)
resourcePath = Path.GetFullPath(resourcePath)
return resourcePath
#Helper to convert an array of domNodes, sets each name based on its type, and returns a c# array of objects
def ConstructDomNodeArray(domNodes):
ret = System.Array.CreateInstance(System.Object, domNodes.Count)
for i in range(domNodes.Count):
name = domNodes[i].Type.Name.Replace("gap:", "")
domNodes[i].SetAttribute(LevelEditorSample.Schema.gameObjectType.nameAttribute, name)
ret[i] = domNodes[i]
return ret
| en | 0.584891 | #Copyright (c) 2014 Sony Computer Entertainment America LLC. See License.txt. #More generic (and cluttered) way if domNode is returned from insertion #Test.Equal(domNode.GetAttribute(domNode.Type.GetAttributeInfo("name")), name, "Verify new object name") #Test.EqualSystem.(Decimal(domNode.GetAttribute(domNode.Type.GetAttributeInfo("translate"))[0]), System.Decimal(x), "Verify new object X pos") #... #internally, angles are stored as radians, but the api/ui displays degrees #need to convert the internal radian to degrees, and use a fuzzy compare #to account for floating point precision differences #This function constructs the full path to a resource under the Data folder. #current directory is something like: #LevelEditor\bin\Debug.vs2010 #Data directory is: #LevelEditor\Data #so the relative path to the data directory is: #Helper to convert an array of domNodes, sets each name based on its type, and returns a c# array of objects | 2.186706 | 2 |
lib/annotation/prodigy_resources/recipe.py | jvasilakes-umn/idisk | 4 | 6613668 | import prodigy
from prodigy.components.loaders import JSONL
"""
Prodigy recipe for annotating pairs of iDISK concepts.
"""
@prodigy.recipe("compare",
dataset=prodigy.recipe_args['dataset'],
input_file=("File containing input data.",
"positional", None, str),
html_file=("File containing HTML template",
"positional", None, str))
def compare(dataset, input_file, html_file):
"""
Prodigy recipe for annotating pairs of iDISK concepts.
"""
stream = JSONL(input_file)
html_template = open(html_file, 'r').read()
stream = add_to_stream(stream, html_template)
return {
"dataset": dataset,
"stream": stream,
"view_id": "choice",
"exclude": [dataset],
"config": {"html_template": html_template}
}
def set_hashes(task, i):
"""
For some reason Prodigy was assigning the same hashes
to every example in the data, which meant that when
it looked at the saved annotations in the dataset to
figure out which to exclude, it excluded all of them.
Setting the _input_hash to the index of the candidate
connection also makes lookup easier when we filter the
candidates according to their annotation.
"""
task["_input_hash"] = i
task["_task_hash"] = -(i+1)
return task
def add_to_stream(stream, html):
for (i, task) in enumerate(stream):
task["html"] = html
task = set_hashes(task, i)
task["options"] = [{"id": 1, "text": "Equal"},
{"id": 2, "text": "Not Equal"},
{"id": 3, "text": "Parent-Child"},
{"id": 4, "text": "Child-Parent"}]
yield task
| import prodigy
from prodigy.components.loaders import JSONL
"""
Prodigy recipe for annotating pairs of iDISK concepts.
"""
@prodigy.recipe("compare",
dataset=prodigy.recipe_args['dataset'],
input_file=("File containing input data.",
"positional", None, str),
html_file=("File containing HTML template",
"positional", None, str))
def compare(dataset, input_file, html_file):
"""
Prodigy recipe for annotating pairs of iDISK concepts.
"""
stream = JSONL(input_file)
html_template = open(html_file, 'r').read()
stream = add_to_stream(stream, html_template)
return {
"dataset": dataset,
"stream": stream,
"view_id": "choice",
"exclude": [dataset],
"config": {"html_template": html_template}
}
def set_hashes(task, i):
"""
For some reason Prodigy was assigning the same hashes
to every example in the data, which meant that when
it looked at the saved annotations in the dataset to
figure out which to exclude, it excluded all of them.
Setting the _input_hash to the index of the candidate
connection also makes lookup easier when we filter the
candidates according to their annotation.
"""
task["_input_hash"] = i
task["_task_hash"] = -(i+1)
return task
def add_to_stream(stream, html):
for (i, task) in enumerate(stream):
task["html"] = html
task = set_hashes(task, i)
task["options"] = [{"id": 1, "text": "Equal"},
{"id": 2, "text": "Not Equal"},
{"id": 3, "text": "Parent-Child"},
{"id": 4, "text": "Child-Parent"}]
yield task
| en | 0.929535 | Prodigy recipe for annotating pairs of iDISK concepts. Prodigy recipe for annotating pairs of iDISK concepts. For some reason Prodigy was assigning the same hashes to every example in the data, which meant that when it looked at the saved annotations in the dataset to figure out which to exclude, it excluded all of them. Setting the _input_hash to the index of the candidate connection also makes lookup easier when we filter the candidates according to their annotation. | 2.282801 | 2 |
test_idun.py | ArneRustad/Master-thesis-cf | 0 | 6613669 | <gh_stars>0
import time
print("Starting script on Idun")
# for i in range(10):
# print("Waiting 10 seconds")
# time.sleep(10)
import torch
print("Finished script on Idun") | import time
print("Starting script on Idun")
# for i in range(10):
# print("Waiting 10 seconds")
# time.sleep(10)
import torch
print("Finished script on Idun") | en | 0.470658 | # for i in range(10): # print("Waiting 10 seconds") # time.sleep(10) | 2.652009 | 3 |
misc/untitled.py | nishishailesh/moving_average_clin_lab | 0 | 6613670 | all record:
{
b'1297254': {
b'Date': b'time: 18-11-2021 11:46:59',
b'': '', b'Patient ID : 1297254': '',
b'Name :': '',
b'Gender :': '',
b'Age :': '',
b'Department :': '',
b'Hospital Name :': '',
b'----------------------': '',
b'FiO2 : 0.21': '',
b'Pat. Temp : 37.0': '',
b'TRef : 26.250': '',
b'BLOOD GAS': '',
b'pCO2': b'24.86 mmHg',
b'pO2': b'56.84 mmHg',
b'HEMATOCRIT': '',
b'Hct': b'29.67 %',
b'SO2': b'92.84 %',
b'Hb': b'9.89 g/dL',
b'ELECTROLYTES': '',
b'Na': b'146.8 mmol/L',
b'K': b'3.26 mmol/L', b'iCa': b'0.93 mmol/L', b'Cl': b'115.6 mmol/L', b'Li': b'NA NA', b'nCa': b'1.00 mmol/L', b'TCa': b'1.99 mmol/L', b'METABOLITES': '', b'GLU': b'104.9 mg/dL', b'LAC': b'1.60 mmol/L', b'CALCULATED PARAMETERS': '', b'HCO3': b'20.99 mmol/L', b'TCO2': b'21.74 mmol/L', b'SBC': b'24.33 mmol/L', b'O2Ct': b'12.94 Vol%', b'pO2%': b'7.48 %', b'BE': b'-1.36 mmol/L', b'BE-B': b'-0.13 mmol/L', b'BE-ECF': b'-1.89 mmol/L', b'Anion Gap': '', b'AG-Na': b'10.24 mmol/L', b'AG-K': b'13.50 mmol/L', b'Alveolar oxygen': '', b'A': b'119.9 mmHg', b'AaDO2': b'63.12 mmHg', b'a/A': b'47.38 %', b'\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0': ''}}
{b'1297254': {
b'Date': b'time: 18-11-2021 11:46:59',
b'': '', b'Patient ID : 1297254': '',
b'Name :': '', b'Gender :': '', b'Age :': '', b'Department :': '', b'Hospital Name :': '',
b'----------------------': '', b'FiO2 : 0.21': '', b'Pat. Temp : 37.0': '', b'TRef : 26.250': '',
b'BLOOD GAS': '',
b'pH': b'7.531',
b'pCO2': b'24.86 mmHg',
b'pO2': b'56.84 mmHg',
b'HEMATOCRIT': '',
b'Hct': b'29.67 %',
b'SO2': b'92.84 %',
b'Hb': b'9.89 g/dL',
b'ELECTROLYTES': '',
b'Na': b'146.8 mmol/L',
b'K': b'3.26 mmol/L',
b'iCa': b'0.93 mmol/L', b'Cl': b'115.6 mmol/L', b'Li': b'NA NA', b'nCa': b'1.00 mmol/L', b'TCa': b'1.99 mmol/L', b'METABOLITES': '', b'GLU': b'104.9 mg/dL', b'LAC': b'1.60 mmol/L', b'CALCULATED PARAMETERS': '', b'HCO3': b'20.99 mmol/L', b'TCO2': b'21.74 mmol/L', b'SBC': b'24.33 mmol/L', b'O2Ct': b'12.94 Vol%', b'pO2%': b'7.48 %', b'BE': b'-1.36 mmol/L', b'BE-B': b'-0.13 mmol/L', b'BE-ECF': b'-1.89 mmol/L', b'Anion Gap': '', b'AG-Na': b'10.24 mmol/L', b'AG-K': b'13.50 mmol/L', b'Alveolar oxygen': '', b'A': b'119.9 mmHg', b'AaDO2': b'63.12 mmHg', b'a/A': b'47.38 %', b'\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0': ''}}
021-11-19 18:03:05,825 data: {
b'Date': b'time: 19-11-2021 17:39:09',
b'': '', b'Patient ID : 1298280': '', b'Name :': '', b'Gender :': '', b'Age :': '', b'Department :': '', b'Hospital Name :': '', b'----------------------': '', b'FiO2 : 0.21': '', b'Pat. Temp : 37.0': '', b'TRef : 26.375': '', b'BLOOD GAS': '',
b'pH': b'7.594',
b'pCO2': b'24.65 mmHg',
b'pO2': b'214.3 mmHg',
b'HEMATOCRIT': '',
b'Hct': b'29.06 %',
b'SO2': b'99.88 %',
b'Hb': b'9.69 g/dL',
b'ELECTROLYTES': '',
b'Na': b'147.8 mmol/L',
b'K': b'2.00 mmol/L',
b'iCa': b'0.80 mmol/L',
b'Cl': b'124.1 mmol/L',
b'Li': b'NA NA',
b'nCa': b'0.89 mmol/L',
b'TCa': b'1.78 mmol/L',
b'METABOLITES': '',
b'GLU': b'66.91 mg/dL',
b'LAC': b'2.17 mmol/L',
b'CALCULATED PARAMETERS': '',
b'HCO3': b'24.09 mmol/L',
b'TCO2': b'24.83 mmol/L', b'SBC': b'27.72 mmol/L', b'O2Ct': b'14.11 Vol%', b'pO2%': b'28.21 %', b'BE': b'2.39 mmol/L', b'BE-B': b'3.69 mmol/L', b'BE-ECF': b'2.23 mmol/L', b'Anion Gap': '', b'AG-Na': b'-0.29 mmol/L', b'AG-K': b'1.71 mmol/L', b'Alveolar oxygen': '', b'A': b'120.2 mmHg', b'AaDO2': b'-94.1 mmHg', b'a/A': b'178.3 %', b'\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0': ''}
2021-11-19 18:03:05,825 pH: b'7.594'
2021-11-19 18:03:05,825 pCO2: b'24.65 mmHg'
2021-11-19 18:03:05,825 Na: b'147.8 mmol/L'
2021-11-19 18:03:05,825 K: b'2.00 mmol/L'
2021-11-19 18:03:05,825 iCa: b'0.80 mmol/L'
2021-11-19 18:03:05,825 Cl: b'124.1 mmol/L'
| all record:
{
b'1297254': {
b'Date': b'time: 18-11-2021 11:46:59',
b'': '', b'Patient ID : 1297254': '',
b'Name :': '',
b'Gender :': '',
b'Age :': '',
b'Department :': '',
b'Hospital Name :': '',
b'----------------------': '',
b'FiO2 : 0.21': '',
b'Pat. Temp : 37.0': '',
b'TRef : 26.250': '',
b'BLOOD GAS': '',
b'pCO2': b'24.86 mmHg',
b'pO2': b'56.84 mmHg',
b'HEMATOCRIT': '',
b'Hct': b'29.67 %',
b'SO2': b'92.84 %',
b'Hb': b'9.89 g/dL',
b'ELECTROLYTES': '',
b'Na': b'146.8 mmol/L',
b'K': b'3.26 mmol/L', b'iCa': b'0.93 mmol/L', b'Cl': b'115.6 mmol/L', b'Li': b'NA NA', b'nCa': b'1.00 mmol/L', b'TCa': b'1.99 mmol/L', b'METABOLITES': '', b'GLU': b'104.9 mg/dL', b'LAC': b'1.60 mmol/L', b'CALCULATED PARAMETERS': '', b'HCO3': b'20.99 mmol/L', b'TCO2': b'21.74 mmol/L', b'SBC': b'24.33 mmol/L', b'O2Ct': b'12.94 Vol%', b'pO2%': b'7.48 %', b'BE': b'-1.36 mmol/L', b'BE-B': b'-0.13 mmol/L', b'BE-ECF': b'-1.89 mmol/L', b'Anion Gap': '', b'AG-Na': b'10.24 mmol/L', b'AG-K': b'13.50 mmol/L', b'Alveolar oxygen': '', b'A': b'119.9 mmHg', b'AaDO2': b'63.12 mmHg', b'a/A': b'47.38 %', b'\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0': ''}}
{b'1297254': {
b'Date': b'time: 18-11-2021 11:46:59',
b'': '', b'Patient ID : 1297254': '',
b'Name :': '', b'Gender :': '', b'Age :': '', b'Department :': '', b'Hospital Name :': '',
b'----------------------': '', b'FiO2 : 0.21': '', b'Pat. Temp : 37.0': '', b'TRef : 26.250': '',
b'BLOOD GAS': '',
b'pH': b'7.531',
b'pCO2': b'24.86 mmHg',
b'pO2': b'56.84 mmHg',
b'HEMATOCRIT': '',
b'Hct': b'29.67 %',
b'SO2': b'92.84 %',
b'Hb': b'9.89 g/dL',
b'ELECTROLYTES': '',
b'Na': b'146.8 mmol/L',
b'K': b'3.26 mmol/L',
b'iCa': b'0.93 mmol/L', b'Cl': b'115.6 mmol/L', b'Li': b'NA NA', b'nCa': b'1.00 mmol/L', b'TCa': b'1.99 mmol/L', b'METABOLITES': '', b'GLU': b'104.9 mg/dL', b'LAC': b'1.60 mmol/L', b'CALCULATED PARAMETERS': '', b'HCO3': b'20.99 mmol/L', b'TCO2': b'21.74 mmol/L', b'SBC': b'24.33 mmol/L', b'O2Ct': b'12.94 Vol%', b'pO2%': b'7.48 %', b'BE': b'-1.36 mmol/L', b'BE-B': b'-0.13 mmol/L', b'BE-ECF': b'-1.89 mmol/L', b'Anion Gap': '', b'AG-Na': b'10.24 mmol/L', b'AG-K': b'13.50 mmol/L', b'Alveolar oxygen': '', b'A': b'119.9 mmHg', b'AaDO2': b'63.12 mmHg', b'a/A': b'47.38 %', b'\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0': ''}}
021-11-19 18:03:05,825 data: {
b'Date': b'time: 19-11-2021 17:39:09',
b'': '', b'Patient ID : 1298280': '', b'Name :': '', b'Gender :': '', b'Age :': '', b'Department :': '', b'Hospital Name :': '', b'----------------------': '', b'FiO2 : 0.21': '', b'Pat. Temp : 37.0': '', b'TRef : 26.375': '', b'BLOOD GAS': '',
b'pH': b'7.594',
b'pCO2': b'24.65 mmHg',
b'pO2': b'214.3 mmHg',
b'HEMATOCRIT': '',
b'Hct': b'29.06 %',
b'SO2': b'99.88 %',
b'Hb': b'9.69 g/dL',
b'ELECTROLYTES': '',
b'Na': b'147.8 mmol/L',
b'K': b'2.00 mmol/L',
b'iCa': b'0.80 mmol/L',
b'Cl': b'124.1 mmol/L',
b'Li': b'NA NA',
b'nCa': b'0.89 mmol/L',
b'TCa': b'1.78 mmol/L',
b'METABOLITES': '',
b'GLU': b'66.91 mg/dL',
b'LAC': b'2.17 mmol/L',
b'CALCULATED PARAMETERS': '',
b'HCO3': b'24.09 mmol/L',
b'TCO2': b'24.83 mmol/L', b'SBC': b'27.72 mmol/L', b'O2Ct': b'14.11 Vol%', b'pO2%': b'28.21 %', b'BE': b'2.39 mmol/L', b'BE-B': b'3.69 mmol/L', b'BE-ECF': b'2.23 mmol/L', b'Anion Gap': '', b'AG-Na': b'-0.29 mmol/L', b'AG-K': b'1.71 mmol/L', b'Alveolar oxygen': '', b'A': b'120.2 mmHg', b'AaDO2': b'-94.1 mmHg', b'a/A': b'178.3 %', b'\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0\xc2\xb0': ''}
2021-11-19 18:03:05,825 pH: b'7.594'
2021-11-19 18:03:05,825 pCO2: b'24.65 mmHg'
2021-11-19 18:03:05,825 Na: b'147.8 mmol/L'
2021-11-19 18:03:05,825 K: b'2.00 mmol/L'
2021-11-19 18:03:05,825 iCa: b'0.80 mmol/L'
2021-11-19 18:03:05,825 Cl: b'124.1 mmol/L'
| none | 1 | 1.225119 | 1 | |
Algoritmos/AproximacaoMinimosQuadrados_CaduSantana.py | CaduSantana/hacktoberfest-2021-codigos | 2 | 6613671 | from sklearn.datasets import make_regression
from matplotlib import pyplot as plt
import numpy as np
from sklearn.linear_model import LinearRegression
# Functions
def gram_schmidt(a):
q = []
for i in range(len(a)):
#orthogonalization
q_tilde = a[i]
for j in range(len(q)):
q_tilde = q_tilde - (q[j] @ a[i])*q[j]
#Test for dependennce
if np.sqrt(sum(q_tilde**2)) <= 1e-10:
print('Vectors are linearly dependent.')
print('GS algorithm terminates at iteration ', i+1)
return q
#Normalization
else:
q_tilde = q_tilde / np.sqrt(sum(q_tilde**2))
q.append(q_tilde)
print('Vectors are linearly independent.')
return q
def QR_factorization(A):
Q_transpose = np.array(gram_schmidt(A.T))
R = Q_transpose @ A
Q = Q_transpose.T
return Q, R
def back_subst(R,b_tilde):
n = R.shape[0]
x = np.zeros(n)
for i in reversed(range(n)):
x[i] = b_tilde[i]
for j in range(i+1,n):
x[i] = x[i] - R[i,j]*x[j]
x[i] = x[i]/R[i,i]
return x
def solve_via_backsub(A,b):
Q,R = QR_factorization(A)
b_tilde = Q.T @ b
x = back_subst(R,b_tilde)
return x
#########
# # Least squares problem
# A = np.array([[2,0],[-1,1],[0,2]])
# b = np.array([1,0,-1])
# x_hat = np.array([1/3, -1/3])
# r_hat = A @ x_hat - b
# print(np.linalg.norm(r_hat))
# x = np.array([1/2, -1/2]) #other value of x
# r = A @ x - b
# print(np.linalg.norm(r))
# print(np.linalg.inv(A.T @ A) @ A.T @ b)
# print(np.linalg.pinv(A) @ b)
# print((A.T @ A) @ x_hat - A.T @ b) #Check that normal equations hold
# # Principio da ortogonalidade
# z = np.array([-1.1,2.3])
# print(A @ z).T @ r_hat)
# z = np.array([5.3, -1.2])
# print((A @ z).T @ r_hat)
# # Resolvendo problemas de quadrados mínimos
# A = np.random.normal(size = (100,20))
# b = np.random.normal(size = 100)
# x1 = solve_via_backsub(A,b)
# x2 = np.linalg.inv(A.T @ A) @ (A.T @ b)
# x3 = np.linalg.pinv(A) @ b
# print(np.linalg.norm(x1-x2))
# print(np.linalg.norm(x2-x3))
# print(np.linalg.norm(x3-x1))
# Exemplo página 234
n = 10 # número de lâmpadas
# posições (x,y) das lâmpadas e altura acima do chão
lamps = np.array([[4.1 ,20.4, 4],
[14.1, 21.3, 3.5],
[22.6, 17.1,6],
[5.5 ,12.3, 4.0],
[12.2, 9.7, 4.0],
[15.3, 13.8, 6],
[21.3, 10.5, 5.5],
[3.9 ,3.3, 5.0],
[13.1, 4.3, 5.0],
[20.3,4.2, 4.5]])
N = 25 # grid size
m = N*N # Número de pixels
# construct m x 2 matrix with coordinates of pixel centers
pixels = np.hstack([np.outer(np.arange(0.5,N,1),np.ones(N)).reshape(m,1), np.outer(np.ones(N),np.arange(0.5,N,1)).reshape(m,1)])
# The m x n matrix A maps lamp powers to pixel intensities.
# A[i,j] is inversely proportional to the squared distance of
# lamp j to pixel i.
A = np.zeros((m,n))
for i in range(m):
for j in range(n):
A[i,j] = 1.0 / (np.linalg.norm(np.hstack([pixels[i,:], 0]) - lamps[j,:])**2)
A = (m / np.sum(A)) * A # scale elements of A
# Least squares solution
x = solve_via_backsub(A, np.ones(m))
rms_ls = (sum((A @ x - 1)**2)/m)**0.5
print(rms_ls)
import matplotlib.pyplot as plt
plt.ion()
plt.hist(A @ x, bins = 25)
plt.show()
plt.pause(10)
# Intensity if all lamp powers are one
rms_uniform = (sum((A @ np.ones(n) - 1)**2)/m)**0.5
print(rms_uniform)
plt.hist(A @ np.ones(n), bins = 25)
plt.show()
plt.pause(10)
| from sklearn.datasets import make_regression
from matplotlib import pyplot as plt
import numpy as np
from sklearn.linear_model import LinearRegression
# Functions
def gram_schmidt(a):
q = []
for i in range(len(a)):
#orthogonalization
q_tilde = a[i]
for j in range(len(q)):
q_tilde = q_tilde - (q[j] @ a[i])*q[j]
#Test for dependennce
if np.sqrt(sum(q_tilde**2)) <= 1e-10:
print('Vectors are linearly dependent.')
print('GS algorithm terminates at iteration ', i+1)
return q
#Normalization
else:
q_tilde = q_tilde / np.sqrt(sum(q_tilde**2))
q.append(q_tilde)
print('Vectors are linearly independent.')
return q
def QR_factorization(A):
Q_transpose = np.array(gram_schmidt(A.T))
R = Q_transpose @ A
Q = Q_transpose.T
return Q, R
def back_subst(R,b_tilde):
n = R.shape[0]
x = np.zeros(n)
for i in reversed(range(n)):
x[i] = b_tilde[i]
for j in range(i+1,n):
x[i] = x[i] - R[i,j]*x[j]
x[i] = x[i]/R[i,i]
return x
def solve_via_backsub(A,b):
Q,R = QR_factorization(A)
b_tilde = Q.T @ b
x = back_subst(R,b_tilde)
return x
#########
# # Least squares problem
# A = np.array([[2,0],[-1,1],[0,2]])
# b = np.array([1,0,-1])
# x_hat = np.array([1/3, -1/3])
# r_hat = A @ x_hat - b
# print(np.linalg.norm(r_hat))
# x = np.array([1/2, -1/2]) #other value of x
# r = A @ x - b
# print(np.linalg.norm(r))
# print(np.linalg.inv(A.T @ A) @ A.T @ b)
# print(np.linalg.pinv(A) @ b)
# print((A.T @ A) @ x_hat - A.T @ b) #Check that normal equations hold
# # Principio da ortogonalidade
# z = np.array([-1.1,2.3])
# print(A @ z).T @ r_hat)
# z = np.array([5.3, -1.2])
# print((A @ z).T @ r_hat)
# # Resolvendo problemas de quadrados mínimos
# A = np.random.normal(size = (100,20))
# b = np.random.normal(size = 100)
# x1 = solve_via_backsub(A,b)
# x2 = np.linalg.inv(A.T @ A) @ (A.T @ b)
# x3 = np.linalg.pinv(A) @ b
# print(np.linalg.norm(x1-x2))
# print(np.linalg.norm(x2-x3))
# print(np.linalg.norm(x3-x1))
# Exemplo página 234
n = 10 # número de lâmpadas
# posições (x,y) das lâmpadas e altura acima do chão
lamps = np.array([[4.1 ,20.4, 4],
[14.1, 21.3, 3.5],
[22.6, 17.1,6],
[5.5 ,12.3, 4.0],
[12.2, 9.7, 4.0],
[15.3, 13.8, 6],
[21.3, 10.5, 5.5],
[3.9 ,3.3, 5.0],
[13.1, 4.3, 5.0],
[20.3,4.2, 4.5]])
N = 25 # grid size
m = N*N # Número de pixels
# construct m x 2 matrix with coordinates of pixel centers
pixels = np.hstack([np.outer(np.arange(0.5,N,1),np.ones(N)).reshape(m,1), np.outer(np.ones(N),np.arange(0.5,N,1)).reshape(m,1)])
# The m x n matrix A maps lamp powers to pixel intensities.
# A[i,j] is inversely proportional to the squared distance of
# lamp j to pixel i.
A = np.zeros((m,n))
for i in range(m):
for j in range(n):
A[i,j] = 1.0 / (np.linalg.norm(np.hstack([pixels[i,:], 0]) - lamps[j,:])**2)
A = (m / np.sum(A)) * A # scale elements of A
# Least squares solution
x = solve_via_backsub(A, np.ones(m))
rms_ls = (sum((A @ x - 1)**2)/m)**0.5
print(rms_ls)
import matplotlib.pyplot as plt
plt.ion()
plt.hist(A @ x, bins = 25)
plt.show()
plt.pause(10)
# Intensity if all lamp powers are one
rms_uniform = (sum((A @ np.ones(n) - 1)**2)/m)**0.5
print(rms_uniform)
plt.hist(A @ np.ones(n), bins = 25)
plt.show()
plt.pause(10)
| en | 0.37897 | # Functions #orthogonalization #Test for dependennce #Normalization ######### # # Least squares problem # A = np.array([[2,0],[-1,1],[0,2]]) # b = np.array([1,0,-1]) # x_hat = np.array([1/3, -1/3]) # r_hat = A @ x_hat - b # print(np.linalg.norm(r_hat)) # x = np.array([1/2, -1/2]) #other value of x # r = A @ x - b # print(np.linalg.norm(r)) # print(np.linalg.inv(A.T @ A) @ A.T @ b) # print(np.linalg.pinv(A) @ b) # print((A.T @ A) @ x_hat - A.T @ b) #Check that normal equations hold # # Principio da ortogonalidade # z = np.array([-1.1,2.3]) # print(A @ z).T @ r_hat) # z = np.array([5.3, -1.2]) # print((A @ z).T @ r_hat) # # Resolvendo problemas de quadrados mínimos # A = np.random.normal(size = (100,20)) # b = np.random.normal(size = 100) # x1 = solve_via_backsub(A,b) # x2 = np.linalg.inv(A.T @ A) @ (A.T @ b) # x3 = np.linalg.pinv(A) @ b # print(np.linalg.norm(x1-x2)) # print(np.linalg.norm(x2-x3)) # print(np.linalg.norm(x3-x1)) # Exemplo página 234 # número de lâmpadas # posições (x,y) das lâmpadas e altura acima do chão # grid size # Número de pixels # construct m x 2 matrix with coordinates of pixel centers # The m x n matrix A maps lamp powers to pixel intensities. # A[i,j] is inversely proportional to the squared distance of # lamp j to pixel i. # scale elements of A # Least squares solution # Intensity if all lamp powers are one | 3.506223 | 4 |
ms/protobufs/protobuf_to_dict.py | jcnelson/syndicate | 16 | 6613672 | # public domain code
# source: https://github.com/benhodgson/protobuf-to-dict/blob/master/src/protobuf_to_dict.py
# accessed 2/12/2013
from googlepb.protobuf.descriptor import FieldDescriptor
__all__ = ["protobuf_to_dict", "TYPE_CALLABLE_MAP"]
TYPE_CALLABLE_MAP = {
FieldDescriptor.TYPE_DOUBLE: float,
FieldDescriptor.TYPE_FLOAT: float,
FieldDescriptor.TYPE_INT32: int,
FieldDescriptor.TYPE_INT64: long,
FieldDescriptor.TYPE_UINT32: int,
FieldDescriptor.TYPE_UINT64: long,
FieldDescriptor.TYPE_SINT32: int,
FieldDescriptor.TYPE_SINT64: long,
FieldDescriptor.TYPE_FIXED32: int,
FieldDescriptor.TYPE_FIXED64: long,
FieldDescriptor.TYPE_SFIXED32: int,
FieldDescriptor.TYPE_SFIXED64: long,
FieldDescriptor.TYPE_BOOL: bool,
FieldDescriptor.TYPE_STRING: unicode,
FieldDescriptor.TYPE_BYTES: lambda b: b.encode("base64"),
FieldDescriptor.TYPE_ENUM: int,
}
def repeated(type_callable):
return lambda value_list: [type_callable(value) for value in value_list]
def enum_label_name(field, value):
return field.enum_type.values_by_number[int(value)].name
def protobuf_to_dict(pb, type_callable_map=TYPE_CALLABLE_MAP, use_enum_labels=False):
result_dict = {}
for field, value in pb.ListFields():
if field.type not in type_callable_map:
raise TypeError("Field %s.%s has unrecognised type id %d" % (
pb.__class__.__name__, field.name, field.type))
type_callable = type_callable_map[field.type]
if use_enum_labels and field.type == FieldDescriptor.TYPE_ENUM:
type_callable = lambda value: enum_label_name(field, value)
if field.label == FieldDescriptor.LABEL_REPEATED:
type_callable = repeated(type_callable)
result_dict[field.name] = type_callable(value)
return result_dict
# recursion!
TYPE_CALLABLE_MAP[FieldDescriptor.TYPE_MESSAGE] = protobuf_to_dict
| # public domain code
# source: https://github.com/benhodgson/protobuf-to-dict/blob/master/src/protobuf_to_dict.py
# accessed 2/12/2013
from googlepb.protobuf.descriptor import FieldDescriptor
__all__ = ["protobuf_to_dict", "TYPE_CALLABLE_MAP"]
TYPE_CALLABLE_MAP = {
FieldDescriptor.TYPE_DOUBLE: float,
FieldDescriptor.TYPE_FLOAT: float,
FieldDescriptor.TYPE_INT32: int,
FieldDescriptor.TYPE_INT64: long,
FieldDescriptor.TYPE_UINT32: int,
FieldDescriptor.TYPE_UINT64: long,
FieldDescriptor.TYPE_SINT32: int,
FieldDescriptor.TYPE_SINT64: long,
FieldDescriptor.TYPE_FIXED32: int,
FieldDescriptor.TYPE_FIXED64: long,
FieldDescriptor.TYPE_SFIXED32: int,
FieldDescriptor.TYPE_SFIXED64: long,
FieldDescriptor.TYPE_BOOL: bool,
FieldDescriptor.TYPE_STRING: unicode,
FieldDescriptor.TYPE_BYTES: lambda b: b.encode("base64"),
FieldDescriptor.TYPE_ENUM: int,
}
def repeated(type_callable):
return lambda value_list: [type_callable(value) for value in value_list]
def enum_label_name(field, value):
return field.enum_type.values_by_number[int(value)].name
def protobuf_to_dict(pb, type_callable_map=TYPE_CALLABLE_MAP, use_enum_labels=False):
result_dict = {}
for field, value in pb.ListFields():
if field.type not in type_callable_map:
raise TypeError("Field %s.%s has unrecognised type id %d" % (
pb.__class__.__name__, field.name, field.type))
type_callable = type_callable_map[field.type]
if use_enum_labels and field.type == FieldDescriptor.TYPE_ENUM:
type_callable = lambda value: enum_label_name(field, value)
if field.label == FieldDescriptor.LABEL_REPEATED:
type_callable = repeated(type_callable)
result_dict[field.name] = type_callable(value)
return result_dict
# recursion!
TYPE_CALLABLE_MAP[FieldDescriptor.TYPE_MESSAGE] = protobuf_to_dict
| en | 0.521397 | # public domain code # source: https://github.com/benhodgson/protobuf-to-dict/blob/master/src/protobuf_to_dict.py # accessed 2/12/2013 # recursion! | 2.298323 | 2 |
zerionAPI/ifb_utilities.py | jhsu98/zws-py | 4 | 6613673 | <reponame>jhsu98/zws-py
from zerionAPI import IFB
from pprint import pprint
import json
import os
import requests
import shutil
import urllib3
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
def exportImages(api, profile_id, page_id, isRecursive=False, directory = '.'):
print(
'Getting page...',
result := api.Pages('GET', profile_id, page_id)
)
if result.status_code == 200:
count = 0
page = result.response
try:
directory = f'{directory}/{page["name"]}'
os.makedirs(directory, exist_ok=True)
except FileExistsError as e:
print(e)
pass
elements_field_grammar = 'name,data_type((="11")|(="18")|(="28")),data_size' if isRecursive else 'name,data_type((="11")|(="28"))'
print(
'Getting elements...',
result := api.Elements('GET', profile_id, page['id'], params={'fields': elements_field_grammar})
)
if result.status_code == 200 and len(result.response) > 0:
elements = result.response
image_elements = [element for element in elements if element['data_type'] in (11, 28)]
subform_elements = [element for element in elements if element['data_type'] == 18]
# Image Element Loop
if len(image_elements) > 0:
print('Getting records...')
result = api.Records('GET', profile_id, page['id'], params={'fields': ','.join([e['name'] for e in image_elements])})
if result.status_code == 200 and len(result.response) > 0:
records = result.response
total = int(result.headers.get('Total-Count'))
print(f'Retrieved {len(records)}/{total} records...')
while len(records) < total:
result = api.Records('GET', profile_id, page['id'], params={'fields': ','.join([e['name'] for e in image_elements]), 'offset': len(records)})
if result.status_code == 200 and len(result.response) > 0:
records += result.response
for record in records:
record_id = record['id']
elements = {key: record[key] for key in record if key != 'id' and record[key] != None}
for element in elements:
r = requests.get(record[element], verify=False, stream=True)
r.raw.decode_content = True
filename = f'{record_id}_{element}.{record[element].split(".")[-1]}'
filepath = f'{directory}/{filename}'
with open(filepath, 'wb') as f:
print(f'Exporting <{record[element]}> as "{filepath}"')
shutil.copyfileobj(r.raw, f)
else:
print('No records found...')
else:
print('No image elements found...')
# Subform Element Loop
if isRecursive and len(subform_elements) > 0:
for element in subform_elements:
print(f'Recursing into {element["name"]}...')
count += exportImages(api, profile_id, element['data_size'], isRecursive, directory=directory)
else:
print('No image elements found...')
return 0
else:
print('Page not found...')
return 0
if __name__ == "__main__":
print('Not directly accessible')
exit() | from zerionAPI import IFB
from pprint import pprint
import json
import os
import requests
import shutil
import urllib3
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
def exportImages(api, profile_id, page_id, isRecursive=False, directory = '.'):
print(
'Getting page...',
result := api.Pages('GET', profile_id, page_id)
)
if result.status_code == 200:
count = 0
page = result.response
try:
directory = f'{directory}/{page["name"]}'
os.makedirs(directory, exist_ok=True)
except FileExistsError as e:
print(e)
pass
elements_field_grammar = 'name,data_type((="11")|(="18")|(="28")),data_size' if isRecursive else 'name,data_type((="11")|(="28"))'
print(
'Getting elements...',
result := api.Elements('GET', profile_id, page['id'], params={'fields': elements_field_grammar})
)
if result.status_code == 200 and len(result.response) > 0:
elements = result.response
image_elements = [element for element in elements if element['data_type'] in (11, 28)]
subform_elements = [element for element in elements if element['data_type'] == 18]
# Image Element Loop
if len(image_elements) > 0:
print('Getting records...')
result = api.Records('GET', profile_id, page['id'], params={'fields': ','.join([e['name'] for e in image_elements])})
if result.status_code == 200 and len(result.response) > 0:
records = result.response
total = int(result.headers.get('Total-Count'))
print(f'Retrieved {len(records)}/{total} records...')
while len(records) < total:
result = api.Records('GET', profile_id, page['id'], params={'fields': ','.join([e['name'] for e in image_elements]), 'offset': len(records)})
if result.status_code == 200 and len(result.response) > 0:
records += result.response
for record in records:
record_id = record['id']
elements = {key: record[key] for key in record if key != 'id' and record[key] != None}
for element in elements:
r = requests.get(record[element], verify=False, stream=True)
r.raw.decode_content = True
filename = f'{record_id}_{element}.{record[element].split(".")[-1]}'
filepath = f'{directory}/{filename}'
with open(filepath, 'wb') as f:
print(f'Exporting <{record[element]}> as "{filepath}"')
shutil.copyfileobj(r.raw, f)
else:
print('No records found...')
else:
print('No image elements found...')
# Subform Element Loop
if isRecursive and len(subform_elements) > 0:
for element in subform_elements:
print(f'Recursing into {element["name"]}...')
count += exportImages(api, profile_id, element['data_size'], isRecursive, directory=directory)
else:
print('No image elements found...')
return 0
else:
print('Page not found...')
return 0
if __name__ == "__main__":
print('Not directly accessible')
exit() | en | 0.357114 | # Image Element Loop # Subform Element Loop | 2.282277 | 2 |
Python Exercises/Mundo1/exercicio18.py | joaopblume/Curso-em-video-exercicios | 0 | 6613674 | # Faça um programa que leia um ângulo qualquer e mostre na tela
# o valor do seno, cosseno e tangente desse ângulos
import math
angulo = int(input('Digite o ângulo desejado: '))
sen = math.sin(math.radians(angulo))
cos = math.cos(math.radians(angulo))
tg = math.tan(math.radians(angulo))
print(f'O ângulo de {angulo} tem seno de {sen:.2f}, cosseno de {cos:.2f} e tangente de {tg:.2f}.')
| # Faça um programa que leia um ângulo qualquer e mostre na tela
# o valor do seno, cosseno e tangente desse ângulos
import math
angulo = int(input('Digite o ângulo desejado: '))
sen = math.sin(math.radians(angulo))
cos = math.cos(math.radians(angulo))
tg = math.tan(math.radians(angulo))
print(f'O ângulo de {angulo} tem seno de {sen:.2f}, cosseno de {cos:.2f} e tangente de {tg:.2f}.')
| pt | 0.863731 | # Faça um programa que leia um ângulo qualquer e mostre na tela # o valor do seno, cosseno e tangente desse ângulos | 3.955292 | 4 |
ample/util/reference_manager.py | fsimkovic/ample | 6 | 6613675 | <filename>ample/util/reference_manager.py
"""Various miscellaneous functions"""
__author__ = "<NAME>, and <NAME>"
__date__ = "16 Jul 2018"
from collections import OrderedDict
import copy
from enum import Enum
import os
from ample.util.ample_util import is_file
from ample.constants import SHARE_DIR
from ample.ensembler.constants import SPICKER_RMSD, SPICKER_TM
class ReferenceManager:
# Section Names
class SECTIONS(Enum):
__order__ = 'GENERAL MODELLING MODEL_PREP MR REFINEMENT DM AUTOBUILD'
GENERAL = 'General'
MODELLING = 'Modelling'
MODEL_PREP = 'Search model preparation'
MR = 'Molecular Replacement'
REFINEMENT = 'Refinement'
DM = 'Main-chain tracing and density modification'
AUTOBUILD = 'Autobuilding'
SEC_TAG = 'h3'
def __init__(self, optd):
self.references = {}
self.ordered_labels = []
self.citation_file_path = None
self.section_labels = OrderedDict()
self.setup_references()
self.setup_sections(optd)
def setup_references(self):
ref_fname = os.path.join(SHARE_DIR, "include", "ample.bib")
if not is_file(ref_fname):
msg = "Cannot find BibTex file containing references. " "Please determine them yourself and cite AMPLE."
return msg
article = {}
entry = False
with open(ref_fname, "r") as fhin:
for line in fhin.readlines():
line = line.strip()
if not line:
continue
elif line.startswith("@"):
# Beginning of all BibTex entry blocks
entry = True
unique_id = line.replace("@article{", "").replace(",", "")
article = {'unique_id': unique_id} # Reset the article dictionary
elif line == "}":
# End of all BibTex entry blocks
entry = False
self.references[article['label']] = article
elif entry:
# BibTex entry block
# Some dirty line handling.
# Not very bulletproof but should do for now
line = line.replace("{", "").replace("}", "")
key, value = [l.strip() for l in line.split("=")]
value = value.rstrip(",").replace("\"", "")
# Save the data to the article entry
article[key] = value
return
def setup_sections(self, optd):
# Create default lists
for section in self.SECTIONS:
self.section_labels[section] = []
# Build up list of program reference labels, ordered by sections
for section in self.SECTIONS:
if section == self.SECTIONS.GENERAL:
self.section_labels[section] = ['AMPLE', 'CCP4', 'AMPLE_COILED-COILS', 'AMPLE_CONTACTS']
elif section == self.SECTIONS.MODELLING:
labels = []
if optd.get('ideal_helices') or optd.get('helical_ensembles'):
labels.append('AMPLE_COILED-COILS')
if optd.get('make_models'):
labels.append('ROSETTA')
if optd.get('nmr_model_in'):
labels.append('AMPLE_NMR')
if optd.get('quark_models'):
labels.append('QUARK')
labels.append('AMPLE_QUARK')
if optd.get('transmembrane'):
labels.append('AMPLE_TRANSMEMBRANE')
self.section_labels[section] = labels
elif section == self.SECTIONS.MODEL_PREP:
labels = []
if not optd.get('import_ensembles'):
labels += ['CCTBX', 'THESEUS', 'GESAMT']
if optd.get('use_scwrl'):
labels.append('SCWRL4')
elif optd['cluster_method'] in [SPICKER_RMSD, SPICKER_TM]:
labels.append('SPICKER')
elif optd['cluster_method'] in ['fast_protein_cluster']:
labels.append('FPC')
self.section_labels[section] = labels
if optd.get('do_mr'):
if section == self.SECTIONS.MR:
labels = ['MRBUMP']
if optd.get('mrbump_programs'):
if 'molrep' in optd['mrbump_programs']:
labels.append('MOLREP')
if 'phaser' in optd['mrbump_programs']:
labels.append('PHASER')
self.section_labels[section] = labels
elif section == self.SECTIONS.REFINEMENT:
self.section_labels[self.SECTIONS.REFINEMENT] = ['REFMAC']
elif section == self.SECTIONS.DM:
if optd.get('use_shelxe'):
self.section_labels[self.SECTIONS.DM].append('SHELXE')
elif section == self.SECTIONS.AUTOBUILD:
labels = []
if optd.get('refine_rebuild_arpwarp') or optd.get('shelxe_rebuild_arpwarp'):
labels += ['ARPWARP']
elif optd.get('refine_rebuild_buccaneer') or optd.get('shelxe_rebuild_buccaneer'):
labels += ['BUCCANEER']
self.section_labels[section] = labels
# Generate ordered list of all relevant reference labels
for section in self.SECTIONS:
if section in self.section_labels:
self.ordered_labels += self.section_labels[section]
return
@property
def methods_as_html(self):
html = (
"<p>This section lists the programs and algorithms that were used in this job and the references that should be cited. "
+ "Numbers in superscript next to program/reference names refer to the number of the program reference in the overall list of references.</p>"
)
for section in self.SECTIONS:
if section == self.SECTIONS.GENERAL:
html += (
'<p>The first 2 references should be cited in all cases.</p>'
+ '<p>If your protein was a coiled-coil protein, please cite reference number 3.</p>'
+ '<p>If coevolutionary contact information was used in the generation of your models, please cite reference number 4.</p>'
)
elif section == self.SECTIONS.MODELLING and len(self.section_labels[self.SECTIONS.MODELLING]):
standfirst = "<p>The following programs or algorithims were used for model building:</p>"
html += self._methods_section_html(self.SECTIONS.MODELLING, standfirst)
elif section == self.SECTIONS.MODEL_PREP and len(self.section_labels[self.SECTIONS.MODEL_PREP]):
standfirst = (
'<p>Model analysis and search model preparation was carried out with the following programs:</p>'
)
html += self._methods_section_html(self.SECTIONS.MODEL_PREP, standfirst)
elif section == self.SECTIONS.MR and len(self.section_labels[self.SECTIONS.MR]):
standfirst = '<p>Molecular Replacement was carried out with the following programs:</p>'
html += self._methods_section_html(self.SECTIONS.MR, standfirst)
elif section == self.SECTIONS.REFINEMENT and len(self.section_labels[self.SECTIONS.REFINEMENT]):
standfirst = '<pRefinement of the MR solutions carried out with the following programs:</p>'
html += self._methods_section_html(self.SECTIONS.REFINEMENT, standfirst)
elif section == self.SECTIONS.DM and len(self.section_labels[self.SECTIONS.DM]):
standfirst = (
'<p>Density modification and main-chain tracing was carried out with the following programs:</p>'
)
html += self._methods_section_html(self.SECTIONS.DM, standfirst)
elif section == self.SECTIONS.AUTOBUILD and len(self.section_labels[self.SECTIONS.AUTOBUILD]):
standfirst = 'Autobuilding of the final structure was carried out with the following programs:</p>'
html += self._methods_section_html(self.SECTIONS.AUTOBUILD, standfirst)
return html
def _methods_section_html(self, section, standfirst):
mysec = self.SECTIONS(section)
html = '<{}>{}</{}>'.format(self.SEC_TAG, mysec.value, self.SEC_TAG)
html += standfirst
html += '<ul>'
for label in self.section_labels[mysec]:
html += "<li>{}<sup>{}</sup></li>".format(label, self.ordered_labels.index(label) + 1)
html += "</ul>"
return html
@property
def citations_as_html(self):
html = '<{}>References</{}>'.format(self.SEC_TAG, self.SEC_TAG)
html += '<ol>'
template_txt = "<li> {author} ({year}). {title}. {journal} {volume}({number}), {pages}. [doi:{doi}]</li>"
for label in self.ordered_labels:
ref = copy.copy(self.references[label])
ref['author'] = ref['author'].split(" and ")[0].split(",")[0] + " et al."
ref['pages'] = ref['pages'].replace("--", "-")
html += template_txt.format(**ref)
html += '</ol>'
return html
@property
def citations_as_text(self):
txt = """A number of programs and algorithms were used within the this run of AMPLE.
The following is a list of citations for this run:
{0}
""".format(
self.citation_list_as_text
)
if self.citation_file_path:
txt += """
A bibtex file with these references has been saved to the following file:
{0}
""".format(
self.citation_file_path
)
return txt
@property
def citation_list_as_text(self):
template_txt = "* {author} ({year}). {title}. {journal} {volume}({number}), {pages}. [doi:{doi}]"
text = ""
for label in self.ordered_labels:
ref = copy.copy(self.references[label])
ref['author'] = ref['author'].split(" and ")[0].split(",")[0] + " et al."
ref['pages'] = ref['pages'].replace("--", "-")
text += template_txt.format(**ref) + os.linesep * 2
return text
def save_citations_to_file(self, optd):
# =========================================================================
# Somewhat a template of how we want to write each article in BibTex format
# =========================================================================
template_bib = (
"@article{{{unique_id},{sep}author = {{{author}}},{sep}doi = {{{doi}}},{sep}"
"journal = {{{journal}}},{sep}number = {{{number}}},{sep}pages = {{{pages}}},{sep}"
"title = {{{{{title}}}}},{sep}volume = {{{volume}}},{sep}year = {{{year}}},{sep}}}{sep}"
)
references_bib = [template_bib.format(sep=os.linesep, **self.references[l]) for l in self.ordered_labels]
ref_fname = os.path.join(optd['work_dir'], optd['name'] + ".bib")
with open(ref_fname, "w") as fhout:
fhout.write(os.linesep.join(references_bib))
self.citation_file_path = ref_fname
return ref_fname
# ======================================================================
# Some default string messages that we need during the program to inform
# the user of certain information
# ======================================================================
header = """
#########################################################################
#########################################################################
#########################################################################
# CCP4: AMPLE - Ab Initio Modelling Molecular Replacement #
#########################################################################
"""
# ======================================================================
# ======================================================================
survey_url = "http://goo.gl/forms/7xP9M4P81O"
footer = """
#########################################################################
#***********************************************************************#
#* How did we do? *#
#* *#
#* Please follow this link and leave some feedback! *#
#* *#
#* {url} *#
#***********************************************************************#
#########################################################################
""".format(
url=survey_url
)
# ======================================================================
# ======================================================================
| <filename>ample/util/reference_manager.py
"""Various miscellaneous functions"""
__author__ = "<NAME>, and <NAME>"
__date__ = "16 Jul 2018"
from collections import OrderedDict
import copy
from enum import Enum
import os
from ample.util.ample_util import is_file
from ample.constants import SHARE_DIR
from ample.ensembler.constants import SPICKER_RMSD, SPICKER_TM
class ReferenceManager:
# Section Names
class SECTIONS(Enum):
__order__ = 'GENERAL MODELLING MODEL_PREP MR REFINEMENT DM AUTOBUILD'
GENERAL = 'General'
MODELLING = 'Modelling'
MODEL_PREP = 'Search model preparation'
MR = 'Molecular Replacement'
REFINEMENT = 'Refinement'
DM = 'Main-chain tracing and density modification'
AUTOBUILD = 'Autobuilding'
SEC_TAG = 'h3'
def __init__(self, optd):
self.references = {}
self.ordered_labels = []
self.citation_file_path = None
self.section_labels = OrderedDict()
self.setup_references()
self.setup_sections(optd)
def setup_references(self):
ref_fname = os.path.join(SHARE_DIR, "include", "ample.bib")
if not is_file(ref_fname):
msg = "Cannot find BibTex file containing references. " "Please determine them yourself and cite AMPLE."
return msg
article = {}
entry = False
with open(ref_fname, "r") as fhin:
for line in fhin.readlines():
line = line.strip()
if not line:
continue
elif line.startswith("@"):
# Beginning of all BibTex entry blocks
entry = True
unique_id = line.replace("@article{", "").replace(",", "")
article = {'unique_id': unique_id} # Reset the article dictionary
elif line == "}":
# End of all BibTex entry blocks
entry = False
self.references[article['label']] = article
elif entry:
# BibTex entry block
# Some dirty line handling.
# Not very bulletproof but should do for now
line = line.replace("{", "").replace("}", "")
key, value = [l.strip() for l in line.split("=")]
value = value.rstrip(",").replace("\"", "")
# Save the data to the article entry
article[key] = value
return
def setup_sections(self, optd):
# Create default lists
for section in self.SECTIONS:
self.section_labels[section] = []
# Build up list of program reference labels, ordered by sections
for section in self.SECTIONS:
if section == self.SECTIONS.GENERAL:
self.section_labels[section] = ['AMPLE', 'CCP4', 'AMPLE_COILED-COILS', 'AMPLE_CONTACTS']
elif section == self.SECTIONS.MODELLING:
labels = []
if optd.get('ideal_helices') or optd.get('helical_ensembles'):
labels.append('AMPLE_COILED-COILS')
if optd.get('make_models'):
labels.append('ROSETTA')
if optd.get('nmr_model_in'):
labels.append('AMPLE_NMR')
if optd.get('quark_models'):
labels.append('QUARK')
labels.append('AMPLE_QUARK')
if optd.get('transmembrane'):
labels.append('AMPLE_TRANSMEMBRANE')
self.section_labels[section] = labels
elif section == self.SECTIONS.MODEL_PREP:
labels = []
if not optd.get('import_ensembles'):
labels += ['CCTBX', 'THESEUS', 'GESAMT']
if optd.get('use_scwrl'):
labels.append('SCWRL4')
elif optd['cluster_method'] in [SPICKER_RMSD, SPICKER_TM]:
labels.append('SPICKER')
elif optd['cluster_method'] in ['fast_protein_cluster']:
labels.append('FPC')
self.section_labels[section] = labels
if optd.get('do_mr'):
if section == self.SECTIONS.MR:
labels = ['MRBUMP']
if optd.get('mrbump_programs'):
if 'molrep' in optd['mrbump_programs']:
labels.append('MOLREP')
if 'phaser' in optd['mrbump_programs']:
labels.append('PHASER')
self.section_labels[section] = labels
elif section == self.SECTIONS.REFINEMENT:
self.section_labels[self.SECTIONS.REFINEMENT] = ['REFMAC']
elif section == self.SECTIONS.DM:
if optd.get('use_shelxe'):
self.section_labels[self.SECTIONS.DM].append('SHELXE')
elif section == self.SECTIONS.AUTOBUILD:
labels = []
if optd.get('refine_rebuild_arpwarp') or optd.get('shelxe_rebuild_arpwarp'):
labels += ['ARPWARP']
elif optd.get('refine_rebuild_buccaneer') or optd.get('shelxe_rebuild_buccaneer'):
labels += ['BUCCANEER']
self.section_labels[section] = labels
# Generate ordered list of all relevant reference labels
for section in self.SECTIONS:
if section in self.section_labels:
self.ordered_labels += self.section_labels[section]
return
@property
def methods_as_html(self):
html = (
"<p>This section lists the programs and algorithms that were used in this job and the references that should be cited. "
+ "Numbers in superscript next to program/reference names refer to the number of the program reference in the overall list of references.</p>"
)
for section in self.SECTIONS:
if section == self.SECTIONS.GENERAL:
html += (
'<p>The first 2 references should be cited in all cases.</p>'
+ '<p>If your protein was a coiled-coil protein, please cite reference number 3.</p>'
+ '<p>If coevolutionary contact information was used in the generation of your models, please cite reference number 4.</p>'
)
elif section == self.SECTIONS.MODELLING and len(self.section_labels[self.SECTIONS.MODELLING]):
standfirst = "<p>The following programs or algorithims were used for model building:</p>"
html += self._methods_section_html(self.SECTIONS.MODELLING, standfirst)
elif section == self.SECTIONS.MODEL_PREP and len(self.section_labels[self.SECTIONS.MODEL_PREP]):
standfirst = (
'<p>Model analysis and search model preparation was carried out with the following programs:</p>'
)
html += self._methods_section_html(self.SECTIONS.MODEL_PREP, standfirst)
elif section == self.SECTIONS.MR and len(self.section_labels[self.SECTIONS.MR]):
standfirst = '<p>Molecular Replacement was carried out with the following programs:</p>'
html += self._methods_section_html(self.SECTIONS.MR, standfirst)
elif section == self.SECTIONS.REFINEMENT and len(self.section_labels[self.SECTIONS.REFINEMENT]):
standfirst = '<pRefinement of the MR solutions carried out with the following programs:</p>'
html += self._methods_section_html(self.SECTIONS.REFINEMENT, standfirst)
elif section == self.SECTIONS.DM and len(self.section_labels[self.SECTIONS.DM]):
standfirst = (
'<p>Density modification and main-chain tracing was carried out with the following programs:</p>'
)
html += self._methods_section_html(self.SECTIONS.DM, standfirst)
elif section == self.SECTIONS.AUTOBUILD and len(self.section_labels[self.SECTIONS.AUTOBUILD]):
standfirst = 'Autobuilding of the final structure was carried out with the following programs:</p>'
html += self._methods_section_html(self.SECTIONS.AUTOBUILD, standfirst)
return html
def _methods_section_html(self, section, standfirst):
mysec = self.SECTIONS(section)
html = '<{}>{}</{}>'.format(self.SEC_TAG, mysec.value, self.SEC_TAG)
html += standfirst
html += '<ul>'
for label in self.section_labels[mysec]:
html += "<li>{}<sup>{}</sup></li>".format(label, self.ordered_labels.index(label) + 1)
html += "</ul>"
return html
@property
def citations_as_html(self):
html = '<{}>References</{}>'.format(self.SEC_TAG, self.SEC_TAG)
html += '<ol>'
template_txt = "<li> {author} ({year}). {title}. {journal} {volume}({number}), {pages}. [doi:{doi}]</li>"
for label in self.ordered_labels:
ref = copy.copy(self.references[label])
ref['author'] = ref['author'].split(" and ")[0].split(",")[0] + " et al."
ref['pages'] = ref['pages'].replace("--", "-")
html += template_txt.format(**ref)
html += '</ol>'
return html
@property
def citations_as_text(self):
txt = """A number of programs and algorithms were used within the this run of AMPLE.
The following is a list of citations for this run:
{0}
""".format(
self.citation_list_as_text
)
if self.citation_file_path:
txt += """
A bibtex file with these references has been saved to the following file:
{0}
""".format(
self.citation_file_path
)
return txt
@property
def citation_list_as_text(self):
template_txt = "* {author} ({year}). {title}. {journal} {volume}({number}), {pages}. [doi:{doi}]"
text = ""
for label in self.ordered_labels:
ref = copy.copy(self.references[label])
ref['author'] = ref['author'].split(" and ")[0].split(",")[0] + " et al."
ref['pages'] = ref['pages'].replace("--", "-")
text += template_txt.format(**ref) + os.linesep * 2
return text
def save_citations_to_file(self, optd):
# =========================================================================
# Somewhat a template of how we want to write each article in BibTex format
# =========================================================================
template_bib = (
"@article{{{unique_id},{sep}author = {{{author}}},{sep}doi = {{{doi}}},{sep}"
"journal = {{{journal}}},{sep}number = {{{number}}},{sep}pages = {{{pages}}},{sep}"
"title = {{{{{title}}}}},{sep}volume = {{{volume}}},{sep}year = {{{year}}},{sep}}}{sep}"
)
references_bib = [template_bib.format(sep=os.linesep, **self.references[l]) for l in self.ordered_labels]
ref_fname = os.path.join(optd['work_dir'], optd['name'] + ".bib")
with open(ref_fname, "w") as fhout:
fhout.write(os.linesep.join(references_bib))
self.citation_file_path = ref_fname
return ref_fname
# ======================================================================
# Some default string messages that we need during the program to inform
# the user of certain information
# ======================================================================
header = """
#########################################################################
#########################################################################
#########################################################################
# CCP4: AMPLE - Ab Initio Modelling Molecular Replacement #
#########################################################################
"""
# ======================================================================
# ======================================================================
survey_url = "http://goo.gl/forms/7xP9M4P81O"
footer = """
#########################################################################
#***********************************************************************#
#* How did we do? *#
#* *#
#* Please follow this link and leave some feedback! *#
#* *#
#* {url} *#
#***********************************************************************#
#########################################################################
""".format(
url=survey_url
)
# ======================================================================
# ======================================================================
| en | 0.454993 | Various miscellaneous functions # Section Names # Beginning of all BibTex entry blocks # Reset the article dictionary # End of all BibTex entry blocks # BibTex entry block # Some dirty line handling. # Not very bulletproof but should do for now # Save the data to the article entry # Create default lists # Build up list of program reference labels, ordered by sections # Generate ordered list of all relevant reference labels A number of programs and algorithms were used within the this run of AMPLE. The following is a list of citations for this run: {0} A bibtex file with these references has been saved to the following file: {0} # ========================================================================= # Somewhat a template of how we want to write each article in BibTex format # ========================================================================= # ====================================================================== # Some default string messages that we need during the program to inform # the user of certain information # ====================================================================== ######################################################################### ######################################################################### ######################################################################### # CCP4: AMPLE - Ab Initio Modelling Molecular Replacement # ######################################################################### # ====================================================================== # ====================================================================== ######################################################################### #***********************************************************************# #* How did we do? *# #* *# #* Please follow this link and leave some feedback! *# #* *# #* {url} *# #***********************************************************************# ######################################################################### # ====================================================================== # ====================================================================== | 2.307497 | 2 |
default.py | cursedzz/Movies | 0 | 6613676 | <filename>default.py<gh_stars>0
import xbmcaddon,os,requests,xbmc,xbmcgui,urllib,urllib2,re,xbmcplugin
def CATEGORIES():
addDir3('Live Tv','http://cursedzz.noads.biz/channels.txt',3,'http://original.livestream.com/filestore/logos/6a941358-6c7f-2ebf-e8ac-b05f4f338270-banner.png','','')
addDir3('Movies','http://cursedzz.noads.biz/movies.txt',4,'https://www.offerpop.com/wp-content/uploads/2014/08/Movies.jpg','','')
def channel():
r = requests.get('http://cursedzz.noads.biz/channels.txt')
match = re.compile('name= (.+?) url= "(.+?)" logo= "(.+?)"').findall(r.content)
for name,link, logo in match:
addLink(name,link,logo,'','')
def Moviess():
r = requests.get('http://cursedzz.noads.biz/movies.txt')
match = re.compile('name= (.+?) url= "(.+?)" logo= "(.+?)"').findall(r.content)
for name,link, logo in match:
addLink(name,link,logo,'','')
def addLink(name,url,image,urlType,fanart):
ok=True
liz=xbmcgui.ListItem(name, iconImage=image, thumbnailImage=image)
liz.setInfo( type="Video", infoLabels={ "Title": name } )
liz.setProperty('IsPlayable','true')
liz.setProperty('fanart_image', fanart)
ok=xbmcplugin.addDirectoryItem(handle=int(sys.argv[1]),url=url,listitem=liz)
def get_params():
param=[]
paramstring=sys.argv[2]
if len(paramstring)>=2:
params=sys.argv[2]
cleanedparams=params.replace('?','')
if (params[len(params)-1]=='/'):
params=params[0:len(params)-2]
pairsofparams=cleanedparams.split('&')
param={}
for i in range(len(pairsofparams)):
splitparams={}
splitparams=pairsofparams[i].split('=')
if (len(splitparams))==2:
param[splitparams[0]]=splitparams[1]
return param
#################################################################################################################
# NEED BELOW CHANGED
def addDir(name,url,mode,iconimage):
u=sys.argv[0]+"?url="+urllib.quote_plus(url)+"&mode="+str(mode)+"&name="+urllib.quote_plus(name)
ok=True
liz=xbmcgui.ListItem(name, iconImage="DefaultFolder.png", thumbnailImage=iconimage)
liz.setInfo( type="Video", infoLabels={ "Title": name } )
ok=xbmcplugin.addDirectoryItem(handle=int(sys.argv[1]),url=u,listitem=liz,isFolder=True)
return ok
def addDir2(name,url,mode,iconimage):
u=sys.argv[0]+"?url="+urllib.quote_plus(url)+"&mode="+str(mode)+"&name="+urllib.quote_plus(name)
ok=True
liz=xbmcgui.ListItem(name, iconImage="DefaultFolder.png", thumbnailImage=iconimage)
liz.setInfo( type="Video", infoLabels={ "Title": name } )
ok=xbmcplugin.addDirectoryItem(handle=int(sys.argv[1]),url=u,listitem=liz,isFolder=False)
return ok
###############################################################################################################
def addDir3(name,url,mode,iconimage,fanart,description):
u=sys.argv[0]+"?url="+urllib.quote_plus(url)+"&mode="+str(mode)+"&name="+urllib.quote_plus(name)+"&iconimage="+urllib.quote_plus(iconimage)+"&fanart="+urllib.quote_plus(fanart)+"&description="+urllib.quote_plus(description)
ok=True
liz=xbmcgui.ListItem(name, iconImage="DefaultFolder.png", thumbnailImage=iconimage)
liz.setInfo( type="Video", infoLabels={ "Title": name, "Plot": description } )
liz.setProperty( "Fanart_Image", fanart )
ok=xbmcplugin.addDirectoryItem(handle=int(sys.argv[1]),url=u,listitem=liz,isFolder=True)
return ok
def setView(content, viewType):
# set content type so library shows more views and info
if content:
xbmcplugin.setContent(int(sys.argv[1]), content)
if ADDON.getSetting('auto-view')=='true':
xbmc.executebuiltin("Container.SetViewMode(%s)" % viewType )
params=get_params()
url=None
name=None
mode=None
iconimage=None
fanart=None
description=None
try:
url=urllib.unquote_plus(params["url"])
except:
pass
try:
name=urllib.unquote_plus(params["name"])
except:
pass
try:
iconimage=urllib.unquote_plus(params["iconimage"])
except:
pass
try:
mode=int(params["mode"])
except:
pass
try:
fanart=urllib.unquote_plus(params["fanart"])
except:
pass
try:
description=urllib.unquote_plus(params["description"])
except:
pass
print "Mode: "+str(mode)
print "URL: "+str(url)
print "Name: "+str(name)
if mode==None or url==None or len(url)<1:
print ""
CATEGORIES()
elif mode==1:
OPEN_URL(url)
elif mode==3:
channel()
elif mode==4:
Moviess()
xbmcplugin.endOfDirectory(int(sys.argv[1]))
| <filename>default.py<gh_stars>0
import xbmcaddon,os,requests,xbmc,xbmcgui,urllib,urllib2,re,xbmcplugin
def CATEGORIES():
addDir3('Live Tv','http://cursedzz.noads.biz/channels.txt',3,'http://original.livestream.com/filestore/logos/6a941358-6c7f-2ebf-e8ac-b05f4f338270-banner.png','','')
addDir3('Movies','http://cursedzz.noads.biz/movies.txt',4,'https://www.offerpop.com/wp-content/uploads/2014/08/Movies.jpg','','')
def channel():
r = requests.get('http://cursedzz.noads.biz/channels.txt')
match = re.compile('name= (.+?) url= "(.+?)" logo= "(.+?)"').findall(r.content)
for name,link, logo in match:
addLink(name,link,logo,'','')
def Moviess():
r = requests.get('http://cursedzz.noads.biz/movies.txt')
match = re.compile('name= (.+?) url= "(.+?)" logo= "(.+?)"').findall(r.content)
for name,link, logo in match:
addLink(name,link,logo,'','')
def addLink(name,url,image,urlType,fanart):
ok=True
liz=xbmcgui.ListItem(name, iconImage=image, thumbnailImage=image)
liz.setInfo( type="Video", infoLabels={ "Title": name } )
liz.setProperty('IsPlayable','true')
liz.setProperty('fanart_image', fanart)
ok=xbmcplugin.addDirectoryItem(handle=int(sys.argv[1]),url=url,listitem=liz)
def get_params():
param=[]
paramstring=sys.argv[2]
if len(paramstring)>=2:
params=sys.argv[2]
cleanedparams=params.replace('?','')
if (params[len(params)-1]=='/'):
params=params[0:len(params)-2]
pairsofparams=cleanedparams.split('&')
param={}
for i in range(len(pairsofparams)):
splitparams={}
splitparams=pairsofparams[i].split('=')
if (len(splitparams))==2:
param[splitparams[0]]=splitparams[1]
return param
#################################################################################################################
# NEED BELOW CHANGED
def addDir(name,url,mode,iconimage):
u=sys.argv[0]+"?url="+urllib.quote_plus(url)+"&mode="+str(mode)+"&name="+urllib.quote_plus(name)
ok=True
liz=xbmcgui.ListItem(name, iconImage="DefaultFolder.png", thumbnailImage=iconimage)
liz.setInfo( type="Video", infoLabels={ "Title": name } )
ok=xbmcplugin.addDirectoryItem(handle=int(sys.argv[1]),url=u,listitem=liz,isFolder=True)
return ok
def addDir2(name,url,mode,iconimage):
u=sys.argv[0]+"?url="+urllib.quote_plus(url)+"&mode="+str(mode)+"&name="+urllib.quote_plus(name)
ok=True
liz=xbmcgui.ListItem(name, iconImage="DefaultFolder.png", thumbnailImage=iconimage)
liz.setInfo( type="Video", infoLabels={ "Title": name } )
ok=xbmcplugin.addDirectoryItem(handle=int(sys.argv[1]),url=u,listitem=liz,isFolder=False)
return ok
###############################################################################################################
def addDir3(name,url,mode,iconimage,fanart,description):
u=sys.argv[0]+"?url="+urllib.quote_plus(url)+"&mode="+str(mode)+"&name="+urllib.quote_plus(name)+"&iconimage="+urllib.quote_plus(iconimage)+"&fanart="+urllib.quote_plus(fanart)+"&description="+urllib.quote_plus(description)
ok=True
liz=xbmcgui.ListItem(name, iconImage="DefaultFolder.png", thumbnailImage=iconimage)
liz.setInfo( type="Video", infoLabels={ "Title": name, "Plot": description } )
liz.setProperty( "Fanart_Image", fanart )
ok=xbmcplugin.addDirectoryItem(handle=int(sys.argv[1]),url=u,listitem=liz,isFolder=True)
return ok
def setView(content, viewType):
# set content type so library shows more views and info
if content:
xbmcplugin.setContent(int(sys.argv[1]), content)
if ADDON.getSetting('auto-view')=='true':
xbmc.executebuiltin("Container.SetViewMode(%s)" % viewType )
params=get_params()
url=None
name=None
mode=None
iconimage=None
fanart=None
description=None
try:
url=urllib.unquote_plus(params["url"])
except:
pass
try:
name=urllib.unquote_plus(params["name"])
except:
pass
try:
iconimage=urllib.unquote_plus(params["iconimage"])
except:
pass
try:
mode=int(params["mode"])
except:
pass
try:
fanart=urllib.unquote_plus(params["fanart"])
except:
pass
try:
description=urllib.unquote_plus(params["description"])
except:
pass
print "Mode: "+str(mode)
print "URL: "+str(url)
print "Name: "+str(name)
if mode==None or url==None or len(url)<1:
print ""
CATEGORIES()
elif mode==1:
OPEN_URL(url)
elif mode==3:
channel()
elif mode==4:
Moviess()
xbmcplugin.endOfDirectory(int(sys.argv[1]))
| de | 0.733191 | ################################################################################################################# # NEED BELOW CHANGED ############################################################################################################### # set content type so library shows more views and info | 2.390607 | 2 |
CustomWidgets/CCountUp.py | PythonDesktopApps/PyQtCustomWidgets | 0 | 6613677 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on 2019年7月31日
@author: Irony
@site: https://pyqt5.com https://github.com/892768447
@email: <EMAIL>
@file: CustomWidgets.CCountUp
@description: Digital animation
"""
from PyQt5.QtCore import QTimeLine, QEasingCurve
from PyQt5.QtGui import QFont
from PyQt5.QtWidgets import QLabel
__Author__ = 'Irony'
__Copyright__ = 'Copyright (c) 2019 Irony'
__Version__ = 1.0
class CCountUp(QLabel):
def __init__(self, *args, **kwargs):
super(CCountUp, self).__init__(*args, **kwargs)
self.isFloat = False # Is it a decimal?
font = self.font() or QFont()
font.setBold(True)
self.setFont(font)
self.timeline = QTimeLine(6000, self)
self.timeline.setEasingCurve(QEasingCurve.OutExpo)
self.timeline.frameChanged.connect(self.onFrameChanged)
def pause(self):
"""pause
"""
self.timeline.setPaused(True)
def resume(self):
"""continue
"""
self.timeline.resume()
def isPaused(self):
"""Will it be paused?
"""
return self.timeline.state() == QTimeLine.Paused
def reset(self):
"""Reset
"""
self.timeline.stop()
self.isFloat = False # Is it a decimal?
self.setText('0')
def onFrameChanged(self, value):
if self.isFloat:
value = round(value / 100.0 + 0.00001, 2)
value = str(format(value, ','))
self.setText(value + '0' if value.endswith('.0') else value)
def setDuration(self, duration):
"""Set up animation duration
:param duration:
"""
self.timeline.setDuration(duration)
def setNum(self, number):
"""Set numbers
:param number: int or float
"""
if isinstance(number, int):
self.isFloat = False
self.timeline.setFrameRange(0, number)
elif isinstance(number, float):
self.isFloat = True
self.timeline.setFrameRange(0, number * 100)
self.timeline.stop()
self.setText('0')
self.timeline.start()
| #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on 2019年7月31日
@author: Irony
@site: https://pyqt5.com https://github.com/892768447
@email: <EMAIL>
@file: CustomWidgets.CCountUp
@description: Digital animation
"""
from PyQt5.QtCore import QTimeLine, QEasingCurve
from PyQt5.QtGui import QFont
from PyQt5.QtWidgets import QLabel
__Author__ = 'Irony'
__Copyright__ = 'Copyright (c) 2019 Irony'
__Version__ = 1.0
class CCountUp(QLabel):
def __init__(self, *args, **kwargs):
super(CCountUp, self).__init__(*args, **kwargs)
self.isFloat = False # Is it a decimal?
font = self.font() or QFont()
font.setBold(True)
self.setFont(font)
self.timeline = QTimeLine(6000, self)
self.timeline.setEasingCurve(QEasingCurve.OutExpo)
self.timeline.frameChanged.connect(self.onFrameChanged)
def pause(self):
"""pause
"""
self.timeline.setPaused(True)
def resume(self):
"""continue
"""
self.timeline.resume()
def isPaused(self):
"""Will it be paused?
"""
return self.timeline.state() == QTimeLine.Paused
def reset(self):
"""Reset
"""
self.timeline.stop()
self.isFloat = False # Is it a decimal?
self.setText('0')
def onFrameChanged(self, value):
if self.isFloat:
value = round(value / 100.0 + 0.00001, 2)
value = str(format(value, ','))
self.setText(value + '0' if value.endswith('.0') else value)
def setDuration(self, duration):
"""Set up animation duration
:param duration:
"""
self.timeline.setDuration(duration)
def setNum(self, number):
"""Set numbers
:param number: int or float
"""
if isinstance(number, int):
self.isFloat = False
self.timeline.setFrameRange(0, number)
elif isinstance(number, float):
self.isFloat = True
self.timeline.setFrameRange(0, number * 100)
self.timeline.stop()
self.setText('0')
self.timeline.start()
| en | 0.395234 | #!/usr/bin/env python # -*- coding: utf-8 -*- Created on 2019年7月31日
@author: Irony
@site: https://pyqt5.com https://github.com/892768447
@email: <EMAIL>
@file: CustomWidgets.CCountUp
@description: Digital animation # Is it a decimal? pause continue Will it be paused? Reset # Is it a decimal? Set up animation duration
:param duration: Set numbers
:param number: int or float | 2.998317 | 3 |
app/code.py | martinlindholdt/dj-service | 0 | 6613678 | <gh_stars>0
import os, sys, random
from bottle.bottle import route, run, template
from time import sleep
rndwait = False
def setWait():
if rndwait:
return random.randint(0, 5)
else:
return 0
@route('/hello/<name>')
def index(name):
return template('<b>Hello, {{name}}!</b>', name=name)
@route('/')
def index():
waittime = setWait()
sleep(waittime)
host = os.getenv('HOSTNAME', 'unknown host')
version = os.getenv('VERSION', 'unknown version')
return template('{{host}} running {{version}} waiting {{wait}} seconds', host=host, version= version, wait=waittime)
@route('/random/<state>')
def doRandom(state):
global rndwait
if state == "on" or state == "true":
rndwait = True
else:
rndwait = False
return template('Random repsonsetime 0-5 seconds now {{rndwait}}</b>', rndwait=rndwait)
@route('/crash')
def crachApp():
sys.stderr.close() # rather ugly hack but there are no exit function
return "<h1>Goodbye cruel world...</h2>"
@route('/health')
def alive():
return "true"
run(host='0.0.0.0', port=8080, reloader=True)
| import os, sys, random
from bottle.bottle import route, run, template
from time import sleep
rndwait = False
def setWait():
if rndwait:
return random.randint(0, 5)
else:
return 0
@route('/hello/<name>')
def index(name):
return template('<b>Hello, {{name}}!</b>', name=name)
@route('/')
def index():
waittime = setWait()
sleep(waittime)
host = os.getenv('HOSTNAME', 'unknown host')
version = os.getenv('VERSION', 'unknown version')
return template('{{host}} running {{version}} waiting {{wait}} seconds', host=host, version= version, wait=waittime)
@route('/random/<state>')
def doRandom(state):
global rndwait
if state == "on" or state == "true":
rndwait = True
else:
rndwait = False
return template('Random repsonsetime 0-5 seconds now {{rndwait}}</b>', rndwait=rndwait)
@route('/crash')
def crachApp():
sys.stderr.close() # rather ugly hack but there are no exit function
return "<h1>Goodbye cruel world...</h2>"
@route('/health')
def alive():
return "true"
run(host='0.0.0.0', port=8080, reloader=True) | en | 0.889674 | # rather ugly hack but there are no exit function | 2.167588 | 2 |
utility.py | FaiZaman/Steganograph-app-y | 0 | 6613679 | """
Utility functions
"""
import os
import cv2
import numpy as np
from unidecode import unidecode
# convert any message to a binary string
def message_to_binary(message):
ascii_message = unidecode(message)
binary_message = ''.join(format(ord(char), '08b') for char in ascii_message)
return binary_message
# convert any integer into 8-bit binary value
def integer_to_binary(integer):
binary_value = format(integer, "08b")
return binary_value
# convert a binary string into a UTF-8 string message
def binary_to_string(binary_message, delimiter):
delimiter_length = len(delimiter) * -1
delimiter_present = False
# split into bytes
message_bytes = [binary_message[i : i + 8] for i in range(0, len(binary_message), 8)]
message = ""
# convert each byte and append to message
for byte in message_bytes:
char = chr(int(byte, 2))
message += char
if message[delimiter_length:] == delimiter: # reached the delimiter
message = message[:delimiter_length]
delimiter_present = True
break
return message, delimiter_present
def is_message_complete(binary_message, delimiter):
# split into bytes
message_bytes = [binary_message[i : i + 8] for i in range(0, len(binary_message), 8)]
message = ""
# convert each byte and append to message
for byte in message_bytes:
char = chr(int(byte, 2))
message += char
# if delimiter is in message then it is complete
if delimiter in message:
return True
return False
# set all the LSBs to zero before detecting edges so same edges are detected in embedding and extraction
def mask_LSB(image):
# uses binary 11111100 to AND all pixels in image to reset 2 LSBs to 0
mask = np.full(image.shape, 252, np.uint8)
masked_image = cv2.bitwise_and(image, mask)
return masked_image
def save_image(save_path, image_name, time_string, stego):
#cv2.imwrite(os.path.join(save_path, image_name), stego)
saved_image = cv2.imwrite(os.path.join(save_path, '{0}_{1}'.format(time_string, image_name)), stego)
return saved_image
def save_message(save_path, time_string, message):
file_path = os.path.join(save_path, "{0}.txt".format(time_string))
message_file = open(file_path, "w")
try:
message_file.write(message)
message_file.close()
return True
except UnicodeEncodeError:
message_file.close()
os.remove(file_path)
return False
| """
Utility functions
"""
import os
import cv2
import numpy as np
from unidecode import unidecode
# convert any message to a binary string
def message_to_binary(message):
ascii_message = unidecode(message)
binary_message = ''.join(format(ord(char), '08b') for char in ascii_message)
return binary_message
# convert any integer into 8-bit binary value
def integer_to_binary(integer):
binary_value = format(integer, "08b")
return binary_value
# convert a binary string into a UTF-8 string message
def binary_to_string(binary_message, delimiter):
delimiter_length = len(delimiter) * -1
delimiter_present = False
# split into bytes
message_bytes = [binary_message[i : i + 8] for i in range(0, len(binary_message), 8)]
message = ""
# convert each byte and append to message
for byte in message_bytes:
char = chr(int(byte, 2))
message += char
if message[delimiter_length:] == delimiter: # reached the delimiter
message = message[:delimiter_length]
delimiter_present = True
break
return message, delimiter_present
def is_message_complete(binary_message, delimiter):
# split into bytes
message_bytes = [binary_message[i : i + 8] for i in range(0, len(binary_message), 8)]
message = ""
# convert each byte and append to message
for byte in message_bytes:
char = chr(int(byte, 2))
message += char
# if delimiter is in message then it is complete
if delimiter in message:
return True
return False
# set all the LSBs to zero before detecting edges so same edges are detected in embedding and extraction
def mask_LSB(image):
# uses binary 11111100 to AND all pixels in image to reset 2 LSBs to 0
mask = np.full(image.shape, 252, np.uint8)
masked_image = cv2.bitwise_and(image, mask)
return masked_image
def save_image(save_path, image_name, time_string, stego):
#cv2.imwrite(os.path.join(save_path, image_name), stego)
saved_image = cv2.imwrite(os.path.join(save_path, '{0}_{1}'.format(time_string, image_name)), stego)
return saved_image
def save_message(save_path, time_string, message):
file_path = os.path.join(save_path, "{0}.txt".format(time_string))
message_file = open(file_path, "w")
try:
message_file.write(message)
message_file.close()
return True
except UnicodeEncodeError:
message_file.close()
os.remove(file_path)
return False
| en | 0.689055 | Utility functions # convert any message to a binary string # convert any integer into 8-bit binary value # convert a binary string into a UTF-8 string message # split into bytes # convert each byte and append to message # reached the delimiter # split into bytes # convert each byte and append to message # if delimiter is in message then it is complete # set all the LSBs to zero before detecting edges so same edges are detected in embedding and extraction # uses binary 11111100 to AND all pixels in image to reset 2 LSBs to 0 #cv2.imwrite(os.path.join(save_path, image_name), stego) | 3.242614 | 3 |
huaweicloud-sdk-cpts/huaweicloudsdkcpts/v1/model/reportdetails_info.py | huaweicloud/huaweicloud-sdk-python-v3 | 64 | 6613680 | # coding: utf-8
import re
import six
from huaweicloudsdkcore.utils.http_utils import sanitize_for_serialization
class ReportdetailsInfo:
"""
Attributes:
openapi_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
sensitive_list = []
openapi_types = {
'data': 'list[ReportdetailItemInfo]',
'page_index': 'int',
'page_size': 'int',
'total': 'int'
}
attribute_map = {
'data': 'data',
'page_index': 'pageIndex',
'page_size': 'pageSize',
'total': 'total'
}
def __init__(self, data=None, page_index=None, page_size=None, total=None):
"""ReportdetailsInfo - a model defined in huaweicloud sdk"""
self._data = None
self._page_index = None
self._page_size = None
self._total = None
self.discriminator = None
if data is not None:
self.data = data
if page_index is not None:
self.page_index = page_index
if page_size is not None:
self.page_size = page_size
if total is not None:
self.total = total
@property
def data(self):
"""Gets the data of this ReportdetailsInfo.
data
:return: The data of this ReportdetailsInfo.
:rtype: list[ReportdetailItemInfo]
"""
return self._data
@data.setter
def data(self, data):
"""Sets the data of this ReportdetailsInfo.
data
:param data: The data of this ReportdetailsInfo.
:type: list[ReportdetailItemInfo]
"""
self._data = data
@property
def page_index(self):
"""Gets the page_index of this ReportdetailsInfo.
pageIndex
:return: The page_index of this ReportdetailsInfo.
:rtype: int
"""
return self._page_index
@page_index.setter
def page_index(self, page_index):
"""Sets the page_index of this ReportdetailsInfo.
pageIndex
:param page_index: The page_index of this ReportdetailsInfo.
:type: int
"""
self._page_index = page_index
@property
def page_size(self):
"""Gets the page_size of this ReportdetailsInfo.
pageSize
:return: The page_size of this ReportdetailsInfo.
:rtype: int
"""
return self._page_size
@page_size.setter
def page_size(self, page_size):
"""Sets the page_size of this ReportdetailsInfo.
pageSize
:param page_size: The page_size of this ReportdetailsInfo.
:type: int
"""
self._page_size = page_size
@property
def total(self):
"""Gets the total of this ReportdetailsInfo.
total
:return: The total of this ReportdetailsInfo.
:rtype: int
"""
return self._total
@total.setter
def total(self, total):
"""Sets the total of this ReportdetailsInfo.
total
:param total: The total of this ReportdetailsInfo.
:type: int
"""
self._total = total
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
if attr in self.sensitive_list:
result[attr] = "****"
else:
result[attr] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
import simplejson as json
if six.PY2:
import sys
reload(sys)
sys.setdefaultencoding("utf-8")
return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)
def __repr__(self):
"""For `print`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, ReportdetailsInfo):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
| # coding: utf-8
import re
import six
from huaweicloudsdkcore.utils.http_utils import sanitize_for_serialization
class ReportdetailsInfo:
"""
Attributes:
openapi_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
sensitive_list = []
openapi_types = {
'data': 'list[ReportdetailItemInfo]',
'page_index': 'int',
'page_size': 'int',
'total': 'int'
}
attribute_map = {
'data': 'data',
'page_index': 'pageIndex',
'page_size': 'pageSize',
'total': 'total'
}
def __init__(self, data=None, page_index=None, page_size=None, total=None):
"""ReportdetailsInfo - a model defined in huaweicloud sdk"""
self._data = None
self._page_index = None
self._page_size = None
self._total = None
self.discriminator = None
if data is not None:
self.data = data
if page_index is not None:
self.page_index = page_index
if page_size is not None:
self.page_size = page_size
if total is not None:
self.total = total
@property
def data(self):
"""Gets the data of this ReportdetailsInfo.
data
:return: The data of this ReportdetailsInfo.
:rtype: list[ReportdetailItemInfo]
"""
return self._data
@data.setter
def data(self, data):
"""Sets the data of this ReportdetailsInfo.
data
:param data: The data of this ReportdetailsInfo.
:type: list[ReportdetailItemInfo]
"""
self._data = data
@property
def page_index(self):
"""Gets the page_index of this ReportdetailsInfo.
pageIndex
:return: The page_index of this ReportdetailsInfo.
:rtype: int
"""
return self._page_index
@page_index.setter
def page_index(self, page_index):
"""Sets the page_index of this ReportdetailsInfo.
pageIndex
:param page_index: The page_index of this ReportdetailsInfo.
:type: int
"""
self._page_index = page_index
@property
def page_size(self):
"""Gets the page_size of this ReportdetailsInfo.
pageSize
:return: The page_size of this ReportdetailsInfo.
:rtype: int
"""
return self._page_size
@page_size.setter
def page_size(self, page_size):
"""Sets the page_size of this ReportdetailsInfo.
pageSize
:param page_size: The page_size of this ReportdetailsInfo.
:type: int
"""
self._page_size = page_size
@property
def total(self):
"""Gets the total of this ReportdetailsInfo.
total
:return: The total of this ReportdetailsInfo.
:rtype: int
"""
return self._total
@total.setter
def total(self, total):
"""Sets the total of this ReportdetailsInfo.
total
:param total: The total of this ReportdetailsInfo.
:type: int
"""
self._total = total
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
if attr in self.sensitive_list:
result[attr] = "****"
else:
result[attr] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
import simplejson as json
if six.PY2:
import sys
reload(sys)
sys.setdefaultencoding("utf-8")
return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)
def __repr__(self):
"""For `print`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, ReportdetailsInfo):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
| en | 0.568668 | # coding: utf-8 Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. ReportdetailsInfo - a model defined in huaweicloud sdk Gets the data of this ReportdetailsInfo. data :return: The data of this ReportdetailsInfo. :rtype: list[ReportdetailItemInfo] Sets the data of this ReportdetailsInfo. data :param data: The data of this ReportdetailsInfo. :type: list[ReportdetailItemInfo] Gets the page_index of this ReportdetailsInfo. pageIndex :return: The page_index of this ReportdetailsInfo. :rtype: int Sets the page_index of this ReportdetailsInfo. pageIndex :param page_index: The page_index of this ReportdetailsInfo. :type: int Gets the page_size of this ReportdetailsInfo. pageSize :return: The page_size of this ReportdetailsInfo. :rtype: int Sets the page_size of this ReportdetailsInfo. pageSize :param page_size: The page_size of this ReportdetailsInfo. :type: int Gets the total of this ReportdetailsInfo. total :return: The total of this ReportdetailsInfo. :rtype: int Sets the total of this ReportdetailsInfo. total :param total: The total of this ReportdetailsInfo. :type: int Returns the model properties as a dict Returns the string representation of the model For `print` Returns true if both objects are equal Returns true if both objects are not equal | 2.488631 | 2 |
lakegallery/map/migrations/0001_initial.py | TNRIS/lake-gallery | 3 | 6613681 | # -*- coding: utf-8 -*-
# Generated by Django 1.11.4 on 2017-09-12 19:32
from __future__ import unicode_literals
import django.contrib.gis.db.models.fields
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='MajorReservoirs',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('res_name', models.CharField(max_length=50)),
('type', models.CharField(max_length=50)),
('status', models.CharField(max_length=50)),
('res_lbl', models.CharField(max_length=100)),
('region', models.CharField(max_length=50)),
('geom', django.contrib.gis.db.models.fields.MultiPolygonField(srid=4326)),
],
),
migrations.CreateModel(
name='RWPAs',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('objectid', models.BigIntegerField()),
('reg_name', models.CharField(max_length=25)),
('letter', models.CharField(max_length=1)),
('shape_leng', models.FloatField()),
('shape_area', models.FloatField()),
('geom', django.contrib.gis.db.models.fields.MultiPolygonField(srid=4326)),
],
),
]
| # -*- coding: utf-8 -*-
# Generated by Django 1.11.4 on 2017-09-12 19:32
from __future__ import unicode_literals
import django.contrib.gis.db.models.fields
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='MajorReservoirs',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('res_name', models.CharField(max_length=50)),
('type', models.CharField(max_length=50)),
('status', models.CharField(max_length=50)),
('res_lbl', models.CharField(max_length=100)),
('region', models.CharField(max_length=50)),
('geom', django.contrib.gis.db.models.fields.MultiPolygonField(srid=4326)),
],
),
migrations.CreateModel(
name='RWPAs',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('objectid', models.BigIntegerField()),
('reg_name', models.CharField(max_length=25)),
('letter', models.CharField(max_length=1)),
('shape_leng', models.FloatField()),
('shape_area', models.FloatField()),
('geom', django.contrib.gis.db.models.fields.MultiPolygonField(srid=4326)),
],
),
]
| en | 0.765513 | # -*- coding: utf-8 -*- # Generated by Django 1.11.4 on 2017-09-12 19:32 | 1.654024 | 2 |
setup.py | RonenNess/grepfunc | 8 | 6613682 | from distutils.core import setup
setup(
name='grepfunc',
packages=['grepfunc'],
package_data={'grepfunc' : ["*.py", "README.md"], },
version='1.0.3',
description='Unix\'s grep, implemented as a Python function.',
author='<NAME>',
author_email='<EMAIL>',
url='https://github.com/RonenNess/grepfunc',
download_url='https://github.com/RonenNess/grepfunc/tarball/1.0.3',
keywords=['grep', 'regex', 'filter', 'search', 'strings'],
classifiers=[],
) | from distutils.core import setup
setup(
name='grepfunc',
packages=['grepfunc'],
package_data={'grepfunc' : ["*.py", "README.md"], },
version='1.0.3',
description='Unix\'s grep, implemented as a Python function.',
author='<NAME>',
author_email='<EMAIL>',
url='https://github.com/RonenNess/grepfunc',
download_url='https://github.com/RonenNess/grepfunc/tarball/1.0.3',
keywords=['grep', 'regex', 'filter', 'search', 'strings'],
classifiers=[],
) | none | 1 | 1.240308 | 1 | |
utils.py | afshinrahimi/mmner | 74 | 6613683 | <filename>utils.py
from io import open
import logging
import pdb
import random
import os
import numpy as np
import shutil
from collections import Counter
import tarfile
import json
import random
from collections import defaultdict
import io
import gzip
import sys
import argparse
np.random.seed(7)
random.seed(7)
logging.basicConfig(format='%(asctime)s %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p', level=logging.INFO)
def check_num_records(filename):
records = 0
with open(filename, 'r', encoding='utf-8') as fin:
for line in fin:
if line.strip() == '':
records += 1
logging.info('{} records in {}'.format(records, filename))
return records
def check_num_records_by_content(filehandle, filename):
records = 0
for line in filehandle:
line = line.decode('utf-8')
if line.strip() == '':
records += 1
logging.info('{} records in {}'.format(records, filename))
return records
def extract_records(input_file, output_file, num_records):
records = 0
all_records = []
with open(input_file, 'r', encoding='utf-8') as fin:
record = []
for line in fin:
line = line.strip()
if 'docstart' in line.lower():
continue
if line == '' and len(record) > 0:
all_records.append(record)
record = []
elif line != '':
record.append(line)
all_records.append(record)
selected_records = all_records[0:num_records]
with open(output_file, 'w', encoding='utf-8') as fout:
for i, record in enumerate(selected_records):
for r in record:
fout.write(r + '\n')
fout.write('\n')
def create_train_dev_test_from_one_file(conll_file, lang, output_dir):
records = 0
all_records = []
with open(conll_file, 'r', encoding='utf-8') as fin:
record = []
for line in fin:
line = line.strip()
if 'docstart' in line.lower():
continue
if line == '' and len(record) > 0:
all_records.append(record)
record = []
elif line != '':
fields = line.split()
if len(fields) > 1:
line = fields[0] + '\t' + fields[-1]
record.append(line)
if len(record) > 0:
all_records.append(record)
logging.info('{} sentences for {} detected.'.format(len(all_records), lang))
train_100 = all_records[0:100]
train_1000 = all_records[0:1000]
train_10000 = all_records[0:10000]
test_count = dev_count = min(len(all_records) - 10000 // 2, 10000)
dev = all_records[10000:10000 + dev_count]
test = all_records[10000 + dev_count: 10000 + dev_count + test_count]
train_100_file = os.path.join(output_dir, '{}.train.{}'.format(lang, 100))
train_1000_file = os.path.join(output_dir, '{}.train.{}'.format(lang, 1000))
train_10000_file = os.path.join(output_dir, '{}.train.{}'.format(lang, 10000))
dev_file = os.path.join(output_dir, '{}.dev'.format(lang))
test_file = os.path.join(output_dir, '{}.test'.format(lang))
file_records = [(train_100, train_100_file), (train_1000, train_1000_file), (train_10000, train_10000_file), (dev, dev_file), (test, test_file)]
for file_record in file_records:
with open(file_record[1], 'w', encoding='utf-8') as fout:
for i, record in enumerate(file_record[0]):
for r in record:
fout.write(r + '\n')
fout.write('\n')
logging.info('created train dev test splits for {} in {}'.format(lang, output_dir))
def add_lang_to_each_word(dir_name, name_index, lang_separator=':', embfile=None):
if embfile:
files = [embfile]
else:
files = [f for f in os.listdir(dir_name) if os.path.isfile(os.path.join(dir_name, f))]
for file in files:
if not embfile:
if file[-5:] == 'multi' or 'cca-59' in str(file):
continue
lang = file.split('.')[name_index]
print('lang:{}'.format(lang))
filename = os.path.join(dir_name, file)
lines = []
with open(filename, 'r', encoding='utf-8') as fin:
for line in fin:
if line.strip() != '':
line = lang + lang_separator + line
lines.append(line)
with open(filename + '.multi', 'w', encoding='utf-8') as fout:
for line in lines:
fout.write(line)
def create_ner_training_datasets():
for lang in ['de', 'en', 'nl']:
for i in [100, 1000, 10000]:
extract_records(input_file='./datasets/ner/{}.train'.format(lang), output_file='./datasets/ner/{}.train.{}'.format(lang, i), num_records=i)
check_num_records('./datasets/ner/{}.train.{}'.format(lang, i))
def collect_vocab_and_tags(dir_name, fname=None):
'''
:param dir_name:
:return set of all the vocab (+$UNK$ and +$NUM$ in all the files in the directory (expect all the files to be in conll2003 format):
'''
tags = set()
vocab = set()
chars = set()
files = [f for f in os.listdir(dir_name) if os.path.isfile(os.path.join(dir_name, f))] if not fname else [os.path.join(dir_name, fname)]
for file in files:
#we just want the files where language id is added to the beginning of the words
if file[-5:] != 'multi':
continue
filename = os.path.join(dir_name, file)
with open(filename, 'r', encoding='utf-8') as fin:
for line in fin:
if line.strip() != '' and 'docstart' not in line.lower():
fields = line.split()
if len(fields) > 1:
word, tag = fields[0], fields[-1]
else:
logging.info('warning: error in line {} in file {}'.format(line, filename))
vocab.add(word.lower())
tags.add(tag)
chars.update(word)
return vocab, tags, chars
def collect_vocab_and_tags_panx(panx_dir_name, fname=None):
'''
:param dir_name: contains tar.gz files each with train test dev conll files
:return set of all the vocab (+$UNK$ and +$NUM$ in all the files in the directory (expect all the files to be in conll2003 format):
important note: all vocab in wikiembs :https://fasttext.cc/docs/en/pretrained-vectors.html are lowercase while
words in wikiemb+crawlemb: https://fasttext.cc/docs/en/crawl-vectors.html have upper case words so it will make a huge
difference in terms of OOVs and how we match words in NER datasets to words in the pre-trained word embeddings.
'''
tags = set()
vocab = set()
chars = set()
files = [f for f in os.listdir(panx_dir_name) if os.path.isfile(os.path.join(panx_dir_name, f))] if not fname else [fname]
for file in files:
targz_file = os.path.join(panx_dir_name, file)
tar = tarfile.open(targz_file, "r:gz")
for member in tar.getmembers():
#don't collect the vocabulary of extra annotated data
if member.name == 'extra':
continue
bio_file = tar.extractfile(member)
for line in bio_file:
line = line.decode('utf-8')
if line.strip() != '' and 'docstart' not in line.lower():
fields = line.split()
if len(fields) > 1:
word, tag = fields[0], fields[-1]
else:
logging.info('warning: error in line {} in file {}'.format(line, targz_file))
vocab.add(word)
tags.add(tag)
chars.update(word)
tar.close()
return vocab, tags, chars
def collect_embedding_vocabs(dir_name, embfile=None):
'''
:param dir_name: directory where all the word embeddings with different languages are located
expected that lang id- (e.g. en-) is added to the beginning of each word so that exact words in different
languages are distinguishable
:return: set of vocab
'''
vocab_emb = set()
filename = os.path.join(dir_name, emb_file)
with gzip.open(filename, 'rt', encoding='utf-8') if 'gz' in embfile else open(filename, 'r', encoding='utf-8') as fin:
for line in fin:
if line.strip() != '':
word = line.split(' ')[0]
vocab_emb.add(word)
vocab_lang_distribution = [v.split(':')[0] for v in vocab_emb]
vocab_lang_distribution = Counter(vocab_lang_distribution)
logging.info('embedding file vocab stats: {}'.format(str(vocab_lang_distribution)))
return vocab_emb
def write_list(items, output_file):
with open(output_file, 'w', encoding='utf-8') as fout:
for i, item in enumerate(sorted(items)):
if i != len(items) - 1:
fout.write('{}\n'.format(item))
else:
fout.write(item)
def read_list(filename):
items = []
with open(filename, 'r', encoding='utf-8') as fin:
for item in fin:
items.append(item.strip())
return items
def write_vocab_tags_chars_embs(vocab_ner, tags, chars, vocab_emb, output_dir):
write_list(tags, os.path.join(output_dir, 'tags.txt'))
write_list(chars, os.path.join(output_dir, 'chars.txt'))
'''
now we should add any vocab that might have uppercase in vocab_ner
but is lowercase in vocab_emmb.
note that lowercase=True should be turned to lowercase=False in mconfig file to reflect this.
question: what about the cases when a lower case word is in NER but an uppercase is in emb?
'''
vocab = vocab_ner & vocab_emb
for v in vocab_ner:
vlow = v.lower()
if v == vlow:
continue
if v not in vocab_emb and vlow in vocab_emb:
vocab.add(vlow)
vocab_lang_distribution = [v.split(':')[0] for v in vocab]
vocab_lang_distribution = Counter(vocab_lang_distribution)
logging.info(vocab_lang_distribution)
with open(os.path.join(output_dir, 'stats'), 'w') as fout:
fout.write(str(vocab_lang_distribution))
vocab.add('$UNK$')
vocab.add('$NUM$')
write_list(vocab, os.path.join(output_dir, 'words.txt'))
def build_vocab(conll_dir, emb_dir, output_dir, emb_file=None, panx=False, fname=None):
if panx:
vocab, tags, chars = collect_vocab_and_tags_panx(conll_dir, fname=fname)
else:
vocab, tags, chars = collect_vocab_and_tags(conll_dir)
vocab_emb = collect_embedding_vocabs(emb_dir, emb_file)
write_vocab_tags_chars_embs(vocab, tags, chars, vocab_emb, output_dir)
return vocab, tags, chars
def trim_embs(emb_dir, vocab_file, output_dir, dim, emb_file=None):
vocab = read_list(vocab_file)
vocabset = set(vocab)
vocab_emb = set()
embeddings = np.zeros([len(vocab), dim])
filename = os.path.join(emb_dir, emb_file)
with gzip.open(filename, 'rt', encoding='utf-8') if 'gz' in emb_file else open(filename, 'r', encoding='utf-8') as fin:
for line in fin:
line = line.strip().split(' ')
word = line[0]
if word not in vocabset:
continue
embedding = [float(x) for x in line[1:]]
word_idx = vocab.index(word)
embeddings[word_idx] = np.asarray(embedding)
np.savez_compressed(os.path.join(output_dir, 'trimmed_embs.npz'), embeddings=embeddings)
def data_to_byteio(records, name):
data = ''
for i, record in enumerate(records):
for r in record:
data += r + '\n'
data += '\n'
encoded_data = data.encode('utf8')
data_byteio = io.BytesIO(encoded_data)
tarinfo = tarfile.TarInfo(name)
tarinfo.size = len(encoded_data)
return data_byteio, tarinfo
def annotation_to_byteio(records, name):
data = ''
for i, record in enumerate(records):
data += '\n'.join([str(c) for c in record]) + '\n\n'
encoded_data = data.encode('utf8')
data_byteio = io.BytesIO(encoded_data)
tarinfo = tarfile.TarInfo(name)
tarinfo.size = len(encoded_data)
return data_byteio, tarinfo
def annotation_to_byteio_yuan(records, name):
data = ''
for i, record in enumerate(records):
data += '\t'.join([str(c) for c in record]) + '\n'
encoded_data = data.encode('utf8')
data_byteio = io.BytesIO(encoded_data)
tarinfo = tarfile.TarInfo(name)
tarinfo.size = len(encoded_data)
return data_byteio, tarinfo
def write_panx_preprocessed(filehandle, filename, output_dir):
langid = filename.split('.')[0]
records = 0
all_records = []
record = []
tag_records = defaultdict(set)
last_label_in_record = None
for line in filehandle:
line = line.decode('utf-8')
line = line.strip()
if 'docstart' in line.lower():
continue
if line == '' and len(record) > 0:
all_records.append(record)
if last_label_in_record:
tag_records[last_label_in_record].add(len(all_records) - 1)
last_label_in_record = None
record = []
elif line != '':
fields = line.split()
if len(fields) > 1:
line = langid + ':' + fields[0] + '\t' + fields[-1]
if 'B-' in fields[-1]:
last_label_in_record = fields[-1]
record.append(line)
if len(record) > 0:
all_records.append(record)
#find the minimum count of B- tag, used for stratified sampling
min_count_tag = min([len(v) for v in tag_records.values()])
#now sample min_count_tag records from each tag
new_records = []
for _, records in tag_records.items():
new_records.extend(random.sample(records, min_count_tag))
#new records contains indices of items in all_records
random.seed(0)
np.random.seed(0)
random.shuffle(new_records)
all_records = [all_records[i] for i in new_records]
total_records = len(all_records)
if total_records > 30000:
num_recs = 10000
elif total_records > 3000:
num_recs = 1000
elif total_records > 300:
num_recs = 100
else:
return
num_train = min((total_records - 2 * num_recs) - (total_records - 2 * num_recs) % 1000, 20000)
num_train = max((num_train // 5000) * 5000, num_recs)
train_set = all_records[0:num_train]
dev_set = all_records[num_train:num_train + num_recs]
test_set = all_records[num_train + num_recs:num_train + 2 * num_recs]
extra_set = all_records[num_train + 2 * num_recs:]
print(langid, 'train', num_train, 'dev/test', num_recs)
tar = tarfile.open(os.path.join(output_dir, filename), "w:gz")
train_data, train_tarinfo = data_to_byteio(train_set, 'train')
dev_data, dev_tarinfo = data_to_byteio(dev_set, 'dev')
test_data, test_tarinfo = data_to_byteio(test_set, 'test')
extra_data, extra_tarinfo = data_to_byteio(extra_set, 'extra')
tar.addfile(tarinfo=train_tarinfo, fileobj=train_data)
tar.addfile(tarinfo=dev_tarinfo, fileobj=dev_data)
tar.addfile(tarinfo=test_tarinfo, fileobj=test_data)
tar.addfile(tarinfo=extra_tarinfo, fileobj=extra_data)
tar.close()
def get_cca59_languages(cca_59_file):
lang_count = Counter()
with gzip.open(cca_59_file, 'rt', encoding='utf-8') if 'gz' in cca_59_file else io.open(cca_59_file, 'r', encoding='utf-8') as fin:
for line in fin:
lang = line.split(':')[0]
lang_count[lang] += 1
return lang_count
def panx_to_dataset(input_dir, output_dir, supported_langs=None, count=False):
files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
if count:
file_record = {}
for file in files:
targz_file = os.path.join(input_dir, file)
tar = tarfile.open(targz_file, "r:gz")
for member in tar.getmembers():
if '.bio' in member.name:
bio_file = tar.extractfile(member)
if bio_file is not None:
num_records = check_num_records_by_content(bio_file, file)
file_record[file] = num_records
file_record = Counter(file_record)
print(file_record.most_common())
with open(os.path.join(output_dir, 'lang_stats.json'), 'w') as fout:
json.dump(file_record, fout)
for file in files:
langid = file.split('.')[0]
if supported_langs and langid not in supported_langs:
continue
targz_file_in = os.path.join(input_dir, file)
targz_file_out = os.path.join(output_dir, file)
tar = tarfile.open(targz_file_in, "r:gz")
for member in tar.getmembers():
if '.bio' in member.name:
bio_file = tar.extractfile(member)
if bio_file is not None:
write_panx_preprocessed(bio_file, file, output_dir)
tar.close()
def parse_args(argv):
"""
Parse commandline arguments.
Arguments:
argv -- An argument list without the program name.
"""
parser = argparse.ArgumentParser()
parser.add_argument('--embs_dir', type=str, required=True, help='directory containing the complete word embeddings')
parser.add_argument('--ner_built_dir', type=str, required=True, help='directory where built output (trimmed embs + vocab) will be written into.')
args = parser.parse_args(argv)
return args
if __name__ == '__main__':
args = parse_args(sys.argv[1:])
logging.info(args)
#facebook or allenai multilingual embeddings?
embs_dir = args.embs_dir
ner_built_dir = args.ner_built_dir
#raw ner downloaded from panx
wiki_ner_input_dir = './datasets/panx2_all/'
#directory for converted raw ner data to train/test/dev stratified
wiki_ner_output_dir = './datasets/panx_datasets' #'./datasets/panx2_supported45'
wiki_embsupported_languages = ['af', 'ar', 'bg', 'bn', 'bs', 'ca', 'cs', 'da', 'de', 'el', 'en', 'es', 'et',
'fa', 'fi', 'fr', 'he', 'hi', 'hr', 'hu', 'id', 'it', 'lt', 'lv', 'mk', 'ms', 'nl',
'no', 'pl', 'pt', 'ro', 'ru', 'sk', 'sl', 'sq', 'sv', 'ta', 'tl',
'tr', 'uk', 'vi']
print(f"num langs: {len(wiki_embsupported_languages)}")
create_ner_datasets = False
if create_ner_datasets:
#only run this once to create stratified train/dev/test splits from wikiann, and save them.
panx_to_dataset(wiki_ner_input_dir, wiki_ner_output_dir, supported_langs=None)#wiki_embsupported_languages)
sys.exit(0)
all_chars = set()
if not os.path.exists(ner_built_dir):
os.mkdir(ner_built_dir)
for lang in wiki_embsupported_languages:
output_dir = os.path.join(ner_built_dir , f'builtdata_{lang}')
emb_file = lang + '.multi.gz'
#create_train_dev_test_from_one_file(conll_file='./datasets/originals/ner/de/wikiann-de.bio', lang='de', output_dir=ner_dir)
#create_train_dev_test_from_one_file(conll_file='./datasets/originals/ner/en/wikiann-en.bio', lang='en', output_dir=ner_dir)
#create_train_dev_test_from_one_file(conll_file='./datasets/originals/ner/nl/wikiann-nl.bio', lang='nl', output_dir=ner_dir)
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
print(f"directory exists:{output_dir}")
os.mkdir(output_dir)
#for panx langid is already added to conll terms
#add_lang_to_each_word(ner_dir, name_index=0, lang_separator=':')
#embeddings already have lang identifier
vocab, tags, chars = build_vocab(wiki_ner_output_dir, os.path.join(embs_dir, lang), output_dir, emb_file=emb_file, panx=True, fname='{}.tar.gz'.format(lang))
all_chars = all_chars | chars
trim_embs(os.path.join(embs_dir, lang), os.path.join(output_dir, 'words.txt'), output_dir, 300, emb_file=emb_file)
write_list(all_chars, os.path.join(ner_built_dir, 'chars.txt'))
write_list(tags, os.path.join(ner_built_dir, 'tags.txt'))
| <filename>utils.py
from io import open
import logging
import pdb
import random
import os
import numpy as np
import shutil
from collections import Counter
import tarfile
import json
import random
from collections import defaultdict
import io
import gzip
import sys
import argparse
np.random.seed(7)
random.seed(7)
logging.basicConfig(format='%(asctime)s %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p', level=logging.INFO)
def check_num_records(filename):
records = 0
with open(filename, 'r', encoding='utf-8') as fin:
for line in fin:
if line.strip() == '':
records += 1
logging.info('{} records in {}'.format(records, filename))
return records
def check_num_records_by_content(filehandle, filename):
records = 0
for line in filehandle:
line = line.decode('utf-8')
if line.strip() == '':
records += 1
logging.info('{} records in {}'.format(records, filename))
return records
def extract_records(input_file, output_file, num_records):
records = 0
all_records = []
with open(input_file, 'r', encoding='utf-8') as fin:
record = []
for line in fin:
line = line.strip()
if 'docstart' in line.lower():
continue
if line == '' and len(record) > 0:
all_records.append(record)
record = []
elif line != '':
record.append(line)
all_records.append(record)
selected_records = all_records[0:num_records]
with open(output_file, 'w', encoding='utf-8') as fout:
for i, record in enumerate(selected_records):
for r in record:
fout.write(r + '\n')
fout.write('\n')
def create_train_dev_test_from_one_file(conll_file, lang, output_dir):
records = 0
all_records = []
with open(conll_file, 'r', encoding='utf-8') as fin:
record = []
for line in fin:
line = line.strip()
if 'docstart' in line.lower():
continue
if line == '' and len(record) > 0:
all_records.append(record)
record = []
elif line != '':
fields = line.split()
if len(fields) > 1:
line = fields[0] + '\t' + fields[-1]
record.append(line)
if len(record) > 0:
all_records.append(record)
logging.info('{} sentences for {} detected.'.format(len(all_records), lang))
train_100 = all_records[0:100]
train_1000 = all_records[0:1000]
train_10000 = all_records[0:10000]
test_count = dev_count = min(len(all_records) - 10000 // 2, 10000)
dev = all_records[10000:10000 + dev_count]
test = all_records[10000 + dev_count: 10000 + dev_count + test_count]
train_100_file = os.path.join(output_dir, '{}.train.{}'.format(lang, 100))
train_1000_file = os.path.join(output_dir, '{}.train.{}'.format(lang, 1000))
train_10000_file = os.path.join(output_dir, '{}.train.{}'.format(lang, 10000))
dev_file = os.path.join(output_dir, '{}.dev'.format(lang))
test_file = os.path.join(output_dir, '{}.test'.format(lang))
file_records = [(train_100, train_100_file), (train_1000, train_1000_file), (train_10000, train_10000_file), (dev, dev_file), (test, test_file)]
for file_record in file_records:
with open(file_record[1], 'w', encoding='utf-8') as fout:
for i, record in enumerate(file_record[0]):
for r in record:
fout.write(r + '\n')
fout.write('\n')
logging.info('created train dev test splits for {} in {}'.format(lang, output_dir))
def add_lang_to_each_word(dir_name, name_index, lang_separator=':', embfile=None):
if embfile:
files = [embfile]
else:
files = [f for f in os.listdir(dir_name) if os.path.isfile(os.path.join(dir_name, f))]
for file in files:
if not embfile:
if file[-5:] == 'multi' or 'cca-59' in str(file):
continue
lang = file.split('.')[name_index]
print('lang:{}'.format(lang))
filename = os.path.join(dir_name, file)
lines = []
with open(filename, 'r', encoding='utf-8') as fin:
for line in fin:
if line.strip() != '':
line = lang + lang_separator + line
lines.append(line)
with open(filename + '.multi', 'w', encoding='utf-8') as fout:
for line in lines:
fout.write(line)
def create_ner_training_datasets():
for lang in ['de', 'en', 'nl']:
for i in [100, 1000, 10000]:
extract_records(input_file='./datasets/ner/{}.train'.format(lang), output_file='./datasets/ner/{}.train.{}'.format(lang, i), num_records=i)
check_num_records('./datasets/ner/{}.train.{}'.format(lang, i))
def collect_vocab_and_tags(dir_name, fname=None):
'''
:param dir_name:
:return set of all the vocab (+$UNK$ and +$NUM$ in all the files in the directory (expect all the files to be in conll2003 format):
'''
tags = set()
vocab = set()
chars = set()
files = [f for f in os.listdir(dir_name) if os.path.isfile(os.path.join(dir_name, f))] if not fname else [os.path.join(dir_name, fname)]
for file in files:
#we just want the files where language id is added to the beginning of the words
if file[-5:] != 'multi':
continue
filename = os.path.join(dir_name, file)
with open(filename, 'r', encoding='utf-8') as fin:
for line in fin:
if line.strip() != '' and 'docstart' not in line.lower():
fields = line.split()
if len(fields) > 1:
word, tag = fields[0], fields[-1]
else:
logging.info('warning: error in line {} in file {}'.format(line, filename))
vocab.add(word.lower())
tags.add(tag)
chars.update(word)
return vocab, tags, chars
def collect_vocab_and_tags_panx(panx_dir_name, fname=None):
'''
:param dir_name: contains tar.gz files each with train test dev conll files
:return set of all the vocab (+$UNK$ and +$NUM$ in all the files in the directory (expect all the files to be in conll2003 format):
important note: all vocab in wikiembs :https://fasttext.cc/docs/en/pretrained-vectors.html are lowercase while
words in wikiemb+crawlemb: https://fasttext.cc/docs/en/crawl-vectors.html have upper case words so it will make a huge
difference in terms of OOVs and how we match words in NER datasets to words in the pre-trained word embeddings.
'''
tags = set()
vocab = set()
chars = set()
files = [f for f in os.listdir(panx_dir_name) if os.path.isfile(os.path.join(panx_dir_name, f))] if not fname else [fname]
for file in files:
targz_file = os.path.join(panx_dir_name, file)
tar = tarfile.open(targz_file, "r:gz")
for member in tar.getmembers():
#don't collect the vocabulary of extra annotated data
if member.name == 'extra':
continue
bio_file = tar.extractfile(member)
for line in bio_file:
line = line.decode('utf-8')
if line.strip() != '' and 'docstart' not in line.lower():
fields = line.split()
if len(fields) > 1:
word, tag = fields[0], fields[-1]
else:
logging.info('warning: error in line {} in file {}'.format(line, targz_file))
vocab.add(word)
tags.add(tag)
chars.update(word)
tar.close()
return vocab, tags, chars
def collect_embedding_vocabs(dir_name, embfile=None):
'''
:param dir_name: directory where all the word embeddings with different languages are located
expected that lang id- (e.g. en-) is added to the beginning of each word so that exact words in different
languages are distinguishable
:return: set of vocab
'''
vocab_emb = set()
filename = os.path.join(dir_name, emb_file)
with gzip.open(filename, 'rt', encoding='utf-8') if 'gz' in embfile else open(filename, 'r', encoding='utf-8') as fin:
for line in fin:
if line.strip() != '':
word = line.split(' ')[0]
vocab_emb.add(word)
vocab_lang_distribution = [v.split(':')[0] for v in vocab_emb]
vocab_lang_distribution = Counter(vocab_lang_distribution)
logging.info('embedding file vocab stats: {}'.format(str(vocab_lang_distribution)))
return vocab_emb
def write_list(items, output_file):
with open(output_file, 'w', encoding='utf-8') as fout:
for i, item in enumerate(sorted(items)):
if i != len(items) - 1:
fout.write('{}\n'.format(item))
else:
fout.write(item)
def read_list(filename):
items = []
with open(filename, 'r', encoding='utf-8') as fin:
for item in fin:
items.append(item.strip())
return items
def write_vocab_tags_chars_embs(vocab_ner, tags, chars, vocab_emb, output_dir):
write_list(tags, os.path.join(output_dir, 'tags.txt'))
write_list(chars, os.path.join(output_dir, 'chars.txt'))
'''
now we should add any vocab that might have uppercase in vocab_ner
but is lowercase in vocab_emmb.
note that lowercase=True should be turned to lowercase=False in mconfig file to reflect this.
question: what about the cases when a lower case word is in NER but an uppercase is in emb?
'''
vocab = vocab_ner & vocab_emb
for v in vocab_ner:
vlow = v.lower()
if v == vlow:
continue
if v not in vocab_emb and vlow in vocab_emb:
vocab.add(vlow)
vocab_lang_distribution = [v.split(':')[0] for v in vocab]
vocab_lang_distribution = Counter(vocab_lang_distribution)
logging.info(vocab_lang_distribution)
with open(os.path.join(output_dir, 'stats'), 'w') as fout:
fout.write(str(vocab_lang_distribution))
vocab.add('$UNK$')
vocab.add('$NUM$')
write_list(vocab, os.path.join(output_dir, 'words.txt'))
def build_vocab(conll_dir, emb_dir, output_dir, emb_file=None, panx=False, fname=None):
if panx:
vocab, tags, chars = collect_vocab_and_tags_panx(conll_dir, fname=fname)
else:
vocab, tags, chars = collect_vocab_and_tags(conll_dir)
vocab_emb = collect_embedding_vocabs(emb_dir, emb_file)
write_vocab_tags_chars_embs(vocab, tags, chars, vocab_emb, output_dir)
return vocab, tags, chars
def trim_embs(emb_dir, vocab_file, output_dir, dim, emb_file=None):
vocab = read_list(vocab_file)
vocabset = set(vocab)
vocab_emb = set()
embeddings = np.zeros([len(vocab), dim])
filename = os.path.join(emb_dir, emb_file)
with gzip.open(filename, 'rt', encoding='utf-8') if 'gz' in emb_file else open(filename, 'r', encoding='utf-8') as fin:
for line in fin:
line = line.strip().split(' ')
word = line[0]
if word not in vocabset:
continue
embedding = [float(x) for x in line[1:]]
word_idx = vocab.index(word)
embeddings[word_idx] = np.asarray(embedding)
np.savez_compressed(os.path.join(output_dir, 'trimmed_embs.npz'), embeddings=embeddings)
def data_to_byteio(records, name):
data = ''
for i, record in enumerate(records):
for r in record:
data += r + '\n'
data += '\n'
encoded_data = data.encode('utf8')
data_byteio = io.BytesIO(encoded_data)
tarinfo = tarfile.TarInfo(name)
tarinfo.size = len(encoded_data)
return data_byteio, tarinfo
def annotation_to_byteio(records, name):
data = ''
for i, record in enumerate(records):
data += '\n'.join([str(c) for c in record]) + '\n\n'
encoded_data = data.encode('utf8')
data_byteio = io.BytesIO(encoded_data)
tarinfo = tarfile.TarInfo(name)
tarinfo.size = len(encoded_data)
return data_byteio, tarinfo
def annotation_to_byteio_yuan(records, name):
data = ''
for i, record in enumerate(records):
data += '\t'.join([str(c) for c in record]) + '\n'
encoded_data = data.encode('utf8')
data_byteio = io.BytesIO(encoded_data)
tarinfo = tarfile.TarInfo(name)
tarinfo.size = len(encoded_data)
return data_byteio, tarinfo
def write_panx_preprocessed(filehandle, filename, output_dir):
langid = filename.split('.')[0]
records = 0
all_records = []
record = []
tag_records = defaultdict(set)
last_label_in_record = None
for line in filehandle:
line = line.decode('utf-8')
line = line.strip()
if 'docstart' in line.lower():
continue
if line == '' and len(record) > 0:
all_records.append(record)
if last_label_in_record:
tag_records[last_label_in_record].add(len(all_records) - 1)
last_label_in_record = None
record = []
elif line != '':
fields = line.split()
if len(fields) > 1:
line = langid + ':' + fields[0] + '\t' + fields[-1]
if 'B-' in fields[-1]:
last_label_in_record = fields[-1]
record.append(line)
if len(record) > 0:
all_records.append(record)
#find the minimum count of B- tag, used for stratified sampling
min_count_tag = min([len(v) for v in tag_records.values()])
#now sample min_count_tag records from each tag
new_records = []
for _, records in tag_records.items():
new_records.extend(random.sample(records, min_count_tag))
#new records contains indices of items in all_records
random.seed(0)
np.random.seed(0)
random.shuffle(new_records)
all_records = [all_records[i] for i in new_records]
total_records = len(all_records)
if total_records > 30000:
num_recs = 10000
elif total_records > 3000:
num_recs = 1000
elif total_records > 300:
num_recs = 100
else:
return
num_train = min((total_records - 2 * num_recs) - (total_records - 2 * num_recs) % 1000, 20000)
num_train = max((num_train // 5000) * 5000, num_recs)
train_set = all_records[0:num_train]
dev_set = all_records[num_train:num_train + num_recs]
test_set = all_records[num_train + num_recs:num_train + 2 * num_recs]
extra_set = all_records[num_train + 2 * num_recs:]
print(langid, 'train', num_train, 'dev/test', num_recs)
tar = tarfile.open(os.path.join(output_dir, filename), "w:gz")
train_data, train_tarinfo = data_to_byteio(train_set, 'train')
dev_data, dev_tarinfo = data_to_byteio(dev_set, 'dev')
test_data, test_tarinfo = data_to_byteio(test_set, 'test')
extra_data, extra_tarinfo = data_to_byteio(extra_set, 'extra')
tar.addfile(tarinfo=train_tarinfo, fileobj=train_data)
tar.addfile(tarinfo=dev_tarinfo, fileobj=dev_data)
tar.addfile(tarinfo=test_tarinfo, fileobj=test_data)
tar.addfile(tarinfo=extra_tarinfo, fileobj=extra_data)
tar.close()
def get_cca59_languages(cca_59_file):
lang_count = Counter()
with gzip.open(cca_59_file, 'rt', encoding='utf-8') if 'gz' in cca_59_file else io.open(cca_59_file, 'r', encoding='utf-8') as fin:
for line in fin:
lang = line.split(':')[0]
lang_count[lang] += 1
return lang_count
def panx_to_dataset(input_dir, output_dir, supported_langs=None, count=False):
files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
if count:
file_record = {}
for file in files:
targz_file = os.path.join(input_dir, file)
tar = tarfile.open(targz_file, "r:gz")
for member in tar.getmembers():
if '.bio' in member.name:
bio_file = tar.extractfile(member)
if bio_file is not None:
num_records = check_num_records_by_content(bio_file, file)
file_record[file] = num_records
file_record = Counter(file_record)
print(file_record.most_common())
with open(os.path.join(output_dir, 'lang_stats.json'), 'w') as fout:
json.dump(file_record, fout)
for file in files:
langid = file.split('.')[0]
if supported_langs and langid not in supported_langs:
continue
targz_file_in = os.path.join(input_dir, file)
targz_file_out = os.path.join(output_dir, file)
tar = tarfile.open(targz_file_in, "r:gz")
for member in tar.getmembers():
if '.bio' in member.name:
bio_file = tar.extractfile(member)
if bio_file is not None:
write_panx_preprocessed(bio_file, file, output_dir)
tar.close()
def parse_args(argv):
"""
Parse commandline arguments.
Arguments:
argv -- An argument list without the program name.
"""
parser = argparse.ArgumentParser()
parser.add_argument('--embs_dir', type=str, required=True, help='directory containing the complete word embeddings')
parser.add_argument('--ner_built_dir', type=str, required=True, help='directory where built output (trimmed embs + vocab) will be written into.')
args = parser.parse_args(argv)
return args
if __name__ == '__main__':
args = parse_args(sys.argv[1:])
logging.info(args)
#facebook or allenai multilingual embeddings?
embs_dir = args.embs_dir
ner_built_dir = args.ner_built_dir
#raw ner downloaded from panx
wiki_ner_input_dir = './datasets/panx2_all/'
#directory for converted raw ner data to train/test/dev stratified
wiki_ner_output_dir = './datasets/panx_datasets' #'./datasets/panx2_supported45'
wiki_embsupported_languages = ['af', 'ar', 'bg', 'bn', 'bs', 'ca', 'cs', 'da', 'de', 'el', 'en', 'es', 'et',
'fa', 'fi', 'fr', 'he', 'hi', 'hr', 'hu', 'id', 'it', 'lt', 'lv', 'mk', 'ms', 'nl',
'no', 'pl', 'pt', 'ro', 'ru', 'sk', 'sl', 'sq', 'sv', 'ta', 'tl',
'tr', 'uk', 'vi']
print(f"num langs: {len(wiki_embsupported_languages)}")
create_ner_datasets = False
if create_ner_datasets:
#only run this once to create stratified train/dev/test splits from wikiann, and save them.
panx_to_dataset(wiki_ner_input_dir, wiki_ner_output_dir, supported_langs=None)#wiki_embsupported_languages)
sys.exit(0)
all_chars = set()
if not os.path.exists(ner_built_dir):
os.mkdir(ner_built_dir)
for lang in wiki_embsupported_languages:
output_dir = os.path.join(ner_built_dir , f'builtdata_{lang}')
emb_file = lang + '.multi.gz'
#create_train_dev_test_from_one_file(conll_file='./datasets/originals/ner/de/wikiann-de.bio', lang='de', output_dir=ner_dir)
#create_train_dev_test_from_one_file(conll_file='./datasets/originals/ner/en/wikiann-en.bio', lang='en', output_dir=ner_dir)
#create_train_dev_test_from_one_file(conll_file='./datasets/originals/ner/nl/wikiann-nl.bio', lang='nl', output_dir=ner_dir)
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
print(f"directory exists:{output_dir}")
os.mkdir(output_dir)
#for panx langid is already added to conll terms
#add_lang_to_each_word(ner_dir, name_index=0, lang_separator=':')
#embeddings already have lang identifier
vocab, tags, chars = build_vocab(wiki_ner_output_dir, os.path.join(embs_dir, lang), output_dir, emb_file=emb_file, panx=True, fname='{}.tar.gz'.format(lang))
all_chars = all_chars | chars
trim_embs(os.path.join(embs_dir, lang), os.path.join(output_dir, 'words.txt'), output_dir, 300, emb_file=emb_file)
write_list(all_chars, os.path.join(ner_built_dir, 'chars.txt'))
write_list(tags, os.path.join(ner_built_dir, 'tags.txt'))
| en | 0.697125 | :param dir_name: :return set of all the vocab (+$UNK$ and +$NUM$ in all the files in the directory (expect all the files to be in conll2003 format): #we just want the files where language id is added to the beginning of the words :param dir_name: contains tar.gz files each with train test dev conll files :return set of all the vocab (+$UNK$ and +$NUM$ in all the files in the directory (expect all the files to be in conll2003 format): important note: all vocab in wikiembs :https://fasttext.cc/docs/en/pretrained-vectors.html are lowercase while words in wikiemb+crawlemb: https://fasttext.cc/docs/en/crawl-vectors.html have upper case words so it will make a huge difference in terms of OOVs and how we match words in NER datasets to words in the pre-trained word embeddings. #don't collect the vocabulary of extra annotated data :param dir_name: directory where all the word embeddings with different languages are located expected that lang id- (e.g. en-) is added to the beginning of each word so that exact words in different languages are distinguishable :return: set of vocab now we should add any vocab that might have uppercase in vocab_ner but is lowercase in vocab_emmb. note that lowercase=True should be turned to lowercase=False in mconfig file to reflect this. question: what about the cases when a lower case word is in NER but an uppercase is in emb? #find the minimum count of B- tag, used for stratified sampling #now sample min_count_tag records from each tag #new records contains indices of items in all_records Parse commandline arguments. Arguments: argv -- An argument list without the program name. #facebook or allenai multilingual embeddings? #raw ner downloaded from panx #directory for converted raw ner data to train/test/dev stratified #'./datasets/panx2_supported45' #only run this once to create stratified train/dev/test splits from wikiann, and save them. #wiki_embsupported_languages) #create_train_dev_test_from_one_file(conll_file='./datasets/originals/ner/de/wikiann-de.bio', lang='de', output_dir=ner_dir) #create_train_dev_test_from_one_file(conll_file='./datasets/originals/ner/en/wikiann-en.bio', lang='en', output_dir=ner_dir) #create_train_dev_test_from_one_file(conll_file='./datasets/originals/ner/nl/wikiann-nl.bio', lang='nl', output_dir=ner_dir) #for panx langid is already added to conll terms #add_lang_to_each_word(ner_dir, name_index=0, lang_separator=':') #embeddings already have lang identifier | 2.437597 | 2 |
evaluate_sket.py | ExaNLP/sket | 4 | 6613684 | <reponame>ExaNLP/sket<gh_stars>1-10
import numpy as np
import json
import glob
import os
import argparse
from sklearn.metrics import hamming_loss, accuracy_score, classification_report
parser = argparse.ArgumentParser()
parser.add_argument('--gt', default='./ground_truth/lung/aoec/lung_labels_allDS.json', type=str, help='Ground truth file.')
parser.add_argument('--outputs', default='./outputs/labels/aoec/lung/*.json', type=str, help='SKET results file.')
parser.add_argument('--use_case', default='lung', choices=['colon', 'cervix', 'lung'], help='Considered use-case.')
parser.add_argument('--hospital', default='aoec', choices=['aoec', 'radboud'], help='Considered hospital.')
parser.add_argument('--debug', default=False, action='store_true', help='Whether to use evaluation for debugging purposes.')
args = parser.parse_args()
label2class = {
'cervix': {
'Normal glands': 'glands_norm',
'Normal squamous': 'squamous_norm',
'Cancer - squamous cell carcinoma in situ': 'cancer_scc_insitu',
'Low grade dysplasia': 'lgd',
'Cancer - squamous cell carcinoma invasive': 'cancer_scc_inv',
'High grade dysplasia': 'hgd',
'Koilocytes': 'koilocytes',
'Cancer - adenocarcinoma invasive': 'cancer_adeno_inv',
'Cancer - adenocarcinoma in situ': 'cancer_adeno_insitu',
'HPV infection present': 'hpv'
},
'colon': {
'Hyperplastic polyp': 'hyperplastic',
'Cancer': 'cancer',
'Adenomatous polyp - high grade dysplasia': 'hgd',
'Adenomatous polyp - low grade dysplasia': 'lgd',
'Non-informative': 'ni'
},
'lung': {
'No cancer': 'no_cancer',
'Cancer - non-small cell cancer, adenocarcinoma': 'cancer_nscc_adeno',
'Cancer - non-small cell cancer, squamous cell carcinoma': 'cancer_nscc_squamous',
'Cancer - small cell cancer': 'cancer_scc',
'Cancer - non-small cell cancer, large cell carcinoma': 'cancer_nscc_large'
}
}
def main():
# create path for debugging
debug_path = './logs/debug/' + args.hospital + '/' + args.use_case + '/'
os.makedirs(os.path.dirname(debug_path), exist_ok=True)
# read ground-truth
with open(args.gt, 'r') as gtf:
ground_truth = json.load(gtf)
gt = {}
# prepare ground-truth for evaluation
if args.use_case == 'colon' and args.hospital == 'aoec':
gt = ground_truth
else:
ground_truth = ground_truth['ground_truth']
for data in ground_truth:
rid = data['report_id_not_hashed']
if len(rid.split('_')) == 3 and args.hospital == 'aoec': # contains codeint info not present within new processed reports
rid = rid.split('_')
rid = rid[0] + '_' + rid[2]
gt[rid] = {label2class[args.use_case][label]: 0 for label in label2class[args.use_case].keys()}
for datum in data['labels']:
label = label2class[args.use_case][datum['label']]
if label in gt[rid]:
gt[rid][label] = 1
# gt name
gt_name = args.gt.split('/')[-1].split('.')[0]
# read SKET results
if '*.json' == args.outputs.split('/')[-1]: # read files
# read file paths
rsfps = glob.glob(args.outputs)
# set dict
rs = {}
for rsfp in rsfps:
with open(rsfp, 'r') as rsf:
rs.update(json.load(rsf))
else: # read file
with open(args.outputs, 'r') as rsf:
rs = json.load(rsf)
sket = {}
# prepare SKET results for evaluation
for rid, rdata in rs.items():
if args.use_case == 'colon' and args.hospital == 'aoec' and '2ndDS' in args.gt:
rid = rid.split('_')[0]
if args.hospital == 'radboud' and args.use_case == 'colon':
sket[rid] = rdata['labels']
else:
sket[rid] = rdata
# fix class order to avoid inconsistencies
rids = list(sket.keys())
classes = list(sket[rids[0]].keys())
# obtain ground-truth and SKET scores
gt_scores = []
sket_scores = []
if args.debug: # open output for debugging
debugf = open(debug_path + gt_name + '.txt', 'w+')
for rid in gt.keys():
gt_rscores = []
sket_rscores = []
if rid not in sket:
print('skipped gt record: {}'.format(rid))
continue
if args.debug:
first = True
for c in classes:
gt_rscores.append(gt[rid][c])
sket_rscores.append(sket[rid][c])
if args.debug: # perform debugging
if gt[rid][c] != sket[rid][c]: # store info for debugging purposes
if first: # first occurence
debugf.write('\nReport ID: {}\n'.format(rid))
first = False
debugf.write(c + ': gt = {}, sket = {}\n'.format(gt[rid][c], sket[rid][c]))
gt_scores.append(gt_rscores)
sket_scores.append(sket_rscores)
if args.debug: # close output for debugging
debugf.close()
# convert to numpy
gt_scores = np.array(gt_scores)
sket_scores = np.array(sket_scores)
# compute evaluation measures
print('Compute evaluation measures')
# exact match accuracy & hamming loss
print("Accuracy (exact match): {}".format(accuracy_score(gt_scores, sket_scores)))
print("Hamming loss: {}\n".format(hamming_loss(gt_scores, sket_scores)))
# compute classification report
print("Classification report:")
print(classification_report(y_true=gt_scores, y_pred=sket_scores, target_names=classes))
if __name__ == "__main__":
main()
| import numpy as np
import json
import glob
import os
import argparse
from sklearn.metrics import hamming_loss, accuracy_score, classification_report
parser = argparse.ArgumentParser()
parser.add_argument('--gt', default='./ground_truth/lung/aoec/lung_labels_allDS.json', type=str, help='Ground truth file.')
parser.add_argument('--outputs', default='./outputs/labels/aoec/lung/*.json', type=str, help='SKET results file.')
parser.add_argument('--use_case', default='lung', choices=['colon', 'cervix', 'lung'], help='Considered use-case.')
parser.add_argument('--hospital', default='aoec', choices=['aoec', 'radboud'], help='Considered hospital.')
parser.add_argument('--debug', default=False, action='store_true', help='Whether to use evaluation for debugging purposes.')
args = parser.parse_args()
label2class = {
'cervix': {
'Normal glands': 'glands_norm',
'Normal squamous': 'squamous_norm',
'Cancer - squamous cell carcinoma in situ': 'cancer_scc_insitu',
'Low grade dysplasia': 'lgd',
'Cancer - squamous cell carcinoma invasive': 'cancer_scc_inv',
'High grade dysplasia': 'hgd',
'Koilocytes': 'koilocytes',
'Cancer - adenocarcinoma invasive': 'cancer_adeno_inv',
'Cancer - adenocarcinoma in situ': 'cancer_adeno_insitu',
'HPV infection present': 'hpv'
},
'colon': {
'Hyperplastic polyp': 'hyperplastic',
'Cancer': 'cancer',
'Adenomatous polyp - high grade dysplasia': 'hgd',
'Adenomatous polyp - low grade dysplasia': 'lgd',
'Non-informative': 'ni'
},
'lung': {
'No cancer': 'no_cancer',
'Cancer - non-small cell cancer, adenocarcinoma': 'cancer_nscc_adeno',
'Cancer - non-small cell cancer, squamous cell carcinoma': 'cancer_nscc_squamous',
'Cancer - small cell cancer': 'cancer_scc',
'Cancer - non-small cell cancer, large cell carcinoma': 'cancer_nscc_large'
}
}
def main():
# create path for debugging
debug_path = './logs/debug/' + args.hospital + '/' + args.use_case + '/'
os.makedirs(os.path.dirname(debug_path), exist_ok=True)
# read ground-truth
with open(args.gt, 'r') as gtf:
ground_truth = json.load(gtf)
gt = {}
# prepare ground-truth for evaluation
if args.use_case == 'colon' and args.hospital == 'aoec':
gt = ground_truth
else:
ground_truth = ground_truth['ground_truth']
for data in ground_truth:
rid = data['report_id_not_hashed']
if len(rid.split('_')) == 3 and args.hospital == 'aoec': # contains codeint info not present within new processed reports
rid = rid.split('_')
rid = rid[0] + '_' + rid[2]
gt[rid] = {label2class[args.use_case][label]: 0 for label in label2class[args.use_case].keys()}
for datum in data['labels']:
label = label2class[args.use_case][datum['label']]
if label in gt[rid]:
gt[rid][label] = 1
# gt name
gt_name = args.gt.split('/')[-1].split('.')[0]
# read SKET results
if '*.json' == args.outputs.split('/')[-1]: # read files
# read file paths
rsfps = glob.glob(args.outputs)
# set dict
rs = {}
for rsfp in rsfps:
with open(rsfp, 'r') as rsf:
rs.update(json.load(rsf))
else: # read file
with open(args.outputs, 'r') as rsf:
rs = json.load(rsf)
sket = {}
# prepare SKET results for evaluation
for rid, rdata in rs.items():
if args.use_case == 'colon' and args.hospital == 'aoec' and '2ndDS' in args.gt:
rid = rid.split('_')[0]
if args.hospital == 'radboud' and args.use_case == 'colon':
sket[rid] = rdata['labels']
else:
sket[rid] = rdata
# fix class order to avoid inconsistencies
rids = list(sket.keys())
classes = list(sket[rids[0]].keys())
# obtain ground-truth and SKET scores
gt_scores = []
sket_scores = []
if args.debug: # open output for debugging
debugf = open(debug_path + gt_name + '.txt', 'w+')
for rid in gt.keys():
gt_rscores = []
sket_rscores = []
if rid not in sket:
print('skipped gt record: {}'.format(rid))
continue
if args.debug:
first = True
for c in classes:
gt_rscores.append(gt[rid][c])
sket_rscores.append(sket[rid][c])
if args.debug: # perform debugging
if gt[rid][c] != sket[rid][c]: # store info for debugging purposes
if first: # first occurence
debugf.write('\nReport ID: {}\n'.format(rid))
first = False
debugf.write(c + ': gt = {}, sket = {}\n'.format(gt[rid][c], sket[rid][c]))
gt_scores.append(gt_rscores)
sket_scores.append(sket_rscores)
if args.debug: # close output for debugging
debugf.close()
# convert to numpy
gt_scores = np.array(gt_scores)
sket_scores = np.array(sket_scores)
# compute evaluation measures
print('Compute evaluation measures')
# exact match accuracy & hamming loss
print("Accuracy (exact match): {}".format(accuracy_score(gt_scores, sket_scores)))
print("Hamming loss: {}\n".format(hamming_loss(gt_scores, sket_scores)))
# compute classification report
print("Classification report:")
print(classification_report(y_true=gt_scores, y_pred=sket_scores, target_names=classes))
if __name__ == "__main__":
main() | en | 0.820588 | # create path for debugging # read ground-truth # prepare ground-truth for evaluation # contains codeint info not present within new processed reports # gt name # read SKET results # read files # read file paths # set dict # read file # prepare SKET results for evaluation # fix class order to avoid inconsistencies # obtain ground-truth and SKET scores # open output for debugging # perform debugging # store info for debugging purposes # first occurence # close output for debugging # convert to numpy # compute evaluation measures # exact match accuracy & hamming loss # compute classification report | 2.359874 | 2 |
tests/asm_logical/test_asm_ora.py | CyberZHG/mos-6502-restricted-assembler | 0 | 6613685 | from unittest import TestCase
from asm_6502 import Assembler
class TestAssembleORA(TestCase):
def setUp(self) -> None:
self.assembler = Assembler()
def test_ora_immediate(self):
code = "ORA #$10"
results = self.assembler.assemble(code, add_entry=False)
self.assertEqual([
(0x0000, [0x09, 0x10]),
], results)
def test_ora_zero_page(self):
code = "ORA $10"
results = self.assembler.assemble(code, add_entry=False)
self.assertEqual([
(0x0000, [0x05, 0x10]),
], results)
def test_ora_zero_page_x(self):
code = "ORA $10,X"
results = self.assembler.assemble(code, add_entry=False)
self.assertEqual([
(0x0000, [0x15, 0x10]),
], results)
def test_ora_absolute(self):
code = "ORA $ABCD"
results = self.assembler.assemble(code, add_entry=False)
self.assertEqual([
(0x0000, [0x0D, 0xCD, 0xAB]),
], results)
def test_ora_absolute_indexed(self):
code = "ORA $ABCD,X\n" \
"ORA $ABCD,Y"
results = self.assembler.assemble(code, add_entry=False)
self.assertEqual([
(0x0000, [0x1D, 0xCD, 0xAB, 0x19, 0xCD, 0xAB]),
], results)
def test_ora_indexed_indirect(self):
code = "ORA ($10,X)"
results = self.assembler.assemble(code, add_entry=False)
self.assertEqual([
(0x0000, [0x01, 0x10]),
], results)
def test_ora_indirect_indexed(self):
code = "ORA ($10),Y"
results = self.assembler.assemble(code, add_entry=False)
self.assertEqual([
(0x0000, [0x11, 0x10]),
], results)
| from unittest import TestCase
from asm_6502 import Assembler
class TestAssembleORA(TestCase):
def setUp(self) -> None:
self.assembler = Assembler()
def test_ora_immediate(self):
code = "ORA #$10"
results = self.assembler.assemble(code, add_entry=False)
self.assertEqual([
(0x0000, [0x09, 0x10]),
], results)
def test_ora_zero_page(self):
code = "ORA $10"
results = self.assembler.assemble(code, add_entry=False)
self.assertEqual([
(0x0000, [0x05, 0x10]),
], results)
def test_ora_zero_page_x(self):
code = "ORA $10,X"
results = self.assembler.assemble(code, add_entry=False)
self.assertEqual([
(0x0000, [0x15, 0x10]),
], results)
def test_ora_absolute(self):
code = "ORA $ABCD"
results = self.assembler.assemble(code, add_entry=False)
self.assertEqual([
(0x0000, [0x0D, 0xCD, 0xAB]),
], results)
def test_ora_absolute_indexed(self):
code = "ORA $ABCD,X\n" \
"ORA $ABCD,Y"
results = self.assembler.assemble(code, add_entry=False)
self.assertEqual([
(0x0000, [0x1D, 0xCD, 0xAB, 0x19, 0xCD, 0xAB]),
], results)
def test_ora_indexed_indirect(self):
code = "ORA ($10,X)"
results = self.assembler.assemble(code, add_entry=False)
self.assertEqual([
(0x0000, [0x01, 0x10]),
], results)
def test_ora_indirect_indexed(self):
code = "ORA ($10),Y"
results = self.assembler.assemble(code, add_entry=False)
self.assertEqual([
(0x0000, [0x11, 0x10]),
], results)
| none | 1 | 2.601373 | 3 | |
nicos_mlz/labs/battery/setups/battery.py | jkrueger1/nicos | 12 | 6613686 | <gh_stars>10-100
description = 'Battery temperature sensors'
includes = ['battery01', 'battery02', 'battery03']
| description = 'Battery temperature sensors'
includes = ['battery01', 'battery02', 'battery03'] | none | 1 | 1.076291 | 1 | |
apps/simauth/apps.py | gilsonbp/simcapital | 0 | 6613687 | from django.apps import AppConfig
class SIMAuthConfig(AppConfig):
name = 'apps.simauth'
label = 'simauth'
verbose_name = 'SIM Auth'
| from django.apps import AppConfig
class SIMAuthConfig(AppConfig):
name = 'apps.simauth'
label = 'simauth'
verbose_name = 'SIM Auth'
| none | 1 | 1.217937 | 1 | |
pydarkstar/tests/test_item.py | Korrbit/pydarkstar | 18 | 6613688 | <reponame>Korrbit/pydarkstar
import unittest
import_error = False
try:
from ..item import Item
except ImportError:
import_error = True
Item = None
class TestCase00(unittest.TestCase):
def test_import(self):
self.assertFalse(import_error)
class TestCase01(unittest.TestCase):
def setUp(self):
if import_error:
self.skipTest('ImportError')
def test_init(self):
i0 = Item(0, 'A')
self.assertEqual(i0.itemid, 0)
self.assertEqual(i0.name, 'A')
def test_price01(self):
Item(0, price01=+1)
with self.assertRaises(ValueError):
Item(0, price01=+0)
with self.assertRaises(ValueError):
Item(0, price01=-1)
def test_price12(self):
Item(0, price12=+1)
with self.assertRaises(ValueError):
Item(0, price12=+0)
with self.assertRaises(ValueError):
Item(0, price12=-1)
def test_stock01(self):
Item(0, stock01=+0)
with self.assertRaises(ValueError):
Item(0, stock01=-1)
def test_stock12(self):
Item(0, stock12=+0)
with self.assertRaises(ValueError):
Item(0, stock12=-1)
def test_rate01(self):
i = Item(0)
self.assertEqual(i.rate01, 1.0)
Item(0, rate01=0.0)
Item(0, rate01=0.5)
Item(0, rate01=1.0)
with self.assertRaises(ValueError):
Item(0, rate01=-1.5)
with self.assertRaises(ValueError):
Item(0, rate01=+1.5)
def test_rate12(self):
i = Item(0)
self.assertEqual(i.rate12, 1.0)
Item(0, rate12=0.0)
Item(0, rate12=0.5)
Item(0, rate12=1.0)
with self.assertRaises(ValueError):
Item(0, rate12=-1.5)
with self.assertRaises(ValueError):
Item(0, rate12=+1.5)
| import unittest
import_error = False
try:
from ..item import Item
except ImportError:
import_error = True
Item = None
class TestCase00(unittest.TestCase):
def test_import(self):
self.assertFalse(import_error)
class TestCase01(unittest.TestCase):
def setUp(self):
if import_error:
self.skipTest('ImportError')
def test_init(self):
i0 = Item(0, 'A')
self.assertEqual(i0.itemid, 0)
self.assertEqual(i0.name, 'A')
def test_price01(self):
Item(0, price01=+1)
with self.assertRaises(ValueError):
Item(0, price01=+0)
with self.assertRaises(ValueError):
Item(0, price01=-1)
def test_price12(self):
Item(0, price12=+1)
with self.assertRaises(ValueError):
Item(0, price12=+0)
with self.assertRaises(ValueError):
Item(0, price12=-1)
def test_stock01(self):
Item(0, stock01=+0)
with self.assertRaises(ValueError):
Item(0, stock01=-1)
def test_stock12(self):
Item(0, stock12=+0)
with self.assertRaises(ValueError):
Item(0, stock12=-1)
def test_rate01(self):
i = Item(0)
self.assertEqual(i.rate01, 1.0)
Item(0, rate01=0.0)
Item(0, rate01=0.5)
Item(0, rate01=1.0)
with self.assertRaises(ValueError):
Item(0, rate01=-1.5)
with self.assertRaises(ValueError):
Item(0, rate01=+1.5)
def test_rate12(self):
i = Item(0)
self.assertEqual(i.rate12, 1.0)
Item(0, rate12=0.0)
Item(0, rate12=0.5)
Item(0, rate12=1.0)
with self.assertRaises(ValueError):
Item(0, rate12=-1.5)
with self.assertRaises(ValueError):
Item(0, rate12=+1.5) | none | 1 | 3.158523 | 3 | |
newBlock.py | Anubhav-Bhargava/Decentralized-Authentication-System | 5 | 6613689 | <reponame>Anubhav-Bhargava/Decentralized-Authentication-System<gh_stars>1-10
from block import *
import datetime as dt
def next_block(last_block, data):
this_index = last_block.index + 1
this_timestamp = dt.datetime.now()
# A one level deep copy of data has been created since data is modified repeatedly
# in the calling function and if data is a direct pointer, it leads to modification
# of old data in the chain.
this_data = data[:]
this_prev_hash = last_block.hash
return Block(this_index, this_timestamp, this_data, this_prev_hash)
def add_block(form, data, blockchain):
'''i = 1
while form.get("roll_no{}".format(i)):
data[-1].append(form.get("roll_no{}".format(i)))
i += 1'''
previous_block = blockchain[-1]
block_to_add = next_block(previous_block, data)
blockchain.append(block_to_add)
previous_block = block_to_add
return "Block #{} has been added to the blockchain!".format(block_to_add.index)
| from block import *
import datetime as dt
def next_block(last_block, data):
this_index = last_block.index + 1
this_timestamp = dt.datetime.now()
# A one level deep copy of data has been created since data is modified repeatedly
# in the calling function and if data is a direct pointer, it leads to modification
# of old data in the chain.
this_data = data[:]
this_prev_hash = last_block.hash
return Block(this_index, this_timestamp, this_data, this_prev_hash)
def add_block(form, data, blockchain):
'''i = 1
while form.get("roll_no{}".format(i)):
data[-1].append(form.get("roll_no{}".format(i)))
i += 1'''
previous_block = blockchain[-1]
block_to_add = next_block(previous_block, data)
blockchain.append(block_to_add)
previous_block = block_to_add
return "Block #{} has been added to the blockchain!".format(block_to_add.index) | en | 0.813729 | # A one level deep copy of data has been created since data is modified repeatedly # in the calling function and if data is a direct pointer, it leads to modification # of old data in the chain. i = 1 while form.get("roll_no{}".format(i)): data[-1].append(form.get("roll_no{}".format(i))) i += 1 #{} has been added to the blockchain!".format(block_to_add.index) | 3.122696 | 3 |
day02/3-post.py | Mhh123/spider | 0 | 6613690 | import urllib.request
import urllib.parse
post_url = 'http://fanyi.baidu.com/sug'
word = input('请输入您要查询到单词')
data = {
'kw': word,
}
# 对表单数据进行处理，先转化为字符串，再转化为字节格式
data = urllib.parse.urlencode(data).encode('utf8')
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 6.1;Win64; x64) AppleWebkit/537.36 ('
'KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36',
}
request = urllib.request.Request(post_url, headers=headers)
response = urllib.request.urlopen(request, data=data)
print(response.read().decode('utf8'))
| import urllib.request
import urllib.parse
post_url = 'http://fanyi.baidu.com/sug'
word = input('请输入您要查询到单词')
data = {
'kw': word,
}
# 对表单数据进行处理，先转化为字符串，再转化为字节格式
data = urllib.parse.urlencode(data).encode('utf8')
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 6.1;Win64; x64) AppleWebkit/537.36 ('
'KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36',
}
request = urllib.request.Request(post_url, headers=headers)
response = urllib.request.urlopen(request, data=data)
print(response.read().decode('utf8'))
| zh | 0.968124 | # 对表单数据进行处理，先转化为字符串，再转化为字节格式 | 3.391943 | 3 |
__checkdIfIntegerDifferByTen.py | simdevex/01.Basics | 0 | 6613691 | '''
Python program to test a list of one hundred integers
between 0 and 999, which all differ by ten from one another. Return
true or false.
Input:
[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150,
160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280,
290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410,
420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540,
550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670,
680, 690, 700, 710, 720, 730, 740, 750, 760, 770, 780, 790, 800,
810, 820, 830, 840, 850, 860, 870, 880, 890, 900, 910, 920, 930,
940, 950, 960, 970, 980, 990]
Output:
True
Input:
[0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240, 260,
280, 300, 320, 340, 360, 380, 400, 420, 440, 460, 480, 500, 520,
540, 560, 580, 600, 620, 640, 660, 680, 700, 720, 740, 760, 780,
800, 820, 840, 860, 880, 900, 920, 940, 960, 980]
Output:
False
'''
#License: https://bit.ly/3oLErEI
def test(li):
#all() function returns True if all items in an iterable are true, otherwise it returns False.
#If the iterable object is empty, the all() function also returns True. all() only works on iteratable
#Long single line return statement
'''
for i in li:
for j in li:
if i != j :
return (i in range(1000) and abs(i - j) >= 10) and len(set(li)) == 100
'''
return all(i in range(1000) and abs(i - j) >= 10 for i in li for j in li if i != j) and len(set(li)) == 100
nums = list(range(0, 1000, 10))
print("Original list:")
print(nums)
print("Check whether the said list contains one hundred integers between 0 and 999 which all differ by ten from one another:")
print(test(nums))
nums = list(range(0, 1000, 20))
print("Original list:")
print(nums)
print("Check whether the said list contains one hundred integers between 0 and 999 which all differ by ten from one another:")
print(test(nums))
| '''
Python program to test a list of one hundred integers
between 0 and 999, which all differ by ten from one another. Return
true or false.
Input:
[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150,
160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280,
290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410,
420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540,
550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670,
680, 690, 700, 710, 720, 730, 740, 750, 760, 770, 780, 790, 800,
810, 820, 830, 840, 850, 860, 870, 880, 890, 900, 910, 920, 930,
940, 950, 960, 970, 980, 990]
Output:
True
Input:
[0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240, 260,
280, 300, 320, 340, 360, 380, 400, 420, 440, 460, 480, 500, 520,
540, 560, 580, 600, 620, 640, 660, 680, 700, 720, 740, 760, 780,
800, 820, 840, 860, 880, 900, 920, 940, 960, 980]
Output:
False
'''
#License: https://bit.ly/3oLErEI
def test(li):
#all() function returns True if all items in an iterable are true, otherwise it returns False.
#If the iterable object is empty, the all() function also returns True. all() only works on iteratable
#Long single line return statement
'''
for i in li:
for j in li:
if i != j :
return (i in range(1000) and abs(i - j) >= 10) and len(set(li)) == 100
'''
return all(i in range(1000) and abs(i - j) >= 10 for i in li for j in li if i != j) and len(set(li)) == 100
nums = list(range(0, 1000, 10))
print("Original list:")
print(nums)
print("Check whether the said list contains one hundred integers between 0 and 999 which all differ by ten from one another:")
print(test(nums))
nums = list(range(0, 1000, 20))
print("Original list:")
print(nums)
print("Check whether the said list contains one hundred integers between 0 and 999 which all differ by ten from one another:")
print(test(nums))
| en | 0.360962 | Python program to test a list of one hundred integers between 0 and 999, which all differ by ten from one another. Return true or false. Input: [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, 700, 710, 720, 730, 740, 750, 760, 770, 780, 790, 800, 810, 820, 830, 840, 850, 860, 870, 880, 890, 900, 910, 920, 930, 940, 950, 960, 970, 980, 990] Output: True Input: [0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240, 260, 280, 300, 320, 340, 360, 380, 400, 420, 440, 460, 480, 500, 520, 540, 560, 580, 600, 620, 640, 660, 680, 700, 720, 740, 760, 780, 800, 820, 840, 860, 880, 900, 920, 940, 960, 980] Output: False #License: https://bit.ly/3oLErEI #all() function returns True if all items in an iterable are true, otherwise it returns False. #If the iterable object is empty, the all() function also returns True. all() only works on iteratable #Long single line return statement for i in li: for j in li: if i != j : return (i in range(1000) and abs(i - j) >= 10) and len(set(li)) == 100 | 3.747006 | 4 |
phylip2eigenstrat.py | btmartin721/phylip2eigenstrat | 2 | 6613692 | #!/usr/bin/env python
import argparse
from datastruct import Struct
def get_arguments():
parser = argparse.ArgumentParser(description="Generates Eigenstrat .snp and .ind files \
for use with AdmixTools software package")
parser.add_argument("-p", "--phylip", type=str, required=True, help="Input filename in PHYLIP format")
parser.add_argument("-i", "--ind", type=str, required=False, help="ind output filename; Default = out.ind",
nargs="?", default="out.ind")
parser.add_argument("-n", "--snp", type=str, required=False, help="snp output filename; Default = out.snp",
nargs="?", default="out.snp")
parser.add_argument("-s", "--start", type=int, required=False, nargs="?", default="1",
help="Specify first character of sample ID to be used as pattern for population ID; default=1")
parser.add_argument("-e", "--end", type=int, required=False, nargs="?", default="4",
help="Specify last character of sample ID to be used as pattern for population ID; default=4")
args = parser.parse_args()
return args
def read_phylip(line):
line = line.rstrip("\r\n")
ids_loci = line.strip().split(None, 1)
ids = ids_loci[0]
loc = ids_loci[1]
return ids, loc, header
def make_indfile(ID, file, pattern):
file.write(ID + "\t" + "U" + "\t" + pattern + "\n")
def make_snpfile(file, header):
initial = 0.000000
numInd, loci = header.split()
for i in range(1, int(loci)+1):
file.write("a" + "_" + str(i) + "\t" + "1" + "\t" + '%0.6f' % initial + "\t" + str(i) + "\n")
initial += 0.000010
def get_unique_identifiers(pattern, hit, number):
if not hit:
dataset.make_dict(number, hit, pattern)
elif pattern not in hit:
number += 1
dataset.make_dict(number, hit, pattern)
return number
#####################################################################################################
##################################MAIN###############################################################
#####################################################################################################
args = get_arguments()
unique_ids = dict()
popnum = 1
with open(args.phylip, "r") as fin:
with open(args.ind, "w") as indout:
with open(args.snp, "w") as snpout:
header = fin.readline()
header = header.rstrip()
for lines in fin:
ids, loc, header = read_phylip(lines)
dataset = Struct(ids, loc)
patt = dataset.id[args.start-1:args.end]
popnum = get_unique_identifiers(patt, unique_ids, popnum) # Returns popID and adds 1 for each unique ID
popid = unique_ids[patt] # dictionary with unique ids (key), popID (value)
make_indfile(dataset.id, indout, str(patt))
make_snpfile(snpout, header) | #!/usr/bin/env python
import argparse
from datastruct import Struct
def get_arguments():
parser = argparse.ArgumentParser(description="Generates Eigenstrat .snp and .ind files \
for use with AdmixTools software package")
parser.add_argument("-p", "--phylip", type=str, required=True, help="Input filename in PHYLIP format")
parser.add_argument("-i", "--ind", type=str, required=False, help="ind output filename; Default = out.ind",
nargs="?", default="out.ind")
parser.add_argument("-n", "--snp", type=str, required=False, help="snp output filename; Default = out.snp",
nargs="?", default="out.snp")
parser.add_argument("-s", "--start", type=int, required=False, nargs="?", default="1",
help="Specify first character of sample ID to be used as pattern for population ID; default=1")
parser.add_argument("-e", "--end", type=int, required=False, nargs="?", default="4",
help="Specify last character of sample ID to be used as pattern for population ID; default=4")
args = parser.parse_args()
return args
def read_phylip(line):
line = line.rstrip("\r\n")
ids_loci = line.strip().split(None, 1)
ids = ids_loci[0]
loc = ids_loci[1]
return ids, loc, header
def make_indfile(ID, file, pattern):
file.write(ID + "\t" + "U" + "\t" + pattern + "\n")
def make_snpfile(file, header):
initial = 0.000000
numInd, loci = header.split()
for i in range(1, int(loci)+1):
file.write("a" + "_" + str(i) + "\t" + "1" + "\t" + '%0.6f' % initial + "\t" + str(i) + "\n")
initial += 0.000010
def get_unique_identifiers(pattern, hit, number):
if not hit:
dataset.make_dict(number, hit, pattern)
elif pattern not in hit:
number += 1
dataset.make_dict(number, hit, pattern)
return number
#####################################################################################################
##################################MAIN###############################################################
#####################################################################################################
args = get_arguments()
unique_ids = dict()
popnum = 1
with open(args.phylip, "r") as fin:
with open(args.ind, "w") as indout:
with open(args.snp, "w") as snpout:
header = fin.readline()
header = header.rstrip()
for lines in fin:
ids, loc, header = read_phylip(lines)
dataset = Struct(ids, loc)
patt = dataset.id[args.start-1:args.end]
popnum = get_unique_identifiers(patt, unique_ids, popnum) # Returns popID and adds 1 for each unique ID
popid = unique_ids[patt] # dictionary with unique ids (key), popID (value)
make_indfile(dataset.id, indout, str(patt))
make_snpfile(snpout, header) | de | 0.737424 | #!/usr/bin/env python ##################################################################################################### ##################################MAIN############################################################### ##################################################################################################### # Returns popID and adds 1 for each unique ID # dictionary with unique ids (key), popID (value) | 2.869272 | 3 |
identity.py | kamens/gae_bingo | 34 | 6613693 | from __future__ import absolute_import
import base64
import logging
import os
import re
from google.appengine.ext import db
from gae_bingo.config import config
from gae_bingo import cookies
from gae_bingo import request_cache
from .models import GAEBingoIdentityModel
IDENTITY_COOKIE_KEY = "gae_b_id"
IDENTITY_COOKIE_AGE = 365 * 24 * 60 * 60 # ~1 year in seconds
CAN_CONTROL_CACHE_KEY = "CAN_CONTROL_CACHE"
IDENTITY_CACHE_KEY = "IDENTITY_CACHE"
LOGGED_IN_IDENTITY_CACHE_KEY = "LOGGED_IN_IDENTITY_CACHE"
ID_TO_PUT_CACHE_KEY = "ID_TO_PUT"
def can_control_experiments():
if request_cache.cache.get(CAN_CONTROL_CACHE_KEY) is None:
request_cache.cache[CAN_CONTROL_CACHE_KEY] = (
config.can_control_experiments())
return request_cache.cache[CAN_CONTROL_CACHE_KEY]
def logged_in_bingo_identity():
if request_cache.cache.get(LOGGED_IN_IDENTITY_CACHE_KEY) is None:
request_cache.cache[LOGGED_IN_IDENTITY_CACHE_KEY] = config.current_logged_in_identity()
return request_cache.cache[LOGGED_IN_IDENTITY_CACHE_KEY]
def flush_caches():
"""Flush the caches associated with the logged in identity.
This is useful if the logged in identity changed for some reason
mid-request.
"""
request_cache.cache.pop(CAN_CONTROL_CACHE_KEY, None)
request_cache.cache.pop(IDENTITY_CACHE_KEY, None)
request_cache.cache.pop(LOGGED_IN_IDENTITY_CACHE_KEY, None)
request_cache.cache.pop(ID_TO_PUT_CACHE_KEY, None)
def identity(identity_val=None):
""" Determines the Bingo identity for the specified user. If no user
is specified, this will attempt to infer one based on cookies/logged in user
identity_val -- a string or instance of GAEBingoIdentityModel specifying
which bingo identity to retrieve.
"""
if identity_val:
# Don't cache for arbitrarily passed in identity_val
return bingo_identity_for_value(identity_val, associate_with_cookie=False)
if request_cache.cache.get(IDENTITY_CACHE_KEY) is None:
if is_bot():
# Just make all bots identify as the same single user so they don't
# bias results. Following simple suggestion in
# http://www.bingocardcreator.com/abingo/faq
request_cache.cache[IDENTITY_CACHE_KEY] = "_gae_bingo_bot"
else:
# Try to get unique (hopefully persistent) identity from user's implementation,
# otherwise grab the current cookie value, otherwise grab random value.
request_cache.cache[IDENTITY_CACHE_KEY] = str(get_logged_in_bingo_identity_value() or get_identity_cookie_value() or get_random_identity_value())
return request_cache.cache[IDENTITY_CACHE_KEY]
def using_logged_in_bingo_identity():
return identity() and identity() == get_logged_in_bingo_identity_value()
def get_logged_in_bingo_identity_value():
val = logged_in_bingo_identity()
return bingo_identity_for_value(val)
def bingo_identity_for_value(val, associate_with_cookie=True):
# We cache the ID we generate here, to put only at the end of the request
if val is None:
return None
if isinstance(val, db.Model):
if isinstance(val, GAEBingoIdentityModel):
# If it's a db.Model that inherited from GAEBingoIdentityModel, return bingo identity
if not val.gae_bingo_identity:
if (is_random_identity_value(get_identity_cookie_value()) and
associate_with_cookie):
# If the current model doesn't have a bingo identity associated w/ it
# and we have a random cookie value already set, associate it with this identity model.
#
# This keeps the user's experience consistent between using the site pre- and post-login.
request_cache.cache[ID_TO_PUT_CACHE_KEY] = get_identity_cookie_value()
else:
# Otherwise just use the key, it's guaranteed to be unique
request_cache.cache[ID_TO_PUT_CACHE_KEY] = str(val.key())
return val.gae_bingo_identity
# If it's just a normal db instance, just use its unique key
return str(val.key())
# Otherwise it's just a plain unique string
return str(val)
def get_random_identity_value():
return "_gae_bingo_random:%s" % base64.urlsafe_b64encode(os.urandom(30))
def is_random_identity_value(val):
return val and val.startswith("_gae_bingo_random")
def get_identity_cookie_value():
cookie_val = cookies.get_cookie_value(IDENTITY_COOKIE_KEY)
if cookie_val:
try:
return base64.urlsafe_b64decode(cookie_val)
except:
pass
return None
def put_id_if_necessary():
"""To be called at the end of a request.
Check to see if we should put() the gae_bingo_identity, and put() it if so.
"""
id_to_put = request_cache.cache.get(ID_TO_PUT_CACHE_KEY)
if id_to_put:
val = config.current_logged_in_identity()
if val is None:
return
if isinstance(val, GAEBingoIdentityModel):
if val.gae_bingo_identity and id_to_put != val.gae_bingo_identity:
logging.warning(
"val.gae_bingo_identity got set to %s unexpectedly,"
"but id_to_put is %s"
% (val.gae_bingo_identity, id_to_put))
else:
# If the UserData has been updated in the course of this
# request current_logged_in_identity might read a stale version
# of the UserData from the request_cache. In order to make
# sure we have the latest userData we will get the the userData
# again.
val = db.get(val.key())
val.gae_bingo_identity = id_to_put
val.put()
# Flush the transaction so the HR datastore doesn't suffer from
# eventual consistency issues when next grabbing this UserData.
db.get(val.key())
def set_identity_cookie_header():
return cookies.set_cookie_value(IDENTITY_COOKIE_KEY,
base64.urlsafe_b64encode(identity()), max_age=IDENTITY_COOKIE_AGE)
def delete_identity_cookie_header():
return cookies.set_cookie_value(IDENTITY_COOKIE_KEY, "")
# I am well aware that this is a far-from-perfect, hacky method of quickly
# determining who's a bot or not. If necessary, in the future we could implement
# a javascript check like a/bingo and django-lean do -- but for now, I'm sticking
# w/ the simplest possible implementation for devs (don't need to add JS in any template code)
# that doesn't strongly bias the statistical outcome (undetected bots aren't a distaster,
# because they shouldn't favor one side over the other).
bot_regex = re.compile("(Baidu|Gigabot|Googlebot|libwww-perl|lwp-trivial|msnbot|SiteUptime|Slurp|WordPress|ZIBB|ZyBorg)", re.IGNORECASE)
def is_bot():
return bool(bot_regex.search(os.environ.get("HTTP_USER_AGENT") or ""))
| from __future__ import absolute_import
import base64
import logging
import os
import re
from google.appengine.ext import db
from gae_bingo.config import config
from gae_bingo import cookies
from gae_bingo import request_cache
from .models import GAEBingoIdentityModel
IDENTITY_COOKIE_KEY = "gae_b_id"
IDENTITY_COOKIE_AGE = 365 * 24 * 60 * 60 # ~1 year in seconds
CAN_CONTROL_CACHE_KEY = "CAN_CONTROL_CACHE"
IDENTITY_CACHE_KEY = "IDENTITY_CACHE"
LOGGED_IN_IDENTITY_CACHE_KEY = "LOGGED_IN_IDENTITY_CACHE"
ID_TO_PUT_CACHE_KEY = "ID_TO_PUT"
def can_control_experiments():
if request_cache.cache.get(CAN_CONTROL_CACHE_KEY) is None:
request_cache.cache[CAN_CONTROL_CACHE_KEY] = (
config.can_control_experiments())
return request_cache.cache[CAN_CONTROL_CACHE_KEY]
def logged_in_bingo_identity():
if request_cache.cache.get(LOGGED_IN_IDENTITY_CACHE_KEY) is None:
request_cache.cache[LOGGED_IN_IDENTITY_CACHE_KEY] = config.current_logged_in_identity()
return request_cache.cache[LOGGED_IN_IDENTITY_CACHE_KEY]
def flush_caches():
"""Flush the caches associated with the logged in identity.
This is useful if the logged in identity changed for some reason
mid-request.
"""
request_cache.cache.pop(CAN_CONTROL_CACHE_KEY, None)
request_cache.cache.pop(IDENTITY_CACHE_KEY, None)
request_cache.cache.pop(LOGGED_IN_IDENTITY_CACHE_KEY, None)
request_cache.cache.pop(ID_TO_PUT_CACHE_KEY, None)
def identity(identity_val=None):
""" Determines the Bingo identity for the specified user. If no user
is specified, this will attempt to infer one based on cookies/logged in user
identity_val -- a string or instance of GAEBingoIdentityModel specifying
which bingo identity to retrieve.
"""
if identity_val:
# Don't cache for arbitrarily passed in identity_val
return bingo_identity_for_value(identity_val, associate_with_cookie=False)
if request_cache.cache.get(IDENTITY_CACHE_KEY) is None:
if is_bot():
# Just make all bots identify as the same single user so they don't
# bias results. Following simple suggestion in
# http://www.bingocardcreator.com/abingo/faq
request_cache.cache[IDENTITY_CACHE_KEY] = "_gae_bingo_bot"
else:
# Try to get unique (hopefully persistent) identity from user's implementation,
# otherwise grab the current cookie value, otherwise grab random value.
request_cache.cache[IDENTITY_CACHE_KEY] = str(get_logged_in_bingo_identity_value() or get_identity_cookie_value() or get_random_identity_value())
return request_cache.cache[IDENTITY_CACHE_KEY]
def using_logged_in_bingo_identity():
return identity() and identity() == get_logged_in_bingo_identity_value()
def get_logged_in_bingo_identity_value():
val = logged_in_bingo_identity()
return bingo_identity_for_value(val)
def bingo_identity_for_value(val, associate_with_cookie=True):
# We cache the ID we generate here, to put only at the end of the request
if val is None:
return None
if isinstance(val, db.Model):
if isinstance(val, GAEBingoIdentityModel):
# If it's a db.Model that inherited from GAEBingoIdentityModel, return bingo identity
if not val.gae_bingo_identity:
if (is_random_identity_value(get_identity_cookie_value()) and
associate_with_cookie):
# If the current model doesn't have a bingo identity associated w/ it
# and we have a random cookie value already set, associate it with this identity model.
#
# This keeps the user's experience consistent between using the site pre- and post-login.
request_cache.cache[ID_TO_PUT_CACHE_KEY] = get_identity_cookie_value()
else:
# Otherwise just use the key, it's guaranteed to be unique
request_cache.cache[ID_TO_PUT_CACHE_KEY] = str(val.key())
return val.gae_bingo_identity
# If it's just a normal db instance, just use its unique key
return str(val.key())
# Otherwise it's just a plain unique string
return str(val)
def get_random_identity_value():
return "_gae_bingo_random:%s" % base64.urlsafe_b64encode(os.urandom(30))
def is_random_identity_value(val):
return val and val.startswith("_gae_bingo_random")
def get_identity_cookie_value():
cookie_val = cookies.get_cookie_value(IDENTITY_COOKIE_KEY)
if cookie_val:
try:
return base64.urlsafe_b64decode(cookie_val)
except:
pass
return None
def put_id_if_necessary():
"""To be called at the end of a request.
Check to see if we should put() the gae_bingo_identity, and put() it if so.
"""
id_to_put = request_cache.cache.get(ID_TO_PUT_CACHE_KEY)
if id_to_put:
val = config.current_logged_in_identity()
if val is None:
return
if isinstance(val, GAEBingoIdentityModel):
if val.gae_bingo_identity and id_to_put != val.gae_bingo_identity:
logging.warning(
"val.gae_bingo_identity got set to %s unexpectedly,"
"but id_to_put is %s"
% (val.gae_bingo_identity, id_to_put))
else:
# If the UserData has been updated in the course of this
# request current_logged_in_identity might read a stale version
# of the UserData from the request_cache. In order to make
# sure we have the latest userData we will get the the userData
# again.
val = db.get(val.key())
val.gae_bingo_identity = id_to_put
val.put()
# Flush the transaction so the HR datastore doesn't suffer from
# eventual consistency issues when next grabbing this UserData.
db.get(val.key())
def set_identity_cookie_header():
return cookies.set_cookie_value(IDENTITY_COOKIE_KEY,
base64.urlsafe_b64encode(identity()), max_age=IDENTITY_COOKIE_AGE)
def delete_identity_cookie_header():
return cookies.set_cookie_value(IDENTITY_COOKIE_KEY, "")
# I am well aware that this is a far-from-perfect, hacky method of quickly
# determining who's a bot or not. If necessary, in the future we could implement
# a javascript check like a/bingo and django-lean do -- but for now, I'm sticking
# w/ the simplest possible implementation for devs (don't need to add JS in any template code)
# that doesn't strongly bias the statistical outcome (undetected bots aren't a distaster,
# because they shouldn't favor one side over the other).
bot_regex = re.compile("(Baidu|Gigabot|Googlebot|libwww-perl|lwp-trivial|msnbot|SiteUptime|Slurp|WordPress|ZIBB|ZyBorg)", re.IGNORECASE)
def is_bot():
return bool(bot_regex.search(os.environ.get("HTTP_USER_AGENT") or ""))
| en | 0.865962 | # ~1 year in seconds Flush the caches associated with the logged in identity. This is useful if the logged in identity changed for some reason mid-request. Determines the Bingo identity for the specified user. If no user is specified, this will attempt to infer one based on cookies/logged in user identity_val -- a string or instance of GAEBingoIdentityModel specifying which bingo identity to retrieve. # Don't cache for arbitrarily passed in identity_val # Just make all bots identify as the same single user so they don't # bias results. Following simple suggestion in # http://www.bingocardcreator.com/abingo/faq # Try to get unique (hopefully persistent) identity from user's implementation, # otherwise grab the current cookie value, otherwise grab random value. # We cache the ID we generate here, to put only at the end of the request # If it's a db.Model that inherited from GAEBingoIdentityModel, return bingo identity # If the current model doesn't have a bingo identity associated w/ it # and we have a random cookie value already set, associate it with this identity model. # # This keeps the user's experience consistent between using the site pre- and post-login. # Otherwise just use the key, it's guaranteed to be unique # If it's just a normal db instance, just use its unique key # Otherwise it's just a plain unique string To be called at the end of a request. Check to see if we should put() the gae_bingo_identity, and put() it if so. # If the UserData has been updated in the course of this # request current_logged_in_identity might read a stale version # of the UserData from the request_cache. In order to make # sure we have the latest userData we will get the the userData # again. # Flush the transaction so the HR datastore doesn't suffer from # eventual consistency issues when next grabbing this UserData. # I am well aware that this is a far-from-perfect, hacky method of quickly # determining who's a bot or not. If necessary, in the future we could implement # a javascript check like a/bingo and django-lean do -- but for now, I'm sticking # w/ the simplest possible implementation for devs (don't need to add JS in any template code) # that doesn't strongly bias the statistical outcome (undetected bots aren't a distaster, # because they shouldn't favor one side over the other). | 2.223537 | 2 |
usaspending_api/broker/tests/integration/test_get_delete_pks_for_afa_keys.py | g4brielvs/usaspending-api | 217 | 6613694 | import pytest
from django.db import connections
from django.test import TestCase
from usaspending_api.broker.helpers.delete_fabs_transactions import get_delete_pks_for_afa_keys
@pytest.mark.usefixtures("broker_db_setup")
class TestThingWithMultipleDatabases(TestCase):
databases = "__all__"
@classmethod
def setUpTestData(cls):
connection = connections["data_broker"]
with connection.cursor() as cursor:
cursor.execute("select count(*) from published_award_financial_assistance")
assert cursor.fetchone()[0] == 0, "Another test somewhere is leaking data"
cursor.execute(
"""
insert into published_award_financial_assistance (
published_award_financial_assistance_id, afa_generated_unique, is_active
) (values
(1, 'abc', false),
(2, 'aBc', false),
(3, 'ABC', true),
(4, 'xyz', false),
(5, 'xYz', false),
(6, 'XYZ', false),
(7, 'lmn', false),
(8, 'opq', true)
)
"""
)
def test_get_delete_pks_for_afa_keys(self):
assert get_delete_pks_for_afa_keys(None) == []
assert get_delete_pks_for_afa_keys([]) == []
assert set(get_delete_pks_for_afa_keys(["abc", "xyZ"])) == {1, 2, 4, 5, 6}
| import pytest
from django.db import connections
from django.test import TestCase
from usaspending_api.broker.helpers.delete_fabs_transactions import get_delete_pks_for_afa_keys
@pytest.mark.usefixtures("broker_db_setup")
class TestThingWithMultipleDatabases(TestCase):
databases = "__all__"
@classmethod
def setUpTestData(cls):
connection = connections["data_broker"]
with connection.cursor() as cursor:
cursor.execute("select count(*) from published_award_financial_assistance")
assert cursor.fetchone()[0] == 0, "Another test somewhere is leaking data"
cursor.execute(
"""
insert into published_award_financial_assistance (
published_award_financial_assistance_id, afa_generated_unique, is_active
) (values
(1, 'abc', false),
(2, 'aBc', false),
(3, 'ABC', true),
(4, 'xyz', false),
(5, 'xYz', false),
(6, 'XYZ', false),
(7, 'lmn', false),
(8, 'opq', true)
)
"""
)
def test_get_delete_pks_for_afa_keys(self):
assert get_delete_pks_for_afa_keys(None) == []
assert get_delete_pks_for_afa_keys([]) == []
assert set(get_delete_pks_for_afa_keys(["abc", "xyZ"])) == {1, 2, 4, 5, 6}
| en | 0.232439 | insert into published_award_financial_assistance ( published_award_financial_assistance_id, afa_generated_unique, is_active ) (values (1, 'abc', false), (2, 'aBc', false), (3, 'ABC', true), (4, 'xyz', false), (5, 'xYz', false), (6, 'XYZ', false), (7, 'lmn', false), (8, 'opq', true) ) | 2.04819 | 2 |
tests/cogctl/cli/test_version.py | operable/cogctl | 3 | 6613695 | <gh_stars>1-10
import re
from cogctl.cli.version import version
def test_version(cogctl):
result = cogctl(version)
assert result.exit_code == 0
expr = re.compile('^cogctl ([a-z0-9\-/])+ \(build: (([a-f0-9]){7}|unknown)\)')
output = result.output.strip()
assert re.fullmatch(expr, output) is not None
| import re
from cogctl.cli.version import version
def test_version(cogctl):
result = cogctl(version)
assert result.exit_code == 0
expr = re.compile('^cogctl ([a-z0-9\-/])+ \(build: (([a-f0-9]){7}|unknown)\)')
output = result.output.strip()
assert re.fullmatch(expr, output) is not None | none | 1 | 2.485997 | 2 | |
bot.py | gothraven/instabot.py | 0 | 6613696 | <gh_stars>0
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
from instabot_py import InstaBot
bot = InstaBot(
login=os.environ.get('INSTA_USER', ''),
password=os.environ.get('INSTA_PASSWORD', ''),
start_at_h=10,
start_at_m=0,
end_at_h=20,
end_at_m=0,
like_per_day=500,
comments_per_day=50,
tag_list=[
'l:212999109', #Los Angeles
'l:6889842', #Paris
'l:219370504', #Algers
'l:213326726', #Warsaw
'l:213385402', #London
# 'change', 'lavieestbelle', 'doglover', 'tweegram', 'nature', 'cool', 'cat', 'cutie', 'onedirection', 'black',
# 'igparis', 'igersparis', 'fuckology', 'red', 'music', 'inspiration', 'dogsofinstagram', 'bestoftheday',
# 'white', 'goodmorning', 'instagramhub', 'school', 'green', 'nofilter', 'iphonesia', 'petsagram',
# 'celibataire', 'doglovers', 'girl', 'pretty', 'travel', 'halloween', 'bored', 'adorable', 'precious',
# 'motivationalquotes', 'equipedefrance', 'clouds', 'puppies', 'ilovedog', 'hair', 'summer', 'blue',
# 'awesome', 'petstagram', 'night', 'versagram', 'dogoftheday', 'quotestoliveby', 'picpets', 'instagramers',
# 'party', 'animals', 'yum', 'dogs', 'igers', 'iphoneonly', 'positivevibes', 'lyon', 'amazing', 'photo',
# 'cute', 'love', 'puppy', 'parisienne', 'pet', 'parisien', 'food', 'bleublancrouge', 'sweet', 'lifequotes',
# 'comment', 'girls', 'repost', 'fuckologyquotes', 'animal', 'parisjetaime', 'family', 'naturephotography',
# 'morningmotivation', 'goodvibes', 'quote', 'igdaily', 'ilovemydog', 'morningvibes', 'quoteoftheday',
# 'lol', 'word', 'friends', 'bestfriend', 'beautiful', 'igaddict', 'instadaily', 'pets', 'indiea',
# 'instamood', 'sun', 'swag', 'life', 'mornings', 'instagood', 'allezlesbleus', 'throwbackthursday',
# 'sunrise', 'me', 'parismonamour', 'poetry', 'funny', 'instagramdogs', 'harrystyles', 'baby', 'happy',
# 'igfrance', 'all_shots', 'fashion', 'ilovedogs', 'ig', 'follow', 'bordeaux', 'smile', 'tagblender',
# 'creativity', 'allezlesbleues', 'lifestyle', 'sunset', 'photooftheday', 'followback', 'photography',
# 'pink', 'inspirationalquotes', 'instahub', 'jj', 'picstitch', 'like', 'dog', 'comments', 'followme',
# 'doggy', 'instalove', 'eyes', 'motivation', 'impatience', 'hot', 'picoftheday', 'tail', 'tea', 'my',
# 'yummy', 'fucklove', 'fitfrenchies', 'tbt', 'instago', 'naturel', 'quotes', 'morning', 'beach', 'art',
# 'jj_forum', 'paris', 'sky', 'pup', 'dogstagram', 'fun', 'bhfyp',
],
tag_blacklist=[],
user_blacklist={},
max_like_for_one_tag=100,
follow_per_day=0, #follow_per_day = 500
follow_time=0, #follow_time=1 * 60 * 60,
unfollow_per_day=0, #unfollow_per_day=300
unfollow_break_min=0, #unfollow_break_min=15 * 60,
unfollow_break_max=0, #unfollow_break_max=30 * 60
log_mod=0,
proxy='',
# List of list of words, each of which will be used to generate comment
# For example: "This shot feels wow!"
comment_list=[['👆', '👌', '💪'],
['😎', '😍', '😉'],
['🤙', '👍']],
# Use unwanted_username_list to block usernames containing a string
## Will do partial matches; i.e. 'mozart' will block 'legend_mozart'
### 'free_followers' will be blocked because it contains 'free'
unwanted_username_list=[
'second', 'stuff', 'art', 'project', 'love', 'life', 'food', 'blog',
'free', 'keren', 'photo', 'graphy', 'indo', 'travel', 'art', 'shop',
'store', 'sex', 'toko', 'jual', 'online', 'murah', 'jam', 'kaos',
'case', 'baju', 'fashion', 'corp', 'tas', 'butik', 'grosir', 'karpet',
'sosis', 'salon', 'skin', 'care', 'cloth', 'tech', 'rental', 'kamera',
'beauty', 'express', 'kredit', 'collection', 'impor', 'preloved',
'follow', 'follower', 'gain', '.id', '_id', 'bags'
],
unfollow_whitelist=[])
bot.mainloop()
| #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
from instabot_py import InstaBot
bot = InstaBot(
login=os.environ.get('INSTA_USER', ''),
password=os.environ.get('INSTA_PASSWORD', ''),
start_at_h=10,
start_at_m=0,
end_at_h=20,
end_at_m=0,
like_per_day=500,
comments_per_day=50,
tag_list=[
'l:212999109', #Los Angeles
'l:6889842', #Paris
'l:219370504', #Algers
'l:213326726', #Warsaw
'l:213385402', #London
# 'change', 'lavieestbelle', 'doglover', 'tweegram', 'nature', 'cool', 'cat', 'cutie', 'onedirection', 'black',
# 'igparis', 'igersparis', 'fuckology', 'red', 'music', 'inspiration', 'dogsofinstagram', 'bestoftheday',
# 'white', 'goodmorning', 'instagramhub', 'school', 'green', 'nofilter', 'iphonesia', 'petsagram',
# 'celibataire', 'doglovers', 'girl', 'pretty', 'travel', 'halloween', 'bored', 'adorable', 'precious',
# 'motivationalquotes', 'equipedefrance', 'clouds', 'puppies', 'ilovedog', 'hair', 'summer', 'blue',
# 'awesome', 'petstagram', 'night', 'versagram', 'dogoftheday', 'quotestoliveby', 'picpets', 'instagramers',
# 'party', 'animals', 'yum', 'dogs', 'igers', 'iphoneonly', 'positivevibes', 'lyon', 'amazing', 'photo',
# 'cute', 'love', 'puppy', 'parisienne', 'pet', 'parisien', 'food', 'bleublancrouge', 'sweet', 'lifequotes',
# 'comment', 'girls', 'repost', 'fuckologyquotes', 'animal', 'parisjetaime', 'family', 'naturephotography',
# 'morningmotivation', 'goodvibes', 'quote', 'igdaily', 'ilovemydog', 'morningvibes', 'quoteoftheday',
# 'lol', 'word', 'friends', 'bestfriend', 'beautiful', 'igaddict', 'instadaily', 'pets', 'indiea',
# 'instamood', 'sun', 'swag', 'life', 'mornings', 'instagood', 'allezlesbleus', 'throwbackthursday',
# 'sunrise', 'me', 'parismonamour', 'poetry', 'funny', 'instagramdogs', 'harrystyles', 'baby', 'happy',
# 'igfrance', 'all_shots', 'fashion', 'ilovedogs', 'ig', 'follow', 'bordeaux', 'smile', 'tagblender',
# 'creativity', 'allezlesbleues', 'lifestyle', 'sunset', 'photooftheday', 'followback', 'photography',
# 'pink', 'inspirationalquotes', 'instahub', 'jj', 'picstitch', 'like', 'dog', 'comments', 'followme',
# 'doggy', 'instalove', 'eyes', 'motivation', 'impatience', 'hot', 'picoftheday', 'tail', 'tea', 'my',
# 'yummy', 'fucklove', 'fitfrenchies', 'tbt', 'instago', 'naturel', 'quotes', 'morning', 'beach', 'art',
# 'jj_forum', 'paris', 'sky', 'pup', 'dogstagram', 'fun', 'bhfyp',
],
tag_blacklist=[],
user_blacklist={},
max_like_for_one_tag=100,
follow_per_day=0, #follow_per_day = 500
follow_time=0, #follow_time=1 * 60 * 60,
unfollow_per_day=0, #unfollow_per_day=300
unfollow_break_min=0, #unfollow_break_min=15 * 60,
unfollow_break_max=0, #unfollow_break_max=30 * 60
log_mod=0,
proxy='',
# List of list of words, each of which will be used to generate comment
# For example: "This shot feels wow!"
comment_list=[['👆', '👌', '💪'],
['😎', '😍', '😉'],
['🤙', '👍']],
# Use unwanted_username_list to block usernames containing a string
## Will do partial matches; i.e. 'mozart' will block 'legend_mozart'
### 'free_followers' will be blocked because it contains 'free'
unwanted_username_list=[
'second', 'stuff', 'art', 'project', 'love', 'life', 'food', 'blog',
'free', 'keren', 'photo', 'graphy', 'indo', 'travel', 'art', 'shop',
'store', 'sex', 'toko', 'jual', 'online', 'murah', 'jam', 'kaos',
'case', 'baju', 'fashion', 'corp', 'tas', 'butik', 'grosir', 'karpet',
'sosis', 'salon', 'skin', 'care', 'cloth', 'tech', 'rental', 'kamera',
'beauty', 'express', 'kredit', 'collection', 'impor', 'preloved',
'follow', 'follower', 'gain', '.id', '_id', 'bags'
],
unfollow_whitelist=[])
bot.mainloop() | en | 0.096848 | #!/usr/bin/env python3 # -*- coding: utf-8 -*- #Los Angeles #Paris #Algers #Warsaw #London # 'change', 'lavieestbelle', 'doglover', 'tweegram', 'nature', 'cool', 'cat', 'cutie', 'onedirection', 'black', # 'igparis', 'igersparis', 'fuckology', 'red', 'music', 'inspiration', 'dogsofinstagram', 'bestoftheday', # 'white', 'goodmorning', 'instagramhub', 'school', 'green', 'nofilter', 'iphonesia', 'petsagram', # 'celibataire', 'doglovers', 'girl', 'pretty', 'travel', 'halloween', 'bored', 'adorable', 'precious', # 'motivationalquotes', 'equipedefrance', 'clouds', 'puppies', 'ilovedog', 'hair', 'summer', 'blue', # 'awesome', 'petstagram', 'night', 'versagram', 'dogoftheday', 'quotestoliveby', 'picpets', 'instagramers', # 'party', 'animals', 'yum', 'dogs', 'igers', 'iphoneonly', 'positivevibes', 'lyon', 'amazing', 'photo', # 'cute', 'love', 'puppy', 'parisienne', 'pet', 'parisien', 'food', 'bleublancrouge', 'sweet', 'lifequotes', # 'comment', 'girls', 'repost', 'fuckologyquotes', 'animal', 'parisjetaime', 'family', 'naturephotography', # 'morningmotivation', 'goodvibes', 'quote', 'igdaily', 'ilovemydog', 'morningvibes', 'quoteoftheday', # 'lol', 'word', 'friends', 'bestfriend', 'beautiful', 'igaddict', 'instadaily', 'pets', 'indiea', # 'instamood', 'sun', 'swag', 'life', 'mornings', 'instagood', 'allezlesbleus', 'throwbackthursday', # 'sunrise', 'me', 'parismonamour', 'poetry', 'funny', 'instagramdogs', 'harrystyles', 'baby', 'happy', # 'igfrance', 'all_shots', 'fashion', 'ilovedogs', 'ig', 'follow', 'bordeaux', 'smile', 'tagblender', # 'creativity', 'allezlesbleues', 'lifestyle', 'sunset', 'photooftheday', 'followback', 'photography', # 'pink', 'inspirationalquotes', 'instahub', 'jj', 'picstitch', 'like', 'dog', 'comments', 'followme', # 'doggy', 'instalove', 'eyes', 'motivation', 'impatience', 'hot', 'picoftheday', 'tail', 'tea', 'my', # 'yummy', 'fucklove', 'fitfrenchies', 'tbt', 'instago', 'naturel', 'quotes', 'morning', 'beach', 'art', # 'jj_forum', 'paris', 'sky', 'pup', 'dogstagram', 'fun', 'bhfyp', #follow_per_day = 500 #follow_time=1 * 60 * 60, #unfollow_per_day=300 #unfollow_break_min=15 * 60, #unfollow_break_max=30 * 60 # List of list of words, each of which will be used to generate comment # For example: "This shot feels wow!" # Use unwanted_username_list to block usernames containing a string ## Will do partial matches; i.e. 'mozart' will block 'legend_mozart' ### 'free_followers' will be blocked because it contains 'free' | 1.960834 | 2 |
attendance.py | LaudateCorpus1/aws-mv-object-detection | 0 | 6613697 | <reponame>LaudateCorpus1/aws-mv-object-detection
# Based off of https://aws.amazon.com/blogs/machine-learning/build-your-own-face-recognition-service-using-amazon-rekognition/
# Using Meraki Python SDK at https://github.com/meraki/dashboard-api-python
import time
from datetime import date
import boto3
import io
import credentials
import meraki
import snapshot
import student_list
from PIL import Image, ImageDraw, ImageFont
from webexteamssdk import WebexTeamsAPI
def attendance(rekognition_api_client, dynamodb_api_client, filename):
# Open stored image and send to Rekognition
image = Image.open(filename)
stream = io.BytesIO()
image.save(stream,format="JPEG")
image_binary = stream.getvalue()
response = rekognition_api_client.detect_faces(
Image={'Bytes':image_binary}
)
all_faces=response['FaceDetails']
draw = ImageDraw.Draw(image)
print('Avengers Assembling...')
attendees = []
for face in all_faces:
box = face['BoundingBox']
x1 = int(box['Left'] * image.size[0]) * 0.9
y1 = int(box['Top'] * image.size[1]) * 0.9
x2 = int(box['Left'] * image.size[0] + box['Width'] * image.size[0]) * 1.10
y2 = int(box['Top'] * image.size[1] + box['Height'] * image.size[1]) * 1.10
image_crop = image.crop((x1,y1,x2,y2))
stream = io.BytesIO()
image_crop.save(stream,format="JPEG")
image_crop_binary = stream.getvalue()
#print('Detecting faces...')
#image_crop.show()
try:
response = rekognition_api_client.search_faces_by_image(
CollectionId='faces',
Image={'Bytes':image_crop_binary}
)
# Initialize averages
confidence_avg = 0
if response['FaceMatches'] == []:
print ("Someone unknown Assembled.")
name = '<NAME>'
imgfont = ImageFont.truetype(font='Arial Unicode.ttf', size=20)
points, left, top = snapshot.draw_bounding_box(image=image, box=box)
draw.line(points, fill='#00d400', width=5)
draw.text(xy=(left,top), text=name, fill='#00d400', font=imgfont)
else:
for match in response['FaceMatches']:
confidence_avg = confidence_avg + match['Face']['Confidence']
face = dynamodb_api_client.get_item(
TableName='faces',
Key={'RekognitionId': {'S': match['Face']['FaceId']}}
)
if len(face) == 2:
name = face['Item']['FullName']['S']
imgfont = ImageFont.truetype(font='Arial Unicode.ttf', size=20)
points, left, top = snapshot.draw_bounding_box(image=image, box=box)
draw.line(points, fill='#00d400', width=5)
draw.text(xy=(left,top), text=name, fill='#00d400', font=imgfont)
confidence_avg = confidence_avg/len(response['FaceMatches'])
attendees += [str(name)]
if name=='Thanos':
print('Thanos is here.')
else:
print('{} Assembled with {} percent confidence.'.format(name,confidence_avg))
except:
print("Something went wrong!")
print('There are {} total Avengers Assembled against Thanos.'.format(len(all_faces)-1))
image.save(filename, "JPEG")
image.show()
return attendees, filename
if __name__ == "__main__":
# Importing variables
api_key = credentials.api_key
baseurl = credentials.base_url
org_id = credentials.organization_id
networks = credentials.networks
cams = credentials.cams
students = student_list.student_list
webex_email = credentials.webex_email
webex_token = credentials.webex_token
# Instantiate Meraki Python SDK Client
dashboard = meraki.DashboardAPI(
api_key=api_key,
base_url=baseurl,
log_file_prefix='./logs/attendance/',
print_console=False)
# Instantiate AWS Python SDK Clients
# Must configure AWS CLI for this to work
# See https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html
rekognition = boto3.client('rekognition', region_name='us-east-2')
dynamodb = boto3.client('dynamodb', region_name='us-east-2')
webex = WebexTeamsAPI(access_token=webex_token)
# Record timestamp for snapshot name
timestr = time.strftime("%Y%m%d-%H%M%S")
file_name = snapshot.snap(
dashboard_api_client=dashboard,
time_string=timestr,
networkIds=networks,
organizationId=org_id,
cameras=cams,
base_url=baseurl,
folder='attendance')
attendees, filename = attendance(
rekognition_api_client=rekognition,
dynamodb_api_client=dynamodb,
filename = file_name)
x = set(attendees)
y = set(students)
z = y.difference(x)
# Generate Attendance Report
filename = file_name
body = "These are the Avengers who Assembled on {}.".format(date.today().isoformat())
body = body + "\n\nAssembled:\n{}".format(x)
body = body + "\n\nDidn't Assemble:\n{}".format(z)
print("Assembled: \n{}".format(x))
print("Didn't Assemble: \n{}".format(z))
webex.messages.create(toPersonEmail=webex_email, text=body, files=[filename]) | # Based off of https://aws.amazon.com/blogs/machine-learning/build-your-own-face-recognition-service-using-amazon-rekognition/
# Using Meraki Python SDK at https://github.com/meraki/dashboard-api-python
import time
from datetime import date
import boto3
import io
import credentials
import meraki
import snapshot
import student_list
from PIL import Image, ImageDraw, ImageFont
from webexteamssdk import WebexTeamsAPI
def attendance(rekognition_api_client, dynamodb_api_client, filename):
# Open stored image and send to Rekognition
image = Image.open(filename)
stream = io.BytesIO()
image.save(stream,format="JPEG")
image_binary = stream.getvalue()
response = rekognition_api_client.detect_faces(
Image={'Bytes':image_binary}
)
all_faces=response['FaceDetails']
draw = ImageDraw.Draw(image)
print('Avengers Assembling...')
attendees = []
for face in all_faces:
box = face['BoundingBox']
x1 = int(box['Left'] * image.size[0]) * 0.9
y1 = int(box['Top'] * image.size[1]) * 0.9
x2 = int(box['Left'] * image.size[0] + box['Width'] * image.size[0]) * 1.10
y2 = int(box['Top'] * image.size[1] + box['Height'] * image.size[1]) * 1.10
image_crop = image.crop((x1,y1,x2,y2))
stream = io.BytesIO()
image_crop.save(stream,format="JPEG")
image_crop_binary = stream.getvalue()
#print('Detecting faces...')
#image_crop.show()
try:
response = rekognition_api_client.search_faces_by_image(
CollectionId='faces',
Image={'Bytes':image_crop_binary}
)
# Initialize averages
confidence_avg = 0
if response['FaceMatches'] == []:
print ("Someone unknown Assembled.")
name = '<NAME>'
imgfont = ImageFont.truetype(font='Arial Unicode.ttf', size=20)
points, left, top = snapshot.draw_bounding_box(image=image, box=box)
draw.line(points, fill='#00d400', width=5)
draw.text(xy=(left,top), text=name, fill='#00d400', font=imgfont)
else:
for match in response['FaceMatches']:
confidence_avg = confidence_avg + match['Face']['Confidence']
face = dynamodb_api_client.get_item(
TableName='faces',
Key={'RekognitionId': {'S': match['Face']['FaceId']}}
)
if len(face) == 2:
name = face['Item']['FullName']['S']
imgfont = ImageFont.truetype(font='Arial Unicode.ttf', size=20)
points, left, top = snapshot.draw_bounding_box(image=image, box=box)
draw.line(points, fill='#00d400', width=5)
draw.text(xy=(left,top), text=name, fill='#00d400', font=imgfont)
confidence_avg = confidence_avg/len(response['FaceMatches'])
attendees += [str(name)]
if name=='Thanos':
print('Thanos is here.')
else:
print('{} Assembled with {} percent confidence.'.format(name,confidence_avg))
except:
print("Something went wrong!")
print('There are {} total Avengers Assembled against Thanos.'.format(len(all_faces)-1))
image.save(filename, "JPEG")
image.show()
return attendees, filename
if __name__ == "__main__":
# Importing variables
api_key = credentials.api_key
baseurl = credentials.base_url
org_id = credentials.organization_id
networks = credentials.networks
cams = credentials.cams
students = student_list.student_list
webex_email = credentials.webex_email
webex_token = credentials.webex_token
# Instantiate Meraki Python SDK Client
dashboard = meraki.DashboardAPI(
api_key=api_key,
base_url=baseurl,
log_file_prefix='./logs/attendance/',
print_console=False)
# Instantiate AWS Python SDK Clients
# Must configure AWS CLI for this to work
# See https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html
rekognition = boto3.client('rekognition', region_name='us-east-2')
dynamodb = boto3.client('dynamodb', region_name='us-east-2')
webex = WebexTeamsAPI(access_token=webex_token)
# Record timestamp for snapshot name
timestr = time.strftime("%Y%m%d-%H%M%S")
file_name = snapshot.snap(
dashboard_api_client=dashboard,
time_string=timestr,
networkIds=networks,
organizationId=org_id,
cameras=cams,
base_url=baseurl,
folder='attendance')
attendees, filename = attendance(
rekognition_api_client=rekognition,
dynamodb_api_client=dynamodb,
filename = file_name)
x = set(attendees)
y = set(students)
z = y.difference(x)
# Generate Attendance Report
filename = file_name
body = "These are the Avengers who Assembled on {}.".format(date.today().isoformat())
body = body + "\n\nAssembled:\n{}".format(x)
body = body + "\n\nDidn't Assemble:\n{}".format(z)
print("Assembled: \n{}".format(x))
print("Didn't Assemble: \n{}".format(z))
webex.messages.create(toPersonEmail=webex_email, text=body, files=[filename]) | en | 0.692673 | # Based off of https://aws.amazon.com/blogs/machine-learning/build-your-own-face-recognition-service-using-amazon-rekognition/ # Using Meraki Python SDK at https://github.com/meraki/dashboard-api-python # Open stored image and send to Rekognition #print('Detecting faces...') #image_crop.show() # Initialize averages # Importing variables # Instantiate Meraki Python SDK Client # Instantiate AWS Python SDK Clients # Must configure AWS CLI for this to work # See https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html # Record timestamp for snapshot name # Generate Attendance Report | 2.929939 | 3 |
Python-Fundamentals/Functions/repeat_string.py | Xamaneone/SoftUni-Intro | 0 | 6613698 | <gh_stars>0
def output ():
print(f"{string}", end="")
string = input()
n = int(input())
for i in range(0, n):
output() | def output ():
print(f"{string}", end="")
string = input()
n = int(input())
for i in range(0, n):
output() | none | 1 | 3.689647 | 4 | |
HW4/AndriiBabii/2.py | kolyasalubov/Lv-677.PythonCore | 0 | 6613699 |
element = [1, 234, 662, 19, 1213, 73, 77]
number_of_elements = len(element)
# Варіант ручного заповнення списку
# number_of_elements = int(input("Input number of elements: "))
# for i in range(number_of_elements):
# element.append(int(input(f"inpute {i} element: ")))
for i in range(number_of_elements):
element[i] = float(element[i])
print(element)
|
element = [1, 234, 662, 19, 1213, 73, 77]
number_of_elements = len(element)
# Варіант ручного заповнення списку
# number_of_elements = int(input("Input number of elements: "))
# for i in range(number_of_elements):
# element.append(int(input(f"inpute {i} element: ")))
for i in range(number_of_elements):
element[i] = float(element[i])
print(element)
| uk | 0.545378 | # Варіант ручного заповнення списку # number_of_elements = int(input("Input number of elements: ")) # for i in range(number_of_elements): # element.append(int(input(f"inpute {i} element: "))) | 3.995475 | 4 |
computer_network_real/9/dhcp.py | mtjin/University_and_AndroidProjects | 1 | 6613700 | <filename>computer_network_real/9/dhcp.py
import socket
import struct
import subprocess
import time
from phue import Bridge
def main():
raw_socket = socket.socket(socket.AF_PACKET,
socket.SOCK_RAW,
socket.ntohs(0x0003))
my_macip = "d0b128275cf9"
#hue bridge connect
bridge = Bridge('192.168.0.218')
bridge.connect()
lights = bridge.lights
#sniffing
while True:
recv_packet = raw_socket.recvfrom(5000)
ethernet_protocol = struct.unpack('!6s6sH', (recv_packet[0])[:14])[2]
if ethernet_protocol == 0x800:
ip_protocol = struct.unpack('!BBHHHBBH4s4s', recv_packet[0][14:34])[6]
if ip_protocol == 17:
udp_src_port = struct.unpack('!H', (recv_packet[0])[34:34+2])[0]
if udp_src_port == 68:
packet_data = recv_packet[0][42:]
#parsing phone mac ip
come_macip = packet_data.hex()[56:68]
print("Come MAC Ip => ", come_macip)
if my_macip == come_macip:
print("My MAC IP !!!!!!!!!!!")
print(recv_packet[0][0:])
print("HEX DATA : ", recv_packet[0][0:].hex()[0:])
phone_ip = struct.unpack('!BBBB', (recv_packet[0])[296:300])
phone_ip = str(phone_ip[0])+ '.'+ str(phone_ip[1]) + '.' + str(phone_ip[2]) +'.' +str(phone_ip[3])
print("PHONE => " , phone_ip)
while True:
# 1 second sleep
time.sleep(1)
# ping to my phone ip that I parsed in packet
status, result = subprocess.getstatusoutput("ping -c1 -w2 " + phone_ip)
if status == 0:
print('respone OK')
lights[0].on = True
lights[0].brightness = int(250)
lights[0].xy = [float(0.9), float(0.9)]
lights[1].on = True
lights[1].brightness = int(250)
lights[1].xy = [float(0.9), float(0.9)]
lights[2].on = True
lights[2].brightness = int(250)
lights[2].xy = [float(0.9), float(0.9)]
else :
print("NOT respond")
lights[0].on = False
lights[0].brightness = int(0)
lights[0].xy = [float(0.0), float(0.0)]
lights[1].on = False
lights[1].brightness = int(0)
lights[1].xy = [float(0.0), float(0.0)]
lights[2].on = False
lights[2].brightness = int(0)
lights[2].xy = [float(0.0), float(0.0)]
break
if __name__ == "__main__":
main()
| <filename>computer_network_real/9/dhcp.py
import socket
import struct
import subprocess
import time
from phue import Bridge
def main():
raw_socket = socket.socket(socket.AF_PACKET,
socket.SOCK_RAW,
socket.ntohs(0x0003))
my_macip = "d0b128275cf9"
#hue bridge connect
bridge = Bridge('192.168.0.218')
bridge.connect()
lights = bridge.lights
#sniffing
while True:
recv_packet = raw_socket.recvfrom(5000)
ethernet_protocol = struct.unpack('!6s6sH', (recv_packet[0])[:14])[2]
if ethernet_protocol == 0x800:
ip_protocol = struct.unpack('!BBHHHBBH4s4s', recv_packet[0][14:34])[6]
if ip_protocol == 17:
udp_src_port = struct.unpack('!H', (recv_packet[0])[34:34+2])[0]
if udp_src_port == 68:
packet_data = recv_packet[0][42:]
#parsing phone mac ip
come_macip = packet_data.hex()[56:68]
print("Come MAC Ip => ", come_macip)
if my_macip == come_macip:
print("My MAC IP !!!!!!!!!!!")
print(recv_packet[0][0:])
print("HEX DATA : ", recv_packet[0][0:].hex()[0:])
phone_ip = struct.unpack('!BBBB', (recv_packet[0])[296:300])
phone_ip = str(phone_ip[0])+ '.'+ str(phone_ip[1]) + '.' + str(phone_ip[2]) +'.' +str(phone_ip[3])
print("PHONE => " , phone_ip)
while True:
# 1 second sleep
time.sleep(1)
# ping to my phone ip that I parsed in packet
status, result = subprocess.getstatusoutput("ping -c1 -w2 " + phone_ip)
if status == 0:
print('respone OK')
lights[0].on = True
lights[0].brightness = int(250)
lights[0].xy = [float(0.9), float(0.9)]
lights[1].on = True
lights[1].brightness = int(250)
lights[1].xy = [float(0.9), float(0.9)]
lights[2].on = True
lights[2].brightness = int(250)
lights[2].xy = [float(0.9), float(0.9)]
else :
print("NOT respond")
lights[0].on = False
lights[0].brightness = int(0)
lights[0].xy = [float(0.0), float(0.0)]
lights[1].on = False
lights[1].brightness = int(0)
lights[1].xy = [float(0.0), float(0.0)]
lights[2].on = False
lights[2].brightness = int(0)
lights[2].xy = [float(0.0), float(0.0)]
break
if __name__ == "__main__":
main()
| en | 0.85908 | #hue bridge connect #sniffing #parsing phone mac ip # 1 second sleep # ping to my phone ip that I parsed in packet | 2.773831 | 3 |
src/HABApp/core/lib/handler.py | DerOetzi/HABApp | 44 | 6613701 | from datetime import date, datetime
from logging.handlers import RotatingFileHandler
class MidnightRotatingFileHandler(RotatingFileHandler):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.last_check: date = datetime.now().date()
def shouldRollover(self, record):
date = datetime.now().date()
if date == self.last_check:
return 0
self.last_check = date
return super().shouldRollover(record)
| from datetime import date, datetime
from logging.handlers import RotatingFileHandler
class MidnightRotatingFileHandler(RotatingFileHandler):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.last_check: date = datetime.now().date()
def shouldRollover(self, record):
date = datetime.now().date()
if date == self.last_check:
return 0
self.last_check = date
return super().shouldRollover(record)
| none | 1 | 3.053758 | 3 | |
sales/tests/__init__.py | marcelomoraes28/real-state-marketplace | 1 | 6613702 | from django.test import TestCase
from sales.models import AreaUnitModel, CityModel, HomeTypeModel, \
PriceHistoryModel, TaxModel, ResidenceModel, ZRentInformationModel, \
ZSaleInformationModel, ZillowModel, AnnouncementModel
class BaseTestCase(TestCase):
def setUp(self):
self.import_data_payload = {'area_unit': 'SqFt', 'bathrooms': '2.0', 'bedrooms': '4',
'home_size': '1372', 'home_type': 'SingleFamily',
'last_sold_date': '', 'last_sold_price': '',
'link': 'https://www.zillow.com/homedetails/7417-Quimby-Ave-West-Hills-CA-91307/19866015_zpid/',
'price': '$739K', 'property_size': '10611',
'rent_price': '', 'rentzestimate_amount': '2850',
'rentzestimate_last_updated': '08/07/2018',
'tax_value': '215083.0', 'tax_year': '2017',
'year_built': '1956', 'zestimate_amount': '709630',
'zestimate_last_updated': '08/07/2018',
'zillow_id': '19866015', 'address': '7417 Quimby Ave',
'city': 'West Hills', 'state': 'CA', 'zipcode': '91307'}
self.area_unit = AreaUnitModel.objects.create(name='Area 51')
self.city = CityModel.objects.create(name='Curitiba', state='Parana')
self.home_type = HomeTypeModel.objects.create(type='Apartment')
self.price_history = PriceHistoryModel.objects.create(sell_price=1000000.00,
rent_price=1000.00)
self.tax = TaxModel.objects.create(tax_value=1240.00, tax_year=1992)
self.residene = ResidenceModel.objects.create(city=self.city,
area_unit=self.area_unit,
address='R. Lothario Boutin 220',
bathrooms=2.5,
bedrooms=2,
home_size=124,
home_type=self.home_type,
property_size=200,
year_built=1994)
self.z_rent_information = ZRentInformationModel.objects.create(rentzestimate_amount=100012.00)
self.z_sale_information = ZSaleInformationModel.objects.create(zestimate_amount=99123.31)
self.zillow = ZillowModel.objects.create(zillow_id=123,
z_rent_information=self.z_rent_information,
z_sale_information=self.z_sale_information)
self.announcement = AnnouncementModel.objects.create(
zillow=self.zillow,
link='localhost:8000',
residence=self.residene,
tax=self.tax
)
self.announcement.price_history.add(self.price_history)
| from django.test import TestCase
from sales.models import AreaUnitModel, CityModel, HomeTypeModel, \
PriceHistoryModel, TaxModel, ResidenceModel, ZRentInformationModel, \
ZSaleInformationModel, ZillowModel, AnnouncementModel
class BaseTestCase(TestCase):
def setUp(self):
self.import_data_payload = {'area_unit': 'SqFt', 'bathrooms': '2.0', 'bedrooms': '4',
'home_size': '1372', 'home_type': 'SingleFamily',
'last_sold_date': '', 'last_sold_price': '',
'link': 'https://www.zillow.com/homedetails/7417-Quimby-Ave-West-Hills-CA-91307/19866015_zpid/',
'price': '$739K', 'property_size': '10611',
'rent_price': '', 'rentzestimate_amount': '2850',
'rentzestimate_last_updated': '08/07/2018',
'tax_value': '215083.0', 'tax_year': '2017',
'year_built': '1956', 'zestimate_amount': '709630',
'zestimate_last_updated': '08/07/2018',
'zillow_id': '19866015', 'address': '7417 Quimby Ave',
'city': 'West Hills', 'state': 'CA', 'zipcode': '91307'}
self.area_unit = AreaUnitModel.objects.create(name='Area 51')
self.city = CityModel.objects.create(name='Curitiba', state='Parana')
self.home_type = HomeTypeModel.objects.create(type='Apartment')
self.price_history = PriceHistoryModel.objects.create(sell_price=1000000.00,
rent_price=1000.00)
self.tax = TaxModel.objects.create(tax_value=1240.00, tax_year=1992)
self.residene = ResidenceModel.objects.create(city=self.city,
area_unit=self.area_unit,
address='R. Lothario Boutin 220',
bathrooms=2.5,
bedrooms=2,
home_size=124,
home_type=self.home_type,
property_size=200,
year_built=1994)
self.z_rent_information = ZRentInformationModel.objects.create(rentzestimate_amount=100012.00)
self.z_sale_information = ZSaleInformationModel.objects.create(zestimate_amount=99123.31)
self.zillow = ZillowModel.objects.create(zillow_id=123,
z_rent_information=self.z_rent_information,
z_sale_information=self.z_sale_information)
self.announcement = AnnouncementModel.objects.create(
zillow=self.zillow,
link='localhost:8000',
residence=self.residene,
tax=self.tax
)
self.announcement.price_history.add(self.price_history)
| none | 1 | 2.20943 | 2 | |
lib/jnpr/healthbot/exception.py | minefuto/healthbot-py-client | 10 | 6613703 | class SchemaError(Exception):
"""
Generated in response to a invalid Schema type
"""
def __init__(self, schema):
self._schema = schema
def __repr__(self):
return "Schema provided is not of {} type".format(self._schema)
__str__ = __repr__
class NotFoundError(Exception):
"""
Generated in response to a invalid Schema type
"""
def __init__(self, detail):
self.detail = detail.get('detail')
self.status = detail.get('status')
def __repr__(self):
return "{}, {}".format(self.status, self.detail)
__str__ = __repr__
class ConnectAuthError(Exception):
"""
Generated if the user-name, password is invalid
"""
def __init__(self, hbot, msg=None):
self.hbot = hbot
self._orig = msg
def __repr__(self):
if self._orig:
return "{}(host: {}, msg: {})".format(self.__class__.__name__,
self.hbot.server,
self._orig)
else:
return "{}({})".format(self.__class__.__name__,
self.hbot.server)
__str__ = __repr__
| class SchemaError(Exception):
"""
Generated in response to a invalid Schema type
"""
def __init__(self, schema):
self._schema = schema
def __repr__(self):
return "Schema provided is not of {} type".format(self._schema)
__str__ = __repr__
class NotFoundError(Exception):
"""
Generated in response to a invalid Schema type
"""
def __init__(self, detail):
self.detail = detail.get('detail')
self.status = detail.get('status')
def __repr__(self):
return "{}, {}".format(self.status, self.detail)
__str__ = __repr__
class ConnectAuthError(Exception):
"""
Generated if the user-name, password is invalid
"""
def __init__(self, hbot, msg=None):
self.hbot = hbot
self._orig = msg
def __repr__(self):
if self._orig:
return "{}(host: {}, msg: {})".format(self.__class__.__name__,
self.hbot.server,
self._orig)
else:
return "{}({})".format(self.__class__.__name__,
self.hbot.server)
__str__ = __repr__
| en | 0.788001 | Generated in response to a invalid Schema type Generated in response to a invalid Schema type Generated if the user-name, password is invalid | 3.084051 | 3 |
intentions/admin.py | gabecm/oit-project | 0 | 6613704 | from django.contrib import admin
# Register your models here.
from intentions.models import Prompt, Entry
class PromptAdmin(admin.ModelAdmin):
fieldsets = [
(None, {'fields': ['prompt_text']}),
('Date Information', {'fields': ['date'], 'classes': ['collapse']}),
]
list_display = ('prompt_text', 'date')
list_filter = ['date']
search_fields = ['prompt_text']
class EntryAdmin(admin.ModelAdmin):
fieldsets = [
(None, {'fields': ['user', 'prompt', 'mood', 'headspace',
'prompt_response', 'public']}),
('Date Information', {'fields': ['date'], 'classes': ['collapse']}),
]
list_display = ('user', 'prompt', 'mood', 'headspace', 'prompt_response', 'public')
list_filter = ['date']
search_fields = ['user']
admin.site.register(Prompt, PromptAdmin)
admin.site.register(Entry, EntryAdmin) | from django.contrib import admin
# Register your models here.
from intentions.models import Prompt, Entry
class PromptAdmin(admin.ModelAdmin):
fieldsets = [
(None, {'fields': ['prompt_text']}),
('Date Information', {'fields': ['date'], 'classes': ['collapse']}),
]
list_display = ('prompt_text', 'date')
list_filter = ['date']
search_fields = ['prompt_text']
class EntryAdmin(admin.ModelAdmin):
fieldsets = [
(None, {'fields': ['user', 'prompt', 'mood', 'headspace',
'prompt_response', 'public']}),
('Date Information', {'fields': ['date'], 'classes': ['collapse']}),
]
list_display = ('user', 'prompt', 'mood', 'headspace', 'prompt_response', 'public')
list_filter = ['date']
search_fields = ['user']
admin.site.register(Prompt, PromptAdmin)
admin.site.register(Entry, EntryAdmin) | en | 0.968259 | # Register your models here. | 1.87547 | 2 |
script_final/train_ext.py | zhaoxueyan1/2018DSB | 97 | 6613705 | # -*- coding: utf-8 -*-
"""
Created on Tue Jan 30 09:45:21 2018
@author: Jackie
"""
import numpy as np
import pandas as pd
from load import load_from_cache, save_to_cache, makefolder
from model_ms1 import generator_1s_v11 as generator_1s
from model_rcnn_weight import UnetRCNN
from params import DsbConfig
from generator import data_generator_multi
def train_generator(config, shuffle = False, augment=False):
isbi = load_from_cache('2009isbi_256')
weebly = load_from_cache('weebly_256')
tnbc = load_from_cache('TNBC_256')
train = load_from_cache('train_final_df')
gen_isbi = data_generator_multi(np.stack(isbi['image'],0), np.stack(isbi['mask'], 0),
config, shuffle=shuffle, augment=augment, batch_size=1,
tp_value=7)
gen_weebly = data_generator_multi(np.stack(weebly['image'],0), np.stack(weebly['mask'], 0),
config, shuffle=shuffle, augment=augment, batch_size=1)
gen_tnbc = data_generator_multi(np.stack(tnbc['image'],0), np.stack(tnbc['mask'], 0),
config, shuffle=shuffle, augment=augment, batch_size=1)
gen_train = data_generator_multi(np.stack(train['image'],0), np.stack(train['mask'], 0),
config, shuffle=shuffle, augment=augment, batch_size=1)
images = np.zeros((config.BATCH_SIZE,)+tuple(config.IMAGE_SHAPE), dtype='float32')
masks = np.zeros((config.BATCH_SIZE,)+tuple(config.IMAGE_SHAPE[:2]), dtype='int32')
while True:
for nb, gen in enumerate([gen_isbi, gen_weebly, gen_tnbc, gen_train]):
image, mask = next(gen)
images[nb] = image[0]
masks[nb] = mask[0]
yield images, masks
def train_1s():
config = DsbConfig()
tp, unet_ratio, opt = 'all', 1, 'sgd'
save_dir = makefolder('..//cache//UnetRCNN',tosecond=True)
weight_fl = '../cache/mask_rcnn_coco.h5'
valid_df = load_from_cache('valid_df')
valid_images = np.stack(valid_df['image'], 0)
valid_masks = np.stack(valid_df['mask'], 0)
#print(len(valid_masks))
model = UnetRCNN(tp, unet_ratio, opt, config, save_dir)
model.load_weights(weight_fl, by_name =True)
train_gen = train_generator(config, shuffle = True, augment= True)
tr_ms1 = generator_1s(train_gen,config, tp)
val_generator = data_generator_multi(valid_images, valid_masks,
config, shuffle = False, augment= False)
val_ms1= generator_1s(val_generator,config, tp)
#model.train_generator(tr_ms1, val_ms1, 1e-2, 1, 'head')
model.train_generator(tr_ms1, val_ms1, 1e-3, 100, 'all')
if __name__ == "__main__":
train_1s()
| # -*- coding: utf-8 -*-
"""
Created on Tue Jan 30 09:45:21 2018
@author: Jackie
"""
import numpy as np
import pandas as pd
from load import load_from_cache, save_to_cache, makefolder
from model_ms1 import generator_1s_v11 as generator_1s
from model_rcnn_weight import UnetRCNN
from params import DsbConfig
from generator import data_generator_multi
def train_generator(config, shuffle = False, augment=False):
isbi = load_from_cache('2009isbi_256')
weebly = load_from_cache('weebly_256')
tnbc = load_from_cache('TNBC_256')
train = load_from_cache('train_final_df')
gen_isbi = data_generator_multi(np.stack(isbi['image'],0), np.stack(isbi['mask'], 0),
config, shuffle=shuffle, augment=augment, batch_size=1,
tp_value=7)
gen_weebly = data_generator_multi(np.stack(weebly['image'],0), np.stack(weebly['mask'], 0),
config, shuffle=shuffle, augment=augment, batch_size=1)
gen_tnbc = data_generator_multi(np.stack(tnbc['image'],0), np.stack(tnbc['mask'], 0),
config, shuffle=shuffle, augment=augment, batch_size=1)
gen_train = data_generator_multi(np.stack(train['image'],0), np.stack(train['mask'], 0),
config, shuffle=shuffle, augment=augment, batch_size=1)
images = np.zeros((config.BATCH_SIZE,)+tuple(config.IMAGE_SHAPE), dtype='float32')
masks = np.zeros((config.BATCH_SIZE,)+tuple(config.IMAGE_SHAPE[:2]), dtype='int32')
while True:
for nb, gen in enumerate([gen_isbi, gen_weebly, gen_tnbc, gen_train]):
image, mask = next(gen)
images[nb] = image[0]
masks[nb] = mask[0]
yield images, masks
def train_1s():
config = DsbConfig()
tp, unet_ratio, opt = 'all', 1, 'sgd'
save_dir = makefolder('..//cache//UnetRCNN',tosecond=True)
weight_fl = '../cache/mask_rcnn_coco.h5'
valid_df = load_from_cache('valid_df')
valid_images = np.stack(valid_df['image'], 0)
valid_masks = np.stack(valid_df['mask'], 0)
#print(len(valid_masks))
model = UnetRCNN(tp, unet_ratio, opt, config, save_dir)
model.load_weights(weight_fl, by_name =True)
train_gen = train_generator(config, shuffle = True, augment= True)
tr_ms1 = generator_1s(train_gen,config, tp)
val_generator = data_generator_multi(valid_images, valid_masks,
config, shuffle = False, augment= False)
val_ms1= generator_1s(val_generator,config, tp)
#model.train_generator(tr_ms1, val_ms1, 1e-2, 1, 'head')
model.train_generator(tr_ms1, val_ms1, 1e-3, 100, 'all')
if __name__ == "__main__":
train_1s()
| en | 0.323118 | # -*- coding: utf-8 -*- Created on Tue Jan 30 09:45:21 2018 @author: Jackie #print(len(valid_masks)) #model.train_generator(tr_ms1, val_ms1, 1e-2, 1, 'head') | 2.014943 | 2 |
simple_curses/window.py | robertblackwell/simple_curses | 0 | 6613706 | import curses
from simple_curses.kurses_ex import *
def newwin_inside(hostwin, h, w, y, x):
"""
Create a new curses window that is fully inside the host window
:param hostwin : curses.win
:param h : non neg int Height of new window
:param w : non neg int Width of new window
:param y : starting row of new window within host window (relative)
:param x : starting column of new window within host (relative)
"""
ybeg, xbeg = hostwin.getbegyx()
ymax, xmax = hostwin.getmaxyx()
win = curses.newwin(h, w, ybeg + y, xbeg + x)
return win
def newwin_after(prev_win, h, w, xstart, y_skip=1):
"""
Create a new curses window starting:
- on the y_skip rows after the prev_win,
- starting at absolute column xstart
:param prev_win : curses.win
:param h : non neg int Height of new window
:param w : non neg int Width of new window
:param y : starting row of new window within host window (relative)
:param x : starting column of new window within host (relative)
"""
ybeg, xbeg = prev_win.getbegyx()
ymax, xmax = prev_win.getmaxyx()
win = curses.newwin(h, w, ybeg + ymax + y_skip - 1, xstart)
return win
def hline_after(prev_win, xstart=0):
"""
Create a new window on the next row after prev_win.
The new window will be for a horizontal line and hence
only needs to be 1 line high.
To underline the previous window it is 2 cols wider than the pre_win
:param prev_win: curses.win
:param xstart : non neg int The starting absolute column for the hline, usually 1 less than prev_win.xbeg
"""
ybeg, xbeg = prev_win.getbegyx()
ymax, xmax = prev_win.getmaxyx()
win = curses.newwin(1, xmax+2, ybeg + ymax, xstart)
return win
def draw_hline(win):
"""
Draw a horizontal line startiing at position 0,0 in win and
as wide as win
"""
win.border(0, 0, curses.ACS_HLINE, curses.ACS_HLINE, curses.ACS_LTEE, curses.ACS_RTEE, curses.ACS_LTEE, curses.ACS_RTEE)
class Window:
@staticmethod
def mainwin():
pass
def newwin(nbr_rows, nbr_cols, ybeg_abs, xbeg_abs):
ymax, xmax = curses.stdscr.getbegyx()
if nbr_rows > ymax or nbr_cols > xmax:
raise ValueError("Window is bigger than stdscr")
cwin = curses.newwin(nbr_rows, nbr_cols, ybeg_abs, xbeg_abs)
w = Window(cwin)
w.parent = curses.stdscr
return w
"""Wrapper for curses.win in order to provide more diagnostics and a single bug fix"""
def __init__(self, cwin, nbr_rows, nbr_cols, ybeg_abs, xbeg_abs):
ymax, xmax = curses.stdscr.getbegyx()
if nbr_rows > ymax or nbr_cols > xmax:
raise ValueError("Window is bigger than stdscr")
self.xbeg = xbeg_abs
self.ybeg = ybeg_abs
self.ymax = nbr_rows
self.xmax = nbr_cols
self.parent = None
self.children = []
self.cwin = cwin
def subwin(self, nbr_lines, nbr_cols, ybeg_rel, xbeg_rel):
neww = make_subwin(self.cwin, nbr_lines, nbr_cols, ybeg_rel, xbeg_rel)
csubwin = Window(csubwin, nbr_lines, nbr_cols, ybeg_rel, xbeg_rel)
csubwin.parent = self
return csubwin | import curses
from simple_curses.kurses_ex import *
def newwin_inside(hostwin, h, w, y, x):
"""
Create a new curses window that is fully inside the host window
:param hostwin : curses.win
:param h : non neg int Height of new window
:param w : non neg int Width of new window
:param y : starting row of new window within host window (relative)
:param x : starting column of new window within host (relative)
"""
ybeg, xbeg = hostwin.getbegyx()
ymax, xmax = hostwin.getmaxyx()
win = curses.newwin(h, w, ybeg + y, xbeg + x)
return win
def newwin_after(prev_win, h, w, xstart, y_skip=1):
"""
Create a new curses window starting:
- on the y_skip rows after the prev_win,
- starting at absolute column xstart
:param prev_win : curses.win
:param h : non neg int Height of new window
:param w : non neg int Width of new window
:param y : starting row of new window within host window (relative)
:param x : starting column of new window within host (relative)
"""
ybeg, xbeg = prev_win.getbegyx()
ymax, xmax = prev_win.getmaxyx()
win = curses.newwin(h, w, ybeg + ymax + y_skip - 1, xstart)
return win
def hline_after(prev_win, xstart=0):
"""
Create a new window on the next row after prev_win.
The new window will be for a horizontal line and hence
only needs to be 1 line high.
To underline the previous window it is 2 cols wider than the pre_win
:param prev_win: curses.win
:param xstart : non neg int The starting absolute column for the hline, usually 1 less than prev_win.xbeg
"""
ybeg, xbeg = prev_win.getbegyx()
ymax, xmax = prev_win.getmaxyx()
win = curses.newwin(1, xmax+2, ybeg + ymax, xstart)
return win
def draw_hline(win):
"""
Draw a horizontal line startiing at position 0,0 in win and
as wide as win
"""
win.border(0, 0, curses.ACS_HLINE, curses.ACS_HLINE, curses.ACS_LTEE, curses.ACS_RTEE, curses.ACS_LTEE, curses.ACS_RTEE)
class Window:
@staticmethod
def mainwin():
pass
def newwin(nbr_rows, nbr_cols, ybeg_abs, xbeg_abs):
ymax, xmax = curses.stdscr.getbegyx()
if nbr_rows > ymax or nbr_cols > xmax:
raise ValueError("Window is bigger than stdscr")
cwin = curses.newwin(nbr_rows, nbr_cols, ybeg_abs, xbeg_abs)
w = Window(cwin)
w.parent = curses.stdscr
return w
"""Wrapper for curses.win in order to provide more diagnostics and a single bug fix"""
def __init__(self, cwin, nbr_rows, nbr_cols, ybeg_abs, xbeg_abs):
ymax, xmax = curses.stdscr.getbegyx()
if nbr_rows > ymax or nbr_cols > xmax:
raise ValueError("Window is bigger than stdscr")
self.xbeg = xbeg_abs
self.ybeg = ybeg_abs
self.ymax = nbr_rows
self.xmax = nbr_cols
self.parent = None
self.children = []
self.cwin = cwin
def subwin(self, nbr_lines, nbr_cols, ybeg_rel, xbeg_rel):
neww = make_subwin(self.cwin, nbr_lines, nbr_cols, ybeg_rel, xbeg_rel)
csubwin = Window(csubwin, nbr_lines, nbr_cols, ybeg_rel, xbeg_rel)
csubwin.parent = self
return csubwin | en | 0.837963 | Create a new curses window that is fully inside the host window :param hostwin : curses.win :param h : non neg int Height of new window :param w : non neg int Width of new window :param y : starting row of new window within host window (relative) :param x : starting column of new window within host (relative) Create a new curses window starting: - on the y_skip rows after the prev_win, - starting at absolute column xstart :param prev_win : curses.win :param h : non neg int Height of new window :param w : non neg int Width of new window :param y : starting row of new window within host window (relative) :param x : starting column of new window within host (relative) Create a new window on the next row after prev_win. The new window will be for a horizontal line and hence only needs to be 1 line high. To underline the previous window it is 2 cols wider than the pre_win :param prev_win: curses.win :param xstart : non neg int The starting absolute column for the hline, usually 1 less than prev_win.xbeg Draw a horizontal line startiing at position 0,0 in win and as wide as win Wrapper for curses.win in order to provide more diagnostics and a single bug fix | 3.857323 | 4 |
pca.py | scooter-dangle/MStream | 69 | 6613707 | <gh_stars>10-100
from sklearn.decomposition import PCA
import numpy as np
import time
import argparse
np.random.seed(0) # For reproducibility
np.seterr(divide="ignore", invalid="ignore")
parser = argparse.ArgumentParser(description="Training for MSTREAM-PCA")
parser.add_argument("--dim", type=int, help="number of dimensions", default=12)
parser.add_argument("--input", help="input file", required=True)
parser.add_argument("--output", help="output file", default="pca.txt")
parser.add_argument(
"--numRecords", type=int, help="number of records for training", default=256
)
args = parser.parse_args()
pca = PCA(n_components=args.dim)
data = np.loadtxt(args.input, delimiter=",")
mean, std = data.mean(0), data.std(0)
new = (data - mean) / std
new[:, std == 0] = 0
t = time.time()
pca.fit(new[: args.numRecords])
new = pca.transform(new)
print("Time for Training PCA is: ", time.time() - t)
np.savetxt(args.output, new, delimiter=",", fmt="%.2f")
| from sklearn.decomposition import PCA
import numpy as np
import time
import argparse
np.random.seed(0) # For reproducibility
np.seterr(divide="ignore", invalid="ignore")
parser = argparse.ArgumentParser(description="Training for MSTREAM-PCA")
parser.add_argument("--dim", type=int, help="number of dimensions", default=12)
parser.add_argument("--input", help="input file", required=True)
parser.add_argument("--output", help="output file", default="pca.txt")
parser.add_argument(
"--numRecords", type=int, help="number of records for training", default=256
)
args = parser.parse_args()
pca = PCA(n_components=args.dim)
data = np.loadtxt(args.input, delimiter=",")
mean, std = data.mean(0), data.std(0)
new = (data - mean) / std
new[:, std == 0] = 0
t = time.time()
pca.fit(new[: args.numRecords])
new = pca.transform(new)
print("Time for Training PCA is: ", time.time() - t)
np.savetxt(args.output, new, delimiter=",", fmt="%.2f") | en | 0.324069 | # For reproducibility | 3.067987 | 3 |
test/time/test_dateUtils.py | liangjz92/zeroinger-utils-python | 0 | 6613708 | <reponame>liangjz92/zeroinger-utils-python<filename>test/time/test_dateUtils.py<gh_stars>0
from unittest import TestCase
from logzero import logger
from zeroinger.time.dateutils import DateUtils
from datetime import datetime
class TestDateUtils(TestCase):
def test_now(self):
now_act = DateUtils.now()
now_gold = datetime.now()
str_act = now_act.strftime('%Y-%m-%d %H:%M:%S')
str_gold = now_gold.strftime('%Y-%m-%d %H:%M:%S')
self.assertEqual(str_act, str_gold, "获取当前时间错误")
def test_date2str(self):
now = datetime.now().replace(2019, 12, 2, 3, 4, 5, 678000)
self.assertEqual('2019-12-02 03:04:05.678', DateUtils.date2str(now))
self.assertEqual('20191202 03:04:05 678', DateUtils.date2str(now, "YYYYMMDD HH:mm:ss SSS"))
def test_str2date(self):
self.assertEqual('2019-12-02 03:04:05.678', DateUtils.date2str(DateUtils.str2date('2019-12-02 03:04:05.678')))
pass
# self.fail()
def test_of(self):
a = DateUtils.of(2019, 1, 2, 3, 4, 5, 6, 7)
self.assertEqual('2019-01-02 03:04:05.006', DateUtils.date2str(a))
b = DateUtils.of(None, None, None, None, 4, None, 6, 7)
c = datetime.now().replace(minute=4, microsecond=6000)
self.assertEqual(DateUtils.date2str(c), DateUtils.date2str(b))
logger.info('{}-{}'.format(DateUtils.date2str(c), DateUtils.date2str(b)))
# self.fail()
def test_set(self):
pass
# self.fail()
def test_add_time(self):
a = DateUtils.of(2019, 1, 2, 3, 4, 5, 6, 7)
b = DateUtils.add_time(a, -1, 1, 1, 1, 1, 1, 1, 1)
logger.info('test_add_time|{}|{}'.format(DateUtils.date2str(a), DateUtils.date2str(b)))
self.assertEqual('2018-02-03 04:05:06.007', DateUtils.date2str(b))
def test_time_delta_with_diff_unit(self):
start = DateUtils.of(2019, 1, 2, 3, 4, 5, 6, 7)
end = DateUtils.of(2020, 2, 2, 4, 4, 6, 7, 8)
logger.info('时间差{}'.format(DateUtils.time_delta_with_diff_unit(end, start)))
# self.fail()
# def test_time_diff_by(self):
# self.fail()
| from unittest import TestCase
from logzero import logger
from zeroinger.time.dateutils import DateUtils
from datetime import datetime
class TestDateUtils(TestCase):
def test_now(self):
now_act = DateUtils.now()
now_gold = datetime.now()
str_act = now_act.strftime('%Y-%m-%d %H:%M:%S')
str_gold = now_gold.strftime('%Y-%m-%d %H:%M:%S')
self.assertEqual(str_act, str_gold, "获取当前时间错误")
def test_date2str(self):
now = datetime.now().replace(2019, 12, 2, 3, 4, 5, 678000)
self.assertEqual('2019-12-02 03:04:05.678', DateUtils.date2str(now))
self.assertEqual('20191202 03:04:05 678', DateUtils.date2str(now, "YYYYMMDD HH:mm:ss SSS"))
def test_str2date(self):
self.assertEqual('2019-12-02 03:04:05.678', DateUtils.date2str(DateUtils.str2date('2019-12-02 03:04:05.678')))
pass
# self.fail()
def test_of(self):
a = DateUtils.of(2019, 1, 2, 3, 4, 5, 6, 7)
self.assertEqual('2019-01-02 03:04:05.006', DateUtils.date2str(a))
b = DateUtils.of(None, None, None, None, 4, None, 6, 7)
c = datetime.now().replace(minute=4, microsecond=6000)
self.assertEqual(DateUtils.date2str(c), DateUtils.date2str(b))
logger.info('{}-{}'.format(DateUtils.date2str(c), DateUtils.date2str(b)))
# self.fail()
def test_set(self):
pass
# self.fail()
def test_add_time(self):
a = DateUtils.of(2019, 1, 2, 3, 4, 5, 6, 7)
b = DateUtils.add_time(a, -1, 1, 1, 1, 1, 1, 1, 1)
logger.info('test_add_time|{}|{}'.format(DateUtils.date2str(a), DateUtils.date2str(b)))
self.assertEqual('2018-02-03 04:05:06.007', DateUtils.date2str(b))
def test_time_delta_with_diff_unit(self):
start = DateUtils.of(2019, 1, 2, 3, 4, 5, 6, 7)
end = DateUtils.of(2020, 2, 2, 4, 4, 6, 7, 8)
logger.info('时间差{}'.format(DateUtils.time_delta_with_diff_unit(end, start)))
# self.fail()
# def test_time_diff_by(self):
# self.fail() | en | 0.325057 | # self.fail() # self.fail() # self.fail() # self.fail() # def test_time_diff_by(self): # self.fail() | 2.967917 | 3 |
rule_engine/rule_executor.py | CUrW-SL/DSS-Framework | 0 | 6613709 | import sys
sys.path.insert(0, '/home/curw/git/DSS-Framework/gen_util')
from controller_util import is_matched
def get_next_pump_configurations(dss_adapter, routines):
dag_info = []
success_routines = evaluate_configuration_logics(dss_adapter, routines)
if len(success_routines) > 0:
for success_routine in success_routines:
dag_name = success_routine['dag_name']
payload = success_routine
dag_info.append({'dag_name': dag_name, 'payload': payload})
else:
print('No triggering_pump_dags found.')
return dag_info
# ((location_name='Yakbedda') and (variable_type='WaterLevel') and ((current_water_level>=alert_water_level) or (current_water_level>=warning_water_level)))
# or
# ((location_name='Kohuwala') and (variable_type='Precipitation') and ((rainfall_intensity>=65.4) or ((last_1_day_rainfall>=150) and (last_3_day_rainfall>=420))))
def evaluate_configuration_logics(dss_adapter, routines):
print('evaluate_configuration_logics|routines : ', routines)
passed_routines = []
for routine in routines:
rule_logic = routine['rule_logic']
if is_matched(rule_logic):
print('evaluate_configuration_logics|rule_logic : ', rule_logic)
location_names = dss_adapter.evaluate_variable_rule_logic(rule_logic)
if location_names is not None and len(location_names) > 0:
passed_routines.append(routine)
print('evaluate_configuration_logics|passed_routines : ', passed_routines)
return passed_routines
| import sys
sys.path.insert(0, '/home/curw/git/DSS-Framework/gen_util')
from controller_util import is_matched
def get_next_pump_configurations(dss_adapter, routines):
dag_info = []
success_routines = evaluate_configuration_logics(dss_adapter, routines)
if len(success_routines) > 0:
for success_routine in success_routines:
dag_name = success_routine['dag_name']
payload = success_routine
dag_info.append({'dag_name': dag_name, 'payload': payload})
else:
print('No triggering_pump_dags found.')
return dag_info
# ((location_name='Yakbedda') and (variable_type='WaterLevel') and ((current_water_level>=alert_water_level) or (current_water_level>=warning_water_level)))
# or
# ((location_name='Kohuwala') and (variable_type='Precipitation') and ((rainfall_intensity>=65.4) or ((last_1_day_rainfall>=150) and (last_3_day_rainfall>=420))))
def evaluate_configuration_logics(dss_adapter, routines):
print('evaluate_configuration_logics|routines : ', routines)
passed_routines = []
for routine in routines:
rule_logic = routine['rule_logic']
if is_matched(rule_logic):
print('evaluate_configuration_logics|rule_logic : ', rule_logic)
location_names = dss_adapter.evaluate_variable_rule_logic(rule_logic)
if location_names is not None and len(location_names) > 0:
passed_routines.append(routine)
print('evaluate_configuration_logics|passed_routines : ', passed_routines)
return passed_routines
| en | 0.477423 | # ((location_name='Yakbedda') and (variable_type='WaterLevel') and ((current_water_level>=alert_water_level) or (current_water_level>=warning_water_level))) # or # ((location_name='Kohuwala') and (variable_type='Precipitation') and ((rainfall_intensity>=65.4) or ((last_1_day_rainfall>=150) and (last_3_day_rainfall>=420)))) | 2.285129 | 2 |
TexturaPedra.py | rodrigomb13/CG | 0 | 6613710 | <filename>TexturaPedra.py
import OpenGL.GLUT as GLUT
import OpenGL.GLU as GLU
import OpenGL.GL as GL
import png
from sys import argv
from math import sin, cos, pi
a = 90.0
b = 0
da = 0.5
background_color = (0.184, 0.211, 0.250, 1)
# variaveis
altura = 3
vertices = 3
raio = 2
modificador = 0.5
#texture = []
def loadtextures():
global texture
texture = GL.glGenTextures(2)
reader = png.Reader(filename='textura.png')
w, h, pixels, metadata = reader.read_flat()
if(metadata['alpha']):
modo = GL.GL_RGBA
else:
modo = GL.GL_RGB
GL.glBindTexture(GL.GL_TEXTURE_2D, texture[0])
GL.glPixelStorei(GL.GL_UNPACK_ALIGNMENT, 1)
GL.glTexImage2D(GL.GL_TEXTURE_2D, 0, modo, w, h, 0, modo, GL.GL_UNSIGNED_BYTE, pixels.tolist())
GL.glTexParameterf(GL.GL_TEXTURE_2D, GL.GL_TEXTURE_WRAP_S, GL.GL_REPEAT)
GL.glTexParameterf(GL.GL_TEXTURE_2D, GL.GL_TEXTURE_WRAP_T, GL.GL_REPEAT)
GL.glTexParameterf(GL.GL_TEXTURE_2D, GL.GL_TEXTURE_MAG_FILTER, GL.GL_NEAREST)
GL.glTexParameterf(GL.GL_TEXTURE_2D, GL.GL_TEXTURE_MIN_FILTER, GL.GL_NEAREST)
GL.glTexEnvf(GL.GL_TEXTURE_ENV, GL.GL_TEXTURE_ENV_MODE, GL.GL_DECAL)
def figura():
polygon_points = []
faces_angle = (2*pi)/vertices
GL.glClear(GL.GL_COLOR_BUFFER_BIT | GL.GL_DEPTH_BUFFER_BIT)
GL.glLoadIdentity()
GL.glPushMatrix()
GL.glTranslatef(0.0, 1.5, -10)
GL.glRotatef(90,1.0,0.0,0.0)
# Rotation
# X axis
GL.glRotatef(a, 0.0, 0.0, 1.0)
# Y axis
GL.glRotatef(b, 0.0, 1.0, 0.0)
# Figura
GL.glBindTexture(GL.GL_TEXTURE_2D, texture[0])
# base
GL.glBegin(GL.GL_POLYGON)
for i in range(vertices):
x = raio * cos(i*faces_angle)
y = raio * sin(i*faces_angle)
polygon_points += [ (x,y) ]
GL.glTexCoord2f(x, y); GL.glVertex3f(x,y,0.0)
GL.glEnd()
# topo
GL.glBegin(GL.GL_POLYGON)
for x,y in polygon_points:
GL.glTexCoord2f(x, y); GL.glVertex3f(modificador*x,modificador*y, altura)
GL.glEnd()
#
GL.glBegin(GL.GL_QUADS)
for i in range(vertices):
GL.glTexCoord2f(0.0, 0.0); GL.glVertex3f(polygon_points[i][0],polygon_points[i][1],0)
GL.glTexCoord2f(0.0, 1.0); GL.glVertex3f(modificador*polygon_points[i][0],modificador*polygon_points[i][1],altura)
GL.glTexCoord2f(1.0, 1.0); GL.glVertex3f(modificador*polygon_points[(i+1)%vertices][0],modificador*polygon_points[(i+1)%vertices][1],altura)
GL.glTexCoord2f(1.0, 0.0); GL.glVertex3f(polygon_points[(i+1)%vertices][0],polygon_points[(i+1)%vertices][1],0)
GL.glEnd()
GL.glPopMatrix()
GLUT.glutSwapBuffers()
def draw():
global a
GL.glClear(GL.GL_COLOR_BUFFER_BIT | GL.GL_DEPTH_BUFFER_BIT)
figura()
# Auto-Rotation
a = a + da
GLUT.glutSwapBuffers()
def timer(i):
GLUT.glutPostRedisplay()
GLUT.glutTimerFunc(10, timer, 1)
def main():
GLUT.glutInit(argv)
GLUT.glutInitDisplayMode(GLUT.GLUT_DOUBLE | GLUT.GLUT_RGBA | GLUT.GLUT_DEPTH | GLUT.GLUT_MULTISAMPLE)
screen_width = GLUT.glutGet(GLUT.GLUT_SCREEN_WIDTH)
screen_height = GLUT.glutGet(GLUT.GLUT_SCREEN_HEIGHT)
window_width = round(2 * screen_width / 3)
window_height = round(2 * screen_height / 3)
GLUT.glutInitWindowSize(window_width, window_height)
GLUT.glutInitWindowPosition(round((screen_width - window_width) / 2), round((screen_height - window_height) / 2))
GLUT.glutCreateWindow("Tronco texturizado")
GLUT.glutDisplayFunc(draw)
loadtextures()
GL.glEnable(GL.GL_MULTISAMPLE)
GL.glEnable(GL.GL_DEPTH_TEST)
GL.glEnable(GL.GL_TEXTURE_2D)
GL.glClearColor(*background_color)
GL.glClearDepth(1.0)
GL.glDepthFunc(GL.GL_LESS)
GL.glShadeModel(GL.GL_SMOOTH)
GL.glMatrixMode(GL.GL_PROJECTION)
# Pre-render camera positioning
GLU.gluPerspective(-45, window_width / window_height, 0.1, 100.0)
GL.glTranslatef(0.0, 0.0, -10)
GL.glMatrixMode(GL.GL_MODELVIEW)
GLUT.glutTimerFunc(10, timer, 1)
GLUT.glutMainLoop()
if(__name__ == '__main__'):
main() | <filename>TexturaPedra.py
import OpenGL.GLUT as GLUT
import OpenGL.GLU as GLU
import OpenGL.GL as GL
import png
from sys import argv
from math import sin, cos, pi
a = 90.0
b = 0
da = 0.5
background_color = (0.184, 0.211, 0.250, 1)
# variaveis
altura = 3
vertices = 3
raio = 2
modificador = 0.5
#texture = []
def loadtextures():
global texture
texture = GL.glGenTextures(2)
reader = png.Reader(filename='textura.png')
w, h, pixels, metadata = reader.read_flat()
if(metadata['alpha']):
modo = GL.GL_RGBA
else:
modo = GL.GL_RGB
GL.glBindTexture(GL.GL_TEXTURE_2D, texture[0])
GL.glPixelStorei(GL.GL_UNPACK_ALIGNMENT, 1)
GL.glTexImage2D(GL.GL_TEXTURE_2D, 0, modo, w, h, 0, modo, GL.GL_UNSIGNED_BYTE, pixels.tolist())
GL.glTexParameterf(GL.GL_TEXTURE_2D, GL.GL_TEXTURE_WRAP_S, GL.GL_REPEAT)
GL.glTexParameterf(GL.GL_TEXTURE_2D, GL.GL_TEXTURE_WRAP_T, GL.GL_REPEAT)
GL.glTexParameterf(GL.GL_TEXTURE_2D, GL.GL_TEXTURE_MAG_FILTER, GL.GL_NEAREST)
GL.glTexParameterf(GL.GL_TEXTURE_2D, GL.GL_TEXTURE_MIN_FILTER, GL.GL_NEAREST)
GL.glTexEnvf(GL.GL_TEXTURE_ENV, GL.GL_TEXTURE_ENV_MODE, GL.GL_DECAL)
def figura():
polygon_points = []
faces_angle = (2*pi)/vertices
GL.glClear(GL.GL_COLOR_BUFFER_BIT | GL.GL_DEPTH_BUFFER_BIT)
GL.glLoadIdentity()
GL.glPushMatrix()
GL.glTranslatef(0.0, 1.5, -10)
GL.glRotatef(90,1.0,0.0,0.0)
# Rotation
# X axis
GL.glRotatef(a, 0.0, 0.0, 1.0)
# Y axis
GL.glRotatef(b, 0.0, 1.0, 0.0)
# Figura
GL.glBindTexture(GL.GL_TEXTURE_2D, texture[0])
# base
GL.glBegin(GL.GL_POLYGON)
for i in range(vertices):
x = raio * cos(i*faces_angle)
y = raio * sin(i*faces_angle)
polygon_points += [ (x,y) ]
GL.glTexCoord2f(x, y); GL.glVertex3f(x,y,0.0)
GL.glEnd()
# topo
GL.glBegin(GL.GL_POLYGON)
for x,y in polygon_points:
GL.glTexCoord2f(x, y); GL.glVertex3f(modificador*x,modificador*y, altura)
GL.glEnd()
#
GL.glBegin(GL.GL_QUADS)
for i in range(vertices):
GL.glTexCoord2f(0.0, 0.0); GL.glVertex3f(polygon_points[i][0],polygon_points[i][1],0)
GL.glTexCoord2f(0.0, 1.0); GL.glVertex3f(modificador*polygon_points[i][0],modificador*polygon_points[i][1],altura)
GL.glTexCoord2f(1.0, 1.0); GL.glVertex3f(modificador*polygon_points[(i+1)%vertices][0],modificador*polygon_points[(i+1)%vertices][1],altura)
GL.glTexCoord2f(1.0, 0.0); GL.glVertex3f(polygon_points[(i+1)%vertices][0],polygon_points[(i+1)%vertices][1],0)
GL.glEnd()
GL.glPopMatrix()
GLUT.glutSwapBuffers()
def draw():
global a
GL.glClear(GL.GL_COLOR_BUFFER_BIT | GL.GL_DEPTH_BUFFER_BIT)
figura()
# Auto-Rotation
a = a + da
GLUT.glutSwapBuffers()
def timer(i):
GLUT.glutPostRedisplay()
GLUT.glutTimerFunc(10, timer, 1)
def main():
GLUT.glutInit(argv)
GLUT.glutInitDisplayMode(GLUT.GLUT_DOUBLE | GLUT.GLUT_RGBA | GLUT.GLUT_DEPTH | GLUT.GLUT_MULTISAMPLE)
screen_width = GLUT.glutGet(GLUT.GLUT_SCREEN_WIDTH)
screen_height = GLUT.glutGet(GLUT.GLUT_SCREEN_HEIGHT)
window_width = round(2 * screen_width / 3)
window_height = round(2 * screen_height / 3)
GLUT.glutInitWindowSize(window_width, window_height)
GLUT.glutInitWindowPosition(round((screen_width - window_width) / 2), round((screen_height - window_height) / 2))
GLUT.glutCreateWindow("Tronco texturizado")
GLUT.glutDisplayFunc(draw)
loadtextures()
GL.glEnable(GL.GL_MULTISAMPLE)
GL.glEnable(GL.GL_DEPTH_TEST)
GL.glEnable(GL.GL_TEXTURE_2D)
GL.glClearColor(*background_color)
GL.glClearDepth(1.0)
GL.glDepthFunc(GL.GL_LESS)
GL.glShadeModel(GL.GL_SMOOTH)
GL.glMatrixMode(GL.GL_PROJECTION)
# Pre-render camera positioning
GLU.gluPerspective(-45, window_width / window_height, 0.1, 100.0)
GL.glTranslatef(0.0, 0.0, -10)
GL.glMatrixMode(GL.GL_MODELVIEW)
GLUT.glutTimerFunc(10, timer, 1)
GLUT.glutMainLoop()
if(__name__ == '__main__'):
main() | en | 0.64151 | # variaveis #texture = [] # Rotation # X axis # Y axis # Figura # base # topo # # Auto-Rotation # Pre-render camera positioning | 2.621208 | 3 |
example_script.py | piccolo-orm/targ | 12 | 6613711 | import asyncio
import decimal
import typing as t
from targ import CLI
def echo(message: str):
"""
Echo back the message.
:param message:
What will be printed out.
"""
print(message)
def add(a: int, b: int):
"""
Add the two numbers.
:param a:
The first number.
:param b:
The second number.
"""
print(a + b)
def say_hello(name: str, greeting: str = "hello"):
"""
Greet someone.
Example usage on the command line:
say_hello daniel --greeting='bonjour'
>>> bonjour daniel
:param name:
The person to greet.
:param greeting:
What to say to the person.
"""
print(f"{greeting} {name}")
# print_address --number=1 --street="Royal Avenue" --postcode="XYZ 123"
# --city=London
def print_address(
number: int, street: str, postcode: str, city: t.Optional[str] = None
):
"""
Print out the full address.
:param number:
House number, e.g. 8
:street:
Street name, e.g. "Royal Avenue"
:postcode:
e.g. "XYZ 123"
:city:
e.g. London
"""
address = f"{number} {street}"
if city is not None:
address += f", {city}"
address += f", {postcode}"
print(address)
def print_pi(precise: bool = False):
"""
Print out the digits of Pi.
:param precise:
If set, then more digits are printed out.
"""
if precise:
print("3.14159265")
else:
print("3.14")
def compound_interest(interest_rate: float, years: int):
"""
Work out the compound interest over the given number of years.
:param interest_rate:
The annual interest rate e.g. 0.05
:param years:
The number of years over which to compound.
"""
print(((interest_rate + 1) ** years) - 1)
def compound_interest_decimal(interest_rate: decimal.Decimal, years: int):
"""
Work out the compound interest over the given number of years.
:param interest_rate:
The annual interest rate e.g. 0.05
:param years:
The number of years over which to compound.
"""
print(((interest_rate + 1) ** years) - 1)
def create(username: str):
"""
Create a new user.
:param username:
The new user's username.
"""
print(f"Creating {username}")
async def timer(seconds: int):
"""
Countdown for a number of seconds.
:param seconds:
The number of seconds to countdown.
"""
print(f"Sleeping for {seconds}")
await asyncio.sleep(seconds)
print("Finished")
if __name__ == "__main__":
cli = CLI()
cli.register(say_hello)
cli.register(echo)
cli.register(add, aliases=["+"])
cli.register(print_pi)
cli.register(compound_interest)
cli.register(compound_interest_decimal)
cli.register(create, group_name="user")
cli.register(timer)
cli.register(add, command_name="sum")
cli.register(print_address)
cli.run()
| import asyncio
import decimal
import typing as t
from targ import CLI
def echo(message: str):
"""
Echo back the message.
:param message:
What will be printed out.
"""
print(message)
def add(a: int, b: int):
"""
Add the two numbers.
:param a:
The first number.
:param b:
The second number.
"""
print(a + b)
def say_hello(name: str, greeting: str = "hello"):
"""
Greet someone.
Example usage on the command line:
say_hello daniel --greeting='bonjour'
>>> bonjour daniel
:param name:
The person to greet.
:param greeting:
What to say to the person.
"""
print(f"{greeting} {name}")
# print_address --number=1 --street="Royal Avenue" --postcode="XYZ 123"
# --city=London
def print_address(
number: int, street: str, postcode: str, city: t.Optional[str] = None
):
"""
Print out the full address.
:param number:
House number, e.g. 8
:street:
Street name, e.g. "Royal Avenue"
:postcode:
e.g. "XYZ 123"
:city:
e.g. London
"""
address = f"{number} {street}"
if city is not None:
address += f", {city}"
address += f", {postcode}"
print(address)
def print_pi(precise: bool = False):
"""
Print out the digits of Pi.
:param precise:
If set, then more digits are printed out.
"""
if precise:
print("3.14159265")
else:
print("3.14")
def compound_interest(interest_rate: float, years: int):
"""
Work out the compound interest over the given number of years.
:param interest_rate:
The annual interest rate e.g. 0.05
:param years:
The number of years over which to compound.
"""
print(((interest_rate + 1) ** years) - 1)
def compound_interest_decimal(interest_rate: decimal.Decimal, years: int):
"""
Work out the compound interest over the given number of years.
:param interest_rate:
The annual interest rate e.g. 0.05
:param years:
The number of years over which to compound.
"""
print(((interest_rate + 1) ** years) - 1)
def create(username: str):
"""
Create a new user.
:param username:
The new user's username.
"""
print(f"Creating {username}")
async def timer(seconds: int):
"""
Countdown for a number of seconds.
:param seconds:
The number of seconds to countdown.
"""
print(f"Sleeping for {seconds}")
await asyncio.sleep(seconds)
print("Finished")
if __name__ == "__main__":
cli = CLI()
cli.register(say_hello)
cli.register(echo)
cli.register(add, aliases=["+"])
cli.register(print_pi)
cli.register(compound_interest)
cli.register(compound_interest_decimal)
cli.register(create, group_name="user")
cli.register(timer)
cli.register(add, command_name="sum")
cli.register(print_address)
cli.run()
| en | 0.756556 | Echo back the message. :param message: What will be printed out. Add the two numbers. :param a: The first number. :param b: The second number. Greet someone. Example usage on the command line: say_hello daniel --greeting='bonjour' >>> bonjour daniel :param name: The person to greet. :param greeting: What to say to the person. # print_address --number=1 --street="Royal Avenue" --postcode="XYZ 123" # --city=London Print out the full address. :param number: House number, e.g. 8 :street: Street name, e.g. "Royal Avenue" :postcode: e.g. "XYZ 123" :city: e.g. London Print out the digits of Pi. :param precise: If set, then more digits are printed out. Work out the compound interest over the given number of years. :param interest_rate: The annual interest rate e.g. 0.05 :param years: The number of years over which to compound. Work out the compound interest over the given number of years. :param interest_rate: The annual interest rate e.g. 0.05 :param years: The number of years over which to compound. Create a new user. :param username: The new user's username. Countdown for a number of seconds. :param seconds: The number of seconds to countdown. | 3.973352 | 4 |
multissh/utils.py | JaanPorkon/MultiSSH | 0 | 6613712 | from tldextract import tldextract
def get_hostname(hostname):
h_data = tldextract.extract(hostname)
if h_data.subdomain:
hostname = h_data.subdomain
else:
if h_data.registered_domain:
hostname = h_data.registered_domain
return hostname
def print_str(message, str_type='info', hostname=None):
if str_type == 'info':
color = ConsoleColor.WHITE
elif str_type == 'success':
color = ConsoleColor.GREEN
elif str_type == 'error':
color = ConsoleColor.RED
else:
color = ConsoleColor.WHITE
if hostname:
print('%s[%s]: %s%s%s' % (ConsoleColor.HEADER, hostname, color, message, ConsoleColor.END))
else:
print('%s%s%s' % (color, message, ConsoleColor.END))
def print_error(message, hostname=None):
print_str(message, 'error', hostname)
def print_success(message, hostname=None):
print_str(message, 'success', hostname)
def parse_credentials(data):
data = data.split(',')
if len(data) != 4:
print_error('Invalid credential line: %s' % ','.join(data))
return False
return {
'host': data[0],
'port': data[1],
'username': data[2],
'password': data[3]
}
def parse_response(response):
lines = []
for line in response:
line = line.strip()
if line != '':
lines.append(line)
return '\n'.join(lines)
def get_input():
return input('%s%s%s' % (ConsoleColor.GREEN, 'multissh$: ', ConsoleColor.END)).strip()
class ConsoleColor:
GREEN = '\033[92m'
RED = '\033[91m'
HEADER = '\033[95m'
END = '\033[0m'
WHITE = '\33[37m'
| from tldextract import tldextract
def get_hostname(hostname):
h_data = tldextract.extract(hostname)
if h_data.subdomain:
hostname = h_data.subdomain
else:
if h_data.registered_domain:
hostname = h_data.registered_domain
return hostname
def print_str(message, str_type='info', hostname=None):
if str_type == 'info':
color = ConsoleColor.WHITE
elif str_type == 'success':
color = ConsoleColor.GREEN
elif str_type == 'error':
color = ConsoleColor.RED
else:
color = ConsoleColor.WHITE
if hostname:
print('%s[%s]: %s%s%s' % (ConsoleColor.HEADER, hostname, color, message, ConsoleColor.END))
else:
print('%s%s%s' % (color, message, ConsoleColor.END))
def print_error(message, hostname=None):
print_str(message, 'error', hostname)
def print_success(message, hostname=None):
print_str(message, 'success', hostname)
def parse_credentials(data):
data = data.split(',')
if len(data) != 4:
print_error('Invalid credential line: %s' % ','.join(data))
return False
return {
'host': data[0],
'port': data[1],
'username': data[2],
'password': data[3]
}
def parse_response(response):
lines = []
for line in response:
line = line.strip()
if line != '':
lines.append(line)
return '\n'.join(lines)
def get_input():
return input('%s%s%s' % (ConsoleColor.GREEN, 'multissh$: ', ConsoleColor.END)).strip()
class ConsoleColor:
GREEN = '\033[92m'
RED = '\033[91m'
HEADER = '\033[95m'
END = '\033[0m'
WHITE = '\33[37m'
| none | 1 | 2.770997 | 3 | |
scripts/utils/camera.py | tmralmeida/tensorrt-yolov4 | 7 | 6613713 | """camera.py
This code is a tiny and custom version of https://github.com/jkjung-avt/tensorrt_demos#yolov4
"""
import cv2
import numpy as np
import threading
USB_GSTREAMER = True
def add_camera_args(parser):
"""Add parser augment for camera options."""
parser.add_argument("--video_path",
type = str,
default = None,
help = "use a video file as input")
parser.add_argument("--image_path",
type = str,
default = None,
help = "use an image file as input",
required = False)
parser.add_argument("--video_dev",
type = int,
default = None,
help = "device number e.g.: 0",
required = False)
parser.add_argument("--width",
dest="image_width",
help = "image width value",
default = 640,
type = int)
parser.add_argument("--height",
dest="image_height",
help = "image height value",
default = 480,
type = int)
return parser
def open_cam_usb(dev, width, height):
"""Open a USB webcam"""
if USB_GSTREAMER:
gst_str = ("v4l2src device=/dev/video{} ! "
"video/x-raw, width=(int){}, height=(int){} ! "
"videoconvert ! appsink").format(dev, width, height)
return cv2.VideoCapture(gst_str, cv2.CAP_GSTREAMER)
else:
return cv2.VideoCapture(dev)
def open_cam_onboard(width, height):
"""Open the Jetson onboard camera."""
gst_elements = str(subprocess.check_output('gst-inspect-1.0'))
if 'nvcamerasrc' in gst_elements:
# On versions of L4T prior to 28.1, you might need to add
# 'flip-method=2' into gst_str below.
gst_str = ('nvcamerasrc ! '
'video/x-raw(memory:NVMM), '
'width=(int)2592, height=(int)1458, '
'format=(string)I420, framerate=(fraction)30/1 ! '
'nvvidconv ! '
'video/x-raw, width=(int){}, height=(int){}, '
'format=(string)BGRx ! '
'videoconvert ! appsink').format(width, height)
elif 'nvarguscamerasrc' in gst_elements:
gst_str = ('nvarguscamerasrc ! '
'video/x-raw(memory:NVMM), '
'width=(int)1920, height=(int)1080, '
'format=(string)NV12, framerate=(fraction)30/1 ! '
'nvvidconv flip-method=2 ! '
'video/x-raw, width=(int){}, height=(int){}, '
'format=(string)BGRx ! '
'videoconvert ! appsink').format(width, height)
else:
raise RuntimeError('onboard camera source not found!')
return cv2.VideoCapture(gst_str, cv2.CAP_GSTREAMER)
def grab_img(cam):
"""This 'grab_img' function is designed to be run in the sub-thread.
Once started, this thread continues to grab a new image and put it
into the global 'img_handle', until 'thread_running' is set to False.
"""
while cam.thread_running:
_, cam.img_handle = cam.cap.read()
if cam.img_handle is None:
logging.warning('grab_img(): cap.read() returns None...')
break
cam.thread_running = False
class Camera():
"""Camera class which supports reading images from theses video sources:
1. Video file
2. Image (jpg, png, etc.) file, repeating indefinitely
3. USB webcam
4. Jetson onboard camera
"""
def __init__(self, args):
self.args = args
self.is_opened = False
self.use_thread = False
self.thread_running = False
self.img_handle = None
self.img_width = 0
self.img_height = 0
self.cap = None
self.thread = None
def open(self):
"""Open camera based on command line arguments."""
assert self.cap is None, 'Camera is already opened!'
args = self.args
if args.video_path is not None:
self.cap = cv2.VideoCapture(args.video_path)
self.use_thread = False
elif args.image_path is not None:
self.cap = "OK"
self.img_handle = cv2.imread(args.image_path)
if self.img_handle is not None:
self.is_opened = True
self.img_height, self.img_width, _ = self.img_handle.shape
self.use_thread = False
elif args.video_dev is not None:
self.cap = open_cam_usb(
args.video_dev,
args.image_width,
args.image_height
)
self.use_thread = True
else: # by default, use the jetson onboard camera
self.cap = open_cam_onboard(
args.image_width,
args.image_height
)
self.use_thread = True
if self.cap != "OK":
if self.cap.isOpened():
_, img = self.cap.read()
if img is not None:
self.img_height, self.img_width, _ = img.shape
self.is_opened = True
def start(self):
assert not self.thread_running
if self.use_thread:
self.thread_running = True
self.thread = threading.Thread(target=grab_img, args=(self,))
self.thread.start()
def stop(self):
self.thread_running = False
if self.use_thread:
self.thread.join()
def read(self):
if self.args.video_path is not None:
_, img = self.cap.read()
if img is None:
#logging.warning('grab_img(): cap.read() returns None...')
# looping around
self.cap.release()
self.cap = cv2.VideoCapture(self.args.video_path)
_, img = self.cap.read()
return img
elif self.args.image_path is not None:
return np.copy(self.img_handle)
else:
return self.img_handle
def release(self):
assert not self.thread_running
if self.cap != 'OK':
self.cap.release() | """camera.py
This code is a tiny and custom version of https://github.com/jkjung-avt/tensorrt_demos#yolov4
"""
import cv2
import numpy as np
import threading
USB_GSTREAMER = True
def add_camera_args(parser):
"""Add parser augment for camera options."""
parser.add_argument("--video_path",
type = str,
default = None,
help = "use a video file as input")
parser.add_argument("--image_path",
type = str,
default = None,
help = "use an image file as input",
required = False)
parser.add_argument("--video_dev",
type = int,
default = None,
help = "device number e.g.: 0",
required = False)
parser.add_argument("--width",
dest="image_width",
help = "image width value",
default = 640,
type = int)
parser.add_argument("--height",
dest="image_height",
help = "image height value",
default = 480,
type = int)
return parser
def open_cam_usb(dev, width, height):
"""Open a USB webcam"""
if USB_GSTREAMER:
gst_str = ("v4l2src device=/dev/video{} ! "
"video/x-raw, width=(int){}, height=(int){} ! "
"videoconvert ! appsink").format(dev, width, height)
return cv2.VideoCapture(gst_str, cv2.CAP_GSTREAMER)
else:
return cv2.VideoCapture(dev)
def open_cam_onboard(width, height):
"""Open the Jetson onboard camera."""
gst_elements = str(subprocess.check_output('gst-inspect-1.0'))
if 'nvcamerasrc' in gst_elements:
# On versions of L4T prior to 28.1, you might need to add
# 'flip-method=2' into gst_str below.
gst_str = ('nvcamerasrc ! '
'video/x-raw(memory:NVMM), '
'width=(int)2592, height=(int)1458, '
'format=(string)I420, framerate=(fraction)30/1 ! '
'nvvidconv ! '
'video/x-raw, width=(int){}, height=(int){}, '
'format=(string)BGRx ! '
'videoconvert ! appsink').format(width, height)
elif 'nvarguscamerasrc' in gst_elements:
gst_str = ('nvarguscamerasrc ! '
'video/x-raw(memory:NVMM), '
'width=(int)1920, height=(int)1080, '
'format=(string)NV12, framerate=(fraction)30/1 ! '
'nvvidconv flip-method=2 ! '
'video/x-raw, width=(int){}, height=(int){}, '
'format=(string)BGRx ! '
'videoconvert ! appsink').format(width, height)
else:
raise RuntimeError('onboard camera source not found!')
return cv2.VideoCapture(gst_str, cv2.CAP_GSTREAMER)
def grab_img(cam):
"""This 'grab_img' function is designed to be run in the sub-thread.
Once started, this thread continues to grab a new image and put it
into the global 'img_handle', until 'thread_running' is set to False.
"""
while cam.thread_running:
_, cam.img_handle = cam.cap.read()
if cam.img_handle is None:
logging.warning('grab_img(): cap.read() returns None...')
break
cam.thread_running = False
class Camera():
"""Camera class which supports reading images from theses video sources:
1. Video file
2. Image (jpg, png, etc.) file, repeating indefinitely
3. USB webcam
4. Jetson onboard camera
"""
def __init__(self, args):
self.args = args
self.is_opened = False
self.use_thread = False
self.thread_running = False
self.img_handle = None
self.img_width = 0
self.img_height = 0
self.cap = None
self.thread = None
def open(self):
"""Open camera based on command line arguments."""
assert self.cap is None, 'Camera is already opened!'
args = self.args
if args.video_path is not None:
self.cap = cv2.VideoCapture(args.video_path)
self.use_thread = False
elif args.image_path is not None:
self.cap = "OK"
self.img_handle = cv2.imread(args.image_path)
if self.img_handle is not None:
self.is_opened = True
self.img_height, self.img_width, _ = self.img_handle.shape
self.use_thread = False
elif args.video_dev is not None:
self.cap = open_cam_usb(
args.video_dev,
args.image_width,
args.image_height
)
self.use_thread = True
else: # by default, use the jetson onboard camera
self.cap = open_cam_onboard(
args.image_width,
args.image_height
)
self.use_thread = True
if self.cap != "OK":
if self.cap.isOpened():
_, img = self.cap.read()
if img is not None:
self.img_height, self.img_width, _ = img.shape
self.is_opened = True
def start(self):
assert not self.thread_running
if self.use_thread:
self.thread_running = True
self.thread = threading.Thread(target=grab_img, args=(self,))
self.thread.start()
def stop(self):
self.thread_running = False
if self.use_thread:
self.thread.join()
def read(self):
if self.args.video_path is not None:
_, img = self.cap.read()
if img is None:
#logging.warning('grab_img(): cap.read() returns None...')
# looping around
self.cap.release()
self.cap = cv2.VideoCapture(self.args.video_path)
_, img = self.cap.read()
return img
elif self.args.image_path is not None:
return np.copy(self.img_handle)
else:
return self.img_handle
def release(self):
assert not self.thread_running
if self.cap != 'OK':
self.cap.release() | en | 0.797078 | camera.py This code is a tiny and custom version of https://github.com/jkjung-avt/tensorrt_demos#yolov4 Add parser augment for camera options. Open a USB webcam Open the Jetson onboard camera. # On versions of L4T prior to 28.1, you might need to add # 'flip-method=2' into gst_str below. This 'grab_img' function is designed to be run in the sub-thread. Once started, this thread continues to grab a new image and put it into the global 'img_handle', until 'thread_running' is set to False. Camera class which supports reading images from theses video sources: 1. Video file 2. Image (jpg, png, etc.) file, repeating indefinitely 3. USB webcam 4. Jetson onboard camera Open camera based on command line arguments. # by default, use the jetson onboard camera #logging.warning('grab_img(): cap.read() returns None...') # looping around | 2.818461 | 3 |
forms.py | Abdulaziz-Hassan/todolist-website | 0 | 6613714 | <filename>forms.py
from flask_wtf import FlaskForm, RecaptchaField
from wtforms import StringField, SubmitField, PasswordField
from wtforms.validators import DataRequired, Email, Length
from flask_ckeditor import CKEditorField
class RegisterForm(FlaskForm):
name = StringField("Name", validators=[DataRequired()])
email = StringField("Email", validators=[DataRequired(), Email(message=f"Enter a valid email.")])
password = PasswordField("Password", validators=[DataRequired(), Length(min=6, max=35)])
recaptcha = RecaptchaField()
submit = SubmitField("Register")
class LoginForm(FlaskForm):
email = StringField("Email", validators=[DataRequired()])
password = PasswordField("Password", validators=[DataRequired()])
submit = SubmitField("Log In")
class TODOItemForm(FlaskForm):
title = StringField("Title", validators=[DataRequired()])
description = CKEditorField("Todo item description")
submit = SubmitField("Add")
cancel = SubmitField("Cancel")
| <filename>forms.py
from flask_wtf import FlaskForm, RecaptchaField
from wtforms import StringField, SubmitField, PasswordField
from wtforms.validators import DataRequired, Email, Length
from flask_ckeditor import CKEditorField
class RegisterForm(FlaskForm):
name = StringField("Name", validators=[DataRequired()])
email = StringField("Email", validators=[DataRequired(), Email(message=f"Enter a valid email.")])
password = PasswordField("Password", validators=[DataRequired(), Length(min=6, max=35)])
recaptcha = RecaptchaField()
submit = SubmitField("Register")
class LoginForm(FlaskForm):
email = StringField("Email", validators=[DataRequired()])
password = PasswordField("Password", validators=[DataRequired()])
submit = SubmitField("Log In")
class TODOItemForm(FlaskForm):
title = StringField("Title", validators=[DataRequired()])
description = CKEditorField("Todo item description")
submit = SubmitField("Add")
cancel = SubmitField("Cancel")
| none | 1 | 2.71832 | 3 | |
DLM.py | pbretz99/Slip-Detection | 0 | 6613715 | '''
DLM Models and Relevant Functionality
'''
# Libraries
import numpy as np
import matplotlib.pyplot as plt
from numpy import pi, sin, cos
from scipy.linalg import block_diag
# Local Code
from Utilities import load_data, check_shape, check_square
from Matrix_Utilities import poly_mats, trig_mats, trig_inits
class Results:
def __init__(self):
self.m = None
self.C = None
self.forecast = []
self.filter = []
self.innovation = []
self.obs_var = []
def append(self, ret):
self.m = ret['m']
self.C = ret['C']
self.forecast.append(ret['forecast'][0,0])
self.filter.append(ret['filter'][0,0])
self.innovation.append(ret['innovation'][0,0])
self.obs_var.append(ret['obs_var'][0,0])
def point_estimate(self):
return np.array(self.filter)
def standardized_error(self):
innovation = np.array(self.innovation)
obs_var = np.array(self.obs_var)
return innovation / np.sqrt(obs_var)
class ResultsDiscount(Results):
def __init__(self):
super().__init__()
self.alpha = []
self.beta = []
def append(self, ret):
self.forecast.append(ret['forecast'][0,0])
self.filter.append(ret['filter'][0,0])
self.innovation.append(ret['innovation'][0,0])
self.obs_var.append(ret['obs_var'][0,0])
self.alpha.append(ret['alpha'])
self.beta.append(ret['beta'])
def var_point_estimate(self):
alpha = np.array(self.alpha)
beta = np.array(self.beta)
return beta / (alpha - 1)
# Filter a sample
def filter_sample(Model, Data, init, final, set_init=True, discount_model=True, reset_to_zero=False):
Temp_Model = Model.copy()
if set_init: Temp_Model.m[0,0] = Data[init]
if reset_to_zero: Temp_Model.m[0,0] = 0
if discount_model: results = ResultsDiscount()
else: results = Results()
for t in range(init, final):
ret = Temp_Model.filter(Data[t], return_results=True)
results.append(ret)
return results
# DLM parent class
class DLM:
def __init__(self, m, C, G, F, W, V):
# State
self.m = check_shape(m)
self.C = check_square(C)
# Forecast matrix
self.G = check_square(G)
self.G_T = np.transpose(check_shape(G))
# Observation matrix
self.F = check_shape(F, column=False)
self.F_T = np.transpose(check_shape(F, column=False))
# Forecast covariance
self.W = check_square(W)
# Observation covariance
self.V = check_square(V)
def copy(self):
return DLM(self.m, self.C, self.G, self.F, self.W, self.V)
def to_discount(self, df, alpha, beta):
return DLMDiscount(self.m, self.C, self.G, self.F, df, alpha, beta)
def add_model(self, M):
# State
self.m = np.concatenate((self.m, M.m))
self.C = block_diag(self.C, M.C)
# Forecast matrix
self.G = block_diag(self.G, M.G)
self.G_T = block_diag(self.G_T, M.G_T)
# Observation matrix
self.F = np.concatenate((self.F, M.F), axis=1)
self.F_T = np.concatenate((self.F_T, M.F_T))
# Forecast covariance
self.W = block_diag(self.W, M.W)
# Observation covariance
self.V = self.V + M.V
def set_inits(self, results):
self.m = results.m
self.C = results.C
def filter(self, z, return_results=False):
# Forecast step
self.m, self.C = self.forecast()
# Data assimilation step
ret = self.data_assimilation(z)
self.m, self.C = ret['m'], ret['C']
if return_results: return ret
def forecast(self):
# Forecast distribution parameters
m_forecast = np.dot(self.G, self.m)
C_forecast = np.dot(self.G, np.dot(self.C, self.G_T)) + self.W
return m_forecast, C_forecast
def data_assimilation(self, obs):
# Predictive distribution parameters
f = np.dot(self.F, self.m)
Q = np.dot(self.F, np.dot(self.C, self.F_T)) + self.V
# Forecast error
innovation = obs - f
# Kalman gain
K = self.K_gain(Q)
# Assimilate data
m_analysis = self.m + np.dot(K, innovation)
C_analysis = np.dot((np.identity(self.C.shape[0]) - np.dot(K, self.F)), self.C)
ret = {'m': m_analysis, 'C': C_analysis}
# Optional returns
ret['forecast'] = f
ret['filter'] = m_analysis
ret['innovation'] = innovation
ret['obs_var'] = Q
return ret
# Get Kalman Gain, given Q
def K_gain(self, Q):
Q_inv = np.linalg.inv(Q)
K = np.dot(self.C, np.dot(self.F_T, Q_inv))
return K
# Print attributes
def print_model(self):
text_G = '\nForecast Matrix G = \n'
text_F = '\nObservation Matrix F = \n'
text_W = '\nForecast Covariance W = \n'
text_V = '\nObservation Covariance V = \n'
print(text_G, self.G, text_F, self.F, text_W, self.W, text_V, self.V)
# Polynomial model
class DLMPoly(DLM):
def __init__(self, m, C, W_list, V):
G, F, W, V = poly_mats(W_list, V)
super().__init__(m, C, G, F, W, V)
# Periodic model
class DLMTrig(DLM):
def __init__(self, init_var, omega, q, trig_var, V):
G, F, W, V = trig_mats(omega, q, trig_var, V)
m, C = trig_inits(q, init_var)
super().__init__(m, C, G, F, W, V)
# Discount model
class DLMDiscount(DLM):
def __init__(self, m, C, G, F, df, alpha, beta):
W = np.identity(C.shape[0])
V = np.array([[1]])
super().__init__(m, C, G, F, W, V)
self.df = df
self.alpha = alpha
self.beta = beta
def copy(self):
return DLMDiscount(self.m, self.C, self.G, self.F, self.df, self.alpha, self.beta)
def filter(self, z, return_results=False):
# Forecast step
self.m, self.C, self.alpha, self.beta = self.forecast()
# Data assimilation step
ret = self.data_assimilation(z)
self.m, self.C, self.alpha, self.beta = ret['m'], ret['C'], ret['alpha'], ret['beta']
if return_results: return ret
def forecast(self):
# Forecast distribution parameters
m_forecast = np.dot(self.G, self.m)
C_forecast = (1 / self.df) * np.dot(self.G, np.dot(self.C, self.G_T))
return m_forecast, C_forecast, self.alpha, self.beta
def data_assimilation(self, obs):
# Predictive distribution parameters
f = np.dot(self.F, self.m)
Q = np.dot(self.F, np.dot(self.C, self.F_T)) + self.V
Q_inv = np.linalg.inv(Q)
# Forecast error
innovation = obs - f
# Kalman gain
K = self.K_gain(Q)
# Assimilate data
m_analysis = self.m + np.dot(K, innovation)
C_analysis = np.dot((np.identity(self.C.shape[0]) - np.dot(K, self.F)), self.C)
alpha_analysis = self.alpha + 0.5
beta_analysis = self.beta + 0.5 * np.dot(np.transpose(innovation), np.dot(Q_inv, innovation))
ret = {'m': m_analysis, 'C': C_analysis, 'alpha': alpha_analysis, 'beta': beta_analysis[0,0]}
# Optional returns
ret['forecast'] = f
ret['filter'] = m_analysis
ret['innovation'] = innovation
ret['obs_var'] = Q * self.beta / (self.alpha - 1)
return ret
| '''
DLM Models and Relevant Functionality
'''
# Libraries
import numpy as np
import matplotlib.pyplot as plt
from numpy import pi, sin, cos
from scipy.linalg import block_diag
# Local Code
from Utilities import load_data, check_shape, check_square
from Matrix_Utilities import poly_mats, trig_mats, trig_inits
class Results:
def __init__(self):
self.m = None
self.C = None
self.forecast = []
self.filter = []
self.innovation = []
self.obs_var = []
def append(self, ret):
self.m = ret['m']
self.C = ret['C']
self.forecast.append(ret['forecast'][0,0])
self.filter.append(ret['filter'][0,0])
self.innovation.append(ret['innovation'][0,0])
self.obs_var.append(ret['obs_var'][0,0])
def point_estimate(self):
return np.array(self.filter)
def standardized_error(self):
innovation = np.array(self.innovation)
obs_var = np.array(self.obs_var)
return innovation / np.sqrt(obs_var)
class ResultsDiscount(Results):
def __init__(self):
super().__init__()
self.alpha = []
self.beta = []
def append(self, ret):
self.forecast.append(ret['forecast'][0,0])
self.filter.append(ret['filter'][0,0])
self.innovation.append(ret['innovation'][0,0])
self.obs_var.append(ret['obs_var'][0,0])
self.alpha.append(ret['alpha'])
self.beta.append(ret['beta'])
def var_point_estimate(self):
alpha = np.array(self.alpha)
beta = np.array(self.beta)
return beta / (alpha - 1)
# Filter a sample
def filter_sample(Model, Data, init, final, set_init=True, discount_model=True, reset_to_zero=False):
Temp_Model = Model.copy()
if set_init: Temp_Model.m[0,0] = Data[init]
if reset_to_zero: Temp_Model.m[0,0] = 0
if discount_model: results = ResultsDiscount()
else: results = Results()
for t in range(init, final):
ret = Temp_Model.filter(Data[t], return_results=True)
results.append(ret)
return results
# DLM parent class
class DLM:
def __init__(self, m, C, G, F, W, V):
# State
self.m = check_shape(m)
self.C = check_square(C)
# Forecast matrix
self.G = check_square(G)
self.G_T = np.transpose(check_shape(G))
# Observation matrix
self.F = check_shape(F, column=False)
self.F_T = np.transpose(check_shape(F, column=False))
# Forecast covariance
self.W = check_square(W)
# Observation covariance
self.V = check_square(V)
def copy(self):
return DLM(self.m, self.C, self.G, self.F, self.W, self.V)
def to_discount(self, df, alpha, beta):
return DLMDiscount(self.m, self.C, self.G, self.F, df, alpha, beta)
def add_model(self, M):
# State
self.m = np.concatenate((self.m, M.m))
self.C = block_diag(self.C, M.C)
# Forecast matrix
self.G = block_diag(self.G, M.G)
self.G_T = block_diag(self.G_T, M.G_T)
# Observation matrix
self.F = np.concatenate((self.F, M.F), axis=1)
self.F_T = np.concatenate((self.F_T, M.F_T))
# Forecast covariance
self.W = block_diag(self.W, M.W)
# Observation covariance
self.V = self.V + M.V
def set_inits(self, results):
self.m = results.m
self.C = results.C
def filter(self, z, return_results=False):
# Forecast step
self.m, self.C = self.forecast()
# Data assimilation step
ret = self.data_assimilation(z)
self.m, self.C = ret['m'], ret['C']
if return_results: return ret
def forecast(self):
# Forecast distribution parameters
m_forecast = np.dot(self.G, self.m)
C_forecast = np.dot(self.G, np.dot(self.C, self.G_T)) + self.W
return m_forecast, C_forecast
def data_assimilation(self, obs):
# Predictive distribution parameters
f = np.dot(self.F, self.m)
Q = np.dot(self.F, np.dot(self.C, self.F_T)) + self.V
# Forecast error
innovation = obs - f
# Kalman gain
K = self.K_gain(Q)
# Assimilate data
m_analysis = self.m + np.dot(K, innovation)
C_analysis = np.dot((np.identity(self.C.shape[0]) - np.dot(K, self.F)), self.C)
ret = {'m': m_analysis, 'C': C_analysis}
# Optional returns
ret['forecast'] = f
ret['filter'] = m_analysis
ret['innovation'] = innovation
ret['obs_var'] = Q
return ret
# Get Kalman Gain, given Q
def K_gain(self, Q):
Q_inv = np.linalg.inv(Q)
K = np.dot(self.C, np.dot(self.F_T, Q_inv))
return K
# Print attributes
def print_model(self):
text_G = '\nForecast Matrix G = \n'
text_F = '\nObservation Matrix F = \n'
text_W = '\nForecast Covariance W = \n'
text_V = '\nObservation Covariance V = \n'
print(text_G, self.G, text_F, self.F, text_W, self.W, text_V, self.V)
# Polynomial model
class DLMPoly(DLM):
def __init__(self, m, C, W_list, V):
G, F, W, V = poly_mats(W_list, V)
super().__init__(m, C, G, F, W, V)
# Periodic model
class DLMTrig(DLM):
def __init__(self, init_var, omega, q, trig_var, V):
G, F, W, V = trig_mats(omega, q, trig_var, V)
m, C = trig_inits(q, init_var)
super().__init__(m, C, G, F, W, V)
# Discount model
class DLMDiscount(DLM):
def __init__(self, m, C, G, F, df, alpha, beta):
W = np.identity(C.shape[0])
V = np.array([[1]])
super().__init__(m, C, G, F, W, V)
self.df = df
self.alpha = alpha
self.beta = beta
def copy(self):
return DLMDiscount(self.m, self.C, self.G, self.F, self.df, self.alpha, self.beta)
def filter(self, z, return_results=False):
# Forecast step
self.m, self.C, self.alpha, self.beta = self.forecast()
# Data assimilation step
ret = self.data_assimilation(z)
self.m, self.C, self.alpha, self.beta = ret['m'], ret['C'], ret['alpha'], ret['beta']
if return_results: return ret
def forecast(self):
# Forecast distribution parameters
m_forecast = np.dot(self.G, self.m)
C_forecast = (1 / self.df) * np.dot(self.G, np.dot(self.C, self.G_T))
return m_forecast, C_forecast, self.alpha, self.beta
def data_assimilation(self, obs):
# Predictive distribution parameters
f = np.dot(self.F, self.m)
Q = np.dot(self.F, np.dot(self.C, self.F_T)) + self.V
Q_inv = np.linalg.inv(Q)
# Forecast error
innovation = obs - f
# Kalman gain
K = self.K_gain(Q)
# Assimilate data
m_analysis = self.m + np.dot(K, innovation)
C_analysis = np.dot((np.identity(self.C.shape[0]) - np.dot(K, self.F)), self.C)
alpha_analysis = self.alpha + 0.5
beta_analysis = self.beta + 0.5 * np.dot(np.transpose(innovation), np.dot(Q_inv, innovation))
ret = {'m': m_analysis, 'C': C_analysis, 'alpha': alpha_analysis, 'beta': beta_analysis[0,0]}
# Optional returns
ret['forecast'] = f
ret['filter'] = m_analysis
ret['innovation'] = innovation
ret['obs_var'] = Q * self.beta / (self.alpha - 1)
return ret
| en | 0.491085 | DLM Models and Relevant Functionality # Libraries # Local Code # Filter a sample # DLM parent class # State # Forecast matrix # Observation matrix # Forecast covariance # Observation covariance # State # Forecast matrix # Observation matrix # Forecast covariance # Observation covariance # Forecast step # Data assimilation step # Forecast distribution parameters # Predictive distribution parameters # Forecast error # Kalman gain # Assimilate data # Optional returns # Get Kalman Gain, given Q # Print attributes # Polynomial model # Periodic model # Discount model # Forecast step # Data assimilation step # Forecast distribution parameters # Predictive distribution parameters # Forecast error # Kalman gain # Assimilate data # Optional returns | 2.65647 | 3 |
scGNNsp_space/plot_results.py | CyanStarNight/single_cell_spatial_image | 5 | 6613716 | <filename>scGNNsp_space/plot_results.py
#plot directly
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def plotResults(dataName, dirName='S',para='_8_euclidean_Grid_dummy_add_0.5'):
arr = np.load('/storage/htc/joshilab/wangjue/scGNNsp/'+dataName+'/coords_array.npy')
df = pd.read_csv('/storage/htc/joshilab/wangjue/scGNNsp/outputdir'+dirName+'-'+dataName+'_0.3/'+dataName+para+'_results.txt')
# sns.lmplot('population', 'Area', data=df, hue='continent', fit_reg=False)
# sns.lmplot(data=df, fit_reg=False)
color_labels = df['Celltype'].unique()
print(color_labels)
# List of colors in the color palettes
rgb_values = sns.color_palette("Set2", len(color_labels))
# Map continents to the colors
color_map = dict(zip(color_labels, rgb_values))
# Finally use the mapped values
plt.scatter(arr[:,0], arr[:,1], c=df['Celltype'].map(color_map), s=10)
plt.show()
plt.savefig(str(dataName)+'-'+dirName+'-'+para+'.png')
plt.close()
# Basic Spatial
print('Basic Spatial')
plotResults('151507_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151508_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151509_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151510_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151669_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151670_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151671_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151672_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151673_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151674_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151675_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151676_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('18-64_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('2-5_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('2-8_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('T4857_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
# Best Spatial
print('Best Spatial')
plotResults('151507_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151508_cpm', para='_40_euclidean_GridEx4_dummy_add_0.5_intersect')
plotResults('151509_cpm', para='_40_euclidean_GridEx4_dummy_add_0.5_intersect')
plotResults('151510_cpm', para='_64_euclidean_GridEx7_dummy_add_0.5_intersect')
plotResults('151669_cpm', para='_104_euclidean_GridEx12_dummy_add_0.5_intersect')
plotResults('151670_cpm', para='_32_euclidean_GridEx3_dummy_add_0.5_intersect')
plotResults('151671_cpm', para='_96_euclidean_GridEx11_dummy_add_0.5_intersect')
plotResults('151672_cpm', para='_48_euclidean_GridEx5_dummy_add_0.5_intersect')
plotResults('151673_cpm', para='_16_euclidean_GridEx_dummy_add_0.5_intersect')
plotResults('151674_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151675_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151676_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('18-64_cpm', para='_32_euclidean_GridEx3_dummy_add_0.5_intersect')
plotResults('2-5_cpm', para='_32_euclidean_GridEx3_dummy_add_0.5_intersect')
plotResults('2-8_cpm', para='_16_euclidean_GridEx_dummy_add_0.5_intersect')
plotResults('T4857_cpm', para='_16_euclidean_GridEx_dummy_add_0.5_intersect')
# Best Hybrid
print('Best Hybrid')
plotResults('151507_cpm', dirName='H', para='_200_euclidean_NA_dummy_add_0.5_intersect_16_GridEx')
plotResults('151508_cpm', dirName='H', para='_2000_euclidean_NA_dummy_add_0.5_intersect_160_GridEx19')
plotResults('151509_cpm', dirName='H', para='_500_euclidean_NA_dummy_add_0.5_intersect_200_GridEx24')
plotResults('151510_cpm', dirName='H', para='_50_euclidean_NA_dummy_add_0.5_intersect_80_GridEx9')
plotResults('151669_cpm', dirName='H', para='_1000_euclidean_NA_dummy_add_0.5_intersect_200_GridEx24')
plotResults('151670_cpm', dirName='H', para='_1000_euclidean_NA_dummy_add_0.5_intersect_160_GridEx19')
plotResults('151671_cpm', dirName='H', para='_100_euclidean_NA_dummy_add_0.5_intersect_80_GridEx9')
plotResults('151672_cpm', dirName='H', para='_2000_euclidean_NA_dummy_add_0.5_intersect_80_GridEx9')
plotResults('151673_cpm', dirName='H', para='_2000_euclidean_NA_dummy_add_0.5_intersect_40_GridEx4')
plotResults('151674_cpm', dirName='H', para='_10_euclidean_NA_dummy_add_0.5_intersect_16_GridEx')
plotResults('151675_cpm', dirName='H', para='_10_euclidean_NA_dummy_add_0.5_intersect_40_GridEx4')
plotResults('151676_cpm', dirName='H', para='_200_euclidean_NA_dummy_add_0.5_intersect_16_GridEx')
plotResults('18-64_cpm', dirName='H', para='_1000_euclidean_NA_dummy_add_0.5_intersect_80_GridEx9')
plotResults('2-5_cpm', dirName='H', para='_200_euclidean_NA_dummy_add_0.5_intersect_120_GridEx14')
plotResults('2-8_cpm', dirName='H', para='_2000_euclidean_NA_dummy_add_0.5_intersect_16_GridEx')
plotResults('T4857_cpm', dirName='H', para='_50_euclidean_NA_dummy_add_0.5_intersect_80_GridEx9')
# Best Hybrid geom
print('Best Hybrid')
plotResults('151507_cpm', dirName='H', para='_2000_euclidean_NA_geom_lowf_add_0.5_intersect_80_GridEx9')
plotResults('151508_cpm', dirName='H', para='_2000_euclidean_NA_geom_lowf_add_0.5_intersect_120_GridEx14')
plotResults('151509_cpm', dirName='H', para='_2000_euclidean_NA_geom_lowf_add_0.5_intersect_200_GridEx24')
plotResults('151510_cpm', dirName='H', para='_1000_euclidean_NA_geom_lowf_add_0.5_intersect_120_GridEx14')
plotResults('151669_cpm', dirName='H', para='_2000_euclidean_NA_geom_lowf_add_0.5_intersect_240_GridEx29')
plotResults('151670_cpm', dirName='H', para='_2000_euclidean_NA_geom_lowf_add_0.5_intersect_240_GridEx29')
plotResults('151671_cpm', dirName='H', para='_200_euclidean_NA_geom_lowf_add_0.5_intersect_80_GridEx9')
plotResults('151672_cpm', dirName='H', para='_100_euclidean_NA_geom_lowf_add_0.5_intersect_160_GridEx19')
plotResults('151673_cpm', dirName='H', para='_1000_euclidean_NA_geom_lowf_add_0.5_intersect_16_GridEx')
plotResults('151674_cpm', dirName='H', para='_10_euclidean_NA_geom_lowf_add_0.5_intersect_8_Grid')
plotResults('151675_cpm', dirName='H', para='_10_euclidean_NA_geom_lowf_add_0.5_intersect_40_GridEx4')
plotResults('151676_cpm', dirName='H', para='_2000_euclidean_NA_geom_lowf_add_0.5_intersect_40_GridEx4')
plotResults('18-64_cpm', dirName='H', para='_10_euclidean_NA_geom_lowf_add_0.5_intersect_240_GridEx29')
plotResults('2-5_cpm', dirName='H', para='_100_euclidean_NA_geom_lowf_add_0.5_intersect_120_GridEx14')
plotResults('2-8_cpm', dirName='H', para='_500_euclidean_NA_geom_lowf_add_0.5_intersect_80_GridEx9')
plotResults('T4857_cpm', dirName='H', para='_10_euclidean_NA_geom_lowf_add_0.5_intersect_200_GridEx24')
# plotResults('151507_sctransform')
# plotResults('151508_sctransform')
# plotResults('151509_sctransform')
# plotResults('151510_sctransform')
# plotResults('151669_sctransform')
# plotResults('151670_sctransform')
# plotResults('151671_sctransform')
# plotResults('151672_sctransform')
# plotResults('151673_sctransform')
# plotResults('151674_sctransform')
# plotResults('151675_sctransform')
# plotResults('151676_sctransform')
# plotResults('18-64_sctransform')
# plotResults('2-5_sctransform')
# plotResults('2-8_sctransform')
# plotResults('T4857_sctransform')
##########
# #plot
# t=np.load('defaultPE.npy')
# tmp=[]
# i=0
# for j in range(10):
# tmp.append(t[i,j]+t[i,j+10])
# plt.plot(tmp,'y.')
# tmp=[]
# i=2
# for j in range(10):
# tmp.append(t[i,j]+t[i,j+10])
# plt.plot(tmp,'b.')
# tmp=[]
# i=20
# for j in range(10):
# tmp.append(t[i,j]+t[i,j+10])
# plt.plot(tmp,'r.')
# plt.show()
# ##########
# tmp=[]
# i=0
# for j in range(10):
# tmp.append(t[i,j])
# plt.plot(tmp,'y.')
# tmp1=[]
# i=2
# for j in range(10):
# tmp1.append(t[i,j])
# plt.plot(tmp1,'b.')
# tmp2=[]
# i=10
# for j in range(10):
# tmp2.append(t[i,j])
# plt.plot(tmp2,'r.')
# plt.show() | <filename>scGNNsp_space/plot_results.py
#plot directly
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def plotResults(dataName, dirName='S',para='_8_euclidean_Grid_dummy_add_0.5'):
arr = np.load('/storage/htc/joshilab/wangjue/scGNNsp/'+dataName+'/coords_array.npy')
df = pd.read_csv('/storage/htc/joshilab/wangjue/scGNNsp/outputdir'+dirName+'-'+dataName+'_0.3/'+dataName+para+'_results.txt')
# sns.lmplot('population', 'Area', data=df, hue='continent', fit_reg=False)
# sns.lmplot(data=df, fit_reg=False)
color_labels = df['Celltype'].unique()
print(color_labels)
# List of colors in the color palettes
rgb_values = sns.color_palette("Set2", len(color_labels))
# Map continents to the colors
color_map = dict(zip(color_labels, rgb_values))
# Finally use the mapped values
plt.scatter(arr[:,0], arr[:,1], c=df['Celltype'].map(color_map), s=10)
plt.show()
plt.savefig(str(dataName)+'-'+dirName+'-'+para+'.png')
plt.close()
# Basic Spatial
print('Basic Spatial')
plotResults('151507_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151508_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151509_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151510_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151669_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151670_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151671_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151672_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151673_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151674_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151675_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151676_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('18-64_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('2-5_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('2-8_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('T4857_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
# Best Spatial
print('Best Spatial')
plotResults('151507_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151508_cpm', para='_40_euclidean_GridEx4_dummy_add_0.5_intersect')
plotResults('151509_cpm', para='_40_euclidean_GridEx4_dummy_add_0.5_intersect')
plotResults('151510_cpm', para='_64_euclidean_GridEx7_dummy_add_0.5_intersect')
plotResults('151669_cpm', para='_104_euclidean_GridEx12_dummy_add_0.5_intersect')
plotResults('151670_cpm', para='_32_euclidean_GridEx3_dummy_add_0.5_intersect')
plotResults('151671_cpm', para='_96_euclidean_GridEx11_dummy_add_0.5_intersect')
plotResults('151672_cpm', para='_48_euclidean_GridEx5_dummy_add_0.5_intersect')
plotResults('151673_cpm', para='_16_euclidean_GridEx_dummy_add_0.5_intersect')
plotResults('151674_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151675_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('151676_cpm', para='_8_euclidean_Grid_dummy_add_0.5')
plotResults('18-64_cpm', para='_32_euclidean_GridEx3_dummy_add_0.5_intersect')
plotResults('2-5_cpm', para='_32_euclidean_GridEx3_dummy_add_0.5_intersect')
plotResults('2-8_cpm', para='_16_euclidean_GridEx_dummy_add_0.5_intersect')
plotResults('T4857_cpm', para='_16_euclidean_GridEx_dummy_add_0.5_intersect')
# Best Hybrid
print('Best Hybrid')
plotResults('151507_cpm', dirName='H', para='_200_euclidean_NA_dummy_add_0.5_intersect_16_GridEx')
plotResults('151508_cpm', dirName='H', para='_2000_euclidean_NA_dummy_add_0.5_intersect_160_GridEx19')
plotResults('151509_cpm', dirName='H', para='_500_euclidean_NA_dummy_add_0.5_intersect_200_GridEx24')
plotResults('151510_cpm', dirName='H', para='_50_euclidean_NA_dummy_add_0.5_intersect_80_GridEx9')
plotResults('151669_cpm', dirName='H', para='_1000_euclidean_NA_dummy_add_0.5_intersect_200_GridEx24')
plotResults('151670_cpm', dirName='H', para='_1000_euclidean_NA_dummy_add_0.5_intersect_160_GridEx19')
plotResults('151671_cpm', dirName='H', para='_100_euclidean_NA_dummy_add_0.5_intersect_80_GridEx9')
plotResults('151672_cpm', dirName='H', para='_2000_euclidean_NA_dummy_add_0.5_intersect_80_GridEx9')
plotResults('151673_cpm', dirName='H', para='_2000_euclidean_NA_dummy_add_0.5_intersect_40_GridEx4')
plotResults('151674_cpm', dirName='H', para='_10_euclidean_NA_dummy_add_0.5_intersect_16_GridEx')
plotResults('151675_cpm', dirName='H', para='_10_euclidean_NA_dummy_add_0.5_intersect_40_GridEx4')
plotResults('151676_cpm', dirName='H', para='_200_euclidean_NA_dummy_add_0.5_intersect_16_GridEx')
plotResults('18-64_cpm', dirName='H', para='_1000_euclidean_NA_dummy_add_0.5_intersect_80_GridEx9')
plotResults('2-5_cpm', dirName='H', para='_200_euclidean_NA_dummy_add_0.5_intersect_120_GridEx14')
plotResults('2-8_cpm', dirName='H', para='_2000_euclidean_NA_dummy_add_0.5_intersect_16_GridEx')
plotResults('T4857_cpm', dirName='H', para='_50_euclidean_NA_dummy_add_0.5_intersect_80_GridEx9')
# Best Hybrid geom
print('Best Hybrid')
plotResults('151507_cpm', dirName='H', para='_2000_euclidean_NA_geom_lowf_add_0.5_intersect_80_GridEx9')
plotResults('151508_cpm', dirName='H', para='_2000_euclidean_NA_geom_lowf_add_0.5_intersect_120_GridEx14')
plotResults('151509_cpm', dirName='H', para='_2000_euclidean_NA_geom_lowf_add_0.5_intersect_200_GridEx24')
plotResults('151510_cpm', dirName='H', para='_1000_euclidean_NA_geom_lowf_add_0.5_intersect_120_GridEx14')
plotResults('151669_cpm', dirName='H', para='_2000_euclidean_NA_geom_lowf_add_0.5_intersect_240_GridEx29')
plotResults('151670_cpm', dirName='H', para='_2000_euclidean_NA_geom_lowf_add_0.5_intersect_240_GridEx29')
plotResults('151671_cpm', dirName='H', para='_200_euclidean_NA_geom_lowf_add_0.5_intersect_80_GridEx9')
plotResults('151672_cpm', dirName='H', para='_100_euclidean_NA_geom_lowf_add_0.5_intersect_160_GridEx19')
plotResults('151673_cpm', dirName='H', para='_1000_euclidean_NA_geom_lowf_add_0.5_intersect_16_GridEx')
plotResults('151674_cpm', dirName='H', para='_10_euclidean_NA_geom_lowf_add_0.5_intersect_8_Grid')
plotResults('151675_cpm', dirName='H', para='_10_euclidean_NA_geom_lowf_add_0.5_intersect_40_GridEx4')
plotResults('151676_cpm', dirName='H', para='_2000_euclidean_NA_geom_lowf_add_0.5_intersect_40_GridEx4')
plotResults('18-64_cpm', dirName='H', para='_10_euclidean_NA_geom_lowf_add_0.5_intersect_240_GridEx29')
plotResults('2-5_cpm', dirName='H', para='_100_euclidean_NA_geom_lowf_add_0.5_intersect_120_GridEx14')
plotResults('2-8_cpm', dirName='H', para='_500_euclidean_NA_geom_lowf_add_0.5_intersect_80_GridEx9')
plotResults('T4857_cpm', dirName='H', para='_10_euclidean_NA_geom_lowf_add_0.5_intersect_200_GridEx24')
# plotResults('151507_sctransform')
# plotResults('151508_sctransform')
# plotResults('151509_sctransform')
# plotResults('151510_sctransform')
# plotResults('151669_sctransform')
# plotResults('151670_sctransform')
# plotResults('151671_sctransform')
# plotResults('151672_sctransform')
# plotResults('151673_sctransform')
# plotResults('151674_sctransform')
# plotResults('151675_sctransform')
# plotResults('151676_sctransform')
# plotResults('18-64_sctransform')
# plotResults('2-5_sctransform')
# plotResults('2-8_sctransform')
# plotResults('T4857_sctransform')
##########
# #plot
# t=np.load('defaultPE.npy')
# tmp=[]
# i=0
# for j in range(10):
# tmp.append(t[i,j]+t[i,j+10])
# plt.plot(tmp,'y.')
# tmp=[]
# i=2
# for j in range(10):
# tmp.append(t[i,j]+t[i,j+10])
# plt.plot(tmp,'b.')
# tmp=[]
# i=20
# for j in range(10):
# tmp.append(t[i,j]+t[i,j+10])
# plt.plot(tmp,'r.')
# plt.show()
# ##########
# tmp=[]
# i=0
# for j in range(10):
# tmp.append(t[i,j])
# plt.plot(tmp,'y.')
# tmp1=[]
# i=2
# for j in range(10):
# tmp1.append(t[i,j])
# plt.plot(tmp1,'b.')
# tmp2=[]
# i=10
# for j in range(10):
# tmp2.append(t[i,j])
# plt.plot(tmp2,'r.')
# plt.show() | en | 0.143765 | #plot directly # sns.lmplot('population', 'Area', data=df, hue='continent', fit_reg=False) # sns.lmplot(data=df, fit_reg=False) # List of colors in the color palettes # Map continents to the colors # Finally use the mapped values # Basic Spatial # Best Spatial # Best Hybrid # Best Hybrid geom # plotResults('151507_sctransform') # plotResults('151508_sctransform') # plotResults('151509_sctransform') # plotResults('151510_sctransform') # plotResults('151669_sctransform') # plotResults('151670_sctransform') # plotResults('151671_sctransform') # plotResults('151672_sctransform') # plotResults('151673_sctransform') # plotResults('151674_sctransform') # plotResults('151675_sctransform') # plotResults('151676_sctransform') # plotResults('18-64_sctransform') # plotResults('2-5_sctransform') # plotResults('2-8_sctransform') # plotResults('T4857_sctransform') ########## # #plot # t=np.load('defaultPE.npy') # tmp=[] # i=0 # for j in range(10): # tmp.append(t[i,j]+t[i,j+10]) # plt.plot(tmp,'y.') # tmp=[] # i=2 # for j in range(10): # tmp.append(t[i,j]+t[i,j+10]) # plt.plot(tmp,'b.') # tmp=[] # i=20 # for j in range(10): # tmp.append(t[i,j]+t[i,j+10]) # plt.plot(tmp,'r.') # plt.show() # ########## # tmp=[] # i=0 # for j in range(10): # tmp.append(t[i,j]) # plt.plot(tmp,'y.') # tmp1=[] # i=2 # for j in range(10): # tmp1.append(t[i,j]) # plt.plot(tmp1,'b.') # tmp2=[] # i=10 # for j in range(10): # tmp2.append(t[i,j]) # plt.plot(tmp2,'r.') # plt.show() | 2.6961 | 3 |
docker/generate.py | qooba/feast-benchmark | 0 | 6613717 | <gh_stars>0
import pandas as pd
import numpy as np
from datetime import datetime, timezone
from sklearn.datasets import make_hastie_10_2
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
def generate_entities(size):
return np.random.choice(size, size=size, replace=False)
def generate_data(entities, year=2021, month=10, day=1) -> pd.DataFrame:
n_samples=len(entities)
X, y = make_hastie_10_2(n_samples=n_samples, random_state=0)
df = pd.DataFrame(X, columns=["f0", "f1", "f2", "f3", "f4", "f5", "f6", "f7", "f8", "f9"])
df["y"]=y
df['entity_id'] = entities
df['datetime'] = pd.to_datetime(
np.random.randint(
datetime(year, month, day, 0,tzinfo=timezone.utc).timestamp(),
datetime(year, month, day, 22,tzinfo=timezone.utc).timestamp(),
size=n_samples),
unit="s", #utc=True
)
df['created'] = pd.to_datetime(
datetime.now(), #utc=True
)
return df
entities=generate_entities(1000000)
entity_df = pd.DataFrame(data=entities, columns=['entity_id'])
entity_df["event_timestamp"]=datetime(2021, 1, 14, 23, 59, 42, tzinfo=timezone.utc)
entity_df=entity_df[entity_df.entity_id == 100]
#entity_df=entity_df[entity_df.entity_id < 500]
entity_df.to_parquet('./dataset/entity_df.parquet')
all_data=[]
for d in range(1,15):
data=generate_data(entities,month=1, day=d)
all_data.append(data)
all_dd=pd.concat(all_data)
all_dd.set_index('datetime')
all_dd.to_parquet("./dataset/all_data.parquet")
| import pandas as pd
import numpy as np
from datetime import datetime, timezone
from sklearn.datasets import make_hastie_10_2
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
def generate_entities(size):
return np.random.choice(size, size=size, replace=False)
def generate_data(entities, year=2021, month=10, day=1) -> pd.DataFrame:
n_samples=len(entities)
X, y = make_hastie_10_2(n_samples=n_samples, random_state=0)
df = pd.DataFrame(X, columns=["f0", "f1", "f2", "f3", "f4", "f5", "f6", "f7", "f8", "f9"])
df["y"]=y
df['entity_id'] = entities
df['datetime'] = pd.to_datetime(
np.random.randint(
datetime(year, month, day, 0,tzinfo=timezone.utc).timestamp(),
datetime(year, month, day, 22,tzinfo=timezone.utc).timestamp(),
size=n_samples),
unit="s", #utc=True
)
df['created'] = pd.to_datetime(
datetime.now(), #utc=True
)
return df
entities=generate_entities(1000000)
entity_df = pd.DataFrame(data=entities, columns=['entity_id'])
entity_df["event_timestamp"]=datetime(2021, 1, 14, 23, 59, 42, tzinfo=timezone.utc)
entity_df=entity_df[entity_df.entity_id == 100]
#entity_df=entity_df[entity_df.entity_id < 500]
entity_df.to_parquet('./dataset/entity_df.parquet')
all_data=[]
for d in range(1,15):
data=generate_data(entities,month=1, day=d)
all_data.append(data)
all_dd=pd.concat(all_data)
all_dd.set_index('datetime')
all_dd.to_parquet("./dataset/all_data.parquet") | en | 0.647864 | #utc=True #utc=True #entity_df=entity_df[entity_df.entity_id < 500] | 2.640442 | 3 |
dali/test/python/test_dali_tf_dataset_pipelines.py | lvtengda/DALI | 0 | 6613718 | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 nvidia.dali as dali
import nvidia.dali.fn as fn
import nvidia.dali.plugin.tf as dali_tf
from nvidia.dali import pipeline_def
import tensorflow as tf
from test_utils import RandomlyShapedDataIterator
import numpy as np
from nose.tools import nottest
class RandomSampleIterator:
def __init__(self,
max_shape=(10, 600, 800, 3),
dtype_sample=np.uint8(0),
start=0,
stop=1e100,
min_shape=None,
seed=42):
self.start = start
self.stop = stop
self.min_shape = min_shape
self.max_shape = max_shape
# As tf passes only tensors to the iterator, we pass a dummy value of which we take the type
self.dtype = dtype_sample.dtype
self.seed = seed
def __iter__(self):
self.n = self.start
self.random_iter = iter(RandomlyShapedDataIterator(batch_size=1, min_shape=self.min_shape,
max_shape=self.max_shape, seed=self.seed, dtype=self.dtype))
return self
def __next__(self):
if self.n <= self.stop:
result = self.n
self.n += 1
ret = self.random_iter.next()[0]
return ret
else:
raise StopIteration
class FixedSampleIterator:
def __init__(self, value):
self.value = value
def __iter__(self):
return self
def __next__(self):
return self.value
class InfiniteSampleIterator:
def __init__(self, start_value):
self.value = start_value
def __iter__(self):
return self
def __next__(self):
result = self.value
self.value = self.value + np.array(1, dtype=self.value.dtype)
return result
@pipeline_def
def one_input_pipeline(def_for_dataset, device, source, external_source_device):
"""Pipeline accepting single input via external source
Parameters
----------
def_for_dataset : bool
True if this pipeline will be converted to TF Dataset
device : str
device that the Dataset will be placed ("cpu" or "gpu")
source : callable
callback for the external source in baseline pipeline otherwise None
external_source_device : str
Device that we want the external source in TF dataset to be placed
"""
if def_for_dataset:
# We use no copy when the input memory is matching the external source placement,
# so the Dataset's placement is the same as external source's device
input = fn.external_source(name="input_placeholder",
no_copy=(device == external_source_device),
device=external_source_device)
else:
input = fn.external_source(name="actual_input",
source=source,
batch=False,
device=external_source_device)
input = input if device == 'cpu' else input.gpu()
processed = fn.cast(input + 10, dtype=dali.types.INT32)
input_padded, processed_padded = fn.pad([input, processed])
return input_padded, processed_padded
# Test that uses Tensor and Repeat (infinite) datasets as inputs to DALI pipeline
def external_source_converter_with_fixed_value(shape, dtype, tensor):
def to_dataset(pipeline_desc, device_str):
with tf.device('/cpu:0'):
input_dataset = tf.data.Dataset.from_tensors(tensor).repeat()
# If we place DALIDataset on GPU we need the remote call + manual data transfer
if "gpu" in device_str:
input_dataset = input_dataset.apply(tf.data.experimental.copy_to_device('/gpu:0'))
dataset_pipeline, shapes, dtypes = pipeline_desc
with tf.device(device_str):
dali_dataset = dali_tf.experimental.DALIDatasetWithInputs(
input_datasets=input_dataset,
input_names="input_placeholder",
pipeline=dataset_pipeline,
batch_size=dataset_pipeline.batch_size,
output_shapes=shapes,
output_dtypes=dtypes,
num_threads=dataset_pipeline.num_threads,
device_id=dataset_pipeline.device_id)
return dali_dataset
return to_dataset
# Test that uses Generator dataset as inputs to DALI pipeline
def external_source_converter_with_callback(input_iterator, shape, dtype, *args):
def to_dataset(pipeline_desc, device_str):
with tf.device('/cpu:0'):
_args = (shape, dtype(0)) + tuple(args)
out_shape = tuple(None for _ in shape)
tf_type = tf.dtypes.as_dtype(dtype)
input_dataset = tf.data.Dataset.from_generator(
input_iterator, output_types=tf_type, output_shapes=out_shape, args=_args)
# If we place DALIDataset on GPU we need the remote call + manual data transfer
if "gpu" in device_str:
input_dataset = input_dataset.apply(tf.data.experimental.copy_to_device('/gpu:0'))
dataset_pipeline, shapes, dtypes = pipeline_desc
with tf.device(device_str):
dali_dataset = dali_tf.experimental.DALIDatasetWithInputs(
input_datasets=input_dataset,
input_names="input_placeholder",
pipeline=dataset_pipeline,
batch_size=dataset_pipeline.batch_size,
output_shapes=shapes,
output_dtypes=dtypes,
num_threads=dataset_pipeline.num_threads,
device_id=dataset_pipeline.device_id)
return dali_dataset
return to_dataset
@nottest
def external_source_tester(shape, dtype, source=None, external_source_device="cpu"):
def get_external_source_pipeline_getter(batch_size, num_threads, device, device_id=0,
shard_id=0, num_shards=1, def_for_dataset=False):
pipe = one_input_pipeline(def_for_dataset,
device,
source,
external_source_device,
batch_size=batch_size,
num_threads=num_threads,
device_id=device_id)
batch_shape = (batch_size,) + tuple(None for _ in shape)
return pipe, (batch_shape, batch_shape), (tf.dtypes.as_dtype(dtype), tf.int32)
return get_external_source_pipeline_getter
@pipeline_def
def many_input_pipeline(def_for_dataset, device, sources, input_names):
""" Pipeline accepting multiple inputs via external source
Parameters
----------
def_for_dataset : bool
True if this pipeline will be converted to TF Dataset
device : str
device that the Dataset will be placed ("cpu" or "gpu")
sources : list of callables
callbacks for the external sources in baseline pipeline otherwise None
input_names : list of str
Names of inputs placeholder for TF
"""
inputs = []
if def_for_dataset:
for input_name in input_names:
input = fn.external_source(name=input_name)
input = input if device == 'cpu' else input.gpu()
inputs.append(input)
else:
for source in sources:
input = fn.external_source(source=source, batch=False)
input = input if device == 'cpu' else input.gpu()
inputs.append(input)
processed = []
for input in inputs:
processed.append(fn.cast(input + 10, dtype=dali.types.INT32))
results = fn.pad(inputs + processed)
return tuple(results)
# Test that uses multiple Generator dataset as inputs to DALI pipeline
def external_source_converter_multiple(start_values, input_names):
def to_dataset(pipeline_desc, device_str):
with tf.device('/cpu:0'):
input_datasets = []
for value, name in zip(start_values, input_names):
tf_type = tf.dtypes.as_dtype(value.dtype)
shape = value.shape
input_dataset = tf.data.Dataset.from_generator(
InfiniteSampleIterator, output_types=tf_type, output_shapes=shape, args=(value,))
# If we place DALIDataset on GPU we need the remote call + manual data transfer
if "gpu" in device_str:
input_dataset = input_dataset.apply(tf.data.experimental.copy_to_device('/gpu:0'))
input_datasets.append(input_dataset)
dataset_pipeline, shapes, dtypes = pipeline_desc
with tf.device(device_str):
dali_dataset = dali_tf.experimental.DALIDatasetWithInputs(
input_datasets=tuple(input_datasets),
input_names=tuple(input_names),
pipeline=dataset_pipeline,
batch_size=dataset_pipeline.batch_size,
output_shapes=shapes,
output_dtypes=dtypes,
num_threads=dataset_pipeline.num_threads,
device_id=dataset_pipeline.device_id)
return dali_dataset
return to_dataset
@nottest
def external_source_tester_multiple(start_values, input_names):
def get_external_source_pipeline_getter(batch_size, num_threads, device, device_id=0,
shard_id=0, num_shards=1, def_for_dataset=False):
sources = [InfiniteSampleIterator(start_value) for start_value in start_values]
output_shapes = [((batch_size, ) + tuple(None for _ in start_value.shape))
for start_value in start_values]
output_shapes = tuple(output_shapes + output_shapes)
output_dtypes = tuple(
[tf.dtypes.as_dtype(start_value.dtype)
for start_value in start_values] + [tf.int32] * len(start_values))
pipe = many_input_pipeline(def_for_dataset,
device,
sources,
input_names,
batch_size=batch_size,
num_threads=num_threads,
device_id=device_id)
return pipe, output_shapes, output_dtypes
return get_external_source_pipeline_getter
| # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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 nvidia.dali as dali
import nvidia.dali.fn as fn
import nvidia.dali.plugin.tf as dali_tf
from nvidia.dali import pipeline_def
import tensorflow as tf
from test_utils import RandomlyShapedDataIterator
import numpy as np
from nose.tools import nottest
class RandomSampleIterator:
def __init__(self,
max_shape=(10, 600, 800, 3),
dtype_sample=np.uint8(0),
start=0,
stop=1e100,
min_shape=None,
seed=42):
self.start = start
self.stop = stop
self.min_shape = min_shape
self.max_shape = max_shape
# As tf passes only tensors to the iterator, we pass a dummy value of which we take the type
self.dtype = dtype_sample.dtype
self.seed = seed
def __iter__(self):
self.n = self.start
self.random_iter = iter(RandomlyShapedDataIterator(batch_size=1, min_shape=self.min_shape,
max_shape=self.max_shape, seed=self.seed, dtype=self.dtype))
return self
def __next__(self):
if self.n <= self.stop:
result = self.n
self.n += 1
ret = self.random_iter.next()[0]
return ret
else:
raise StopIteration
class FixedSampleIterator:
def __init__(self, value):
self.value = value
def __iter__(self):
return self
def __next__(self):
return self.value
class InfiniteSampleIterator:
def __init__(self, start_value):
self.value = start_value
def __iter__(self):
return self
def __next__(self):
result = self.value
self.value = self.value + np.array(1, dtype=self.value.dtype)
return result
@pipeline_def
def one_input_pipeline(def_for_dataset, device, source, external_source_device):
"""Pipeline accepting single input via external source
Parameters
----------
def_for_dataset : bool
True if this pipeline will be converted to TF Dataset
device : str
device that the Dataset will be placed ("cpu" or "gpu")
source : callable
callback for the external source in baseline pipeline otherwise None
external_source_device : str
Device that we want the external source in TF dataset to be placed
"""
if def_for_dataset:
# We use no copy when the input memory is matching the external source placement,
# so the Dataset's placement is the same as external source's device
input = fn.external_source(name="input_placeholder",
no_copy=(device == external_source_device),
device=external_source_device)
else:
input = fn.external_source(name="actual_input",
source=source,
batch=False,
device=external_source_device)
input = input if device == 'cpu' else input.gpu()
processed = fn.cast(input + 10, dtype=dali.types.INT32)
input_padded, processed_padded = fn.pad([input, processed])
return input_padded, processed_padded
# Test that uses Tensor and Repeat (infinite) datasets as inputs to DALI pipeline
def external_source_converter_with_fixed_value(shape, dtype, tensor):
def to_dataset(pipeline_desc, device_str):
with tf.device('/cpu:0'):
input_dataset = tf.data.Dataset.from_tensors(tensor).repeat()
# If we place DALIDataset on GPU we need the remote call + manual data transfer
if "gpu" in device_str:
input_dataset = input_dataset.apply(tf.data.experimental.copy_to_device('/gpu:0'))
dataset_pipeline, shapes, dtypes = pipeline_desc
with tf.device(device_str):
dali_dataset = dali_tf.experimental.DALIDatasetWithInputs(
input_datasets=input_dataset,
input_names="input_placeholder",
pipeline=dataset_pipeline,
batch_size=dataset_pipeline.batch_size,
output_shapes=shapes,
output_dtypes=dtypes,
num_threads=dataset_pipeline.num_threads,
device_id=dataset_pipeline.device_id)
return dali_dataset
return to_dataset
# Test that uses Generator dataset as inputs to DALI pipeline
def external_source_converter_with_callback(input_iterator, shape, dtype, *args):
def to_dataset(pipeline_desc, device_str):
with tf.device('/cpu:0'):
_args = (shape, dtype(0)) + tuple(args)
out_shape = tuple(None for _ in shape)
tf_type = tf.dtypes.as_dtype(dtype)
input_dataset = tf.data.Dataset.from_generator(
input_iterator, output_types=tf_type, output_shapes=out_shape, args=_args)
# If we place DALIDataset on GPU we need the remote call + manual data transfer
if "gpu" in device_str:
input_dataset = input_dataset.apply(tf.data.experimental.copy_to_device('/gpu:0'))
dataset_pipeline, shapes, dtypes = pipeline_desc
with tf.device(device_str):
dali_dataset = dali_tf.experimental.DALIDatasetWithInputs(
input_datasets=input_dataset,
input_names="input_placeholder",
pipeline=dataset_pipeline,
batch_size=dataset_pipeline.batch_size,
output_shapes=shapes,
output_dtypes=dtypes,
num_threads=dataset_pipeline.num_threads,
device_id=dataset_pipeline.device_id)
return dali_dataset
return to_dataset
@nottest
def external_source_tester(shape, dtype, source=None, external_source_device="cpu"):
def get_external_source_pipeline_getter(batch_size, num_threads, device, device_id=0,
shard_id=0, num_shards=1, def_for_dataset=False):
pipe = one_input_pipeline(def_for_dataset,
device,
source,
external_source_device,
batch_size=batch_size,
num_threads=num_threads,
device_id=device_id)
batch_shape = (batch_size,) + tuple(None for _ in shape)
return pipe, (batch_shape, batch_shape), (tf.dtypes.as_dtype(dtype), tf.int32)
return get_external_source_pipeline_getter
@pipeline_def
def many_input_pipeline(def_for_dataset, device, sources, input_names):
""" Pipeline accepting multiple inputs via external source
Parameters
----------
def_for_dataset : bool
True if this pipeline will be converted to TF Dataset
device : str
device that the Dataset will be placed ("cpu" or "gpu")
sources : list of callables
callbacks for the external sources in baseline pipeline otherwise None
input_names : list of str
Names of inputs placeholder for TF
"""
inputs = []
if def_for_dataset:
for input_name in input_names:
input = fn.external_source(name=input_name)
input = input if device == 'cpu' else input.gpu()
inputs.append(input)
else:
for source in sources:
input = fn.external_source(source=source, batch=False)
input = input if device == 'cpu' else input.gpu()
inputs.append(input)
processed = []
for input in inputs:
processed.append(fn.cast(input + 10, dtype=dali.types.INT32))
results = fn.pad(inputs + processed)
return tuple(results)
# Test that uses multiple Generator dataset as inputs to DALI pipeline
def external_source_converter_multiple(start_values, input_names):
def to_dataset(pipeline_desc, device_str):
with tf.device('/cpu:0'):
input_datasets = []
for value, name in zip(start_values, input_names):
tf_type = tf.dtypes.as_dtype(value.dtype)
shape = value.shape
input_dataset = tf.data.Dataset.from_generator(
InfiniteSampleIterator, output_types=tf_type, output_shapes=shape, args=(value,))
# If we place DALIDataset on GPU we need the remote call + manual data transfer
if "gpu" in device_str:
input_dataset = input_dataset.apply(tf.data.experimental.copy_to_device('/gpu:0'))
input_datasets.append(input_dataset)
dataset_pipeline, shapes, dtypes = pipeline_desc
with tf.device(device_str):
dali_dataset = dali_tf.experimental.DALIDatasetWithInputs(
input_datasets=tuple(input_datasets),
input_names=tuple(input_names),
pipeline=dataset_pipeline,
batch_size=dataset_pipeline.batch_size,
output_shapes=shapes,
output_dtypes=dtypes,
num_threads=dataset_pipeline.num_threads,
device_id=dataset_pipeline.device_id)
return dali_dataset
return to_dataset
@nottest
def external_source_tester_multiple(start_values, input_names):
def get_external_source_pipeline_getter(batch_size, num_threads, device, device_id=0,
shard_id=0, num_shards=1, def_for_dataset=False):
sources = [InfiniteSampleIterator(start_value) for start_value in start_values]
output_shapes = [((batch_size, ) + tuple(None for _ in start_value.shape))
for start_value in start_values]
output_shapes = tuple(output_shapes + output_shapes)
output_dtypes = tuple(
[tf.dtypes.as_dtype(start_value.dtype)
for start_value in start_values] + [tf.int32] * len(start_values))
pipe = many_input_pipeline(def_for_dataset,
device,
sources,
input_names,
batch_size=batch_size,
num_threads=num_threads,
device_id=device_id)
return pipe, output_shapes, output_dtypes
return get_external_source_pipeline_getter
| en | 0.76997 | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # 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. # As tf passes only tensors to the iterator, we pass a dummy value of which we take the type Pipeline accepting single input via external source Parameters ---------- def_for_dataset : bool True if this pipeline will be converted to TF Dataset device : str device that the Dataset will be placed ("cpu" or "gpu") source : callable callback for the external source in baseline pipeline otherwise None external_source_device : str Device that we want the external source in TF dataset to be placed # We use no copy when the input memory is matching the external source placement, # so the Dataset's placement is the same as external source's device # Test that uses Tensor and Repeat (infinite) datasets as inputs to DALI pipeline # If we place DALIDataset on GPU we need the remote call + manual data transfer # Test that uses Generator dataset as inputs to DALI pipeline # If we place DALIDataset on GPU we need the remote call + manual data transfer Pipeline accepting multiple inputs via external source Parameters ---------- def_for_dataset : bool True if this pipeline will be converted to TF Dataset device : str device that the Dataset will be placed ("cpu" or "gpu") sources : list of callables callbacks for the external sources in baseline pipeline otherwise None input_names : list of str Names of inputs placeholder for TF # Test that uses multiple Generator dataset as inputs to DALI pipeline # If we place DALIDataset on GPU we need the remote call + manual data transfer | 2.3176 | 2 |
utils/iqc_report.py | uees/happyWork | 0 | 6613719 | import random
import re
from datetime import datetime
from openpyxl import load_workbook
from common import module_path
from database import IQCMaterial
def generate(filename, end_row=None):
wb = load_workbook(filename)
ws = wb.get_sheet_by_name('供应商来料质量统计表')
if not end_row:
end_row = ws.max_row
for row in ws.iter_rows('B7:G{}'.format(end_row)):
template_wb = load_workbook('%s/templates/iqc.xlsx' % module_path())
template_ws = template_wb.get_sheet_by_name('Sheet1')
material_name, incoming_date, supplier, qc_result, substandard_items, amount = [
cell.value for cell in row]
_material_name = ''
if isinstance(material_name, str):
_material_name = re.sub(r'[\u4e00-\u9fa5]+', '', material_name) # 去掉中文
if not _material_name:
continue
material = IQCMaterial.query.filter(IQCMaterial.name.ilike('%' + _material_name + '%')).first()
if not material or material.qc_items == '免检':
continue
if isinstance(incoming_date, datetime):
incoming_date = datetime.strftime(incoming_date, '%Y-%m-%d') # 转为字符串
if _material_name.upper() in ['0.25L', '0.3L', '1L', '5L', '6L', '20L',
'0.25KG', '0.3KG', '1KG', '5KG', '6KG',
'20KG']:
unit = '套'
else:
unit = 'kg'
template_ws.cell('B5').value = incoming_date
template_ws.cell('B6').value = material_name
template_ws.cell('D6').value = supplier
template_ws.cell('D7').value = '%s%s' % (amount, unit)
template_ws.cell('D8').value = material.qc_method
qc_items = material.qc_items.split('、')
row = 11
for item in qc_items:
template_ws.cell('A{}'.format(row)).value = item
if item == '细度':
if _material_name.find('A0084') >= 0:
template_ws.cell('C{}'.format(row)).value = '<25μm'
elif _material_name.find('A0085') >= 0 or \
_material_name.find('A0088') >= 0:
template_ws.cell('C{}'.format(row)).value = '<17.5μm'
else:
template_ws.cell('C{}'.format(row)).value = '<20μm'
elif item == '软化点':
if _material_name == 'A0016' or _material_name == 'A0016A'\
or _material_name == 'A0016B':
template_ws.cell('C{}'.format(row)).value = \
'%s℃' % round(random.uniform(27, 30), 1)
elif _material_name == 'A0016F':
template_ws.cell('C{}'.format(row)).value = \
'%s℃' % round(random.uniform(32, 35), 1)
else:
template_ws.cell('C{}'.format(row)).value = '√'
elif item == '环氧值':
if _material_name == 'A0016' or _material_name == 'A0016A'\
or _material_name == 'A0016B':
template_ws.cell('C{}'.format(row)).value = \
'%s mol/100g' % round(random.uniform(0.515, 0.535), 3)
elif _material_name == 'A0016F':
template_ws.cell('C{}'.format(row)).value = \
'%s mol/100g' % round(random.uniform(0.56, 0.59), 3)
else:
template_ws.cell('C{}'.format(row)).value = '√'
elif item == '馏程':
if _material_name.find('A0055') >= 0 or \
_material_name.find('A0063') >= 0:
template_ws.cell('C{}'.format(row)).value = \
'%s~%s℃' % (str(180 + random.randint(1, 9)),
str(220 - random.randint(1, 9)))
elif _material_name.find('A0058') >= 0:
template_ws.cell('C{}'.format(row)).value = \
'%s~%s℃' % (str(135 + random.randint(1, 5)),
str(150 - random.randint(1, 5)))
else:
template_ws.cell('C{}'.format(row)).value = '√'
else:
template_ws.cell('C{}'.format(row)).value = '√'
row += 1
template_ws.merge_cells('B11:B{}'.format(10 + len(qc_items)))
template_ws.cell('B11').value = material.spec
new_filename = '%s-%s-%s-%s.xlsx' % (incoming_date,
random.randint(1, 99),
material_name,
supplier)
template_wb.save('%s/reports/IQC/%s' % (module_path(), new_filename))
| import random
import re
from datetime import datetime
from openpyxl import load_workbook
from common import module_path
from database import IQCMaterial
def generate(filename, end_row=None):
wb = load_workbook(filename)
ws = wb.get_sheet_by_name('供应商来料质量统计表')
if not end_row:
end_row = ws.max_row
for row in ws.iter_rows('B7:G{}'.format(end_row)):
template_wb = load_workbook('%s/templates/iqc.xlsx' % module_path())
template_ws = template_wb.get_sheet_by_name('Sheet1')
material_name, incoming_date, supplier, qc_result, substandard_items, amount = [
cell.value for cell in row]
_material_name = ''
if isinstance(material_name, str):
_material_name = re.sub(r'[\u4e00-\u9fa5]+', '', material_name) # 去掉中文
if not _material_name:
continue
material = IQCMaterial.query.filter(IQCMaterial.name.ilike('%' + _material_name + '%')).first()
if not material or material.qc_items == '免检':
continue
if isinstance(incoming_date, datetime):
incoming_date = datetime.strftime(incoming_date, '%Y-%m-%d') # 转为字符串
if _material_name.upper() in ['0.25L', '0.3L', '1L', '5L', '6L', '20L',
'0.25KG', '0.3KG', '1KG', '5KG', '6KG',
'20KG']:
unit = '套'
else:
unit = 'kg'
template_ws.cell('B5').value = incoming_date
template_ws.cell('B6').value = material_name
template_ws.cell('D6').value = supplier
template_ws.cell('D7').value = '%s%s' % (amount, unit)
template_ws.cell('D8').value = material.qc_method
qc_items = material.qc_items.split('、')
row = 11
for item in qc_items:
template_ws.cell('A{}'.format(row)).value = item
if item == '细度':
if _material_name.find('A0084') >= 0:
template_ws.cell('C{}'.format(row)).value = '<25μm'
elif _material_name.find('A0085') >= 0 or \
_material_name.find('A0088') >= 0:
template_ws.cell('C{}'.format(row)).value = '<17.5μm'
else:
template_ws.cell('C{}'.format(row)).value = '<20μm'
elif item == '软化点':
if _material_name == 'A0016' or _material_name == 'A0016A'\
or _material_name == 'A0016B':
template_ws.cell('C{}'.format(row)).value = \
'%s℃' % round(random.uniform(27, 30), 1)
elif _material_name == 'A0016F':
template_ws.cell('C{}'.format(row)).value = \
'%s℃' % round(random.uniform(32, 35), 1)
else:
template_ws.cell('C{}'.format(row)).value = '√'
elif item == '环氧值':
if _material_name == 'A0016' or _material_name == 'A0016A'\
or _material_name == 'A0016B':
template_ws.cell('C{}'.format(row)).value = \
'%s mol/100g' % round(random.uniform(0.515, 0.535), 3)
elif _material_name == 'A0016F':
template_ws.cell('C{}'.format(row)).value = \
'%s mol/100g' % round(random.uniform(0.56, 0.59), 3)
else:
template_ws.cell('C{}'.format(row)).value = '√'
elif item == '馏程':
if _material_name.find('A0055') >= 0 or \
_material_name.find('A0063') >= 0:
template_ws.cell('C{}'.format(row)).value = \
'%s~%s℃' % (str(180 + random.randint(1, 9)),
str(220 - random.randint(1, 9)))
elif _material_name.find('A0058') >= 0:
template_ws.cell('C{}'.format(row)).value = \
'%s~%s℃' % (str(135 + random.randint(1, 5)),
str(150 - random.randint(1, 5)))
else:
template_ws.cell('C{}'.format(row)).value = '√'
else:
template_ws.cell('C{}'.format(row)).value = '√'
row += 1
template_ws.merge_cells('B11:B{}'.format(10 + len(qc_items)))
template_ws.cell('B11').value = material.spec
new_filename = '%s-%s-%s-%s.xlsx' % (incoming_date,
random.randint(1, 99),
material_name,
supplier)
template_wb.save('%s/reports/IQC/%s' % (module_path(), new_filename))
| zh | 0.973493 | # 去掉中文 # 转为字符串 | 2.321203 | 2 |
engines/flow/extensions/send_email.py | NunoEdgarGFlowHub/rhizome | 8 | 6613720 | <reponame>NunoEdgarGFlowHub/rhizome<gh_stars>1-10
"""Send emails."""
import smtplib
from email.message import EmailMessage
from jinja2 import Template
from flow.chatbot_engine import Extension
class SendEmail(Extension):
"""SendEmail plugin - defined .flow function sendEmail to send emails."""
def __init__(self, flow):
super().__init__(flow)
class_name = self.__class__.__module__ + '.' + self.__class__.__name__
flow.register_dot_flow_function('sendEmail', {
'class': class_name, 'method': 'sendEmail'})
def sendEmail(self, args):
"""Send email."""
node = args[0]
params = dict()
smtp_config = self.flow.dotbot['bot']['smtp']
flow_vars = self.flow.session.get_var(self.flow.user_id)
msg = EmailMessage()
msg['Subject'] = self.flow.template_engine.render(self.flow, node['info']['subject'], flow_vars)
msg['From'] = smtp_config['email']
msg['To'] = node['info']['recipient']
body = self.flow.template_engine.render(self.flow, node['info']['body'], flow_vars)
body = body.replace('\n', '<br />')
if node['info'].get('formId', None): # if there is a form defined, build form and add it to the message body
flow_form = self.flow.session.get(self.flow.user_id, f"formVars.{node['info']['formId']}")
form_template = "{% for form, qa_pair in form_xxxxx.items() %}{{qa_pair.question}}: {{qa_pair.answer}}<br />{% endfor %}"
body += "<br /><br />" + Template(form_template).render({'form_xxxxx': flow_form})
msg.set_content("Please see this email with an html compatible email client\n")
msg.add_alternative(f"""\
<html>
<head></head>
<body>
{body}
</body>
</html>
""", subtype='html')
self.flow.logger.debug(f"Sending email through {smtp_config['server']['host']}:{smtp_config['server']['port']} to {node['info']['recipient']}")
smtp = smtplib.SMTP(smtp_config['server']['host'], smtp_config['server']['port'])
smtp.set_debuglevel(1)
if smtp_config['server'].get('username', "") and smtp_config['server'].get('password', ""):
smtp.login(smtp_config['server']['username'], smtp_config['server']['password'])
smtp.send_message(msg)
smtp.quit()
| """Send emails."""
import smtplib
from email.message import EmailMessage
from jinja2 import Template
from flow.chatbot_engine import Extension
class SendEmail(Extension):
"""SendEmail plugin - defined .flow function sendEmail to send emails."""
def __init__(self, flow):
super().__init__(flow)
class_name = self.__class__.__module__ + '.' + self.__class__.__name__
flow.register_dot_flow_function('sendEmail', {
'class': class_name, 'method': 'sendEmail'})
def sendEmail(self, args):
"""Send email."""
node = args[0]
params = dict()
smtp_config = self.flow.dotbot['bot']['smtp']
flow_vars = self.flow.session.get_var(self.flow.user_id)
msg = EmailMessage()
msg['Subject'] = self.flow.template_engine.render(self.flow, node['info']['subject'], flow_vars)
msg['From'] = smtp_config['email']
msg['To'] = node['info']['recipient']
body = self.flow.template_engine.render(self.flow, node['info']['body'], flow_vars)
body = body.replace('\n', '<br />')
if node['info'].get('formId', None): # if there is a form defined, build form and add it to the message body
flow_form = self.flow.session.get(self.flow.user_id, f"formVars.{node['info']['formId']}")
form_template = "{% for form, qa_pair in form_xxxxx.items() %}{{qa_pair.question}}: {{qa_pair.answer}}<br />{% endfor %}"
body += "<br /><br />" + Template(form_template).render({'form_xxxxx': flow_form})
msg.set_content("Please see this email with an html compatible email client\n")
msg.add_alternative(f"""\
<html>
<head></head>
<body>
{body}
</body>
</html>
""", subtype='html')
self.flow.logger.debug(f"Sending email through {smtp_config['server']['host']}:{smtp_config['server']['port']} to {node['info']['recipient']}")
smtp = smtplib.SMTP(smtp_config['server']['host'], smtp_config['server']['port'])
smtp.set_debuglevel(1)
if smtp_config['server'].get('username', "") and smtp_config['server'].get('password', ""):
smtp.login(smtp_config['server']['username'], smtp_config['server']['password'])
smtp.send_message(msg)
smtp.quit() | en | 0.629237 | Send emails. SendEmail plugin - defined .flow function sendEmail to send emails. Send email. # if there is a form defined, build form and add it to the message body \ <html> <head></head> <body> {body} </body> </html> | 2.592585 | 3 |
python/tvm/relay/qnn/op/canonicalizations.py | XiaoSong9905/tvm | 1 | 6613721 | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
"""Consist of utilities and methods for lowering QNN into mainline relay."""
from typing import Callable
import numpy as np
import tvm
from tvm import relay
def run_const_expr(expr: "relay.Expr") -> np.ndarray:
"""Evaluate a const expression, receiving result as np array."""
mod = tvm.IRModule.from_expr(expr)
vm_exe = relay.create_executor("vm", mod=mod)
return vm_exe.evaluate()().asnumpy()
def create_integer_lookup_table(
floating_point_func: Callable[[np.ndarray], np.ndarray],
input_scale: "relay.Expr",
input_zero_point: "relay.Expr",
output_scale: "relay.Expr",
output_zero_point: "relay.Expr",
in_axis: int = -1,
out_axis: int = -1,
in_dtype: str = "uint8",
out_dtype: str = "uint8",
) -> np.ndarray:
"""
Return a table where each input indexes to the output quantizing the given function.
Note this also supports mapping unsigned and signed integers to each other.
Args:
floating_point_func: The numpy function which this table is to approximate
input_scale: The scale of the quantized input tensor.
input_zero_point: The zero point of the quantized input tensor.
output_scale: The scale of the quantized output tensor.
output_zero_point: The zero point of the quantized output tensor.
in_axis: The axis for multi-channel quantization of the input if applicable.
out_axis: The axis for multi-channel quantization of the output if applicable.
in_dtype: The dtype of the input tensor.
out_dtype: The wanted dtype of the output tensor.
Returns:
A numpy array where values in quantized space will index to the output in quantized space
approximating the given function.
"""
if not np.issubdtype(np.dtype(in_dtype), np.integer) or not np.issubdtype(
np.dtype(out_dtype), np.integer
):
raise ValueError(
f"Only integer dtypes allowed got {in_dtype} and {out_dtype} for in and out dtypes."
)
dtype_info = np.iinfo(in_dtype)
num_bits = dtype_info.bits
# Use TVMs quantization methods via relay to be consistent
# inputs_quantized = np.array(range(dtype_info.min, dtype_info.max + 1)).astype(in_dtype)
# First generate a list of all num_bit integer patterns
inputs_quantized = np.array(range(0, 2 ** num_bits), dtype=f"uint{num_bits}")
# Reinterpret bits as the real datatype
# Note what we are doing here is a bit tricky, the canonical view of our lookup table
# is using the uintX version. When we run the lookup in the relay graph, we cast the
# bit pattern back into this form.
inputs_quantized = inputs_quantized.view(in_dtype)
inputs_quantized = relay.const(inputs_quantized, dtype=in_dtype)
inputs_dequantized = run_const_expr(
relay.qnn.op.dequantize(
inputs_quantized,
input_scale=input_scale,
input_zero_point=input_zero_point,
axis=in_axis,
)
)
output_dequantized = relay.const(floating_point_func(inputs_dequantized))
output_quantized = run_const_expr(
relay.qnn.op.quantize(
output_dequantized, output_scale, output_zero_point, out_axis, out_dtype
)
)
return output_quantized
def create_integer_lookup_op(
input_arg: "relay.Expr",
floating_point_func: Callable[[np.array], np.array],
in_scale: "relay.Expr",
in_zero_point: "relay.Expr",
out_scale: "relay.Expr",
out_zero_point: "relay.Expr",
in_axis: int = -1,
out_axis: int = -1,
in_dtype: str = "uint8",
out_dtype: str = "uint8",
) -> "relay.Expr":
"""
Create a quantized version of the given floating point unary operation using table lookup.
Args:
input_arg: The quantized input to the final function.
floating_point_func: The numpy function which this table is to approximate
in_scale: The scale of the quantized input tensor.
in_zero_point: The zero point of the quantized input tensor.
out_scale: The scale of the quantized output tensor.
out_zero_point: The zero point of the quantized output tensor.
in_axis: The axis for multi-channel quantization of the input if applicable.
out_axis: The axis for multi-channel quantization of the output if applicable.
in_dtype: The dtype of the input tensor.
out_dtype: The wanted dtype of the output tensor.
Returns:
A Relay expression representing a quantized version of the given function.
"""
# TODO: handle multi-channel q, below will fail with multi-channel q
in_scale = in_scale.data.numpy().item()
in_zero_point = in_zero_point.data.numpy().item()
out_scale = out_scale.data.numpy().item()
out_zero_point = out_zero_point.data.numpy().item()
lookup_table = create_integer_lookup_table(
floating_point_func,
relay.const(in_scale),
relay.const(in_zero_point, dtype="int32"),
relay.const(out_scale),
relay.const(out_zero_point, dtype="int32"),
in_axis=in_axis,
in_dtype=in_dtype,
out_axis=out_axis,
out_dtype=out_dtype,
)
in_dtype_info = np.iinfo(in_dtype)
in_dtype_num_bits = in_dtype_info.bits
lookup_table = relay.const(lookup_table)
index_tensor = relay.reinterpret(input_arg, f"uint{in_dtype_num_bits}")
result = relay.take(lookup_table, index_tensor, axis=0, mode="fast")
return result
| # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
"""Consist of utilities and methods for lowering QNN into mainline relay."""
from typing import Callable
import numpy as np
import tvm
from tvm import relay
def run_const_expr(expr: "relay.Expr") -> np.ndarray:
"""Evaluate a const expression, receiving result as np array."""
mod = tvm.IRModule.from_expr(expr)
vm_exe = relay.create_executor("vm", mod=mod)
return vm_exe.evaluate()().asnumpy()
def create_integer_lookup_table(
floating_point_func: Callable[[np.ndarray], np.ndarray],
input_scale: "relay.Expr",
input_zero_point: "relay.Expr",
output_scale: "relay.Expr",
output_zero_point: "relay.Expr",
in_axis: int = -1,
out_axis: int = -1,
in_dtype: str = "uint8",
out_dtype: str = "uint8",
) -> np.ndarray:
"""
Return a table where each input indexes to the output quantizing the given function.
Note this also supports mapping unsigned and signed integers to each other.
Args:
floating_point_func: The numpy function which this table is to approximate
input_scale: The scale of the quantized input tensor.
input_zero_point: The zero point of the quantized input tensor.
output_scale: The scale of the quantized output tensor.
output_zero_point: The zero point of the quantized output tensor.
in_axis: The axis for multi-channel quantization of the input if applicable.
out_axis: The axis for multi-channel quantization of the output if applicable.
in_dtype: The dtype of the input tensor.
out_dtype: The wanted dtype of the output tensor.
Returns:
A numpy array where values in quantized space will index to the output in quantized space
approximating the given function.
"""
if not np.issubdtype(np.dtype(in_dtype), np.integer) or not np.issubdtype(
np.dtype(out_dtype), np.integer
):
raise ValueError(
f"Only integer dtypes allowed got {in_dtype} and {out_dtype} for in and out dtypes."
)
dtype_info = np.iinfo(in_dtype)
num_bits = dtype_info.bits
# Use TVMs quantization methods via relay to be consistent
# inputs_quantized = np.array(range(dtype_info.min, dtype_info.max + 1)).astype(in_dtype)
# First generate a list of all num_bit integer patterns
inputs_quantized = np.array(range(0, 2 ** num_bits), dtype=f"uint{num_bits}")
# Reinterpret bits as the real datatype
# Note what we are doing here is a bit tricky, the canonical view of our lookup table
# is using the uintX version. When we run the lookup in the relay graph, we cast the
# bit pattern back into this form.
inputs_quantized = inputs_quantized.view(in_dtype)
inputs_quantized = relay.const(inputs_quantized, dtype=in_dtype)
inputs_dequantized = run_const_expr(
relay.qnn.op.dequantize(
inputs_quantized,
input_scale=input_scale,
input_zero_point=input_zero_point,
axis=in_axis,
)
)
output_dequantized = relay.const(floating_point_func(inputs_dequantized))
output_quantized = run_const_expr(
relay.qnn.op.quantize(
output_dequantized, output_scale, output_zero_point, out_axis, out_dtype
)
)
return output_quantized
def create_integer_lookup_op(
input_arg: "relay.Expr",
floating_point_func: Callable[[np.array], np.array],
in_scale: "relay.Expr",
in_zero_point: "relay.Expr",
out_scale: "relay.Expr",
out_zero_point: "relay.Expr",
in_axis: int = -1,
out_axis: int = -1,
in_dtype: str = "uint8",
out_dtype: str = "uint8",
) -> "relay.Expr":
"""
Create a quantized version of the given floating point unary operation using table lookup.
Args:
input_arg: The quantized input to the final function.
floating_point_func: The numpy function which this table is to approximate
in_scale: The scale of the quantized input tensor.
in_zero_point: The zero point of the quantized input tensor.
out_scale: The scale of the quantized output tensor.
out_zero_point: The zero point of the quantized output tensor.
in_axis: The axis for multi-channel quantization of the input if applicable.
out_axis: The axis for multi-channel quantization of the output if applicable.
in_dtype: The dtype of the input tensor.
out_dtype: The wanted dtype of the output tensor.
Returns:
A Relay expression representing a quantized version of the given function.
"""
# TODO: handle multi-channel q, below will fail with multi-channel q
in_scale = in_scale.data.numpy().item()
in_zero_point = in_zero_point.data.numpy().item()
out_scale = out_scale.data.numpy().item()
out_zero_point = out_zero_point.data.numpy().item()
lookup_table = create_integer_lookup_table(
floating_point_func,
relay.const(in_scale),
relay.const(in_zero_point, dtype="int32"),
relay.const(out_scale),
relay.const(out_zero_point, dtype="int32"),
in_axis=in_axis,
in_dtype=in_dtype,
out_axis=out_axis,
out_dtype=out_dtype,
)
in_dtype_info = np.iinfo(in_dtype)
in_dtype_num_bits = in_dtype_info.bits
lookup_table = relay.const(lookup_table)
index_tensor = relay.reinterpret(input_arg, f"uint{in_dtype_num_bits}")
result = relay.take(lookup_table, index_tensor, axis=0, mode="fast")
return result
| en | 0.75485 | # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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. Consist of utilities and methods for lowering QNN into mainline relay. Evaluate a const expression, receiving result as np array. Return a table where each input indexes to the output quantizing the given function. Note this also supports mapping unsigned and signed integers to each other. Args: floating_point_func: The numpy function which this table is to approximate input_scale: The scale of the quantized input tensor. input_zero_point: The zero point of the quantized input tensor. output_scale: The scale of the quantized output tensor. output_zero_point: The zero point of the quantized output tensor. in_axis: The axis for multi-channel quantization of the input if applicable. out_axis: The axis for multi-channel quantization of the output if applicable. in_dtype: The dtype of the input tensor. out_dtype: The wanted dtype of the output tensor. Returns: A numpy array where values in quantized space will index to the output in quantized space approximating the given function. # Use TVMs quantization methods via relay to be consistent # inputs_quantized = np.array(range(dtype_info.min, dtype_info.max + 1)).astype(in_dtype) # First generate a list of all num_bit integer patterns # Reinterpret bits as the real datatype # Note what we are doing here is a bit tricky, the canonical view of our lookup table # is using the uintX version. When we run the lookup in the relay graph, we cast the # bit pattern back into this form. Create a quantized version of the given floating point unary operation using table lookup. Args: input_arg: The quantized input to the final function. floating_point_func: The numpy function which this table is to approximate in_scale: The scale of the quantized input tensor. in_zero_point: The zero point of the quantized input tensor. out_scale: The scale of the quantized output tensor. out_zero_point: The zero point of the quantized output tensor. in_axis: The axis for multi-channel quantization of the input if applicable. out_axis: The axis for multi-channel quantization of the output if applicable. in_dtype: The dtype of the input tensor. out_dtype: The wanted dtype of the output tensor. Returns: A Relay expression representing a quantized version of the given function. # TODO: handle multi-channel q, below will fail with multi-channel q | 2.022499 | 2 |
getThreads.py | tinkerNamedFerro/biz_insights | 0 | 6613722 | <filename>getThreads.py
import requests
import bs4
import sys, getopt, os
import re
import pprint
import json
import hashlib
import openpyxl
import datetime
import time
from joblib import Parallel, delayed
from tqdm import tqdm
from selenium import webdriver
from CoinDict import *
from scrapers.ChanOfficial import *
from scrapers.ChanArchieve import *
from mongo_db.tickerTable import *
import multiprocessing as mp
def ThreadIDGet():
try:
os.remove('tids.txt')
except:
print("Did NOT delete tids.txt")
url = 'http://boards.4chan.org/biz/catalog'
driver = webdriver.Chrome()
driver.get(url)
res = driver.page_source
soup = bs4.BeautifulSoup(res, 'lxml')
body = soup.find('body')
content = body.find(id="content")
thread = content.find(id="threads")
ThR = re.compile(r'(thread-)(\d\d\d\d\d\d\d\d)')
threadlist = ThR.findall(str(thread))
ThreadIDs = open("tids.txt", 'a')
for i in range(1, len(threadlist)):
ThreadIDs.write(threadlist[i][1] + '\n')
print('IDs Obtained')
driver.close()
ThreadIDs.close()
def TextGet():
ThreadIDGet()
try:
os.remove('text.txt')
except:
print("Did NOT delete text.txt")
tids = open('tids.txt','r')
tlist = tids.readlines()
print("Scanning threads")
tickerDb = MongoDB_Biz_Ticker_Mentions()
for i in tqdm(range(0, len(tlist)-1)):
# threadJson = fullThreadScrape(tlist[i][:-2],url)
tickerOnlyScrape(tlist[i],tickerDb)
print('Scrape Complete')
def TextGetArchieve(fromPage, toPage):
# page = 7000
for page in range(fromPage,toPage):
try:
tlist = getTidsOnPage(page)
# print("Scanning threads")
tickerDb = MongoDB_Biz_Ticker_Mentions()
# for i in tqdm(range(0, len(tlist)-1)):
for i in range(0, len(tlist)-1):
# threadJson = fullThreadScrape(tlist[i][:-2],url)
tickerOnlyScrapeArchieve(tlist[i],tickerDb)
except Exception as e: print("ERROR:" + e)
print("PAGE IS: " + str(page))
def Count(row):
file = open('text.txt', 'r')
postlist = file.readlines()
checklist = [row['aka'][0],row['aka'][0].lower(),(row['name']),(row['name'].lower())]
Count = 0
print(checklist)
for i in range(0,len(postlist)):
for x in checklist:
if x in postlist[i].split():
Count += 1
break
else:
continue
file.close()
return Count
# NewBook('tester')
# while True:
# while True:
# ThreadIDGet()
# TextGet()
# Update()
def main(argv):
# Generate list of coins
generateCurrenciesList()
# Load json file for coin list
with open('data.json') as json_file:
CD = json.load(json_file)
startPage = 0
endPage = 0
parallelCount = 0
try:
opts, args = getopt.getopt(argv,'s:e:p:')
except getopt.GetoptError:
print ('startBrainWallet.py -s <startPage> -e <endPage> -p <parallelCount>')
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print ('startBrainWallet.py -s <startPage> -e <endPage> -p <parallelCount>')
sys.exit()
elif opt in ("-s", "--start"):
startPage = int(arg)
elif opt in ("-e", "--end"):
endPage = int(arg)
elif opt in ("-p", "--parallelCount"):
parallelCount = int(arg)
if parallelCount == 0:
print(startPage)
TextGetArchieve(startPage,endPage)
elif parallelCount == -1:
while True:
TextGetArchieve(startPage,endPage)
else:
# Get work load for each worker
pageSegmentsDealt = (endPage - startPage)/parallelCount
# Init pool
pool = mp.Pool(parallelCount)
for instance in range(0,int(parallelCount)):
instanceStartPage = int(round(startPage+(instance*pageSegmentsDealt)))
instanceEndPage = int(round(startPage+((instance+1) *pageSegmentsDealt)))
pool.apply_async(TextGetArchieve, args=(instanceStartPage, instanceEndPage))
# print(values)
pool.close()
# prevent freeze support error for windows https://minerl.io/docs/notes/windows.html
if __name__ == '__main__':
main(sys.argv[1:])
| <filename>getThreads.py
import requests
import bs4
import sys, getopt, os
import re
import pprint
import json
import hashlib
import openpyxl
import datetime
import time
from joblib import Parallel, delayed
from tqdm import tqdm
from selenium import webdriver
from CoinDict import *
from scrapers.ChanOfficial import *
from scrapers.ChanArchieve import *
from mongo_db.tickerTable import *
import multiprocessing as mp
def ThreadIDGet():
try:
os.remove('tids.txt')
except:
print("Did NOT delete tids.txt")
url = 'http://boards.4chan.org/biz/catalog'
driver = webdriver.Chrome()
driver.get(url)
res = driver.page_source
soup = bs4.BeautifulSoup(res, 'lxml')
body = soup.find('body')
content = body.find(id="content")
thread = content.find(id="threads")
ThR = re.compile(r'(thread-)(\d\d\d\d\d\d\d\d)')
threadlist = ThR.findall(str(thread))
ThreadIDs = open("tids.txt", 'a')
for i in range(1, len(threadlist)):
ThreadIDs.write(threadlist[i][1] + '\n')
print('IDs Obtained')
driver.close()
ThreadIDs.close()
def TextGet():
ThreadIDGet()
try:
os.remove('text.txt')
except:
print("Did NOT delete text.txt")
tids = open('tids.txt','r')
tlist = tids.readlines()
print("Scanning threads")
tickerDb = MongoDB_Biz_Ticker_Mentions()
for i in tqdm(range(0, len(tlist)-1)):
# threadJson = fullThreadScrape(tlist[i][:-2],url)
tickerOnlyScrape(tlist[i],tickerDb)
print('Scrape Complete')
def TextGetArchieve(fromPage, toPage):
# page = 7000
for page in range(fromPage,toPage):
try:
tlist = getTidsOnPage(page)
# print("Scanning threads")
tickerDb = MongoDB_Biz_Ticker_Mentions()
# for i in tqdm(range(0, len(tlist)-1)):
for i in range(0, len(tlist)-1):
# threadJson = fullThreadScrape(tlist[i][:-2],url)
tickerOnlyScrapeArchieve(tlist[i],tickerDb)
except Exception as e: print("ERROR:" + e)
print("PAGE IS: " + str(page))
def Count(row):
file = open('text.txt', 'r')
postlist = file.readlines()
checklist = [row['aka'][0],row['aka'][0].lower(),(row['name']),(row['name'].lower())]
Count = 0
print(checklist)
for i in range(0,len(postlist)):
for x in checklist:
if x in postlist[i].split():
Count += 1
break
else:
continue
file.close()
return Count
# NewBook('tester')
# while True:
# while True:
# ThreadIDGet()
# TextGet()
# Update()
def main(argv):
# Generate list of coins
generateCurrenciesList()
# Load json file for coin list
with open('data.json') as json_file:
CD = json.load(json_file)
startPage = 0
endPage = 0
parallelCount = 0
try:
opts, args = getopt.getopt(argv,'s:e:p:')
except getopt.GetoptError:
print ('startBrainWallet.py -s <startPage> -e <endPage> -p <parallelCount>')
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print ('startBrainWallet.py -s <startPage> -e <endPage> -p <parallelCount>')
sys.exit()
elif opt in ("-s", "--start"):
startPage = int(arg)
elif opt in ("-e", "--end"):
endPage = int(arg)
elif opt in ("-p", "--parallelCount"):
parallelCount = int(arg)
if parallelCount == 0:
print(startPage)
TextGetArchieve(startPage,endPage)
elif parallelCount == -1:
while True:
TextGetArchieve(startPage,endPage)
else:
# Get work load for each worker
pageSegmentsDealt = (endPage - startPage)/parallelCount
# Init pool
pool = mp.Pool(parallelCount)
for instance in range(0,int(parallelCount)):
instanceStartPage = int(round(startPage+(instance*pageSegmentsDealt)))
instanceEndPage = int(round(startPage+((instance+1) *pageSegmentsDealt)))
pool.apply_async(TextGetArchieve, args=(instanceStartPage, instanceEndPage))
# print(values)
pool.close()
# prevent freeze support error for windows https://minerl.io/docs/notes/windows.html
if __name__ == '__main__':
main(sys.argv[1:])
| en | 0.549888 | # threadJson = fullThreadScrape(tlist[i][:-2],url) # page = 7000 # print("Scanning threads") # for i in tqdm(range(0, len(tlist)-1)): # threadJson = fullThreadScrape(tlist[i][:-2],url) # NewBook('tester') # while True: # while True: # ThreadIDGet() # TextGet() # Update() # Generate list of coins # Load json file for coin list # Get work load for each worker # Init pool # print(values) # prevent freeze support error for windows https://minerl.io/docs/notes/windows.html | 2.577859 | 3 |
setup.py | senavs/BitJoy | 0 | 6613723 | import setuptools
with open('README.md') as file:
long_description = file.read()
setuptools.setup(
name='bitjoy',
version='1.1',
license='MIT',
description='Bit, Bytes and Logical Gates Abstraction.',
author='<NAME>',
author_email='<EMAIL>',
long_description=long_description,
long_description_content_type='text/markdown',
url='https://github.com/senavs/BitJoy',
keywords=['bitjoy', 'bit', 'bytes', 'logical-operators',
'int_to_bytes', 'half-adder', 'full-adder',
'boolean', 'gates', 'abstraction'],
packages=['bitjoy', 'bitjoy.dtypes', 'bitjoy.utils'],
classifiers=[
'Programming Language :: Python :: 3',
'License :: OSI Approved :: MIT License',
'Operating System :: OS Independent',
],
python_required='>=3.6'
)
| import setuptools
with open('README.md') as file:
long_description = file.read()
setuptools.setup(
name='bitjoy',
version='1.1',
license='MIT',
description='Bit, Bytes and Logical Gates Abstraction.',
author='<NAME>',
author_email='<EMAIL>',
long_description=long_description,
long_description_content_type='text/markdown',
url='https://github.com/senavs/BitJoy',
keywords=['bitjoy', 'bit', 'bytes', 'logical-operators',
'int_to_bytes', 'half-adder', 'full-adder',
'boolean', 'gates', 'abstraction'],
packages=['bitjoy', 'bitjoy.dtypes', 'bitjoy.utils'],
classifiers=[
'Programming Language :: Python :: 3',
'License :: OSI Approved :: MIT License',
'Operating System :: OS Independent',
],
python_required='>=3.6'
)
| none | 1 | 1.508226 | 2 | |
go/py/blah.py | brightpuddle/playground | 0 | 6613724 | <reponame>brightpuddle/playground<gh_stars>0
print('w00t')
| print('w00t') | none | 1 | 1.1543 | 1 | |
xautodl/xmodels/transformers_quantum.py | Joey61Liuyi/AutoDL-Projects | 817 | 6613725 | <gh_stars>100-1000
#####################################################
# Copyright (c) <NAME> [GitHub D-X-Y], 2021.06 #
#####################################################
# Vision Transformer: arxiv.org/pdf/2010.11929.pdf #
#####################################################
import copy, math
from functools import partial
from typing import Optional, Text, List
import torch
import torch.nn as nn
import torch.nn.functional as F
from xautodl import spaces
from xautodl import xlayers
from xautodl.xlayers import weight_init
class SuperQuaT(xlayers.SuperModule):
"""The super transformer for transformer."""
def __init__(
self,
image_size,
patch_size,
num_classes,
dim,
depth,
heads,
mlp_multiplier=4,
channels=3,
dropout=0.0,
att_dropout=0.0,
):
super(SuperQuaT, self).__init__()
image_height, image_width = pair(image_size)
patch_height, patch_width = pair(patch_size)
if image_height % patch_height != 0 or image_width % patch_width != 0:
raise ValueError("Image dimensions must be divisible by the patch size.")
num_patches = (image_height // patch_height) * (image_width // patch_width)
patch_dim = channels * patch_height * patch_width
self.to_patch_embedding = xlayers.SuperSequential(
xlayers.SuperReArrange(
"b c (h p1) (w p2) -> b (h w) (p1 p2 c)",
p1=patch_height,
p2=patch_width,
),
xlayers.SuperLinear(patch_dim, dim),
)
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
self.dropout = nn.Dropout(dropout)
# build the transformer encode layers
layers = []
for ilayer in range(depth):
layers.append(
xlayers.SuperTransformerEncoderLayer(
dim,
heads,
False,
mlp_multiplier,
dropout=dropout,
att_dropout=att_dropout,
)
)
self.backbone = xlayers.SuperSequential(*layers)
self.cls_head = xlayers.SuperSequential(
xlayers.SuperLayerNorm1D(dim), xlayers.SuperLinear(dim, num_classes)
)
weight_init.trunc_normal_(self.cls_token, std=0.02)
self.apply(_init_weights)
@property
def abstract_search_space(self):
raise NotImplementedError
def apply_candidate(self, abstract_child: spaces.VirtualNode):
super(SuperQuaT, self).apply_candidate(abstract_child)
raise NotImplementedError
def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
raise NotImplementedError
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
tensors = self.to_patch_embedding(input)
batch, seq, _ = tensors.shape
cls_tokens = self.cls_token.expand(batch, -1, -1)
feats = torch.cat((cls_tokens, tensors), dim=1)
feats = feats + self.pos_embedding[:, : seq + 1, :]
feats = self.dropout(feats)
feats = self.backbone(feats)
x = feats[:, 0] # the features for cls-token
return self.cls_head(x)
def get_transformer(config):
if isinstance(config, str) and config.lower() in name2config:
config = name2config[config.lower()]
if not isinstance(config, dict):
raise ValueError("Invalid Configuration: {:}".format(config))
model_type = config.get("type", "vit").lower()
if model_type == "vit":
model = SuperQuaT(
image_size=config.get("image_size"),
patch_size=config.get("patch_size"),
num_classes=config.get("num_classes"),
dim=config.get("dim"),
depth=config.get("depth"),
heads=config.get("heads"),
dropout=config.get("dropout"),
att_dropout=config.get("att_dropout"),
)
else:
raise ValueError("Unknown model type: {:}".format(model_type))
return model
| #####################################################
# Copyright (c) <NAME> [GitHub D-X-Y], 2021.06 #
#####################################################
# Vision Transformer: arxiv.org/pdf/2010.11929.pdf #
#####################################################
import copy, math
from functools import partial
from typing import Optional, Text, List
import torch
import torch.nn as nn
import torch.nn.functional as F
from xautodl import spaces
from xautodl import xlayers
from xautodl.xlayers import weight_init
class SuperQuaT(xlayers.SuperModule):
"""The super transformer for transformer."""
def __init__(
self,
image_size,
patch_size,
num_classes,
dim,
depth,
heads,
mlp_multiplier=4,
channels=3,
dropout=0.0,
att_dropout=0.0,
):
super(SuperQuaT, self).__init__()
image_height, image_width = pair(image_size)
patch_height, patch_width = pair(patch_size)
if image_height % patch_height != 0 or image_width % patch_width != 0:
raise ValueError("Image dimensions must be divisible by the patch size.")
num_patches = (image_height // patch_height) * (image_width // patch_width)
patch_dim = channels * patch_height * patch_width
self.to_patch_embedding = xlayers.SuperSequential(
xlayers.SuperReArrange(
"b c (h p1) (w p2) -> b (h w) (p1 p2 c)",
p1=patch_height,
p2=patch_width,
),
xlayers.SuperLinear(patch_dim, dim),
)
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
self.dropout = nn.Dropout(dropout)
# build the transformer encode layers
layers = []
for ilayer in range(depth):
layers.append(
xlayers.SuperTransformerEncoderLayer(
dim,
heads,
False,
mlp_multiplier,
dropout=dropout,
att_dropout=att_dropout,
)
)
self.backbone = xlayers.SuperSequential(*layers)
self.cls_head = xlayers.SuperSequential(
xlayers.SuperLayerNorm1D(dim), xlayers.SuperLinear(dim, num_classes)
)
weight_init.trunc_normal_(self.cls_token, std=0.02)
self.apply(_init_weights)
@property
def abstract_search_space(self):
raise NotImplementedError
def apply_candidate(self, abstract_child: spaces.VirtualNode):
super(SuperQuaT, self).apply_candidate(abstract_child)
raise NotImplementedError
def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
raise NotImplementedError
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
tensors = self.to_patch_embedding(input)
batch, seq, _ = tensors.shape
cls_tokens = self.cls_token.expand(batch, -1, -1)
feats = torch.cat((cls_tokens, tensors), dim=1)
feats = feats + self.pos_embedding[:, : seq + 1, :]
feats = self.dropout(feats)
feats = self.backbone(feats)
x = feats[:, 0] # the features for cls-token
return self.cls_head(x)
def get_transformer(config):
if isinstance(config, str) and config.lower() in name2config:
config = name2config[config.lower()]
if not isinstance(config, dict):
raise ValueError("Invalid Configuration: {:}".format(config))
model_type = config.get("type", "vit").lower()
if model_type == "vit":
model = SuperQuaT(
image_size=config.get("image_size"),
patch_size=config.get("patch_size"),
num_classes=config.get("num_classes"),
dim=config.get("dim"),
depth=config.get("depth"),
heads=config.get("heads"),
dropout=config.get("dropout"),
att_dropout=config.get("att_dropout"),
)
else:
raise ValueError("Unknown model type: {:}".format(model_type))
return model | de | 0.525542 | ##################################################### # Copyright (c) <NAME> [GitHub D-X-Y], 2021.06 # ##################################################### # Vision Transformer: arxiv.org/pdf/2010.11929.pdf # ##################################################### The super transformer for transformer. # build the transformer encode layers # the features for cls-token | 2.189486 | 2 |
setup.py | ekta1224/jellyfish | 0 | 6613726 | #! /usr/bin/env python
from setuptools import setup
setup(name='Jellyfish',
version='0.1',
description='Tools to plot and analyze N-body simulations of hosts and satellites galaxies',
author='<NAME> and <NAME>',
author_email='<EMAIL>',
install_requieres=['numpy', 'scipy', 'matplotlib', 'astropy', 'pygadgetreader'],
packages=['jellyfish'],
)
| #! /usr/bin/env python
from setuptools import setup
setup(name='Jellyfish',
version='0.1',
description='Tools to plot and analyze N-body simulations of hosts and satellites galaxies',
author='<NAME> and <NAME>',
author_email='<EMAIL>',
install_requieres=['numpy', 'scipy', 'matplotlib', 'astropy', 'pygadgetreader'],
packages=['jellyfish'],
)
| ru | 0.148623 | #! /usr/bin/env python | 1.034745 | 1 |
gestao/contrato/views/equipe_contrato.py | Smartboxweb98/gestao_empresarial | 3 | 6613727 | # -*- coding: utf-8 -*-
from django.shortcuts import render_to_response, get_object_or_404
from django.template import RequestContext
from gestao.contrato.models.contrato.Contrato import Contrato
from gestao.contrato.models.equipe.FuncionarioContrato import FuncionarioContrato
from gestao.recursos_humanos.models.funcionario.FuncionarioCargo import FuncionarioCargo
from gestao.contrato.forms.FuncionarioContratoForm import FuncionarioContratoForm
def equipe_contrato( request, id_contrato):
title = u"Equipe do Contrato"
contrato = get_object_or_404(Contrato, pk=id_contrato)
equipe_dado = FuncionarioContrato.objects.filter(contrato=contrato)
funcionario_contrato = FuncionarioContrato(contrato=contrato)
if request.method == "POST":
form_equipe_contrato = FuncionarioContratoForm(request.POST,
instance=funcionario_contrato)
if form_equipe_contrato.is_valid():
form_equipe_contrato.save()
funcionario_contrato = FuncionarioContrato(contrato=contrato)
form_equipe_contrato = FuncionarioContratoForm(instance=funcionario_contrato)
else:
form_equipe_contrato = FuncionarioContratoForm(instance=funcionario_contrato)
equipe = []
for func in equipe_dado:
cargos = FuncionarioCargo.objects.filter(funcionario=func).order_by("-data_inicial")
if cargos:
equipe.append( ( func, cargos[0] ) )
else:
equipe.append( ( func, None) )
return render_to_response('equipe_contrato.html',locals(),
context_instance=RequestContext(request)) | # -*- coding: utf-8 -*-
from django.shortcuts import render_to_response, get_object_or_404
from django.template import RequestContext
from gestao.contrato.models.contrato.Contrato import Contrato
from gestao.contrato.models.equipe.FuncionarioContrato import FuncionarioContrato
from gestao.recursos_humanos.models.funcionario.FuncionarioCargo import FuncionarioCargo
from gestao.contrato.forms.FuncionarioContratoForm import FuncionarioContratoForm
def equipe_contrato( request, id_contrato):
title = u"Equipe do Contrato"
contrato = get_object_or_404(Contrato, pk=id_contrato)
equipe_dado = FuncionarioContrato.objects.filter(contrato=contrato)
funcionario_contrato = FuncionarioContrato(contrato=contrato)
if request.method == "POST":
form_equipe_contrato = FuncionarioContratoForm(request.POST,
instance=funcionario_contrato)
if form_equipe_contrato.is_valid():
form_equipe_contrato.save()
funcionario_contrato = FuncionarioContrato(contrato=contrato)
form_equipe_contrato = FuncionarioContratoForm(instance=funcionario_contrato)
else:
form_equipe_contrato = FuncionarioContratoForm(instance=funcionario_contrato)
equipe = []
for func in equipe_dado:
cargos = FuncionarioCargo.objects.filter(funcionario=func).order_by("-data_inicial")
if cargos:
equipe.append( ( func, cargos[0] ) )
else:
equipe.append( ( func, None) )
return render_to_response('equipe_contrato.html',locals(),
context_instance=RequestContext(request)) | en | 0.769321 | # -*- coding: utf-8 -*- | 2.000074 | 2 |
kglib/utils/IMF_utils.py | kgullikson88/gullikson-scripts | 4 | 6613728 | <filename>kglib/utils/IMF_utils.py
"""
Various codes to work with the initial mass function. Stolen shamelessly from
<NAME>'s agpy code:
https://code.google.com/p/agpy/source/browse/trunk/agpy/imf.py
"""
from __future__ import print_function, division, absolute_import
import types # I use typechecking. Is there a better way to do this? (see inverse_imf below)
import numpy as np
class MassFunction(object):
"""
Generic Mass Function class
"""
def dndm(self, m, **kwargs):
"""
The differential form of the mass function, d N(M) / dM
"""
return self(m, integral_form=False, **kwargs)
def n_of_m(self, m, **kwargs):
"""
The integral form of the mass function, N(M)
"""
return self(m, integral_form=True, **kwargs)
def integrate(self, mlow, mhigh, **kwargs):
"""
Integrate the mass function over some range
"""
import scipy.integrate
return scipy.integrate.quad(self, mlow, mhigh, **kwargs)
class Salpeter(MassFunction):
def __init__(self, alpha=2.35):
"""
Create a default Salpeter mass function, i.e. a power-law mass function
the Salpeter 1955 IMF: dn/dm ~ m^-2.35
"""
self.alpha = alpha
def __call__(self, m, integral_form=False):
if integral_form:
return m**(-(self.alpha - 1))
else:
return m**(-self.alpha)
# three codes for dn/dlog(m)
salpeter = Salpeter()
class BrokenPowerLaw(MassFunction):
def __init__(self, breaks, mmin, mmax):
self.breaks = breaks
self.normalization = self.integrate(mmin, mmax)[0]
def __call__(self, m, integral_form=False):
zeta = 0
b_low = 0
alp_low = 0
for ii,b in enumerate(self.breaks):
if integral_form:
alp = self.breaks[b] - 1
else:
alp = self.breaks[b]
if b == 'last':
zeta += m**(-alp) * (b_low**(-alp+alp_low)) * (m>b_low)
else:
mask = ((m<b)*(m>b_low))
zeta += m**(-alp) * (b**(-alp+alp_low)) *mask
alp_low = alp
b_low = b
if hasattr(self,'normalization'):
return zeta/self.normalization
else:
return zeta
#kroupa = BrokenPowerLaw(breaks={0.08:-0.3, 0.5:1.3, 'last':2.3},mmin=0.03,mmax=120)
class Kroupa(MassFunction):
def __init__(self, mmin=0.03):
"""
"""
self.mmin = mmin
def __call__(self, m, p1=0.3, p2=1.3, p3=2.3, break1=0.08, break2=0.5, integral_form=False):
"""
Kroupa 2001 IMF (http://arxiv.org/abs/astro-ph/0009005, http://adsabs.harvard.edu/abs/2001MNRAS.322..231K)
"""
m = np.array(m)
binv = ((break1**(-(p1-1)) - self.mmin**(-(p1-1)))/(1-p1) +
(break2**(-(p2-1)) - break1**(-(p2-1))) * (break1**(p2-p1))/(1-p2) +
(- break2**(-(p3-1))) * (break1**(p2-p1)) * (break2**(p3-p2))/(1-p3))
b = 1./binv
c = b * break1**(p2-p1)
d = c * break2**(p3-p2)
zeta = (b*(m**(-(p1))) * (m<break1) +
c*(m**(-(p2))) * (m>=break1) * (m<break2) +
d*(m**(-(p3))) * (m>=break2))
if integral_form:
return zeta * m
else:
return zeta
kroupa = Kroupa()
def chabrier(m, integral=False):
"""
Chabrier 2003 IMF
http://adsabs.harvard.edu/abs/2003PASP..115..763C
(only valid for m < 1 msun)
not sure which of these to use...
integral is NOT IMPLEMENTED
"""
if integral: print("Chabrier integral NOT IMPLEMENTED")
# This system MF can be parameterized by the same type of lognormal form as
# the single MF (eq. [17]), with the same normalization at 1 Msun, with the
# coefficients (Chabrier 2003)
return 0.86 * np.exp(-1*(np.log10(m)-np.log10(0.22))**2/(2*0.57**2))
# This analytic form for the disk MF for single objects below 1 Msun, within these uncertainties, is given by the following lognormal form (Chabrier 2003):
return 0.158 * np.exp(-1*(np.log10(m)-np.log10(0.08))**2/(2*0.69**2))
def schechter(m,A=1,beta=2,m0=100, integral=False):
"""
A Schechter function with arbitrary defaults
(integral may not be correct - exponent hasn't been dealt with at all)
$$ A m^{-\\beta} e^{-m/m_0} $$
Parameters
----------
m : np.ndarray
List of masses for which to compute the Schechter function
A : float
Arbitrary amplitude of the Schechter function
beta : float
Power law exponent
m0 : float
Characteristic mass (mass at which exponential decay takes over)
Returns
-------
p(m) - the (unnormalized) probability of an object of a given mass
as a function of that object's mass
(though you could interpret mass as anything, it's just a number)
"""
if integral: beta -= 1
return A*m**-beta * np.exp(-m/m0)
def modified_schechter(m, m1, **kwargs):
"""
A Schechter function with a low-level exponential cutoff
"
Parameters
----------
m : np.ndarray
List of masses for which to compute the Schechter function
m1 : float
Characteristic minimum mass (exponential decay below this mass)
** See schecter for other parameters **
Returns
-------
p(m) - the (unnormalized) probability of an object of a given mass
as a function of that object's mass
(though you could interpret mass as anything, it's just a number)
"""
return schechter(m, **kwargs) * np.exp(-m1/m)
try:
import scipy
def schechter_cdf(m,A=1,beta=2,m0=100,mmin=10,mmax=None,npts=1e4):
"""
Return the CDF value of a given mass for a set mmin,mmax
mmax will default to 10 m0 if not specified
Analytic integral of the Schechter function:
http://www.wolframalpha.com/input/?i=integral%28x^-a+exp%28-x%2Fm%29+dx%29
"""
if mmax is None:
mmax = 10*m0
# integrate the CDF from the minimum to maximum
# undefined posint = -m0 * mmax**-beta * (mmax/m0)**beta * scipy.special.gammainc(1-beta, mmax/m0)
# undefined negint = -m0 * mmin**-beta * (mmin/m0)**beta * scipy.special.gammainc(1-beta, mmin/m0)
posint = -mmax**(1-beta) * scipy.special.expn(beta, mmax/m0)
negint = -mmin**(1-beta) * scipy.special.expn(beta, mmin/m0)
tot = posint-negint
# normalize by the integral
# undefined ret = (-m0 * m**-beta * (m/m0)**beta * scipy.special.gammainc(1-beta, m/m0)) / tot
ret = (-m**(1-beta) * scipy.special.expn(beta, m/m0) - negint)/ tot
return ret
def sh_cdf_func(**kwargs):
return lambda x: schechter_cdf(x, **kwargs)
except ImportError:
pass
#def schechter_inv(m):
# """
# Return p(m)
# """
# return scipy.interpolate.interp1d(shfun,arange(.1,20,.01),bounds_error=False,fill_value=20.)
def integrate(fn=kroupa, bins=np.logspace(-2,2,500)):
xax = (bins[:-1]+bins[1:])/2.
integral = (bins[1:]-bins[:-1]) * (fn(bins[:-1])+fn(bins[1:])) / 2.
return xax,integral
def m_integrate(fn=kroupa, bins=np.logspace(-2,2,500)):
xax = (bins[:-1]+bins[1:])/2.
integral = xax*(bins[1:]-bins[:-1]) * (fn(bins[:-1])+fn(bins[1:])) / 2.
return xax,integral
def cumint(fn=kroupa, bins=np.logspace(-2,2,500)):
xax,integral = integrate(fn,bins)
return integral.cumsum() / integral.sum()
def m_cumint(fn=kroupa, bins=np.logspace(-2,2,500)):
xax,integral = m_integrate(fn,bins)
return integral.cumsum() / integral.sum()
massfunctions = {'kroupa':kroupa, 'salpeter':salpeter, 'chabrier':chabrier, 'schechter':schechter,'modified_schechter':modified_schechter}
if hasattr(massfunctions, '__iteritems__'):
reverse_mf_dict = {v:k for k,v in massfunctions.iteritems()}
else:
reverse_mf_dict = {v:k for k,v in massfunctions.items()}
# salpeter and schechter selections are arbitrary
mostcommonmass = {'kroupa':0.08, 'salpeter':0.01, 'chabrier':0.23, 'schecter':0.01,'modified_schechter':0.01}
def get_massfunc(massfunc):
if type(massfunc) is types.FunctionType or hasattr(massfunc,'__call__'):
return massfunc
elif type(massfunc) is str:
return massfunctions[massfunc]
else:
raise ValueError("massfunc must either be a string in the set %s or a function" % (",".join(massfunctions.keys())))
def get_massfunc_name(massfunc):
if massfunc in reverse_mf_dict:
return reverse_mf_dict[massfunc]
elif type(massfunc) is str:
return massfunc
elif hasattr(massfunc,'__name__'):
return massfunc.__name__
else:
raise ValueError("invalid mass function")
def inverse_imf(p, nbins=1000, mmin=0.03, mmax=120, massfunc='kroupa', **kwargs):
"""
Inverse mass function
massfunc can be 'kroupa', 'chabrier', 'salpeter', 'schechter', or a function
"""
masses = np.logspace(np.log10(mmin),np.log10(mmax),nbins)
mf = get_massfunc(massfunc)(masses, integral_form=True, **kwargs)
mfcum = mf.cumsum()
mfcum /= mfcum.max() # normalize to sum (cdf)
return np.interp(p, mfcum, masses) | <filename>kglib/utils/IMF_utils.py
"""
Various codes to work with the initial mass function. Stolen shamelessly from
<NAME>'s agpy code:
https://code.google.com/p/agpy/source/browse/trunk/agpy/imf.py
"""
from __future__ import print_function, division, absolute_import
import types # I use typechecking. Is there a better way to do this? (see inverse_imf below)
import numpy as np
class MassFunction(object):
"""
Generic Mass Function class
"""
def dndm(self, m, **kwargs):
"""
The differential form of the mass function, d N(M) / dM
"""
return self(m, integral_form=False, **kwargs)
def n_of_m(self, m, **kwargs):
"""
The integral form of the mass function, N(M)
"""
return self(m, integral_form=True, **kwargs)
def integrate(self, mlow, mhigh, **kwargs):
"""
Integrate the mass function over some range
"""
import scipy.integrate
return scipy.integrate.quad(self, mlow, mhigh, **kwargs)
class Salpeter(MassFunction):
def __init__(self, alpha=2.35):
"""
Create a default Salpeter mass function, i.e. a power-law mass function
the Salpeter 1955 IMF: dn/dm ~ m^-2.35
"""
self.alpha = alpha
def __call__(self, m, integral_form=False):
if integral_form:
return m**(-(self.alpha - 1))
else:
return m**(-self.alpha)
# three codes for dn/dlog(m)
salpeter = Salpeter()
class BrokenPowerLaw(MassFunction):
def __init__(self, breaks, mmin, mmax):
self.breaks = breaks
self.normalization = self.integrate(mmin, mmax)[0]
def __call__(self, m, integral_form=False):
zeta = 0
b_low = 0
alp_low = 0
for ii,b in enumerate(self.breaks):
if integral_form:
alp = self.breaks[b] - 1
else:
alp = self.breaks[b]
if b == 'last':
zeta += m**(-alp) * (b_low**(-alp+alp_low)) * (m>b_low)
else:
mask = ((m<b)*(m>b_low))
zeta += m**(-alp) * (b**(-alp+alp_low)) *mask
alp_low = alp
b_low = b
if hasattr(self,'normalization'):
return zeta/self.normalization
else:
return zeta
#kroupa = BrokenPowerLaw(breaks={0.08:-0.3, 0.5:1.3, 'last':2.3},mmin=0.03,mmax=120)
class Kroupa(MassFunction):
def __init__(self, mmin=0.03):
"""
"""
self.mmin = mmin
def __call__(self, m, p1=0.3, p2=1.3, p3=2.3, break1=0.08, break2=0.5, integral_form=False):
"""
Kroupa 2001 IMF (http://arxiv.org/abs/astro-ph/0009005, http://adsabs.harvard.edu/abs/2001MNRAS.322..231K)
"""
m = np.array(m)
binv = ((break1**(-(p1-1)) - self.mmin**(-(p1-1)))/(1-p1) +
(break2**(-(p2-1)) - break1**(-(p2-1))) * (break1**(p2-p1))/(1-p2) +
(- break2**(-(p3-1))) * (break1**(p2-p1)) * (break2**(p3-p2))/(1-p3))
b = 1./binv
c = b * break1**(p2-p1)
d = c * break2**(p3-p2)
zeta = (b*(m**(-(p1))) * (m<break1) +
c*(m**(-(p2))) * (m>=break1) * (m<break2) +
d*(m**(-(p3))) * (m>=break2))
if integral_form:
return zeta * m
else:
return zeta
kroupa = Kroupa()
def chabrier(m, integral=False):
"""
Chabrier 2003 IMF
http://adsabs.harvard.edu/abs/2003PASP..115..763C
(only valid for m < 1 msun)
not sure which of these to use...
integral is NOT IMPLEMENTED
"""
if integral: print("Chabrier integral NOT IMPLEMENTED")
# This system MF can be parameterized by the same type of lognormal form as
# the single MF (eq. [17]), with the same normalization at 1 Msun, with the
# coefficients (Chabrier 2003)
return 0.86 * np.exp(-1*(np.log10(m)-np.log10(0.22))**2/(2*0.57**2))
# This analytic form for the disk MF for single objects below 1 Msun, within these uncertainties, is given by the following lognormal form (Chabrier 2003):
return 0.158 * np.exp(-1*(np.log10(m)-np.log10(0.08))**2/(2*0.69**2))
def schechter(m,A=1,beta=2,m0=100, integral=False):
"""
A Schechter function with arbitrary defaults
(integral may not be correct - exponent hasn't been dealt with at all)
$$ A m^{-\\beta} e^{-m/m_0} $$
Parameters
----------
m : np.ndarray
List of masses for which to compute the Schechter function
A : float
Arbitrary amplitude of the Schechter function
beta : float
Power law exponent
m0 : float
Characteristic mass (mass at which exponential decay takes over)
Returns
-------
p(m) - the (unnormalized) probability of an object of a given mass
as a function of that object's mass
(though you could interpret mass as anything, it's just a number)
"""
if integral: beta -= 1
return A*m**-beta * np.exp(-m/m0)
def modified_schechter(m, m1, **kwargs):
"""
A Schechter function with a low-level exponential cutoff
"
Parameters
----------
m : np.ndarray
List of masses for which to compute the Schechter function
m1 : float
Characteristic minimum mass (exponential decay below this mass)
** See schecter for other parameters **
Returns
-------
p(m) - the (unnormalized) probability of an object of a given mass
as a function of that object's mass
(though you could interpret mass as anything, it's just a number)
"""
return schechter(m, **kwargs) * np.exp(-m1/m)
try:
import scipy
def schechter_cdf(m,A=1,beta=2,m0=100,mmin=10,mmax=None,npts=1e4):
"""
Return the CDF value of a given mass for a set mmin,mmax
mmax will default to 10 m0 if not specified
Analytic integral of the Schechter function:
http://www.wolframalpha.com/input/?i=integral%28x^-a+exp%28-x%2Fm%29+dx%29
"""
if mmax is None:
mmax = 10*m0
# integrate the CDF from the minimum to maximum
# undefined posint = -m0 * mmax**-beta * (mmax/m0)**beta * scipy.special.gammainc(1-beta, mmax/m0)
# undefined negint = -m0 * mmin**-beta * (mmin/m0)**beta * scipy.special.gammainc(1-beta, mmin/m0)
posint = -mmax**(1-beta) * scipy.special.expn(beta, mmax/m0)
negint = -mmin**(1-beta) * scipy.special.expn(beta, mmin/m0)
tot = posint-negint
# normalize by the integral
# undefined ret = (-m0 * m**-beta * (m/m0)**beta * scipy.special.gammainc(1-beta, m/m0)) / tot
ret = (-m**(1-beta) * scipy.special.expn(beta, m/m0) - negint)/ tot
return ret
def sh_cdf_func(**kwargs):
return lambda x: schechter_cdf(x, **kwargs)
except ImportError:
pass
#def schechter_inv(m):
# """
# Return p(m)
# """
# return scipy.interpolate.interp1d(shfun,arange(.1,20,.01),bounds_error=False,fill_value=20.)
def integrate(fn=kroupa, bins=np.logspace(-2,2,500)):
xax = (bins[:-1]+bins[1:])/2.
integral = (bins[1:]-bins[:-1]) * (fn(bins[:-1])+fn(bins[1:])) / 2.
return xax,integral
def m_integrate(fn=kroupa, bins=np.logspace(-2,2,500)):
xax = (bins[:-1]+bins[1:])/2.
integral = xax*(bins[1:]-bins[:-1]) * (fn(bins[:-1])+fn(bins[1:])) / 2.
return xax,integral
def cumint(fn=kroupa, bins=np.logspace(-2,2,500)):
xax,integral = integrate(fn,bins)
return integral.cumsum() / integral.sum()
def m_cumint(fn=kroupa, bins=np.logspace(-2,2,500)):
xax,integral = m_integrate(fn,bins)
return integral.cumsum() / integral.sum()
massfunctions = {'kroupa':kroupa, 'salpeter':salpeter, 'chabrier':chabrier, 'schechter':schechter,'modified_schechter':modified_schechter}
if hasattr(massfunctions, '__iteritems__'):
reverse_mf_dict = {v:k for k,v in massfunctions.iteritems()}
else:
reverse_mf_dict = {v:k for k,v in massfunctions.items()}
# salpeter and schechter selections are arbitrary
mostcommonmass = {'kroupa':0.08, 'salpeter':0.01, 'chabrier':0.23, 'schecter':0.01,'modified_schechter':0.01}
def get_massfunc(massfunc):
if type(massfunc) is types.FunctionType or hasattr(massfunc,'__call__'):
return massfunc
elif type(massfunc) is str:
return massfunctions[massfunc]
else:
raise ValueError("massfunc must either be a string in the set %s or a function" % (",".join(massfunctions.keys())))
def get_massfunc_name(massfunc):
if massfunc in reverse_mf_dict:
return reverse_mf_dict[massfunc]
elif type(massfunc) is str:
return massfunc
elif hasattr(massfunc,'__name__'):
return massfunc.__name__
else:
raise ValueError("invalid mass function")
def inverse_imf(p, nbins=1000, mmin=0.03, mmax=120, massfunc='kroupa', **kwargs):
"""
Inverse mass function
massfunc can be 'kroupa', 'chabrier', 'salpeter', 'schechter', or a function
"""
masses = np.logspace(np.log10(mmin),np.log10(mmax),nbins)
mf = get_massfunc(massfunc)(masses, integral_form=True, **kwargs)
mfcum = mf.cumsum()
mfcum /= mfcum.max() # normalize to sum (cdf)
return np.interp(p, mfcum, masses) | en | 0.664747 | Various codes to work with the initial mass function. Stolen shamelessly from <NAME>'s agpy code: https://code.google.com/p/agpy/source/browse/trunk/agpy/imf.py # I use typechecking. Is there a better way to do this? (see inverse_imf below) Generic Mass Function class The differential form of the mass function, d N(M) / dM The integral form of the mass function, N(M) Integrate the mass function over some range Create a default Salpeter mass function, i.e. a power-law mass function the Salpeter 1955 IMF: dn/dm ~ m^-2.35 # three codes for dn/dlog(m) #kroupa = BrokenPowerLaw(breaks={0.08:-0.3, 0.5:1.3, 'last':2.3},mmin=0.03,mmax=120) Kroupa 2001 IMF (http://arxiv.org/abs/astro-ph/0009005, http://adsabs.harvard.edu/abs/2001MNRAS.322..231K) Chabrier 2003 IMF http://adsabs.harvard.edu/abs/2003PASP..115..763C (only valid for m < 1 msun) not sure which of these to use... integral is NOT IMPLEMENTED # This system MF can be parameterized by the same type of lognormal form as # the single MF (eq. [17]), with the same normalization at 1 Msun, with the # coefficients (Chabrier 2003) # This analytic form for the disk MF for single objects below 1 Msun, within these uncertainties, is given by the following lognormal form (Chabrier 2003): A Schechter function with arbitrary defaults (integral may not be correct - exponent hasn't been dealt with at all) $$ A m^{-\\beta} e^{-m/m_0} $$ Parameters ---------- m : np.ndarray List of masses for which to compute the Schechter function A : float Arbitrary amplitude of the Schechter function beta : float Power law exponent m0 : float Characteristic mass (mass at which exponential decay takes over) Returns ------- p(m) - the (unnormalized) probability of an object of a given mass as a function of that object's mass (though you could interpret mass as anything, it's just a number) A Schechter function with a low-level exponential cutoff " Parameters ---------- m : np.ndarray List of masses for which to compute the Schechter function m1 : float Characteristic minimum mass (exponential decay below this mass) ** See schecter for other parameters ** Returns ------- p(m) - the (unnormalized) probability of an object of a given mass as a function of that object's mass (though you could interpret mass as anything, it's just a number) Return the CDF value of a given mass for a set mmin,mmax mmax will default to 10 m0 if not specified Analytic integral of the Schechter function: http://www.wolframalpha.com/input/?i=integral%28x^-a+exp%28-x%2Fm%29+dx%29 # integrate the CDF from the minimum to maximum # undefined posint = -m0 * mmax**-beta * (mmax/m0)**beta * scipy.special.gammainc(1-beta, mmax/m0) # undefined negint = -m0 * mmin**-beta * (mmin/m0)**beta * scipy.special.gammainc(1-beta, mmin/m0) # normalize by the integral # undefined ret = (-m0 * m**-beta * (m/m0)**beta * scipy.special.gammainc(1-beta, m/m0)) / tot #def schechter_inv(m): # """ # Return p(m) # """ # return scipy.interpolate.interp1d(shfun,arange(.1,20,.01),bounds_error=False,fill_value=20.) # salpeter and schechter selections are arbitrary Inverse mass function massfunc can be 'kroupa', 'chabrier', 'salpeter', 'schechter', or a function # normalize to sum (cdf) | 2.699579 | 3 |
AverageSpeed.py | ddawx123/Smart-Traffic | 8 | 6613729 | import os
import sys
import http.client
import json
import codecs
import time
import datetime
import sqlite3
import subprocess
import platform
def getData():
conn = http.client.HTTPConnection("172.16.17.32")
conn.request("GET", "/wx/data.php?t=dlzs")
jsonstr = conn.getresponse()
return jsonstr
def Main():
print("绍兴市智慧交通数据分析工具_v1.0内测版\n\n")
if (getData().status != 200):
print("远程服务器连接失败，请检查网络状态。")
exit()
#print(getData().read())
data = json.loads(getData().read())
print("检索到共有" + str(len(data)) + "条路况数据，正在计算。\n")
reqtime = time.strftime("%Y%m%d%H%M%S",time.localtime(time.time()))
fullspeed = 0
for newdata in data:
#print(newdata["speed"])
fullspeed = fullspeed + newdata["speed"]
print("数据分析结束，主城区平均通行速度：" + str(int(fullspeed / len(data))) + " Km/h")
print("\n\n\nCopyright 2012-2017 DingStudio All Rights Reserved")
def LoopExecute():
while(1):
time.sleep(1)
if (platform.system() == "Windows"):
os.system('color 0a')
os.system('cls')
else:
os.system('clear')
Main()
LoopExecute()
| import os
import sys
import http.client
import json
import codecs
import time
import datetime
import sqlite3
import subprocess
import platform
def getData():
conn = http.client.HTTPConnection("172.16.17.32")
conn.request("GET", "/wx/data.php?t=dlzs")
jsonstr = conn.getresponse()
return jsonstr
def Main():
print("绍兴市智慧交通数据分析工具_v1.0内测版\n\n")
if (getData().status != 200):
print("远程服务器连接失败，请检查网络状态。")
exit()
#print(getData().read())
data = json.loads(getData().read())
print("检索到共有" + str(len(data)) + "条路况数据，正在计算。\n")
reqtime = time.strftime("%Y%m%d%H%M%S",time.localtime(time.time()))
fullspeed = 0
for newdata in data:
#print(newdata["speed"])
fullspeed = fullspeed + newdata["speed"]
print("数据分析结束，主城区平均通行速度：" + str(int(fullspeed / len(data))) + " Km/h")
print("\n\n\nCopyright 2012-2017 DingStudio All Rights Reserved")
def LoopExecute():
while(1):
time.sleep(1)
if (platform.system() == "Windows"):
os.system('color 0a')
os.system('cls')
else:
os.system('clear')
Main()
LoopExecute()
| ru | 0.208283 | #print(getData().read()) #print(newdata["speed"]) | 2.466831 | 2 |
15-17. Menu/window-17-pushdownMenu.py | IvanFoke/TkinterLessons | 3 | 6613730 | <filename>15-17. Menu/window-17-pushdownMenu.py
from tkinter import *
from tkinter import messagebox as mb
from tkinter.ttk import Combobox
from child_window import ChildWindow
# from PIL import Image as PilImage
# from PIL import ImageTk, ImageOps
class Window:
def __init__(self, width, height, title="MyWindow", resizable=(False, False), icon=r"resources/feather.ico"):
self.root = Tk()
self.root.title(title)
# self.root.geometry(f"{width}x{height}+200+200")
self.root.geometry("+600+300")
# self.root.resizable(resizable[0], resizable[1])
if icon:
self.root.iconbitmap(icon)
self.auto_save = BooleanVar(value=0)
self.auto_load = BooleanVar(value=0)
self.value = IntVar()
def run(self):
self.draw_widgets()
self.root.mainloop()
def draw_widgets(self):
self.draw_menu()
Label(self.root, text="Just a label").pack()
def draw_menu(self):
menu_bar = Menu(self.root)
file_menu = Menu(menu_bar, tearoff=0)
file_menu.add_command(label="Сохранить", command=self.cmd)
file_menu.add_separator()
file_menu.add_command(label="Выйти", command=self.exit)
edit_menu = Menu(menu_bar, tearoff=0)
parameters_menu = Menu(edit_menu, tearoff=0)
parameters_menu.add_checkbutton(label="Автосохранение", offvalue=0, onvalue=1, variable=self.auto_save)
parameters_menu.add_checkbutton(label="Автозагрузка", offvalue=0, onvalue=1, variable=self.auto_load,
command=self.check_auto_load)
edit_menu.add_cascade(label="Параметры", menu=parameters_menu)
edit_menu.add_separator()
values_menu = Menu(edit_menu, tearoff=0)
values_menu.add_radiobutton(label="Один", value=1, variable=self.value)
values_menu.add_radiobutton(label="Два", value=2, variable=self.value)
values_menu.add_radiobutton(label="Три", value=3, variable=self.value)
edit_menu.add_cascade(label="Значения", menu=values_menu)
info_menu = Menu(menu_bar, tearoff=0)
info_menu.add_command(label="О приложении", command=self.show_info)
menu_bar.add_cascade(label="Файл", menu=file_menu)
menu_bar.add_cascade(label="Настройки", menu=edit_menu)
menu_bar.add_cascade(label="Справка", menu=info_menu)
self.root.configure(menu=menu_bar)
def check_auto_load(self):
if not self.auto_save.get() and self.auto_load.get():
if mb.askyesno("Ошибка", "Автозагрузка без автосохранения. Хотите установить автосохранение?"):
self.auto_save.set(True)
def show_info(self):
mb.showinfo("Информация", "Лучшее графическое приложение на свете")
def auto_save_changed(self):
mb.showinfo("AutoSave", f"Value: {self.auto_save.get()}")
def age_changed(self):
mb.showinfo("Age", f"Value: {self.age.get()}")
def cmd(self):
mb.showinfo("123", "123")
def exit(self):
choice = mb.askyesno("Quit", "Do you want to quit?")
if choice:
self.root.destroy()
def create_child(self, width, height, title="Child", resizable=(False, False), icon=None):
ChildWindow(self.root, width, height, title, resizable, icon)
if __name__ == "__main__":
window = Window(500, 500, "TKINTER")
# window.create_child(200, 100)
window.run()
| <filename>15-17. Menu/window-17-pushdownMenu.py
from tkinter import *
from tkinter import messagebox as mb
from tkinter.ttk import Combobox
from child_window import ChildWindow
# from PIL import Image as PilImage
# from PIL import ImageTk, ImageOps
class Window:
def __init__(self, width, height, title="MyWindow", resizable=(False, False), icon=r"resources/feather.ico"):
self.root = Tk()
self.root.title(title)
# self.root.geometry(f"{width}x{height}+200+200")
self.root.geometry("+600+300")
# self.root.resizable(resizable[0], resizable[1])
if icon:
self.root.iconbitmap(icon)
self.auto_save = BooleanVar(value=0)
self.auto_load = BooleanVar(value=0)
self.value = IntVar()
def run(self):
self.draw_widgets()
self.root.mainloop()
def draw_widgets(self):
self.draw_menu()
Label(self.root, text="Just a label").pack()
def draw_menu(self):
menu_bar = Menu(self.root)
file_menu = Menu(menu_bar, tearoff=0)
file_menu.add_command(label="Сохранить", command=self.cmd)
file_menu.add_separator()
file_menu.add_command(label="Выйти", command=self.exit)
edit_menu = Menu(menu_bar, tearoff=0)
parameters_menu = Menu(edit_menu, tearoff=0)
parameters_menu.add_checkbutton(label="Автосохранение", offvalue=0, onvalue=1, variable=self.auto_save)
parameters_menu.add_checkbutton(label="Автозагрузка", offvalue=0, onvalue=1, variable=self.auto_load,
command=self.check_auto_load)
edit_menu.add_cascade(label="Параметры", menu=parameters_menu)
edit_menu.add_separator()
values_menu = Menu(edit_menu, tearoff=0)
values_menu.add_radiobutton(label="Один", value=1, variable=self.value)
values_menu.add_radiobutton(label="Два", value=2, variable=self.value)
values_menu.add_radiobutton(label="Три", value=3, variable=self.value)
edit_menu.add_cascade(label="Значения", menu=values_menu)
info_menu = Menu(menu_bar, tearoff=0)
info_menu.add_command(label="О приложении", command=self.show_info)
menu_bar.add_cascade(label="Файл", menu=file_menu)
menu_bar.add_cascade(label="Настройки", menu=edit_menu)
menu_bar.add_cascade(label="Справка", menu=info_menu)
self.root.configure(menu=menu_bar)
def check_auto_load(self):
if not self.auto_save.get() and self.auto_load.get():
if mb.askyesno("Ошибка", "Автозагрузка без автосохранения. Хотите установить автосохранение?"):
self.auto_save.set(True)
def show_info(self):
mb.showinfo("Информация", "Лучшее графическое приложение на свете")
def auto_save_changed(self):
mb.showinfo("AutoSave", f"Value: {self.auto_save.get()}")
def age_changed(self):
mb.showinfo("Age", f"Value: {self.age.get()}")
def cmd(self):
mb.showinfo("123", "123")
def exit(self):
choice = mb.askyesno("Quit", "Do you want to quit?")
if choice:
self.root.destroy()
def create_child(self, width, height, title="Child", resizable=(False, False), icon=None):
ChildWindow(self.root, width, height, title, resizable, icon)
if __name__ == "__main__":
window = Window(500, 500, "TKINTER")
# window.create_child(200, 100)
window.run()
| en | 0.216211 | # from PIL import Image as PilImage # from PIL import ImageTk, ImageOps # self.root.geometry(f"{width}x{height}+200+200") # self.root.resizable(resizable[0], resizable[1]) # window.create_child(200, 100) | 2.955921 | 3 |
test/test_analysis/test_plotting.py | sid-marain/EMAworkbench | 0 | 6613731 | <gh_stars>0
'''
Created on 22 jul. 2012
.. codeauthor:: jhkwakkel <j.h.kwakkel (at) tudelft (dot) nl>
'''
from __future__ import (absolute_import, print_function, division,
unicode_literals)
import matplotlib.pyplot as plt
import numpy as np
from ema_workbench.analysis.b_and_w_plotting import set_fig_to_bw
from ema_workbench.analysis.plotting import *
from ema_workbench.analysis.plotting_util import (make_continuous_grouping_specifiers,
filter_scalar_outcomes, group_results, BOXPLOT, KDE,
VIOLIN, HIST, ENV_LIN)
from test import utilities
# don't run these tests using nosetest
# __test__ = False
def test_make_continuous_grouping_specifiers():
array = np.random.randint(1,100, size=(1000,))
categories = make_continuous_grouping_specifiers(array, nr_of_groups=10)
for entry in categories:
print(repr(entry))
print(np.min(array), np.max(array))
def test_filter_scalar_outcomes():
outcomes = {}
for entry in ['a', 'b', 'c']:
outcomes[entry] = np.random.rand(10,100)
for entry in ['d','e','f']:
outcomes[entry] = np.random.rand(10)
outcomes = filter_scalar_outcomes(outcomes)
print(outcomes.keys())
def test_group_results():
results = utilities.load_eng_trans_data()
experiments, outcomes = results
# test indices
groups = {'set1':np.arange(0,11),
'set2':np.arange(11,25),
'set3':np.arange(25,experiments.shape[0])}
groups = group_results(experiments, outcomes,
group_by='index',
grouping_specifiers=groups.values(),
grouping_labels= groups.keys())
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print(experiments.shape[0], total_data)
# test continuous parameter type
array = experiments['average planning and construction period T1']
grouping_specifiers = make_continuous_grouping_specifiers(array, nr_of_groups=5)
groups = group_results(experiments, outcomes,
group_by='average planning and construction period T1',
grouping_specifiers=grouping_specifiers,
grouping_labels = [str(entry) for entry in grouping_specifiers])
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print(experiments.shape[0], total_data)
# test integer type
array = experiments['seed PR T1']
grouping_specifiers = make_continuous_grouping_specifiers(array, nr_of_groups=10)
groups = group_results(experiments, outcomes,
group_by='seed PR T1',
grouping_specifiers=grouping_specifiers,
grouping_labels = [str(entry) for entry in grouping_specifiers])
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print(experiments.shape[0], total_data)
# test categorical type
grouping_specifiers = set(experiments["policy"])
groups = group_results(experiments, outcomes,
group_by='policy',
grouping_specifiers=grouping_specifiers,
grouping_labels = [str(entry) for entry in grouping_specifiers])
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print(experiments.shape[0], total_data)
def test_lines():
experiments, outcomes = utilities.load_eng_trans_data()
lines(experiments, outcomes,
outcomes_to_show="total fraction new technologies",
experiments_to_show=np.arange(0,600, 20),
group_by='policy',
grouping_specifiers='basic policy'
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 2),
group_by='policy',
density=HIST
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 2),
group_by='policy',
density=KDE
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 2),
group_by='policy',
density=BOXPLOT
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 2),
group_by='policy',
density=VIOLIN
)
lines(experiments, outcomes,
group_by='index',
grouping_specifiers = {"blaat": np.arange(1, 100, 2)},
density=KDE,
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=KDE,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy']
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=HIST,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy']
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=BOXPLOT,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy']
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=VIOLIN,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy']
)
plt.draw()
plt.close('all')
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=KDE,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'],
log=True
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=HIST,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'],
log=True
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=BOXPLOT,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'],
log=True
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=VIOLIN,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'],
log=True
)
plt.draw()
plt.close('all')
set_fig_to_bw(lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 20),
group_by='policy',
density=KDE
)[0])
new_outcomes = {}
for key, value in outcomes.items():
new_outcomes[key] = value[0:20, :]
experiments = experiments[0:20]
#no grouping, with density
set_fig_to_bw(lines(experiments, new_outcomes, density=KDE)[0])
set_fig_to_bw(lines(experiments, new_outcomes, density=HIST)[0])
set_fig_to_bw(lines(experiments, new_outcomes, density=BOXPLOT)[0])
set_fig_to_bw(lines(experiments, new_outcomes, density=VIOLIN)[0])
# grouping and density
set_fig_to_bw(lines(experiments, new_outcomes,
group_by='policy',
density='kde')[0])
# grouping, density as histograms
# grouping and density
set_fig_to_bw(lines(experiments, new_outcomes,
group_by='policy',
density='hist',
legend=False)[0])
plt.draw()
plt.close('all')
def test_envelopes():
experiments, outcomes = utilities.load_eng_trans_data()
#testing titles
envelopes(experiments, outcomes,
density=None,
titles=None)
envelopes(experiments, outcomes,
density=None,
titles={})
envelopes(experiments, outcomes,
density=None,
titles={'total fraction new technologies': 'a'})
plt.draw()
plt.close('all')
#testing ylabels
envelopes(experiments, outcomes,
density=None,
ylabels=None)
envelopes(experiments, outcomes,
density=None,
ylabels={})
envelopes(experiments, outcomes,
density=None,
ylabels={'total fraction new technologies': 'a'})
plt.draw()
plt.close('all')
#no grouping no density
envelopes(experiments, outcomes,
titles=None)
set_fig_to_bw(envelopes(experiments, outcomes, density=None)[0])
plt.draw()
plt.close('all')
#no grouping, with density
envelopes(experiments, outcomes, density=KDE)
envelopes(experiments, outcomes, density=HIST)
envelopes(experiments, outcomes, density=BOXPLOT)
envelopes(experiments, outcomes, density=VIOLIN)
set_fig_to_bw(envelopes(experiments, outcomes, density=VIOLIN)[0])
plt.draw()
plt.close('all')
# grouping and density kde
envelopes(experiments, outcomes,
group_by='policy',
density=VIOLIN)
envelopes(experiments, outcomes,
group_by='policy',
density=BOXPLOT)
envelopes(experiments, outcomes,
group_by='policy',
density=KDE,
grouping_specifiers=['no policy', 'adaptive policy'])
envelopes(experiments, outcomes,
group_by='policy',
density=BOXPLOT,
grouping_specifiers=['no policy', 'adaptive policy'])
envelopes(experiments, outcomes,
group_by='policy',
density=KDE)
plt.draw()
plt.close('all')
envelopes(experiments, outcomes,
group_by='policy',
density=VIOLIN)
envelopes(experiments, outcomes,
group_by='policy',
density=BOXPLOT)
envelopes(experiments, outcomes,
group_by='policy',
density=KDE)
envelopes(experiments, outcomes,
group_by='policy',
density=HIST)
plt.draw()
plt.close('all')
envelopes(experiments, outcomes,
group_by='policy',
density=VIOLIN,
log=True)
envelopes(experiments, outcomes,
group_by='policy',
density=BOXPLOT,
log=True)
envelopes(experiments, outcomes,
group_by='policy',
density=KDE,
log=True)
envelopes(experiments, outcomes,
group_by='policy',
density=HIST,
log=True)
plt.draw()
plt.close('all')
# grouping and density hist
envelopes(experiments, outcomes,
group_by='policy',
density=HIST)
envelopes(experiments, outcomes,
group_by='policy',
density=HIST)
set_fig_to_bw(envelopes(experiments, outcomes,
group_by='policy',
density=KDE)[0])
# grouping and density
envelopes(experiments, outcomes,
group_by='policy',
density=KDE,
fill=True)
set_fig_to_bw(envelopes(experiments, outcomes,
group_by='policy',
density=KDE,
fill=True)[0])
plt.draw()
plt.close('all')
def test_kde_over_time():
experiments, outcomes = utilities.load_eng_trans_data()
kde_over_time(experiments, outcomes, log=False)
kde_over_time(experiments, outcomes, log=True)
kde_over_time(experiments, outcomes, group_by='policy',
grouping_specifiers=['no policy', 'adaptive policy'])
plt.draw()
plt.close('all')
def test_multiple_densities():
experiments, outcomes = utilities.load_eng_trans_data()
ooi = 'total fraction new technologies'
multiple_densities(experiments, outcomes,
group_by="policy",
points_in_time = [2010])
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010])
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010, 2100])
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010, 2050, 2100])
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010, 2020, 2050, 2080])
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010, 2020, 2040, 2060, 2100])
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010,2020, 2040, 2060, 2080, 2100],
plot_type=ENV_LIN,
density=KDE,
experiments_to_show=[1,2,10])
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010,2020, 2040, 2060, 2080, 2100],
plot_type=ENV_LIN,
density=HIST,
experiments_to_show=[1,2,10])
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010,2020, 2040, 2060, 2080, 2100],
plot_type=ENV_LIN,
density=BOXPLOT,
experiments_to_show=[1,2,10])
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010,2020, 2040, 2060, 2080, 2100],
plot_type=ENV_LIN,
density=VIOLIN,
experiments_to_show=[1,2,10])
plt.draw()
plt.close('all')
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010,2020, 2040, 2060, 2080, 2100],
plot_type=ENV_LIN,
density=KDE,
experiments_to_show=[1,2,10],
log=True)
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010,2020, 2040, 2060, 2080, 2100],
plot_type=ENV_LIN,
density=HIST,
experiments_to_show=[1,2,10],
log=True)
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010,2020, 2040, 2060, 2080, 2100],
plot_type=ENV_LIN,
density=BOXPLOT,
experiments_to_show=[1,2,10],
log=True)
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010,2020, 2040, 2060, 2080, 2100],
plot_type=ENV_LIN,
density=VIOLIN,
experiments_to_show=[1,2,10],
log=True)
plt.draw()
plt.close('all')
if __name__ == '__main__':
# test_lines()
# test_envelopes()
test_kde_over_time()
# test_multiple_densities()
# test_filter_scalar_outcomes()
# test_group_results()
# test_make_continuous_grouping_specifiers()
| '''
Created on 22 jul. 2012
.. codeauthor:: jhkwakkel <j.h.kwakkel (at) tudelft (dot) nl>
'''
from __future__ import (absolute_import, print_function, division,
unicode_literals)
import matplotlib.pyplot as plt
import numpy as np
from ema_workbench.analysis.b_and_w_plotting import set_fig_to_bw
from ema_workbench.analysis.plotting import *
from ema_workbench.analysis.plotting_util import (make_continuous_grouping_specifiers,
filter_scalar_outcomes, group_results, BOXPLOT, KDE,
VIOLIN, HIST, ENV_LIN)
from test import utilities
# don't run these tests using nosetest
# __test__ = False
def test_make_continuous_grouping_specifiers():
array = np.random.randint(1,100, size=(1000,))
categories = make_continuous_grouping_specifiers(array, nr_of_groups=10)
for entry in categories:
print(repr(entry))
print(np.min(array), np.max(array))
def test_filter_scalar_outcomes():
outcomes = {}
for entry in ['a', 'b', 'c']:
outcomes[entry] = np.random.rand(10,100)
for entry in ['d','e','f']:
outcomes[entry] = np.random.rand(10)
outcomes = filter_scalar_outcomes(outcomes)
print(outcomes.keys())
def test_group_results():
results = utilities.load_eng_trans_data()
experiments, outcomes = results
# test indices
groups = {'set1':np.arange(0,11),
'set2':np.arange(11,25),
'set3':np.arange(25,experiments.shape[0])}
groups = group_results(experiments, outcomes,
group_by='index',
grouping_specifiers=groups.values(),
grouping_labels= groups.keys())
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print(experiments.shape[0], total_data)
# test continuous parameter type
array = experiments['average planning and construction period T1']
grouping_specifiers = make_continuous_grouping_specifiers(array, nr_of_groups=5)
groups = group_results(experiments, outcomes,
group_by='average planning and construction period T1',
grouping_specifiers=grouping_specifiers,
grouping_labels = [str(entry) for entry in grouping_specifiers])
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print(experiments.shape[0], total_data)
# test integer type
array = experiments['seed PR T1']
grouping_specifiers = make_continuous_grouping_specifiers(array, nr_of_groups=10)
groups = group_results(experiments, outcomes,
group_by='seed PR T1',
grouping_specifiers=grouping_specifiers,
grouping_labels = [str(entry) for entry in grouping_specifiers])
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print(experiments.shape[0], total_data)
# test categorical type
grouping_specifiers = set(experiments["policy"])
groups = group_results(experiments, outcomes,
group_by='policy',
grouping_specifiers=grouping_specifiers,
grouping_labels = [str(entry) for entry in grouping_specifiers])
total_data = 0
for value in groups.values():
total_data += value[0].shape[0]
print(experiments.shape[0], total_data)
def test_lines():
experiments, outcomes = utilities.load_eng_trans_data()
lines(experiments, outcomes,
outcomes_to_show="total fraction new technologies",
experiments_to_show=np.arange(0,600, 20),
group_by='policy',
grouping_specifiers='basic policy'
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 2),
group_by='policy',
density=HIST
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 2),
group_by='policy',
density=KDE
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 2),
group_by='policy',
density=BOXPLOT
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 2),
group_by='policy',
density=VIOLIN
)
lines(experiments, outcomes,
group_by='index',
grouping_specifiers = {"blaat": np.arange(1, 100, 2)},
density=KDE,
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=KDE,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy']
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=HIST,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy']
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=BOXPLOT,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy']
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=VIOLIN,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy']
)
plt.draw()
plt.close('all')
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=KDE,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'],
log=True
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=HIST,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'],
log=True
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=BOXPLOT,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'],
log=True
)
lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 30),
group_by='policy',
density=VIOLIN,
show_envelope=True,
grouping_specifiers=['no policy', 'adaptive policy'],
log=True
)
plt.draw()
plt.close('all')
set_fig_to_bw(lines(experiments, outcomes,
experiments_to_show=np.arange(0,600, 20),
group_by='policy',
density=KDE
)[0])
new_outcomes = {}
for key, value in outcomes.items():
new_outcomes[key] = value[0:20, :]
experiments = experiments[0:20]
#no grouping, with density
set_fig_to_bw(lines(experiments, new_outcomes, density=KDE)[0])
set_fig_to_bw(lines(experiments, new_outcomes, density=HIST)[0])
set_fig_to_bw(lines(experiments, new_outcomes, density=BOXPLOT)[0])
set_fig_to_bw(lines(experiments, new_outcomes, density=VIOLIN)[0])
# grouping and density
set_fig_to_bw(lines(experiments, new_outcomes,
group_by='policy',
density='kde')[0])
# grouping, density as histograms
# grouping and density
set_fig_to_bw(lines(experiments, new_outcomes,
group_by='policy',
density='hist',
legend=False)[0])
plt.draw()
plt.close('all')
def test_envelopes():
experiments, outcomes = utilities.load_eng_trans_data()
#testing titles
envelopes(experiments, outcomes,
density=None,
titles=None)
envelopes(experiments, outcomes,
density=None,
titles={})
envelopes(experiments, outcomes,
density=None,
titles={'total fraction new technologies': 'a'})
plt.draw()
plt.close('all')
#testing ylabels
envelopes(experiments, outcomes,
density=None,
ylabels=None)
envelopes(experiments, outcomes,
density=None,
ylabels={})
envelopes(experiments, outcomes,
density=None,
ylabels={'total fraction new technologies': 'a'})
plt.draw()
plt.close('all')
#no grouping no density
envelopes(experiments, outcomes,
titles=None)
set_fig_to_bw(envelopes(experiments, outcomes, density=None)[0])
plt.draw()
plt.close('all')
#no grouping, with density
envelopes(experiments, outcomes, density=KDE)
envelopes(experiments, outcomes, density=HIST)
envelopes(experiments, outcomes, density=BOXPLOT)
envelopes(experiments, outcomes, density=VIOLIN)
set_fig_to_bw(envelopes(experiments, outcomes, density=VIOLIN)[0])
plt.draw()
plt.close('all')
# grouping and density kde
envelopes(experiments, outcomes,
group_by='policy',
density=VIOLIN)
envelopes(experiments, outcomes,
group_by='policy',
density=BOXPLOT)
envelopes(experiments, outcomes,
group_by='policy',
density=KDE,
grouping_specifiers=['no policy', 'adaptive policy'])
envelopes(experiments, outcomes,
group_by='policy',
density=BOXPLOT,
grouping_specifiers=['no policy', 'adaptive policy'])
envelopes(experiments, outcomes,
group_by='policy',
density=KDE)
plt.draw()
plt.close('all')
envelopes(experiments, outcomes,
group_by='policy',
density=VIOLIN)
envelopes(experiments, outcomes,
group_by='policy',
density=BOXPLOT)
envelopes(experiments, outcomes,
group_by='policy',
density=KDE)
envelopes(experiments, outcomes,
group_by='policy',
density=HIST)
plt.draw()
plt.close('all')
envelopes(experiments, outcomes,
group_by='policy',
density=VIOLIN,
log=True)
envelopes(experiments, outcomes,
group_by='policy',
density=BOXPLOT,
log=True)
envelopes(experiments, outcomes,
group_by='policy',
density=KDE,
log=True)
envelopes(experiments, outcomes,
group_by='policy',
density=HIST,
log=True)
plt.draw()
plt.close('all')
# grouping and density hist
envelopes(experiments, outcomes,
group_by='policy',
density=HIST)
envelopes(experiments, outcomes,
group_by='policy',
density=HIST)
set_fig_to_bw(envelopes(experiments, outcomes,
group_by='policy',
density=KDE)[0])
# grouping and density
envelopes(experiments, outcomes,
group_by='policy',
density=KDE,
fill=True)
set_fig_to_bw(envelopes(experiments, outcomes,
group_by='policy',
density=KDE,
fill=True)[0])
plt.draw()
plt.close('all')
def test_kde_over_time():
experiments, outcomes = utilities.load_eng_trans_data()
kde_over_time(experiments, outcomes, log=False)
kde_over_time(experiments, outcomes, log=True)
kde_over_time(experiments, outcomes, group_by='policy',
grouping_specifiers=['no policy', 'adaptive policy'])
plt.draw()
plt.close('all')
def test_multiple_densities():
experiments, outcomes = utilities.load_eng_trans_data()
ooi = 'total fraction new technologies'
multiple_densities(experiments, outcomes,
group_by="policy",
points_in_time = [2010])
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010])
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010, 2100])
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010, 2050, 2100])
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010, 2020, 2050, 2080])
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010, 2020, 2040, 2060, 2100])
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010,2020, 2040, 2060, 2080, 2100],
plot_type=ENV_LIN,
density=KDE,
experiments_to_show=[1,2,10])
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010,2020, 2040, 2060, 2080, 2100],
plot_type=ENV_LIN,
density=HIST,
experiments_to_show=[1,2,10])
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010,2020, 2040, 2060, 2080, 2100],
plot_type=ENV_LIN,
density=BOXPLOT,
experiments_to_show=[1,2,10])
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010,2020, 2040, 2060, 2080, 2100],
plot_type=ENV_LIN,
density=VIOLIN,
experiments_to_show=[1,2,10])
plt.draw()
plt.close('all')
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010,2020, 2040, 2060, 2080, 2100],
plot_type=ENV_LIN,
density=KDE,
experiments_to_show=[1,2,10],
log=True)
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010,2020, 2040, 2060, 2080, 2100],
plot_type=ENV_LIN,
density=HIST,
experiments_to_show=[1,2,10],
log=True)
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010,2020, 2040, 2060, 2080, 2100],
plot_type=ENV_LIN,
density=BOXPLOT,
experiments_to_show=[1,2,10],
log=True)
multiple_densities(experiments, outcomes,
outcomes_to_show=ooi,
group_by="policy",
points_in_time = [2010,2020, 2040, 2060, 2080, 2100],
plot_type=ENV_LIN,
density=VIOLIN,
experiments_to_show=[1,2,10],
log=True)
plt.draw()
plt.close('all')
if __name__ == '__main__':
# test_lines()
# test_envelopes()
test_kde_over_time()
# test_multiple_densities()
# test_filter_scalar_outcomes()
# test_group_results()
# test_make_continuous_grouping_specifiers() | en | 0.40855 | Created on 22 jul. 2012 .. codeauthor:: jhkwakkel <j.h.kwakkel (at) tudelft (dot) nl> # don't run these tests using nosetest # __test__ = False # test indices # test continuous parameter type # test integer type # test categorical type #no grouping, with density # grouping and density # grouping, density as histograms # grouping and density #testing titles #testing ylabels #no grouping no density #no grouping, with density # grouping and density kde # grouping and density hist # grouping and density # test_lines() # test_envelopes() # test_multiple_densities() # test_filter_scalar_outcomes() # test_group_results() # test_make_continuous_grouping_specifiers() | 2.263146 | 2 |
convert_pptx.py | yuhal/ppt-convert | 4 | 6613732 | # -*- coding: utf-8 -*-
# !python3
"""
PPT convert PPTX
"""
from changeOffice import Change
change = Change("./")
change.ppt2pptx()
print(change.get_allPath())
| # -*- coding: utf-8 -*-
# !python3
"""
PPT convert PPTX
"""
from changeOffice import Change
change = Change("./")
change.ppt2pptx()
print(change.get_allPath())
| en | 0.457089 | # -*- coding: utf-8 -*- # !python3 PPT convert PPTX | 2.66839 | 3 |
setup.py | mypleasureteam/mann | 0 | 6613733 | """Setup for Mann on PyPi."""
from distutils.core import setup
setup(
name='mann',
packages=['mypleasure'],
version='0.9.1',
description='A multi-purpose logger and notifier.',
author='<NAME>',
author_email='<EMAIL>',
url='https://github.com/mypleasureteam/mann',
download_url='https://github.com/mypleasureteam/mann/tarball/0.1',
keywords=['logging', 'notification', 'trello', 'slack'],
classifiers=[],
)
| """Setup for Mann on PyPi."""
from distutils.core import setup
setup(
name='mann',
packages=['mypleasure'],
version='0.9.1',
description='A multi-purpose logger and notifier.',
author='<NAME>',
author_email='<EMAIL>',
url='https://github.com/mypleasureteam/mann',
download_url='https://github.com/mypleasureteam/mann/tarball/0.1',
keywords=['logging', 'notification', 'trello', 'slack'],
classifiers=[],
)
| en | 0.499595 | Setup for Mann on PyPi. | 1.343032 | 1 |
magmap/gui/vis_3d.py | kaparna126/magellanmapper | 10 | 6613734 | # 3D visualization in MagellanMapper
import math
from time import time
import numpy as np
from skimage import filters, restoration, transform
from magmap.cv import segmenter
from magmap.io import libmag
from magmap.plot import colormaps, plot_3d
from magmap.settings import config
class Vis3D:
"""3D visualization object for handling Mayavi/VTK tasks.
Attributes:
scene (:class:`mayavi.tools.mlab_scene_model.MlabSceneModel`):
Mayavi scene.
fn_update_coords (func): Callback to update coordinates; defaults to
None.
surfaces (list): List of Mayavi surfaces for each displayed channel;
defaults to None.
blobs (list[:class:`mayavi.modules.glyph.Glyph`]): List of Mayavi
glyphs, where each glyph typically contains many 3D points
representing blob positions; defaults to None.
"""
#: float: Maximum number of points to show.
_MASK_DIVIDEND = 10000.0 # 3D max points
def __init__(self, scene):
"""Initialize a 3D visualization object.
Args:
scene (:class:`mayavi.tools.mlab_scene_model.MlabSceneModel`):
Mayavi scene.
"""
self.scene = scene
# callbacks
self.fn_update_coords = None
# generated Mayavi objects
self.surfaces = None
self.blobs = None
def update_img_display(self, minimum=None, maximum=None, brightness=None,
contrast=None, alpha=None):
"""Update the displayed image settings.
Args:
minimum (float): Minimum intensity.
maximum (float): Maximum intensity.
brightness (float): Brightness gamma.
contrast (float): Contrast factor.
alpha (float): Opacity, from 0-1, where 1 is fully opaque.
Returns:
"""
if self.surfaces:
for surface in self.surfaces:
if alpha is not None:
surface.actor.property.opacity = alpha
def plot_3d_points(self, roi, channel, flipz=False, offset=None):
"""Plots all pixels as points in 3D space.
Points falling below a given threshold will be removed, allowing
the viewer to see through the presumed background to masses within
the region of interest.
Args:
roi (:class:`numpy.ndarray`): Region of interest either as a 3D
``z,y,x`` or 4D ``z,y,x,c`` array.
channel (int): Channel to select, which can be None to indicate all
channels.
flipz (bool): True to invert the ROI along the z-axis to match
the handedness of Matplotlib with z progressing upward;
defaults to False.
offset (Sequence[int]): Origin coordinates in ``z,y,x``; defaults
to None.
Returns:
bool: True if points were rendered, False if no points to render.
"""
print("Plotting ROI as 3D points")
# streamline the image
if roi is None or roi.size < 1: return False
roi = plot_3d.saturate_roi(roi, clip_vmax=98.5, channel=channel)
roi = np.clip(roi, 0.2, 0.8)
roi = restoration.denoise_tv_chambolle(roi, weight=0.1)
# separate parallel arrays for each dimension of all coordinates for
# Mayavi input format, with the ROI itself given as a 1D scalar array ;
# TODO: consider using np.mgrid to construct the x,y,z arrays
time_start = time()
shape = roi.shape
isotropic = plot_3d.get_isotropic_vis(config.roi_profile)
z = np.ones((shape[0], shape[1] * shape[2]))
for i in range(shape[0]):
z[i] = z[i] * i
if flipz:
# invert along z-axis to match handedness of Matplotlib with z up
z *= -1
if offset is not None:
offset = np.copy(offset)
offset[0] *= -1
y = np.ones((shape[0] * shape[1], shape[2]))
for i in range(shape[0]):
for j in range(shape[1]):
y[i * shape[1] + j] = y[i * shape[1] + j] * j
x = np.ones((shape[0] * shape[1], shape[2]))
for i in range(shape[0] * shape[1]):
x[i] = np.arange(shape[2])
if offset is not None:
offset = np.multiply(offset, isotropic)
coords = [z, y, x]
for i, _ in enumerate(coords):
# scale coordinates for isotropy
coords[i] *= isotropic[i]
if offset is not None:
# translate by offset
coords[i] += offset[i]
multichannel, channels = plot_3d.setup_channels(roi, channel, 3)
for chl in channels:
roi_show = roi[..., chl] if multichannel else roi
roi_show_1d = roi_show.reshape(roi_show.size)
if chl == 0:
x = np.reshape(x, roi_show.size)
y = np.reshape(y, roi_show.size)
z = np.reshape(z, roi_show.size)
settings = config.get_roi_profile(chl)
# clear background points to see remaining structures
thresh = 0
if len(np.unique(roi_show)) > 1:
# need > 1 val to threshold
try:
thresh = filters.threshold_otsu(roi_show, 64)
except ValueError as e:
thresh = np.median(roi_show)
print("could not determine Otsu threshold, taking median "
"({}) instead".format(thresh))
thresh *= settings["points_3d_thresh"]
print("removing 3D points below threshold of {}".format(thresh))
remove = np.where(roi_show_1d < thresh)
roi_show_1d = np.delete(roi_show_1d, remove)
# adjust range from 0-1 to region of colormap to use
roi_show_1d = libmag.normalize(roi_show_1d, 0.6, 1.0)
points_len = roi_show_1d.size
if points_len == 0:
print("no 3D points to display")
return False
mask = math.ceil(points_len / self._MASK_DIVIDEND)
print("points: {}, mask: {}".format(points_len, mask))
if any(np.isnan(roi_show_1d)):
# TODO: see if some NaNs are permissible
print("NaN values for 3D points, will not show 3D visualization")
return False
pts = self.scene.mlab.points3d(
np.delete(x, remove), np.delete(y, remove), np.delete(z, remove),
roi_show_1d, mode="sphere",
scale_mode="scalar", mask_points=mask, line_width=1.0, vmax=1.0,
vmin=0.0, transparent=True)
cmap = colormaps.get_cmap(config.cmaps, chl)
if cmap is not None:
pts.module_manager.scalar_lut_manager.lut.table = cmap(
range(0, 256)) * 255
# scale glyphs to partially fill in gaps from isotropic scaling;
# do not use actor scaling as it also translates the points when
# not positioned at the origin
pts.glyph.glyph.scale_factor = 2 * max(isotropic)
# keep visual ordering of surfaces when opacity is reduced
self.scene.renderer.use_depth_peeling = True
print("time for 3D points display: {}".format(time() - time_start))
return True
def plot_3d_surface(self, roi, channel, segment=False, flipz=False,
offset=None):
"""Plots areas with greater intensity as 3D surfaces.
The scene will be cleared before display.
Args:
roi (:class:`numpy.ndarray`): Region of interest either as a 3D
``z,y,x`` or 4D ``z,y,x,c`` array.
channel (int): Channel to select, which can be None to indicate all
channels.
segment (bool): True to denoise and segment ``roi`` before
displaying, which may remove artifacts that might otherwise
lead to spurious surfaces. Defaults to False.
flipz: True to invert ``roi`` along z-axis to match handedness
of Matplotlib with z progressing upward; defaults to False.
offset (Sequence[int]): Origin coordinates in ``z,y,x``; defaults
to None.
Returns:
list: List of Mayavi surfaces for each displayed channel, which
are also stored in :attr:`surfaces`.
"""
# Plot in Mayavi
print("viewing 3D surface")
pipeline = self.scene.mlab.pipeline
settings = config.roi_profile
if flipz:
# invert along z-axis to match handedness of Matplotlib with z up
roi = roi[::-1]
if offset is not None:
# invert z-offset and translate by ROI z-size so ROI is
# mirrored across the xy-plane
offset = np.copy(offset)
offset[0] = -offset[0] - roi.shape[0]
isotropic = plot_3d.get_isotropic_vis(settings)
# saturate to remove noise and normalize values
roi = plot_3d.saturate_roi(roi, channel=channel)
# turn off segmentation if ROI too big (arbitrarily set here as
# > 10 million pixels) to avoid performance hit and since likely showing
# large region of downsampled image anyway, where don't need hi res
num_pixels = np.prod(roi.shape)
to_segment = num_pixels < 10000000
time_start = time()
multichannel, channels = plot_3d.setup_channels(roi, channel, 3)
surfaces = []
for chl in channels:
roi_show = roi[..., chl] if multichannel else roi
# clip to minimize sub-nuclear variation
roi_show = np.clip(roi_show, 0.2, 0.8)
if segment:
# denoising makes for much cleaner images but also seems to
# allow structures to blend together
# TODO: consider segmenting individual structures and rendering
# as separate surfaces to avoid blending
roi_show = restoration.denoise_tv_chambolle(
roi_show, weight=0.1)
# build surface from segmented ROI
if to_segment:
vmin, vmax = np.percentile(roi_show, (40, 70))
walker = segmenter.segment_rw(
roi_show, chl, vmin=vmin, vmax=vmax)
roi_show *= np.subtract(walker[0], 1)
else:
print("deferring segmentation as {} px is above threshold"
.format(num_pixels))
# ROI is in (z, y, x) order, so need to transpose or swap x,z axes
roi_show = np.transpose(roi_show)
surface = pipeline.scalar_field(roi_show)
# Contour -> Surface pipeline
# create the surface
surface = pipeline.contour(surface)
# remove many more extraneous points
surface = pipeline.user_defined(
surface, filter="SmoothPolyDataFilter")
surface.filter.number_of_iterations = 400
surface.filter.relaxation_factor = 0.015
# distinguishing pos vs neg curvatures?
surface = pipeline.user_defined(surface, filter="Curvatures")
surface = self.scene.mlab.pipeline.surface(surface)
module_manager = surface.module_manager
module_manager.scalar_lut_manager.data_range = np.array([-2, 0])
module_manager.scalar_lut_manager.lut_mode = "gray"
'''
# Surface pipleline with contours enabled (similar to above?)
surface = pipeline.contour_surface(
surface, color=(0.7, 1, 0.7), line_width=6.0)
surface.actor.property.representation = 'wireframe'
#surface.actor.property.line_width = 6.0
surface.actor.mapper.scalar_visibility = False
'''
'''
# IsoSurface pipeline
# uses unique IsoSurface module but appears to have
# similar output to contour_surface
surface = pipeline.iso_surface(surface)
# limit contours for simpler surfaces including smaller file sizes;
# TODO: consider making settable as arg or through profile
surface.contour.number_of_contours = 1
try:
# increase min to further reduce complexity
surface.contour.minimum_contour = 0.5
surface.contour.maximum_contour = 0.8
except Exception as e:
print(e)
print("ignoring min/max contour for now")
'''
if offset is not None:
# translate to offset scaled by isotropic factor
surface.actor.actor.position = np.multiply(
offset, isotropic)[::-1]
# scale surfaces, which expands/contracts but does not appear
# to translate the surface position
surface.actor.actor.scale = isotropic[::-1]
surfaces.append(surface)
# keep visual ordering of surfaces when opacity is reduced
self.scene.renderer.use_depth_peeling = True
print("time to render 3D surface: {}".format(time() - time_start))
self.surfaces = surfaces
return surfaces
def _shadow_blob(self, x, y, z, cmap_indices, cmap, scale):
"""Shows blobs as shadows projected parallel to the 3D visualization.
Parmas:
x: Array of x-coordinates of blobs.
y: Array of y-coordinates of blobs.
z: Array of z-coordinates of blobs.
cmap_indices: Indices of blobs for the colormap, usually given as a
simple ascending sequence the same size as the number of blobs.
cmap: The colormap, usually the same as for the segments.
scale: Array of scaled size of each blob.
"""
pts_shadows = self.scene.mlab.points3d(
x, y, z, cmap_indices, mode="2dcircle", scale_mode="none",
scale_factor=scale * 0.8, resolution=20)
pts_shadows.module_manager.scalar_lut_manager.lut.table = cmap
return pts_shadows
def show_blobs(self, segments, segs_in_mask, cmap, roi_offset, roi_size,
show_shadows=False, flipz=None):
"""Show 3D blobs as points.
Args:
segments: Labels from 3D blob detection method.
segs_in_mask: Boolean mask for segments within the ROI; all other
segments are assumed to be from padding and border regions
surrounding the ROI.
cmap (:class:`numpy.ndaarry`): Colormap as a 2D Numpy array in the
format ``[[R, G, B, alpha], ...]``.
roi_offset (Sequence[int]): Region of interest offset in ``z,y,x``.
roi_size (Sequence[int]): Region of interest size in ``z,y,x``.
Used to show the ROI outline.
show_shadows: True if shadows of blobs should be depicted on planes
behind the blobs; defaults to False.
flipz (bool): True to invert blobs along the z-axis to match
the handedness of Matplotlib with z progressing upward;
defaults to False.
Returns:
A 3-element tuple containing ``pts_in``, the 3D points within the
ROI; ``cmap'', the random colormap generated with a color for each
blob, and ``scale``, the current size of the points.
"""
if segments.shape[0] <= 0:
return None, 0
if roi_offset is None:
roi_offset = np.zeros(3, dtype=np.int)
if self.blobs:
for blob in self.blobs:
# remove existing blob glyphs from the pipeline
blob.remove()
settings = config.roi_profile
# copy blobs with duplicate columns to access original values for
# the coordinates callback when a blob is selected
segs = np.concatenate((segments[:, :4], segments[:, :4]), axis=1)
isotropic = plot_3d.get_isotropic_vis(settings)
if flipz:
# invert along z-axis within the same original space, eg to match
# handedness of Matplotlib with z up
segs[:, 0] *= -1
roi_offset = np.copy(roi_offset)
roi_offset[0] *= -1
roi_size = np.copy(roi_size)
roi_size[0] *= -1
segs[:, :3] = np.add(segs[:, :3], roi_offset)
if isotropic is not None:
# adjust position based on isotropic factor
roi_offset = np.multiply(roi_offset, isotropic)
roi_size = np.multiply(roi_size[:3], isotropic)
segs[:, :3] = np.multiply(segs[:, :3], isotropic)
radii = segs[:, 3]
scale = 5 if radii is None else np.mean(np.mean(radii) + np.amax(radii))
print("blob point scaling: {}".format(scale))
# colormap has to be at least 2 colors
segs_in = segs[segs_in_mask]
cmap_indices = np.arange(segs_in.shape[0])
if show_shadows:
# show projections onto side planes, assumed to be at -10 units
# along the given axis
segs_ones = np.ones(segs.shape[0])
# xy
self._shadow_blob(
segs_in[:, 2], segs_in[:, 1], segs_ones * -10, cmap_indices,
cmap, scale)
# xz
shadows = self._shadow_blob(
segs_in[:, 2], segs_in[:, 0], segs_ones * -10, cmap_indices,
cmap, scale)
shadows.actor.actor.orientation = [90, 0, 0]
shadows.actor.actor.position = [0, -20, 0]
# yz
shadows = self._shadow_blob(
segs_in[:, 1], segs_in[:, 0], segs_ones * -10, cmap_indices,
cmap, scale)
shadows.actor.actor.orientation = [90, 90, 0]
shadows.actor.actor.position = [0, 0, 0]
# show blobs within the ROI
points_len = len(segs)
mask = math.ceil(points_len / self._MASK_DIVIDEND)
print("points: {}, mask: {}".format(points_len, mask))
pts_in = None
self.blobs = []
if len(segs_in) > 0:
# each Glyph contains multiple 3D points, one for each blob
pts_in = self.scene.mlab.points3d(
segs_in[:, 2], segs_in[:, 1],
segs_in[:, 0], cmap_indices,
mask_points=mask, scale_mode="none", scale_factor=scale,
resolution=50)
pts_in.module_manager.scalar_lut_manager.lut.table = cmap
self.blobs.append(pts_in)
# show blobs within padding or border region as black and more
# transparent
segs_out_mask = np.logical_not(segs_in_mask)
if np.sum(segs_out_mask) > 0:
self.blobs.append(self.scene.mlab.points3d(
segs[segs_out_mask, 2], segs[segs_out_mask, 1],
segs[segs_out_mask, 0], color=(0, 0, 0),
mask_points=mask, scale_mode="none", scale_factor=scale / 2,
resolution=50, opacity=0.2))
def pick_callback(pick):
# handle picking blobs/glyphs
if pick.actor in pts_in.actor.actors:
# get the blob corresponding to the picked glyph actor
blobi = pick.point_id // glyph_points.shape[0]
else:
# find the closest blob to the pick position
dists = np.linalg.norm(
segs_in[:, :3] - pick.pick_position[::-1], axis=1)
blobi = np.argmin(dists)
if dists[blobi] > max_dist:
# remove blob if not within a tolerated distance
blobi = None
if blobi is None:
# revert outline to full ROI if no blob is found
self.show_roi_outline(roi_offset, roi_size)
else:
# move outline cube to surround picked blob; each glyph has
# has many points, and each point ID maps to a data index
# after floor division by the number of points
z, y, x, r = segs_in[blobi, :4]
outline.bounds = (x - r, x + r, y - r, y + r, z - r, z + r)
if self.fn_update_coords:
# callback to update coordinates using blob's orig coords
self.fn_update_coords(np.add(
segs_in[blobi, 4:7], roi_offset).astype(np.int))
# show ROI outline and make blobs pickable, falling back to closest
# blobs within 20% of the longest ROI edge to be picked if present
outline = self.show_roi_outline(roi_offset, roi_size)
print(outline)
glyph_points = pts_in.glyph.glyph_source.glyph_source.output.points.\
to_array()
max_dist = max(roi_size) * 0.2
self.scene.mlab.gcf().on_mouse_pick(pick_callback)
return pts_in, scale
def _shadow_img2d(self, img2d, shape, axis):
"""Shows a plane along the given axis as a shadow parallel to
the 3D visualization.
Args:
img2d: The plane to show.
shape: Shape of the ROI.
axis: Axis along which the plane lies.
Returns:
The displayed plane.
"""
img2d = np.swapaxes(img2d, 0, 1)
img2d[img2d < 1] = 0
# expands the plane to match the size of the xy plane, with this
# plane in the middle
extra_z = (shape[axis] - shape[0]) // 2
if extra_z > 0:
img2d_full = np.zeros(shape[1] * shape[2])
img2d_full = np.reshape(img2d_full, [shape[1], shape[2]])
img2d_full[:, extra_z:extra_z + img2d.shape[1]] = img2d
img2d = img2d_full
return self.scene.mlab.imshow(img2d, opacity=0.5, colormap="gray")
def plot_2d_shadows(self, roi, flipz=False):
"""Plots 2D shadows in each axis around the 3D visualization.
Args:
roi (:class:`numpy.ndarray`): Region of interest.
flipz (bool): True to invert ``roi`` along z-axis to match
handedness of Matplotlib with z progressing upward; defaults
to False.
"""
# set up shapes, accounting for any isotropic resizing
if flipz:
# invert along z-axis to match handedness of Matplotlib with z up
roi = roi[::-1]
if len(roi.shape) > 2:
# covert 4D to 3D array, using only the 1st channel
roi = roi[:, :, :, 0]
isotropic = plot_3d.get_isotropic_vis(config.roi_profile)
shape = roi.shape
shape_iso = np.multiply(roi.shape, isotropic).astype(np.int)
shape_iso_mid = shape_iso // 2
# TODO: shift z by +10?
# xy-plane, positioned just below the 3D ROI
img2d = roi[shape[0] // 2, :, :]
img2d = transform.resize(
img2d, np.multiply(img2d.shape, isotropic[1:]).astype(np.int),
preserve_range=True)
img2d_mlab = self._shadow_img2d(img2d, shape_iso, 0)
# Mayavi positions are in x,y,z
img2d_mlab.actor.position = [
shape_iso_mid[2], shape_iso_mid[1], -10]
# xz-plane
img2d = roi[:, shape[1] // 2, :]
img2d = transform.resize(
img2d, np.multiply(img2d.shape, isotropic[[0, 2]]).astype(np.int),
preserve_range=True)
img2d_mlab = self._shadow_img2d(img2d, shape_iso, 2)
img2d_mlab.actor.position = [
-10, shape_iso_mid[1], shape_iso_mid[0]]
img2d_mlab.actor.orientation = [90, 90, 0]
# yz-plane
img2d = roi[:, :, shape[2] // 2]
img2d = transform.resize(
img2d, np.multiply(img2d.shape, isotropic[:2]).astype(np.int),
preserve_range=True)
img2d_mlab = self._shadow_img2d(img2d, shape_iso, 1)
img2d_mlab.actor.position = [
shape_iso_mid[2], -10, shape_iso_mid[0]]
img2d_mlab.actor.orientation = [90, 0, 0]
def show_roi_outline(self, roi_offset, roi_size):
"""Show plot outline to show ROI borders.
Args:
roi_offset (Sequence[int]): Region of interest offset in ``z,y,x``.
roi_size (Sequence[int]): Region of interest size in ``z,y,x``.
Returns:
:class:`mayavi.modules.outline.Outline`: Outline object.
"""
# manually calculate extent since the default bounds do not always
# capture all objects and to include any empty border spaces
return self.scene.mlab.outline(
extent=np.array(tuple(zip(roi_offset, np.add(
roi_offset, roi_size)))).ravel()[::-1])
def clear_scene(self):
"""Clear the scene."""
print("Clearing 3D scene")
self.scene.mlab.clf()
| # 3D visualization in MagellanMapper
import math
from time import time
import numpy as np
from skimage import filters, restoration, transform
from magmap.cv import segmenter
from magmap.io import libmag
from magmap.plot import colormaps, plot_3d
from magmap.settings import config
class Vis3D:
"""3D visualization object for handling Mayavi/VTK tasks.
Attributes:
scene (:class:`mayavi.tools.mlab_scene_model.MlabSceneModel`):
Mayavi scene.
fn_update_coords (func): Callback to update coordinates; defaults to
None.
surfaces (list): List of Mayavi surfaces for each displayed channel;
defaults to None.
blobs (list[:class:`mayavi.modules.glyph.Glyph`]): List of Mayavi
glyphs, where each glyph typically contains many 3D points
representing blob positions; defaults to None.
"""
#: float: Maximum number of points to show.
_MASK_DIVIDEND = 10000.0 # 3D max points
def __init__(self, scene):
"""Initialize a 3D visualization object.
Args:
scene (:class:`mayavi.tools.mlab_scene_model.MlabSceneModel`):
Mayavi scene.
"""
self.scene = scene
# callbacks
self.fn_update_coords = None
# generated Mayavi objects
self.surfaces = None
self.blobs = None
def update_img_display(self, minimum=None, maximum=None, brightness=None,
contrast=None, alpha=None):
"""Update the displayed image settings.
Args:
minimum (float): Minimum intensity.
maximum (float): Maximum intensity.
brightness (float): Brightness gamma.
contrast (float): Contrast factor.
alpha (float): Opacity, from 0-1, where 1 is fully opaque.
Returns:
"""
if self.surfaces:
for surface in self.surfaces:
if alpha is not None:
surface.actor.property.opacity = alpha
def plot_3d_points(self, roi, channel, flipz=False, offset=None):
"""Plots all pixels as points in 3D space.
Points falling below a given threshold will be removed, allowing
the viewer to see through the presumed background to masses within
the region of interest.
Args:
roi (:class:`numpy.ndarray`): Region of interest either as a 3D
``z,y,x`` or 4D ``z,y,x,c`` array.
channel (int): Channel to select, which can be None to indicate all
channels.
flipz (bool): True to invert the ROI along the z-axis to match
the handedness of Matplotlib with z progressing upward;
defaults to False.
offset (Sequence[int]): Origin coordinates in ``z,y,x``; defaults
to None.
Returns:
bool: True if points were rendered, False if no points to render.
"""
print("Plotting ROI as 3D points")
# streamline the image
if roi is None or roi.size < 1: return False
roi = plot_3d.saturate_roi(roi, clip_vmax=98.5, channel=channel)
roi = np.clip(roi, 0.2, 0.8)
roi = restoration.denoise_tv_chambolle(roi, weight=0.1)
# separate parallel arrays for each dimension of all coordinates for
# Mayavi input format, with the ROI itself given as a 1D scalar array ;
# TODO: consider using np.mgrid to construct the x,y,z arrays
time_start = time()
shape = roi.shape
isotropic = plot_3d.get_isotropic_vis(config.roi_profile)
z = np.ones((shape[0], shape[1] * shape[2]))
for i in range(shape[0]):
z[i] = z[i] * i
if flipz:
# invert along z-axis to match handedness of Matplotlib with z up
z *= -1
if offset is not None:
offset = np.copy(offset)
offset[0] *= -1
y = np.ones((shape[0] * shape[1], shape[2]))
for i in range(shape[0]):
for j in range(shape[1]):
y[i * shape[1] + j] = y[i * shape[1] + j] * j
x = np.ones((shape[0] * shape[1], shape[2]))
for i in range(shape[0] * shape[1]):
x[i] = np.arange(shape[2])
if offset is not None:
offset = np.multiply(offset, isotropic)
coords = [z, y, x]
for i, _ in enumerate(coords):
# scale coordinates for isotropy
coords[i] *= isotropic[i]
if offset is not None:
# translate by offset
coords[i] += offset[i]
multichannel, channels = plot_3d.setup_channels(roi, channel, 3)
for chl in channels:
roi_show = roi[..., chl] if multichannel else roi
roi_show_1d = roi_show.reshape(roi_show.size)
if chl == 0:
x = np.reshape(x, roi_show.size)
y = np.reshape(y, roi_show.size)
z = np.reshape(z, roi_show.size)
settings = config.get_roi_profile(chl)
# clear background points to see remaining structures
thresh = 0
if len(np.unique(roi_show)) > 1:
# need > 1 val to threshold
try:
thresh = filters.threshold_otsu(roi_show, 64)
except ValueError as e:
thresh = np.median(roi_show)
print("could not determine Otsu threshold, taking median "
"({}) instead".format(thresh))
thresh *= settings["points_3d_thresh"]
print("removing 3D points below threshold of {}".format(thresh))
remove = np.where(roi_show_1d < thresh)
roi_show_1d = np.delete(roi_show_1d, remove)
# adjust range from 0-1 to region of colormap to use
roi_show_1d = libmag.normalize(roi_show_1d, 0.6, 1.0)
points_len = roi_show_1d.size
if points_len == 0:
print("no 3D points to display")
return False
mask = math.ceil(points_len / self._MASK_DIVIDEND)
print("points: {}, mask: {}".format(points_len, mask))
if any(np.isnan(roi_show_1d)):
# TODO: see if some NaNs are permissible
print("NaN values for 3D points, will not show 3D visualization")
return False
pts = self.scene.mlab.points3d(
np.delete(x, remove), np.delete(y, remove), np.delete(z, remove),
roi_show_1d, mode="sphere",
scale_mode="scalar", mask_points=mask, line_width=1.0, vmax=1.0,
vmin=0.0, transparent=True)
cmap = colormaps.get_cmap(config.cmaps, chl)
if cmap is not None:
pts.module_manager.scalar_lut_manager.lut.table = cmap(
range(0, 256)) * 255
# scale glyphs to partially fill in gaps from isotropic scaling;
# do not use actor scaling as it also translates the points when
# not positioned at the origin
pts.glyph.glyph.scale_factor = 2 * max(isotropic)
# keep visual ordering of surfaces when opacity is reduced
self.scene.renderer.use_depth_peeling = True
print("time for 3D points display: {}".format(time() - time_start))
return True
def plot_3d_surface(self, roi, channel, segment=False, flipz=False,
offset=None):
"""Plots areas with greater intensity as 3D surfaces.
The scene will be cleared before display.
Args:
roi (:class:`numpy.ndarray`): Region of interest either as a 3D
``z,y,x`` or 4D ``z,y,x,c`` array.
channel (int): Channel to select, which can be None to indicate all
channels.
segment (bool): True to denoise and segment ``roi`` before
displaying, which may remove artifacts that might otherwise
lead to spurious surfaces. Defaults to False.
flipz: True to invert ``roi`` along z-axis to match handedness
of Matplotlib with z progressing upward; defaults to False.
offset (Sequence[int]): Origin coordinates in ``z,y,x``; defaults
to None.
Returns:
list: List of Mayavi surfaces for each displayed channel, which
are also stored in :attr:`surfaces`.
"""
# Plot in Mayavi
print("viewing 3D surface")
pipeline = self.scene.mlab.pipeline
settings = config.roi_profile
if flipz:
# invert along z-axis to match handedness of Matplotlib with z up
roi = roi[::-1]
if offset is not None:
# invert z-offset and translate by ROI z-size so ROI is
# mirrored across the xy-plane
offset = np.copy(offset)
offset[0] = -offset[0] - roi.shape[0]
isotropic = plot_3d.get_isotropic_vis(settings)
# saturate to remove noise and normalize values
roi = plot_3d.saturate_roi(roi, channel=channel)
# turn off segmentation if ROI too big (arbitrarily set here as
# > 10 million pixels) to avoid performance hit and since likely showing
# large region of downsampled image anyway, where don't need hi res
num_pixels = np.prod(roi.shape)
to_segment = num_pixels < 10000000
time_start = time()
multichannel, channels = plot_3d.setup_channels(roi, channel, 3)
surfaces = []
for chl in channels:
roi_show = roi[..., chl] if multichannel else roi
# clip to minimize sub-nuclear variation
roi_show = np.clip(roi_show, 0.2, 0.8)
if segment:
# denoising makes for much cleaner images but also seems to
# allow structures to blend together
# TODO: consider segmenting individual structures and rendering
# as separate surfaces to avoid blending
roi_show = restoration.denoise_tv_chambolle(
roi_show, weight=0.1)
# build surface from segmented ROI
if to_segment:
vmin, vmax = np.percentile(roi_show, (40, 70))
walker = segmenter.segment_rw(
roi_show, chl, vmin=vmin, vmax=vmax)
roi_show *= np.subtract(walker[0], 1)
else:
print("deferring segmentation as {} px is above threshold"
.format(num_pixels))
# ROI is in (z, y, x) order, so need to transpose or swap x,z axes
roi_show = np.transpose(roi_show)
surface = pipeline.scalar_field(roi_show)
# Contour -> Surface pipeline
# create the surface
surface = pipeline.contour(surface)
# remove many more extraneous points
surface = pipeline.user_defined(
surface, filter="SmoothPolyDataFilter")
surface.filter.number_of_iterations = 400
surface.filter.relaxation_factor = 0.015
# distinguishing pos vs neg curvatures?
surface = pipeline.user_defined(surface, filter="Curvatures")
surface = self.scene.mlab.pipeline.surface(surface)
module_manager = surface.module_manager
module_manager.scalar_lut_manager.data_range = np.array([-2, 0])
module_manager.scalar_lut_manager.lut_mode = "gray"
'''
# Surface pipleline with contours enabled (similar to above?)
surface = pipeline.contour_surface(
surface, color=(0.7, 1, 0.7), line_width=6.0)
surface.actor.property.representation = 'wireframe'
#surface.actor.property.line_width = 6.0
surface.actor.mapper.scalar_visibility = False
'''
'''
# IsoSurface pipeline
# uses unique IsoSurface module but appears to have
# similar output to contour_surface
surface = pipeline.iso_surface(surface)
# limit contours for simpler surfaces including smaller file sizes;
# TODO: consider making settable as arg or through profile
surface.contour.number_of_contours = 1
try:
# increase min to further reduce complexity
surface.contour.minimum_contour = 0.5
surface.contour.maximum_contour = 0.8
except Exception as e:
print(e)
print("ignoring min/max contour for now")
'''
if offset is not None:
# translate to offset scaled by isotropic factor
surface.actor.actor.position = np.multiply(
offset, isotropic)[::-1]
# scale surfaces, which expands/contracts but does not appear
# to translate the surface position
surface.actor.actor.scale = isotropic[::-1]
surfaces.append(surface)
# keep visual ordering of surfaces when opacity is reduced
self.scene.renderer.use_depth_peeling = True
print("time to render 3D surface: {}".format(time() - time_start))
self.surfaces = surfaces
return surfaces
def _shadow_blob(self, x, y, z, cmap_indices, cmap, scale):
"""Shows blobs as shadows projected parallel to the 3D visualization.
Parmas:
x: Array of x-coordinates of blobs.
y: Array of y-coordinates of blobs.
z: Array of z-coordinates of blobs.
cmap_indices: Indices of blobs for the colormap, usually given as a
simple ascending sequence the same size as the number of blobs.
cmap: The colormap, usually the same as for the segments.
scale: Array of scaled size of each blob.
"""
pts_shadows = self.scene.mlab.points3d(
x, y, z, cmap_indices, mode="2dcircle", scale_mode="none",
scale_factor=scale * 0.8, resolution=20)
pts_shadows.module_manager.scalar_lut_manager.lut.table = cmap
return pts_shadows
def show_blobs(self, segments, segs_in_mask, cmap, roi_offset, roi_size,
show_shadows=False, flipz=None):
"""Show 3D blobs as points.
Args:
segments: Labels from 3D blob detection method.
segs_in_mask: Boolean mask for segments within the ROI; all other
segments are assumed to be from padding and border regions
surrounding the ROI.
cmap (:class:`numpy.ndaarry`): Colormap as a 2D Numpy array in the
format ``[[R, G, B, alpha], ...]``.
roi_offset (Sequence[int]): Region of interest offset in ``z,y,x``.
roi_size (Sequence[int]): Region of interest size in ``z,y,x``.
Used to show the ROI outline.
show_shadows: True if shadows of blobs should be depicted on planes
behind the blobs; defaults to False.
flipz (bool): True to invert blobs along the z-axis to match
the handedness of Matplotlib with z progressing upward;
defaults to False.
Returns:
A 3-element tuple containing ``pts_in``, the 3D points within the
ROI; ``cmap'', the random colormap generated with a color for each
blob, and ``scale``, the current size of the points.
"""
if segments.shape[0] <= 0:
return None, 0
if roi_offset is None:
roi_offset = np.zeros(3, dtype=np.int)
if self.blobs:
for blob in self.blobs:
# remove existing blob glyphs from the pipeline
blob.remove()
settings = config.roi_profile
# copy blobs with duplicate columns to access original values for
# the coordinates callback when a blob is selected
segs = np.concatenate((segments[:, :4], segments[:, :4]), axis=1)
isotropic = plot_3d.get_isotropic_vis(settings)
if flipz:
# invert along z-axis within the same original space, eg to match
# handedness of Matplotlib with z up
segs[:, 0] *= -1
roi_offset = np.copy(roi_offset)
roi_offset[0] *= -1
roi_size = np.copy(roi_size)
roi_size[0] *= -1
segs[:, :3] = np.add(segs[:, :3], roi_offset)
if isotropic is not None:
# adjust position based on isotropic factor
roi_offset = np.multiply(roi_offset, isotropic)
roi_size = np.multiply(roi_size[:3], isotropic)
segs[:, :3] = np.multiply(segs[:, :3], isotropic)
radii = segs[:, 3]
scale = 5 if radii is None else np.mean(np.mean(radii) + np.amax(radii))
print("blob point scaling: {}".format(scale))
# colormap has to be at least 2 colors
segs_in = segs[segs_in_mask]
cmap_indices = np.arange(segs_in.shape[0])
if show_shadows:
# show projections onto side planes, assumed to be at -10 units
# along the given axis
segs_ones = np.ones(segs.shape[0])
# xy
self._shadow_blob(
segs_in[:, 2], segs_in[:, 1], segs_ones * -10, cmap_indices,
cmap, scale)
# xz
shadows = self._shadow_blob(
segs_in[:, 2], segs_in[:, 0], segs_ones * -10, cmap_indices,
cmap, scale)
shadows.actor.actor.orientation = [90, 0, 0]
shadows.actor.actor.position = [0, -20, 0]
# yz
shadows = self._shadow_blob(
segs_in[:, 1], segs_in[:, 0], segs_ones * -10, cmap_indices,
cmap, scale)
shadows.actor.actor.orientation = [90, 90, 0]
shadows.actor.actor.position = [0, 0, 0]
# show blobs within the ROI
points_len = len(segs)
mask = math.ceil(points_len / self._MASK_DIVIDEND)
print("points: {}, mask: {}".format(points_len, mask))
pts_in = None
self.blobs = []
if len(segs_in) > 0:
# each Glyph contains multiple 3D points, one for each blob
pts_in = self.scene.mlab.points3d(
segs_in[:, 2], segs_in[:, 1],
segs_in[:, 0], cmap_indices,
mask_points=mask, scale_mode="none", scale_factor=scale,
resolution=50)
pts_in.module_manager.scalar_lut_manager.lut.table = cmap
self.blobs.append(pts_in)
# show blobs within padding or border region as black and more
# transparent
segs_out_mask = np.logical_not(segs_in_mask)
if np.sum(segs_out_mask) > 0:
self.blobs.append(self.scene.mlab.points3d(
segs[segs_out_mask, 2], segs[segs_out_mask, 1],
segs[segs_out_mask, 0], color=(0, 0, 0),
mask_points=mask, scale_mode="none", scale_factor=scale / 2,
resolution=50, opacity=0.2))
def pick_callback(pick):
# handle picking blobs/glyphs
if pick.actor in pts_in.actor.actors:
# get the blob corresponding to the picked glyph actor
blobi = pick.point_id // glyph_points.shape[0]
else:
# find the closest blob to the pick position
dists = np.linalg.norm(
segs_in[:, :3] - pick.pick_position[::-1], axis=1)
blobi = np.argmin(dists)
if dists[blobi] > max_dist:
# remove blob if not within a tolerated distance
blobi = None
if blobi is None:
# revert outline to full ROI if no blob is found
self.show_roi_outline(roi_offset, roi_size)
else:
# move outline cube to surround picked blob; each glyph has
# has many points, and each point ID maps to a data index
# after floor division by the number of points
z, y, x, r = segs_in[blobi, :4]
outline.bounds = (x - r, x + r, y - r, y + r, z - r, z + r)
if self.fn_update_coords:
# callback to update coordinates using blob's orig coords
self.fn_update_coords(np.add(
segs_in[blobi, 4:7], roi_offset).astype(np.int))
# show ROI outline and make blobs pickable, falling back to closest
# blobs within 20% of the longest ROI edge to be picked if present
outline = self.show_roi_outline(roi_offset, roi_size)
print(outline)
glyph_points = pts_in.glyph.glyph_source.glyph_source.output.points.\
to_array()
max_dist = max(roi_size) * 0.2
self.scene.mlab.gcf().on_mouse_pick(pick_callback)
return pts_in, scale
def _shadow_img2d(self, img2d, shape, axis):
"""Shows a plane along the given axis as a shadow parallel to
the 3D visualization.
Args:
img2d: The plane to show.
shape: Shape of the ROI.
axis: Axis along which the plane lies.
Returns:
The displayed plane.
"""
img2d = np.swapaxes(img2d, 0, 1)
img2d[img2d < 1] = 0
# expands the plane to match the size of the xy plane, with this
# plane in the middle
extra_z = (shape[axis] - shape[0]) // 2
if extra_z > 0:
img2d_full = np.zeros(shape[1] * shape[2])
img2d_full = np.reshape(img2d_full, [shape[1], shape[2]])
img2d_full[:, extra_z:extra_z + img2d.shape[1]] = img2d
img2d = img2d_full
return self.scene.mlab.imshow(img2d, opacity=0.5, colormap="gray")
def plot_2d_shadows(self, roi, flipz=False):
"""Plots 2D shadows in each axis around the 3D visualization.
Args:
roi (:class:`numpy.ndarray`): Region of interest.
flipz (bool): True to invert ``roi`` along z-axis to match
handedness of Matplotlib with z progressing upward; defaults
to False.
"""
# set up shapes, accounting for any isotropic resizing
if flipz:
# invert along z-axis to match handedness of Matplotlib with z up
roi = roi[::-1]
if len(roi.shape) > 2:
# covert 4D to 3D array, using only the 1st channel
roi = roi[:, :, :, 0]
isotropic = plot_3d.get_isotropic_vis(config.roi_profile)
shape = roi.shape
shape_iso = np.multiply(roi.shape, isotropic).astype(np.int)
shape_iso_mid = shape_iso // 2
# TODO: shift z by +10?
# xy-plane, positioned just below the 3D ROI
img2d = roi[shape[0] // 2, :, :]
img2d = transform.resize(
img2d, np.multiply(img2d.shape, isotropic[1:]).astype(np.int),
preserve_range=True)
img2d_mlab = self._shadow_img2d(img2d, shape_iso, 0)
# Mayavi positions are in x,y,z
img2d_mlab.actor.position = [
shape_iso_mid[2], shape_iso_mid[1], -10]
# xz-plane
img2d = roi[:, shape[1] // 2, :]
img2d = transform.resize(
img2d, np.multiply(img2d.shape, isotropic[[0, 2]]).astype(np.int),
preserve_range=True)
img2d_mlab = self._shadow_img2d(img2d, shape_iso, 2)
img2d_mlab.actor.position = [
-10, shape_iso_mid[1], shape_iso_mid[0]]
img2d_mlab.actor.orientation = [90, 90, 0]
# yz-plane
img2d = roi[:, :, shape[2] // 2]
img2d = transform.resize(
img2d, np.multiply(img2d.shape, isotropic[:2]).astype(np.int),
preserve_range=True)
img2d_mlab = self._shadow_img2d(img2d, shape_iso, 1)
img2d_mlab.actor.position = [
shape_iso_mid[2], -10, shape_iso_mid[0]]
img2d_mlab.actor.orientation = [90, 0, 0]
def show_roi_outline(self, roi_offset, roi_size):
"""Show plot outline to show ROI borders.
Args:
roi_offset (Sequence[int]): Region of interest offset in ``z,y,x``.
roi_size (Sequence[int]): Region of interest size in ``z,y,x``.
Returns:
:class:`mayavi.modules.outline.Outline`: Outline object.
"""
# manually calculate extent since the default bounds do not always
# capture all objects and to include any empty border spaces
return self.scene.mlab.outline(
extent=np.array(tuple(zip(roi_offset, np.add(
roi_offset, roi_size)))).ravel()[::-1])
def clear_scene(self):
"""Clear the scene."""
print("Clearing 3D scene")
self.scene.mlab.clf()
| en | 0.807034 | # 3D visualization in MagellanMapper 3D visualization object for handling Mayavi/VTK tasks. Attributes: scene (:class:`mayavi.tools.mlab_scene_model.MlabSceneModel`): Mayavi scene. fn_update_coords (func): Callback to update coordinates; defaults to None. surfaces (list): List of Mayavi surfaces for each displayed channel; defaults to None. blobs (list[:class:`mayavi.modules.glyph.Glyph`]): List of Mayavi glyphs, where each glyph typically contains many 3D points representing blob positions; defaults to None. #: float: Maximum number of points to show. # 3D max points Initialize a 3D visualization object. Args: scene (:class:`mayavi.tools.mlab_scene_model.MlabSceneModel`): Mayavi scene. # callbacks # generated Mayavi objects Update the displayed image settings. Args: minimum (float): Minimum intensity. maximum (float): Maximum intensity. brightness (float): Brightness gamma. contrast (float): Contrast factor. alpha (float): Opacity, from 0-1, where 1 is fully opaque. Returns: Plots all pixels as points in 3D space. Points falling below a given threshold will be removed, allowing the viewer to see through the presumed background to masses within the region of interest. Args: roi (:class:`numpy.ndarray`): Region of interest either as a 3D ``z,y,x`` or 4D ``z,y,x,c`` array. channel (int): Channel to select, which can be None to indicate all channels. flipz (bool): True to invert the ROI along the z-axis to match the handedness of Matplotlib with z progressing upward; defaults to False. offset (Sequence[int]): Origin coordinates in ``z,y,x``; defaults to None. Returns: bool: True if points were rendered, False if no points to render. # streamline the image # separate parallel arrays for each dimension of all coordinates for # Mayavi input format, with the ROI itself given as a 1D scalar array ; # TODO: consider using np.mgrid to construct the x,y,z arrays # invert along z-axis to match handedness of Matplotlib with z up # scale coordinates for isotropy # translate by offset # clear background points to see remaining structures # need > 1 val to threshold # adjust range from 0-1 to region of colormap to use # TODO: see if some NaNs are permissible # scale glyphs to partially fill in gaps from isotropic scaling; # do not use actor scaling as it also translates the points when # not positioned at the origin # keep visual ordering of surfaces when opacity is reduced Plots areas with greater intensity as 3D surfaces. The scene will be cleared before display. Args: roi (:class:`numpy.ndarray`): Region of interest either as a 3D ``z,y,x`` or 4D ``z,y,x,c`` array. channel (int): Channel to select, which can be None to indicate all channels. segment (bool): True to denoise and segment ``roi`` before displaying, which may remove artifacts that might otherwise lead to spurious surfaces. Defaults to False. flipz: True to invert ``roi`` along z-axis to match handedness of Matplotlib with z progressing upward; defaults to False. offset (Sequence[int]): Origin coordinates in ``z,y,x``; defaults to None. Returns: list: List of Mayavi surfaces for each displayed channel, which are also stored in :attr:`surfaces`. # Plot in Mayavi # invert along z-axis to match handedness of Matplotlib with z up # invert z-offset and translate by ROI z-size so ROI is # mirrored across the xy-plane # saturate to remove noise and normalize values # turn off segmentation if ROI too big (arbitrarily set here as # > 10 million pixels) to avoid performance hit and since likely showing # large region of downsampled image anyway, where don't need hi res # clip to minimize sub-nuclear variation # denoising makes for much cleaner images but also seems to # allow structures to blend together # TODO: consider segmenting individual structures and rendering # as separate surfaces to avoid blending # build surface from segmented ROI # ROI is in (z, y, x) order, so need to transpose or swap x,z axes # Contour -> Surface pipeline # create the surface # remove many more extraneous points # distinguishing pos vs neg curvatures? # Surface pipleline with contours enabled (similar to above?) surface = pipeline.contour_surface( surface, color=(0.7, 1, 0.7), line_width=6.0) surface.actor.property.representation = 'wireframe' #surface.actor.property.line_width = 6.0 surface.actor.mapper.scalar_visibility = False # IsoSurface pipeline # uses unique IsoSurface module but appears to have # similar output to contour_surface surface = pipeline.iso_surface(surface) # limit contours for simpler surfaces including smaller file sizes; # TODO: consider making settable as arg or through profile surface.contour.number_of_contours = 1 try: # increase min to further reduce complexity surface.contour.minimum_contour = 0.5 surface.contour.maximum_contour = 0.8 except Exception as e: print(e) print("ignoring min/max contour for now") # translate to offset scaled by isotropic factor # scale surfaces, which expands/contracts but does not appear # to translate the surface position # keep visual ordering of surfaces when opacity is reduced Shows blobs as shadows projected parallel to the 3D visualization. Parmas: x: Array of x-coordinates of blobs. y: Array of y-coordinates of blobs. z: Array of z-coordinates of blobs. cmap_indices: Indices of blobs for the colormap, usually given as a simple ascending sequence the same size as the number of blobs. cmap: The colormap, usually the same as for the segments. scale: Array of scaled size of each blob. Show 3D blobs as points. Args: segments: Labels from 3D blob detection method. segs_in_mask: Boolean mask for segments within the ROI; all other segments are assumed to be from padding and border regions surrounding the ROI. cmap (:class:`numpy.ndaarry`): Colormap as a 2D Numpy array in the format ``[[R, G, B, alpha], ...]``. roi_offset (Sequence[int]): Region of interest offset in ``z,y,x``. roi_size (Sequence[int]): Region of interest size in ``z,y,x``. Used to show the ROI outline. show_shadows: True if shadows of blobs should be depicted on planes behind the blobs; defaults to False. flipz (bool): True to invert blobs along the z-axis to match the handedness of Matplotlib with z progressing upward; defaults to False. Returns: A 3-element tuple containing ``pts_in``, the 3D points within the ROI; ``cmap'', the random colormap generated with a color for each blob, and ``scale``, the current size of the points. # remove existing blob glyphs from the pipeline # copy blobs with duplicate columns to access original values for # the coordinates callback when a blob is selected # invert along z-axis within the same original space, eg to match # handedness of Matplotlib with z up # adjust position based on isotropic factor # colormap has to be at least 2 colors # show projections onto side planes, assumed to be at -10 units # along the given axis # xy # xz # yz # show blobs within the ROI # each Glyph contains multiple 3D points, one for each blob # show blobs within padding or border region as black and more # transparent # handle picking blobs/glyphs # get the blob corresponding to the picked glyph actor # find the closest blob to the pick position # remove blob if not within a tolerated distance # revert outline to full ROI if no blob is found # move outline cube to surround picked blob; each glyph has # has many points, and each point ID maps to a data index # after floor division by the number of points # callback to update coordinates using blob's orig coords # show ROI outline and make blobs pickable, falling back to closest # blobs within 20% of the longest ROI edge to be picked if present Shows a plane along the given axis as a shadow parallel to the 3D visualization. Args: img2d: The plane to show. shape: Shape of the ROI. axis: Axis along which the plane lies. Returns: The displayed plane. # expands the plane to match the size of the xy plane, with this # plane in the middle Plots 2D shadows in each axis around the 3D visualization. Args: roi (:class:`numpy.ndarray`): Region of interest. flipz (bool): True to invert ``roi`` along z-axis to match handedness of Matplotlib with z progressing upward; defaults to False. # set up shapes, accounting for any isotropic resizing # invert along z-axis to match handedness of Matplotlib with z up # covert 4D to 3D array, using only the 1st channel # TODO: shift z by +10? # xy-plane, positioned just below the 3D ROI # Mayavi positions are in x,y,z # xz-plane # yz-plane Show plot outline to show ROI borders. Args: roi_offset (Sequence[int]): Region of interest offset in ``z,y,x``. roi_size (Sequence[int]): Region of interest size in ``z,y,x``. Returns: :class:`mayavi.modules.outline.Outline`: Outline object. # manually calculate extent since the default bounds do not always # capture all objects and to include any empty border spaces Clear the scene. | 2.247025 | 2 |
maddpg/common/torch_utils.py | cying9/maddpg | 0 | 6613735 | import torch
from torch import nn
from torch import functional as F
def get_device(disable_cuda=False):
if (not disable_cuda) & torch.cuda.is_available():
device = "cuda"
print(f'Using CUDA ({torch.cuda.get_device_name(torch.cuda.current_device())})')
else:
print('Using CPU')
device = "cpu"
return torch.device(device)
def init_params(model, gain=1.0):
for params in model.parameters():
if len(params.shape) > 1:
nn.init.xavier_uniform_(params.data, gain=gain)
| import torch
from torch import nn
from torch import functional as F
def get_device(disable_cuda=False):
if (not disable_cuda) & torch.cuda.is_available():
device = "cuda"
print(f'Using CUDA ({torch.cuda.get_device_name(torch.cuda.current_device())})')
else:
print('Using CPU')
device = "cpu"
return torch.device(device)
def init_params(model, gain=1.0):
for params in model.parameters():
if len(params.shape) > 1:
nn.init.xavier_uniform_(params.data, gain=gain)
| none | 1 | 2.755692 | 3 | |
overlap/overlap/overlap.py | carlos-ferras/code-challenge | 1 | 6613736 | <filename>overlap/overlap/overlap.py
from typing import Tuple
Line = Tuple[int, int]
def is_number_inside_boundaries(number: int, boundaries: Line) -> bool:
"""Check if a number is contained into the specified boundaries
:param number: The number to check into the boundaries
:param boundaries: Two numbers tuple whit first element < second element
:return: True if the number is inside the boundaries, if not, False
"""
return min(boundaries) <= number <= max(boundaries)
def overlap_ordered(line1: Line, line2: Line) -> bool:
"""Check if two lines in the X axis overlap; the first one must be before or inside the second
:param line1: Two numbers tuple whit first element < second element
:param line2: Two numbers tuple whit first element < second element
:return: True if the lines overlap, if not, False
"""
return is_number_inside_boundaries(line1[0], line2) or is_number_inside_boundaries(line1[1], line2)
def overlap(line1: Line, line2: Line) -> bool:
"""Check if two lines in the X axis overlap
:param line1: Two numbers tuple whit first element < second element
:param line2: Two numbers tuple whit first element < second element
:return: True if the lines overlap, if not, False
"""
return overlap_ordered(line1, line2) or overlap_ordered(line2, line1)
| <filename>overlap/overlap/overlap.py
from typing import Tuple
Line = Tuple[int, int]
def is_number_inside_boundaries(number: int, boundaries: Line) -> bool:
"""Check if a number is contained into the specified boundaries
:param number: The number to check into the boundaries
:param boundaries: Two numbers tuple whit first element < second element
:return: True if the number is inside the boundaries, if not, False
"""
return min(boundaries) <= number <= max(boundaries)
def overlap_ordered(line1: Line, line2: Line) -> bool:
"""Check if two lines in the X axis overlap; the first one must be before or inside the second
:param line1: Two numbers tuple whit first element < second element
:param line2: Two numbers tuple whit first element < second element
:return: True if the lines overlap, if not, False
"""
return is_number_inside_boundaries(line1[0], line2) or is_number_inside_boundaries(line1[1], line2)
def overlap(line1: Line, line2: Line) -> bool:
"""Check if two lines in the X axis overlap
:param line1: Two numbers tuple whit first element < second element
:param line2: Two numbers tuple whit first element < second element
:return: True if the lines overlap, if not, False
"""
return overlap_ordered(line1, line2) or overlap_ordered(line2, line1)
| en | 0.808119 | Check if a number is contained into the specified boundaries :param number: The number to check into the boundaries :param boundaries: Two numbers tuple whit first element < second element :return: True if the number is inside the boundaries, if not, False Check if two lines in the X axis overlap; the first one must be before or inside the second :param line1: Two numbers tuple whit first element < second element :param line2: Two numbers tuple whit first element < second element :return: True if the lines overlap, if not, False Check if two lines in the X axis overlap :param line1: Two numbers tuple whit first element < second element :param line2: Two numbers tuple whit first element < second element :return: True if the lines overlap, if not, False | 3.944636 | 4 |
lib/python/treadmill/sproc/warpgate.py | bretttegartms/treadmill | 133 | 6613737 | <reponame>bretttegartms/treadmill
"""Warpgate client CLI.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
import click
from treadmill import cli
from treadmill.warpgate import client
_LOGGER = logging.getLogger(__name__)
def init():
"""Top level command handler."""
@click.command()
@click.option('--policy-servers', type=cli.LIST,
required=True,
help='Warpgate policy servers')
@click.option('--service-principal', type=str,
default='host',
help='Warpgate service principal.')
@click.option('--policy', type=str, required=True,
envvar='WARPGATE_POLICY',
help='Warpget policy to use')
@click.option('--tun-dev', type=str, required=True,
help='Device to use when establishing tunnels.')
@click.option('--tun-addr', type=str, required=False,
help='Local IP address to use when establishing tunnels.')
def warpgate(policy_servers, service_principal, policy, tun_dev, tun_addr):
"""Run warpgate connection manager.
"""
_LOGGER.info(
'Launch client => %s, tunnel: %s[%s], policy: %s, principal: %s',
policy_servers,
tun_dev, tun_addr,
policy,
service_principal,
)
# Never exits
client.run_client(
policy_servers, service_principal, policy,
tun_dev, tun_addr
)
return warpgate
| """Warpgate client CLI.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
import click
from treadmill import cli
from treadmill.warpgate import client
_LOGGER = logging.getLogger(__name__)
def init():
"""Top level command handler."""
@click.command()
@click.option('--policy-servers', type=cli.LIST,
required=True,
help='Warpgate policy servers')
@click.option('--service-principal', type=str,
default='host',
help='Warpgate service principal.')
@click.option('--policy', type=str, required=True,
envvar='WARPGATE_POLICY',
help='Warpget policy to use')
@click.option('--tun-dev', type=str, required=True,
help='Device to use when establishing tunnels.')
@click.option('--tun-addr', type=str, required=False,
help='Local IP address to use when establishing tunnels.')
def warpgate(policy_servers, service_principal, policy, tun_dev, tun_addr):
"""Run warpgate connection manager.
"""
_LOGGER.info(
'Launch client => %s, tunnel: %s[%s], policy: %s, principal: %s',
policy_servers,
tun_dev, tun_addr,
policy,
service_principal,
)
# Never exits
client.run_client(
policy_servers, service_principal, policy,
tun_dev, tun_addr
)
return warpgate | en | 0.749999 | Warpgate client CLI. Top level command handler. Run warpgate connection manager. # Never exits | 2.201168 | 2 |
scripts/data-importer/main.py | vhermecz/gitariskola | 0 | 6613738 | import urllib.request
import xlrd3 as xlrd
import json
import re
DATA_URL = "http://www.musztydobay.hu/osszescsalidalexcel.xls"
def download_data(url):
"""Download data from URL and return in binary-string"""
response = urllib.request.urlopen(url)
data = response.read()
return data
def extract_from_excel_blob(data):
"""Open binary blob xls and return data from first sheet in list of strings"""
book = xlrd.open_workbook(file_contents = data)
sh = book.sheet_by_index(0)
for rx in range(sh.nrows):
yield [cx.value for cx in sh.row(rx)]
def extract_chords(text):
def splitter(texts, sep):
presult = map(lambda x: x.split(sep), texts)
return [item for sublist in presult for item in sublist]
def cleanse_chordlist(text):
text = re.split('-|\.', text)
text = map(str.strip, text)
text = map(str.lower, text)
text = map(str.capitalize, text)
text = list(filter(lambda x:x not in ['', 'Git'], text))
return text
text = [text.replace('–', '-').replace('…', '').upper()]
text = splitter(text, "V.")
text = splitter(text, "VAGY")
text = splitter(text, "//")
text = splitter(text, "GITISK")
text = [cleanse_chordlist(item) for item in text]
return text
def extract_pageref(text):
result = []
while text:
index = text.find("-")
book = text[0:index+1].strip("-")
text = text[index+1:] if index > -1 else ""
pages = []
while True:
full, page = re.match("(([^,.+]*)[,.+]*)", text).groups()
if page:
pages.append(page.lstrip("0"))
text = text[len(full):]
if not len(text) or not text[0].isdigit():
break
if pages:
book = book.strip()
if book == "C" or book.startswith("G"):
book = "G"
if book == "Cs":
book = "Cs1"
result.append(dict(
book=book,
pages=pages,
))
return result
def convert_to_json_data(data):
"""Convert list of strings to json format"""
if next(data) != ['Dal neve', 'Előadó', 'Könyv', 'Akkord', 'Akkord száma']:
raise ValueError("Check input. Might be broken.")
for idx, (title, performer, pageref, chords, _) in enumerate(data):
chords = [i for i in extract_chords(chords) if i]
pageref = extract_pageref(pageref)
yield dict(
idx=idx,
title=title,
performer=performer,
pagerefs=pageref,
chords=chords,
)
def export_json(data, fname):
"""Export json data to file"""
data = list(data)
with open(fname, "w") as fp:
fp.write("//Autogenerated do not modify\n\nexport const CHORDINFO = " + json.dumps(data, indent=4, sort_keys=True) + ";")
def main():
data = download_data(DATA_URL)
data = extract_from_excel_blob(data)
data = convert_to_json_data(data)
export_json(data, "../../src/config/chordinfo.js")
if __name__ == "__main__":
main()
| import urllib.request
import xlrd3 as xlrd
import json
import re
DATA_URL = "http://www.musztydobay.hu/osszescsalidalexcel.xls"
def download_data(url):
"""Download data from URL and return in binary-string"""
response = urllib.request.urlopen(url)
data = response.read()
return data
def extract_from_excel_blob(data):
"""Open binary blob xls and return data from first sheet in list of strings"""
book = xlrd.open_workbook(file_contents = data)
sh = book.sheet_by_index(0)
for rx in range(sh.nrows):
yield [cx.value for cx in sh.row(rx)]
def extract_chords(text):
def splitter(texts, sep):
presult = map(lambda x: x.split(sep), texts)
return [item for sublist in presult for item in sublist]
def cleanse_chordlist(text):
text = re.split('-|\.', text)
text = map(str.strip, text)
text = map(str.lower, text)
text = map(str.capitalize, text)
text = list(filter(lambda x:x not in ['', 'Git'], text))
return text
text = [text.replace('–', '-').replace('…', '').upper()]
text = splitter(text, "V.")
text = splitter(text, "VAGY")
text = splitter(text, "//")
text = splitter(text, "GITISK")
text = [cleanse_chordlist(item) for item in text]
return text
def extract_pageref(text):
result = []
while text:
index = text.find("-")
book = text[0:index+1].strip("-")
text = text[index+1:] if index > -1 else ""
pages = []
while True:
full, page = re.match("(([^,.+]*)[,.+]*)", text).groups()
if page:
pages.append(page.lstrip("0"))
text = text[len(full):]
if not len(text) or not text[0].isdigit():
break
if pages:
book = book.strip()
if book == "C" or book.startswith("G"):
book = "G"
if book == "Cs":
book = "Cs1"
result.append(dict(
book=book,
pages=pages,
))
return result
def convert_to_json_data(data):
"""Convert list of strings to json format"""
if next(data) != ['Dal neve', 'Előadó', 'Könyv', 'Akkord', 'Akkord száma']:
raise ValueError("Check input. Might be broken.")
for idx, (title, performer, pageref, chords, _) in enumerate(data):
chords = [i for i in extract_chords(chords) if i]
pageref = extract_pageref(pageref)
yield dict(
idx=idx,
title=title,
performer=performer,
pagerefs=pageref,
chords=chords,
)
def export_json(data, fname):
"""Export json data to file"""
data = list(data)
with open(fname, "w") as fp:
fp.write("//Autogenerated do not modify\n\nexport const CHORDINFO = " + json.dumps(data, indent=4, sort_keys=True) + ";")
def main():
data = download_data(DATA_URL)
data = extract_from_excel_blob(data)
data = convert_to_json_data(data)
export_json(data, "../../src/config/chordinfo.js")
if __name__ == "__main__":
main()
| en | 0.712132 | Download data from URL and return in binary-string Open binary blob xls and return data from first sheet in list of strings Convert list of strings to json format Export json data to file | 3.293633 | 3 |
biobb_amber/nab/__init__.py | bioexcel/biobb_amber | 0 | 6613739 | name = "nab"
__all__ = ["nab_build_dna_structure"]
| name = "nab"
__all__ = ["nab_build_dna_structure"]
| none | 1 | 1.010792 | 1 | |
other_code/TIFtoJPG.py | EoNjesajo/SemiSeg_CPS_Reproduction | 0 | 6613740 | import os
import cv2
from skimage.io import imread
file_path = "path to tif data dir"
save_path = "path to save dir"
for file_name in os.listdir(file_path):
print(file_name)
image = imread(file_path+file_name)
image = image / (2**14-1, 2**14-1, 2**14-1, 2**14-1) *255
image = image[...,:3]
name = file_name.split('.')[0] + '.jpg'
cv2.imwrite(os.path.join(save_path,name),image,[int(cv2.IMWRITE_JPEG_QUALITY), 200])
| import os
import cv2
from skimage.io import imread
file_path = "path to tif data dir"
save_path = "path to save dir"
for file_name in os.listdir(file_path):
print(file_name)
image = imread(file_path+file_name)
image = image / (2**14-1, 2**14-1, 2**14-1, 2**14-1) *255
image = image[...,:3]
name = file_name.split('.')[0] + '.jpg'
cv2.imwrite(os.path.join(save_path,name),image,[int(cv2.IMWRITE_JPEG_QUALITY), 200])
| none | 1 | 2.994003 | 3 | |
pyglasstools/thermo/__init__.py | muhammadhasyim/pyglasstools | 1 | 6613741 | <filename>pyglasstools/thermo/__init__.py
R""" Thermodynamic observables.
"""
from pyglasstools.thermo import _thermo
import pyglasstools
from pyglasstools import _pyglasstools, comm, rank, size, solvers_list
import numpy as np
def initialize_global(names,dim):
list_obs = {}
if any("potentialenergy" in s for s in names):
if dim == 2:
list_obs['potentialenergy'] = _thermo.GlobalPotentialEnergy2D("potentialenergy", "SCALAR", False)
elif dim == 3:
list_obs['potentialenergy'] = _thermo.GlobalPotentialEnergy3D("potentialenergy", "SCALAR", False)
if any("virialstress" in s for s in names):
if any("elastic" in s for s in names) is True:
if dim == 2:
list_obs['elasticvirialstress'] = _thermo.GlobalElasticVirialStress2D("elasticvirialstress", "2-TENSOR", False)
elif dim == 3:
list_obs['elasticvirialstress'] = _thermo.GlobalElasticVirialStress3D("elasticvirialstress", "2-TENSOR", False)
if any("elastic" in s for s in names) is False:
if dim == 2:
list_obs['virialstress'] = _thermo.GlobalVirialStress2D("virialstress", "2-TENSOR", False)
elif dim == 3:
list_obs['virialstress'] = _thermo.GlobalVirialStress3D("virialstress", "2-TENSOR", False)
if any("kineticstress" in s for s in names):
if dim == 2:
list_obs['kineticstress'] = _thermo.GlobalKineticStress2D("kineticstress", "2-TENSOR", True)
elif dim == 3:
list_obs['kineticstress'] = _thermo.GlobalKineticStress3D("kineticstress", "2-TENSOR", True)
if any("borntensor" in s for s in names):
if dim == 2:
list_obs['borntensor'] = _thermo.GlobalBornTensor2D("borntensor", "4-TENSOR", False)
elif dim == 3:
list_obs['borntensor'] = _thermo.GlobalBornTensor3D("borntensor", "4-TENSOR", False)
return list_obs
class calculator(object):
global solvers_list
def __init__(self):
#Initialize system data and pair potential of the system
self.manager = _pyglasstools.Manager();
self.thermocalculator = _thermo.ThermoCalculator( pyglasstools.get_sysdata().cppparticledata,
pyglasstools.get_potential().cpppairpotential)
solvers_list.append(self)
def add_observables(self, observables):
for name in observables:
self.thermocalculator.addObservable(observables[name])
def run(self):
self.thermocalculator.compute()
def update(self,frame_num):
pyglasstools.update_sysdata(frame_num);
self.thermocalculator.setSystemData(pyglasstools.get_sysdata().cppparticledata)
| <filename>pyglasstools/thermo/__init__.py
R""" Thermodynamic observables.
"""
from pyglasstools.thermo import _thermo
import pyglasstools
from pyglasstools import _pyglasstools, comm, rank, size, solvers_list
import numpy as np
def initialize_global(names,dim):
list_obs = {}
if any("potentialenergy" in s for s in names):
if dim == 2:
list_obs['potentialenergy'] = _thermo.GlobalPotentialEnergy2D("potentialenergy", "SCALAR", False)
elif dim == 3:
list_obs['potentialenergy'] = _thermo.GlobalPotentialEnergy3D("potentialenergy", "SCALAR", False)
if any("virialstress" in s for s in names):
if any("elastic" in s for s in names) is True:
if dim == 2:
list_obs['elasticvirialstress'] = _thermo.GlobalElasticVirialStress2D("elasticvirialstress", "2-TENSOR", False)
elif dim == 3:
list_obs['elasticvirialstress'] = _thermo.GlobalElasticVirialStress3D("elasticvirialstress", "2-TENSOR", False)
if any("elastic" in s for s in names) is False:
if dim == 2:
list_obs['virialstress'] = _thermo.GlobalVirialStress2D("virialstress", "2-TENSOR", False)
elif dim == 3:
list_obs['virialstress'] = _thermo.GlobalVirialStress3D("virialstress", "2-TENSOR", False)
if any("kineticstress" in s for s in names):
if dim == 2:
list_obs['kineticstress'] = _thermo.GlobalKineticStress2D("kineticstress", "2-TENSOR", True)
elif dim == 3:
list_obs['kineticstress'] = _thermo.GlobalKineticStress3D("kineticstress", "2-TENSOR", True)
if any("borntensor" in s for s in names):
if dim == 2:
list_obs['borntensor'] = _thermo.GlobalBornTensor2D("borntensor", "4-TENSOR", False)
elif dim == 3:
list_obs['borntensor'] = _thermo.GlobalBornTensor3D("borntensor", "4-TENSOR", False)
return list_obs
class calculator(object):
global solvers_list
def __init__(self):
#Initialize system data and pair potential of the system
self.manager = _pyglasstools.Manager();
self.thermocalculator = _thermo.ThermoCalculator( pyglasstools.get_sysdata().cppparticledata,
pyglasstools.get_potential().cpppairpotential)
solvers_list.append(self)
def add_observables(self, observables):
for name in observables:
self.thermocalculator.addObservable(observables[name])
def run(self):
self.thermocalculator.compute()
def update(self,frame_num):
pyglasstools.update_sysdata(frame_num);
self.thermocalculator.setSystemData(pyglasstools.get_sysdata().cppparticledata)
| en | 0.749152 | Thermodynamic observables. #Initialize system data and pair potential of the system | 2.13444 | 2 |
Projects/Online Workouts/w3resource/Dictionary/program-13.py | ivenpoker/Python-Projects | 1 | 6613742 | #!/usr/bin/env python3
############################################################################################
# #
# Program purpose: Creates a dictionary from two lists. #
# Program Author : <NAME> <<EMAIL>> #
# Creation Date : November 28, 2019 #
# #
############################################################################################
import random
def random_list(low: int, high: int, size: int) -> list:
return [random.randint(low, high) for _ in range(size)]
def create_dict(listA: list, listB: list) -> dict:
new_dict = dict()
for (k, v) in zip(listA, listB):
new_dict[k] = v
return new_dict
if __name__ == "__main__":
list_A = random_list(low=0, high=10, size=5)
list_B = random_list(low=0, high=10, size=5)
print(f'List A: {list_A}')
print(f'List B: {list_B}')
print(f'New dictionary: {create_dict(listA=list_A, listB=list_B)}')
| #!/usr/bin/env python3
############################################################################################
# #
# Program purpose: Creates a dictionary from two lists. #
# Program Author : <NAME> <<EMAIL>> #
# Creation Date : November 28, 2019 #
# #
############################################################################################
import random
def random_list(low: int, high: int, size: int) -> list:
return [random.randint(low, high) for _ in range(size)]
def create_dict(listA: list, listB: list) -> dict:
new_dict = dict()
for (k, v) in zip(listA, listB):
new_dict[k] = v
return new_dict
if __name__ == "__main__":
list_A = random_list(low=0, high=10, size=5)
list_B = random_list(low=0, high=10, size=5)
print(f'List A: {list_A}')
print(f'List B: {list_B}')
print(f'New dictionary: {create_dict(listA=list_A, listB=list_B)}')
| de | 0.602497 | #!/usr/bin/env python3 ############################################################################################ # # # Program purpose: Creates a dictionary from two lists. # # Program Author : <NAME> <<EMAIL>> # # Creation Date : November 28, 2019 # # # ############################################################################################ | 4.372369 | 4 |
447/app.py | kaixiang1992/python-flask | 0 | 6613743 | <filename>447/app.py
from flask import Flask, render_template, request, redirect, url_for
app = Flask(__name__)
@app.route('/')
def hello_world():
return render_template('home.html')
@app.route('/login/', methods=['GET', 'POST'])
def login():
# TODO: GET请求返回登录页面
if request.method == 'GET':
return render_template('login.html')
else:
# TODO: 登录后重定向到个人中心页面
username = request.form.get('username')
if username:
return redirect(url_for('profile', username=username), 302)
else:
return redirect(url_for('profile', username='无名氏'), 302)
@app.route('/profile/')
def profile():
# TODO: 登录后显示登录昵称
if request.args.get('username'):
return '登录成功，当前用户：%s' % request.args.get('username')
else:
# TODO: 未登录重定向登录界面
return redirect(url_for('login'), 302)
if __name__ == '__main__':
app.run(debug=True, host='192.168.31.176', port=8080)
| <filename>447/app.py
from flask import Flask, render_template, request, redirect, url_for
app = Flask(__name__)
@app.route('/')
def hello_world():
return render_template('home.html')
@app.route('/login/', methods=['GET', 'POST'])
def login():
# TODO: GET请求返回登录页面
if request.method == 'GET':
return render_template('login.html')
else:
# TODO: 登录后重定向到个人中心页面
username = request.form.get('username')
if username:
return redirect(url_for('profile', username=username), 302)
else:
return redirect(url_for('profile', username='无名氏'), 302)
@app.route('/profile/')
def profile():
# TODO: 登录后显示登录昵称
if request.args.get('username'):
return '登录成功，当前用户：%s' % request.args.get('username')
else:
# TODO: 未登录重定向登录界面
return redirect(url_for('login'), 302)
if __name__ == '__main__':
app.run(debug=True, host='192.168.31.176', port=8080)
| zh | 0.990584 | # TODO: GET请求返回登录页面 # TODO: 登录后重定向到个人中心页面 # TODO: 登录后显示登录昵称 # TODO: 未登录重定向登录界面 | 2.923476 | 3 |
tracardi_pushover_webhook/model/pushover_payload.py | bartdob/pushOver | 0 | 6613744 | from pydantic import BaseModel
from tracardi.domain.entity import Entity
class PushOverAuth(BaseModel):
token: str
user: str
class PushOverConfiguration(BaseModel):
source: Entity
message: str
| from pydantic import BaseModel
from tracardi.domain.entity import Entity
class PushOverAuth(BaseModel):
token: str
user: str
class PushOverConfiguration(BaseModel):
source: Entity
message: str
| none | 1 | 2.137351 | 2 | |
Neural Network/TensorFlow/TensorFlow.py | marcelkotze007/mk007---ML-Python-library | 0 | 6613745 | <filename>Neural Network/TensorFlow/TensorFlow.py
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from datetime import datetime as dt
N = 500
D = 2
M = 30
K = 3
#first cloud is centred at (0, -2)
X1 = np.random.randn(500, 2) + np.array([0, -2])
#second cloud is centred at (2, 2)
X2 = np.random.randn(500, 2) + np.array([2, 2])
#Third cloud is centred at (-2, 2)
X3 = np.random.randn(500, 2) + np.array([-2, 2])
X = np.vstack((X1,X2,X3))
Y = np.array([0]*N + [1]*N + [2]*N)
N1 = len(Y)
T = np.zeros((N1, K))
T[np.arange(N1), Y[:].astype(np.int32)] = 1
def init_weights(shape):
return tf.Variable(tf.random_normal(shape, stddev = 0.01))
def forward(X, W1, b1, W2, b2):
#Z = tf.nn.sigmoid(tf.matmul(X, W1) + b1)
#Z = tf.nn.tanh(tf.matmul(X, W1) + b1)
Z = tf.nn.relu(tf.matmul(X, W1) + b1)
return tf.matmul(Z, W2) + b2
tfX = tf.placeholder(tf.float32, [None, D])
tfY = tf.placeholder(tf.float32, [None, K])
W1 = init_weights([D,M])
b1 = init_weights([M])
W2 = init_weights([M,K])
b2 = init_weights([K])
logits = forward(tfX, W1, b1, W2, b2)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits = logits, labels = tfY))
train_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost)
predict_op = tf.argmax(logits, 1)
sessions = tf.Session()
init = tf.initialize_all_variables()
sessions.run(init)
dt0 = dt.now()
for i in range(10000):
sessions.run(train_op, feed_dict={tfX: X, tfY: T})
pred = sessions.run(predict_op, feed_dict={tfX: X, tfY: T})
print(np.mean(Y == pred))
print(dt.now() - dt0)
| <filename>Neural Network/TensorFlow/TensorFlow.py
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from datetime import datetime as dt
N = 500
D = 2
M = 30
K = 3
#first cloud is centred at (0, -2)
X1 = np.random.randn(500, 2) + np.array([0, -2])
#second cloud is centred at (2, 2)
X2 = np.random.randn(500, 2) + np.array([2, 2])
#Third cloud is centred at (-2, 2)
X3 = np.random.randn(500, 2) + np.array([-2, 2])
X = np.vstack((X1,X2,X3))
Y = np.array([0]*N + [1]*N + [2]*N)
N1 = len(Y)
T = np.zeros((N1, K))
T[np.arange(N1), Y[:].astype(np.int32)] = 1
def init_weights(shape):
return tf.Variable(tf.random_normal(shape, stddev = 0.01))
def forward(X, W1, b1, W2, b2):
#Z = tf.nn.sigmoid(tf.matmul(X, W1) + b1)
#Z = tf.nn.tanh(tf.matmul(X, W1) + b1)
Z = tf.nn.relu(tf.matmul(X, W1) + b1)
return tf.matmul(Z, W2) + b2
tfX = tf.placeholder(tf.float32, [None, D])
tfY = tf.placeholder(tf.float32, [None, K])
W1 = init_weights([D,M])
b1 = init_weights([M])
W2 = init_weights([M,K])
b2 = init_weights([K])
logits = forward(tfX, W1, b1, W2, b2)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits = logits, labels = tfY))
train_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost)
predict_op = tf.argmax(logits, 1)
sessions = tf.Session()
init = tf.initialize_all_variables()
sessions.run(init)
dt0 = dt.now()
for i in range(10000):
sessions.run(train_op, feed_dict={tfX: X, tfY: T})
pred = sessions.run(predict_op, feed_dict={tfX: X, tfY: T})
print(np.mean(Y == pred))
print(dt.now() - dt0)
| en | 0.730465 | #first cloud is centred at (0, -2) #second cloud is centred at (2, 2) #Third cloud is centred at (-2, 2) #Z = tf.nn.sigmoid(tf.matmul(X, W1) + b1) #Z = tf.nn.tanh(tf.matmul(X, W1) + b1) | 3.241101 | 3 |
remote_control/management/commands/runremote.py | adrienemery/auv-control-api | 0 | 6613746 | import logging
from autobahn.asyncio.component import Component, run
from django.core.management.base import BaseCommand
from django.conf import settings
from remote_control.remote_control import RemoteInterface
logging.basicConfig(level=logging.INFO)
class Command(BaseCommand):
def handle(self, *args, **options):
url = settings.CROSSBAR_URL
realm = settings.CROSSBAR_REALM
comp = Component(
transports=url,
realm=realm,
session_factory=RemoteInterface,
)
run([comp])
| import logging
from autobahn.asyncio.component import Component, run
from django.core.management.base import BaseCommand
from django.conf import settings
from remote_control.remote_control import RemoteInterface
logging.basicConfig(level=logging.INFO)
class Command(BaseCommand):
def handle(self, *args, **options):
url = settings.CROSSBAR_URL
realm = settings.CROSSBAR_REALM
comp = Component(
transports=url,
realm=realm,
session_factory=RemoteInterface,
)
run([comp])
| none | 1 | 1.897983 | 2 | |
archive/migrations/0001_initial.py | pastpages/savemy.news | 19 | 6613747 | # -*- coding: utf-8 -*-
# Generated by Django 1.11.7 on 2017-11-04 18:02
from __future__ import unicode_literals
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Clip',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('url', models.URLField()),
],
),
migrations.CreateModel(
name='Memento',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('timestamp', models.DateTimeField(auto_now_add=True, db_index=True)),
('archive', models.CharField(choices=[('archive.org', 'archive.org')], db_index=True, default='archive.org', max_length=1000)),
('url', models.URLField()),
],
),
migrations.AddField(
model_name='clip',
name='memento',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='archive.Memento'),
),
migrations.AddField(
model_name='clip',
name='user',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL),
),
]
| # -*- coding: utf-8 -*-
# Generated by Django 1.11.7 on 2017-11-04 18:02
from __future__ import unicode_literals
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Clip',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('url', models.URLField()),
],
),
migrations.CreateModel(
name='Memento',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('timestamp', models.DateTimeField(auto_now_add=True, db_index=True)),
('archive', models.CharField(choices=[('archive.org', 'archive.org')], db_index=True, default='archive.org', max_length=1000)),
('url', models.URLField()),
],
),
migrations.AddField(
model_name='clip',
name='memento',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='archive.Memento'),
),
migrations.AddField(
model_name='clip',
name='user',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL),
),
]
| en | 0.751848 | # -*- coding: utf-8 -*- # Generated by Django 1.11.7 on 2017-11-04 18:02 | 1.698528 | 2 |
src/paql_eval/sketch_refine/partitioning/quad_tree.py | ahmadchatha/Scalable-PaQL-Queries | 6 | 6613748 | from src.utils.log import log
class QuadTreePartitioning(object):
def __init__(self, db, dataset_size, nbits, cid_type_cast,
data_table_name,
repr_table_name,
clust_attrs, data_attrs, max_clust_size, min_n_clusters, epsilon,
index_table_name,
indexing_attrs,
sr_schema,
obj_type=None):
self.sr_schema = sr_schema
self.labels_ = None
self.N = dataset_size
self.nbits = nbits
self.db = db
self.data_table_name = data_table_name
self.repr_table_name = repr_table_name
self.clust_attrs = clust_attrs
self.data_attrs = data_attrs
self.global_depth = 0
self.max_clust_size = max_clust_size
self.min_n_clusters = min_n_clusters
self.epsilon = epsilon
self.partitioned_cids = set()
self.index_table_name = index_table_name
self.obj_type = obj_type
if self.epsilon is not None:
assert self.obj_type is not None
self.indexing_attrs = indexing_attrs
self.clust_attrs_mask = "".join("1" if attr in self.clust_attrs else "0" for attr in self.indexing_attrs)
log("Clust attrs mask: {}".format(self.clust_attrs_mask))
self.partitioning_sql = None # Will be set later, in fit()
self.aggregating_sql = None # Will be set later, in fit()
self.cid_type_cast = cid_type_cast
self.mask_type_cast = "BIT({})".format(self.nbits)
def store_representatives(self):
"""
Store partitioning to data table and representatives to representative table
and set labels (partition sizes).
"""
empirical_epsilon_max = (
"CASE WHEN avg_{attr} > 0 THEN "
" (radius / avg_{attr})::float "
"ELSE "
" NULL "
"END AS emp_eps_max_{attr}"
)
empirical_epsilon_min = (
"CASE WHEN avg_{attr} > 0 THEN "
" (radius / (avg_{attr} - radius))::float "
"ELSE "
" NULL "
"END AS emp_eps_min_{attr}"
)
# Store representatives
log("Storing representative table '{repr_table}'...".format(repr_table=self.repr_table_name))
self.db.sql_update(
"DROP TABLE IF EXISTS {SR}.{repr_table};\n"
"CREATE TABLE {SR}.{repr_table} AS "
"SELECT cid, {attrs}, cid_size, radius, {emp_eps_max}, {emp_eps_min} "
"FROM {SR}.centroids".format(
SR=self.sr_schema,
repr_table=self.repr_table_name,
attrs=",".join("avg_{attr}::float as {attr}".format(attr=attr) for attr in self.data_attrs),
emp_eps_max=",".join(empirical_epsilon_max.format(attr=attr) for attr in self.clust_attrs),
emp_eps_min=",".join(empirical_epsilon_min.format(attr=attr) for attr in self.clust_attrs),
))
log("Representative table stored.")
# Create index on representative table
log("Creating index on representative table...")
self.db.sql_update(
"CREATE INDEX ON {SR}.{repr_table} (cid)".format(
SR=self.sr_schema,
repr_table=self.repr_table_name))
log("Index on representative table created.")
def fit(self, only_representatives=False, indexing=False):
"""
Labels is a list of cluster labels with same lenght as dataset "data".
Each label labels[i] is a cluster index indicating which of the n_clusters clusters data[i] belongs to.
"""
assert self.max_clust_size is not None or self.min_n_clusters is not None
averages_sql = ", ".join(
"AVG({attr}) AS avg_{attr}".format(attr=attr)
for attr in self.data_attrs)
mins_sql = ", ".join(
"MIN({attr}) AS min_{attr}".format(attr=attr)
for attr in self.clust_attrs)
maxs_sql = ", ".join(
"MAX({attr}) AS max_{attr}".format(attr=attr)
for attr in self.clust_attrs)
# The basic centroid table does not contain the radius yet
centroids_basic = (
"SELECT "
" cid, "
" COUNT(*) AS cid_size, \n"
" {avgs}, \n"
" {mins}, \n"
" {maxs} \n"
"FROM {D} \n"
"GROUP BY cid".format(
D=self.data_table_name,
avgs=averages_sql,
mins=mins_sql,
maxs=maxs_sql))
if self.obj_type is None:
averages_eps_val = None
elif self.obj_type.lower() == "maximize":
averages_eps_val = "avg_{attr} * {epsilon}"
elif self.obj_type.lower() == "minimize":
averages_eps_val = "avg_{attr} * ({epsilon} / (1 + {epsilon}))"
else:
raise Exception("Unknown objective type.")
# This is the complete centroid table, containing radius information
centroids_complete = (
"SELECT "
" cid, \n"
" cid_size, \n"
" {averages}, \n"
" {radius} AS radius \n"
" {averages_eps} \n"
" {radiuses} \n"
"FROM centroids_basic A").format(
averages=",".join("avg_{attr}".format(attr=attr) for attr in self.data_attrs),
radius="GREATEST({})".format(",".join(
"A.avg_{attr} - A.min_{attr}, A.max_{attr} - A.avg_{attr}".format(attr=attr)
for attr in self.clust_attrs)),
# The followings are used when epsilon is set, to ensure a certain clustering quality
averages_eps=("," + ",".join(
("(" + averages_eps_val + ") AS avg_eps_{attr}").format(
attr=attr,
epsilon=self.epsilon)
for attr in self.clust_attrs)) if self.epsilon is not None else "",
radiuses=("," + ",".join(
"GREATEST(A.avg_{attr} - A.min_{attr}, A.max_{attr} - A.avg_{attr}) AS radius_{attr}".format(attr=attr)
for attr in self.clust_attrs)) if self.epsilon is not None else "")
self.db.sql_update("DROP MATERIALIZED VIEW IF EXISTS {SR}.centroids".format(SR=self.sr_schema))
# Create the materialized view that stores the current centroids
self.db.sql_update(
"CREATE MATERIALIZED VIEW {SR}.centroids AS \n"
"WITH centroids_basic AS (\n"
"{centroids_basic} \n"
") \n"
"{centroids_complete} \n"
"WITH NO DATA".format(
SR=self.sr_schema,
centroids_basic=centroids_basic,
centroids_complete=centroids_complete))
self.aggregating_sql = "REFRESH MATERIALIZED VIEW {SR}.centroids".format(SR=self.sr_schema)
# Define partitioning query: keep partitioning only groups that violate the size and radius conditions
neg_size_condition = "A.cid_size > {}".format(self.max_clust_size)
# NOTE: This condition is more fine grained that saying MAX(radius_attr) <= MIN(agv_aggr)
# and it can cause less partitioning, although it still satisfies the approximation bounds.
# So it's preferable.
neg_radius_condition = "FALSE" if self.epsilon is None else " OR ".join(
"A.radius_{attr} > A.avg_eps_{attr}".format(attr=attr) for attr in self.clust_attrs)
self.partitioning_sql = (
"UPDATE {D} D SET cid = ("
# NOTE: THIS PARTITION INDEX SCHEME DOESN'T SUPPORT THE INDEX
"(0::BIT({diff_bits}) || ({internal_cid})::BIT({k})) | (D.cid::BIT({nbits}) << {k})"
")::{cid_type_cast} \n"
"FROM {SR}.centroids A \n"
"WHERE ({neg_size_condition} OR {neg_radius_condition}) \n"
"AND D.cid = A.cid"
"".format(
SR=self.sr_schema,
D=self.data_table_name,
internal_cid=" || ".join(
"(CASE"
" WHEN D.{attr} IS NULL OR A.avg_{attr} IS NULL OR D.{attr} = A.avg_{attr} THEN "
" round(random())::int::bit(1) "
" ELSE "
" (D.{attr} < A.avg_{attr})::int::bit(1) "
"END)".format(attr=attr)
for attr in self.clust_attrs),
k=len(self.clust_attrs),
nbits=self.nbits,
diff_bits=self.nbits - len(self.clust_attrs),
cid_type_cast=self.cid_type_cast,
mask_type_cast=self.mask_type_cast,
neg_size_condition=neg_size_condition,
neg_radius_condition=neg_radius_condition))
##################################################################################
# RUN PARTITIONING PROCESS
##################################################################################
if not only_representatives:
keep = True
tree_level = 0
while keep:
keep = self.partition(tree_level, indexing) > 0
tree_level += 1
else:
log("Only computing the representatives...")
self.db.sql_update(self.aggregating_sql)
log("Representatives computed.")
# Store partitioning to data table
self.store_representatives()
# Clean up
self.db.sql_update("DROP MATERIALIZED VIEW {SR}.centroids".format(SR=self.sr_schema))
def partition(self, tree_level, indexing):
log("aggregating at tree level {}...".format(tree_level))
self.db.sql_update(self.aggregating_sql)
if indexing:
level_index_table_name = "{}_l{}".format(self.index_table_name, tree_level)
# Store the aggregated meta info in separate tables, one for each tree level
log("storing indexing level in table '{}'...".format(level_index_table_name))
self.db.sql_update(
"DROP TABLE IF EXISTS {SR}.{level_index_table_name};"
"CREATE TABLE {SR}.{level_index_table_name} AS "
"SELECT * FROM {SR}.centroids;"
"CREATE INDEX ON {SR}.{level_index_table_name} USING btree (cid)".format(
SR=self.sr_schema,
level_index_table_name=level_index_table_name))
log("analyzing data table...")
self.db.sql_update("ANALYZE {D}".format(D=self.data_table_name))
# Partition only clusters whose size is above the threshold
log("partitioning at tree level {}...".format(tree_level))
print
print self.partitioning_sql.format(tree_level=tree_level)
new_cids_n = self.db.sql_update(self.partitioning_sql.format(tree_level=tree_level))
log("new partitions: {}...".format(new_cids_n))
self.db.commit()
return new_cids_n
| from src.utils.log import log
class QuadTreePartitioning(object):
def __init__(self, db, dataset_size, nbits, cid_type_cast,
data_table_name,
repr_table_name,
clust_attrs, data_attrs, max_clust_size, min_n_clusters, epsilon,
index_table_name,
indexing_attrs,
sr_schema,
obj_type=None):
self.sr_schema = sr_schema
self.labels_ = None
self.N = dataset_size
self.nbits = nbits
self.db = db
self.data_table_name = data_table_name
self.repr_table_name = repr_table_name
self.clust_attrs = clust_attrs
self.data_attrs = data_attrs
self.global_depth = 0
self.max_clust_size = max_clust_size
self.min_n_clusters = min_n_clusters
self.epsilon = epsilon
self.partitioned_cids = set()
self.index_table_name = index_table_name
self.obj_type = obj_type
if self.epsilon is not None:
assert self.obj_type is not None
self.indexing_attrs = indexing_attrs
self.clust_attrs_mask = "".join("1" if attr in self.clust_attrs else "0" for attr in self.indexing_attrs)
log("Clust attrs mask: {}".format(self.clust_attrs_mask))
self.partitioning_sql = None # Will be set later, in fit()
self.aggregating_sql = None # Will be set later, in fit()
self.cid_type_cast = cid_type_cast
self.mask_type_cast = "BIT({})".format(self.nbits)
def store_representatives(self):
"""
Store partitioning to data table and representatives to representative table
and set labels (partition sizes).
"""
empirical_epsilon_max = (
"CASE WHEN avg_{attr} > 0 THEN "
" (radius / avg_{attr})::float "
"ELSE "
" NULL "
"END AS emp_eps_max_{attr}"
)
empirical_epsilon_min = (
"CASE WHEN avg_{attr} > 0 THEN "
" (radius / (avg_{attr} - radius))::float "
"ELSE "
" NULL "
"END AS emp_eps_min_{attr}"
)
# Store representatives
log("Storing representative table '{repr_table}'...".format(repr_table=self.repr_table_name))
self.db.sql_update(
"DROP TABLE IF EXISTS {SR}.{repr_table};\n"
"CREATE TABLE {SR}.{repr_table} AS "
"SELECT cid, {attrs}, cid_size, radius, {emp_eps_max}, {emp_eps_min} "
"FROM {SR}.centroids".format(
SR=self.sr_schema,
repr_table=self.repr_table_name,
attrs=",".join("avg_{attr}::float as {attr}".format(attr=attr) for attr in self.data_attrs),
emp_eps_max=",".join(empirical_epsilon_max.format(attr=attr) for attr in self.clust_attrs),
emp_eps_min=",".join(empirical_epsilon_min.format(attr=attr) for attr in self.clust_attrs),
))
log("Representative table stored.")
# Create index on representative table
log("Creating index on representative table...")
self.db.sql_update(
"CREATE INDEX ON {SR}.{repr_table} (cid)".format(
SR=self.sr_schema,
repr_table=self.repr_table_name))
log("Index on representative table created.")
def fit(self, only_representatives=False, indexing=False):
"""
Labels is a list of cluster labels with same lenght as dataset "data".
Each label labels[i] is a cluster index indicating which of the n_clusters clusters data[i] belongs to.
"""
assert self.max_clust_size is not None or self.min_n_clusters is not None
averages_sql = ", ".join(
"AVG({attr}) AS avg_{attr}".format(attr=attr)
for attr in self.data_attrs)
mins_sql = ", ".join(
"MIN({attr}) AS min_{attr}".format(attr=attr)
for attr in self.clust_attrs)
maxs_sql = ", ".join(
"MAX({attr}) AS max_{attr}".format(attr=attr)
for attr in self.clust_attrs)
# The basic centroid table does not contain the radius yet
centroids_basic = (
"SELECT "
" cid, "
" COUNT(*) AS cid_size, \n"
" {avgs}, \n"
" {mins}, \n"
" {maxs} \n"
"FROM {D} \n"
"GROUP BY cid".format(
D=self.data_table_name,
avgs=averages_sql,
mins=mins_sql,
maxs=maxs_sql))
if self.obj_type is None:
averages_eps_val = None
elif self.obj_type.lower() == "maximize":
averages_eps_val = "avg_{attr} * {epsilon}"
elif self.obj_type.lower() == "minimize":
averages_eps_val = "avg_{attr} * ({epsilon} / (1 + {epsilon}))"
else:
raise Exception("Unknown objective type.")
# This is the complete centroid table, containing radius information
centroids_complete = (
"SELECT "
" cid, \n"
" cid_size, \n"
" {averages}, \n"
" {radius} AS radius \n"
" {averages_eps} \n"
" {radiuses} \n"
"FROM centroids_basic A").format(
averages=",".join("avg_{attr}".format(attr=attr) for attr in self.data_attrs),
radius="GREATEST({})".format(",".join(
"A.avg_{attr} - A.min_{attr}, A.max_{attr} - A.avg_{attr}".format(attr=attr)
for attr in self.clust_attrs)),
# The followings are used when epsilon is set, to ensure a certain clustering quality
averages_eps=("," + ",".join(
("(" + averages_eps_val + ") AS avg_eps_{attr}").format(
attr=attr,
epsilon=self.epsilon)
for attr in self.clust_attrs)) if self.epsilon is not None else "",
radiuses=("," + ",".join(
"GREATEST(A.avg_{attr} - A.min_{attr}, A.max_{attr} - A.avg_{attr}) AS radius_{attr}".format(attr=attr)
for attr in self.clust_attrs)) if self.epsilon is not None else "")
self.db.sql_update("DROP MATERIALIZED VIEW IF EXISTS {SR}.centroids".format(SR=self.sr_schema))
# Create the materialized view that stores the current centroids
self.db.sql_update(
"CREATE MATERIALIZED VIEW {SR}.centroids AS \n"
"WITH centroids_basic AS (\n"
"{centroids_basic} \n"
") \n"
"{centroids_complete} \n"
"WITH NO DATA".format(
SR=self.sr_schema,
centroids_basic=centroids_basic,
centroids_complete=centroids_complete))
self.aggregating_sql = "REFRESH MATERIALIZED VIEW {SR}.centroids".format(SR=self.sr_schema)
# Define partitioning query: keep partitioning only groups that violate the size and radius conditions
neg_size_condition = "A.cid_size > {}".format(self.max_clust_size)
# NOTE: This condition is more fine grained that saying MAX(radius_attr) <= MIN(agv_aggr)
# and it can cause less partitioning, although it still satisfies the approximation bounds.
# So it's preferable.
neg_radius_condition = "FALSE" if self.epsilon is None else " OR ".join(
"A.radius_{attr} > A.avg_eps_{attr}".format(attr=attr) for attr in self.clust_attrs)
self.partitioning_sql = (
"UPDATE {D} D SET cid = ("
# NOTE: THIS PARTITION INDEX SCHEME DOESN'T SUPPORT THE INDEX
"(0::BIT({diff_bits}) || ({internal_cid})::BIT({k})) | (D.cid::BIT({nbits}) << {k})"
")::{cid_type_cast} \n"
"FROM {SR}.centroids A \n"
"WHERE ({neg_size_condition} OR {neg_radius_condition}) \n"
"AND D.cid = A.cid"
"".format(
SR=self.sr_schema,
D=self.data_table_name,
internal_cid=" || ".join(
"(CASE"
" WHEN D.{attr} IS NULL OR A.avg_{attr} IS NULL OR D.{attr} = A.avg_{attr} THEN "
" round(random())::int::bit(1) "
" ELSE "
" (D.{attr} < A.avg_{attr})::int::bit(1) "
"END)".format(attr=attr)
for attr in self.clust_attrs),
k=len(self.clust_attrs),
nbits=self.nbits,
diff_bits=self.nbits - len(self.clust_attrs),
cid_type_cast=self.cid_type_cast,
mask_type_cast=self.mask_type_cast,
neg_size_condition=neg_size_condition,
neg_radius_condition=neg_radius_condition))
##################################################################################
# RUN PARTITIONING PROCESS
##################################################################################
if not only_representatives:
keep = True
tree_level = 0
while keep:
keep = self.partition(tree_level, indexing) > 0
tree_level += 1
else:
log("Only computing the representatives...")
self.db.sql_update(self.aggregating_sql)
log("Representatives computed.")
# Store partitioning to data table
self.store_representatives()
# Clean up
self.db.sql_update("DROP MATERIALIZED VIEW {SR}.centroids".format(SR=self.sr_schema))
def partition(self, tree_level, indexing):
log("aggregating at tree level {}...".format(tree_level))
self.db.sql_update(self.aggregating_sql)
if indexing:
level_index_table_name = "{}_l{}".format(self.index_table_name, tree_level)
# Store the aggregated meta info in separate tables, one for each tree level
log("storing indexing level in table '{}'...".format(level_index_table_name))
self.db.sql_update(
"DROP TABLE IF EXISTS {SR}.{level_index_table_name};"
"CREATE TABLE {SR}.{level_index_table_name} AS "
"SELECT * FROM {SR}.centroids;"
"CREATE INDEX ON {SR}.{level_index_table_name} USING btree (cid)".format(
SR=self.sr_schema,
level_index_table_name=level_index_table_name))
log("analyzing data table...")
self.db.sql_update("ANALYZE {D}".format(D=self.data_table_name))
# Partition only clusters whose size is above the threshold
log("partitioning at tree level {}...".format(tree_level))
print
print self.partitioning_sql.format(tree_level=tree_level)
new_cids_n = self.db.sql_update(self.partitioning_sql.format(tree_level=tree_level))
log("new partitions: {}...".format(new_cids_n))
self.db.commit()
return new_cids_n
| en | 0.721673 | # Will be set later, in fit() # Will be set later, in fit() Store partitioning to data table and representatives to representative table and set labels (partition sizes). # Store representatives # Create index on representative table Labels is a list of cluster labels with same lenght as dataset "data". Each label labels[i] is a cluster index indicating which of the n_clusters clusters data[i] belongs to. # The basic centroid table does not contain the radius yet # This is the complete centroid table, containing radius information # The followings are used when epsilon is set, to ensure a certain clustering quality # Create the materialized view that stores the current centroids # Define partitioning query: keep partitioning only groups that violate the size and radius conditions # NOTE: This condition is more fine grained that saying MAX(radius_attr) <= MIN(agv_aggr) # and it can cause less partitioning, although it still satisfies the approximation bounds. # So it's preferable. # NOTE: THIS PARTITION INDEX SCHEME DOESN'T SUPPORT THE INDEX ################################################################################## # RUN PARTITIONING PROCESS ################################################################################## # Store partitioning to data table # Clean up # Store the aggregated meta info in separate tables, one for each tree level # Partition only clusters whose size is above the threshold | 2.405413 | 2 |
components/matchers.py | vdvchen/SGMNet | 61 | 6613749 | import torch
import numpy as np
import os
from collections import OrderedDict,namedtuple
import sys
ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
sys.path.insert(0, ROOT_DIR)
from sgmnet import matcher as SGM_Model
from superglue import matcher as SG_Model
from utils import evaluation_utils
class GNN_Matcher(object):
def __init__(self,config,model_name):
assert model_name=='SGM' or model_name=='SG'
config=namedtuple('config',config.keys())(*config.values())
self.p_th=config.p_th
self.model = SGM_Model(config) if model_name=='SGM' else SG_Model(config)
self.model.cuda(),self.model.eval()
checkpoint = torch.load(os.path.join(config.model_dir, 'model_best.pth'))
#for ddp model
if list(checkpoint['state_dict'].items())[0][0].split('.')[0]=='module':
new_stat_dict=OrderedDict()
for key,value in checkpoint['state_dict'].items():
new_stat_dict[key[7:]]=value
checkpoint['state_dict']=new_stat_dict
self.model.load_state_dict(checkpoint['state_dict'])
def run(self,test_data):
norm_x1,norm_x2=evaluation_utils.normalize_size(test_data['x1'][:,:2],test_data['size1']),\
evaluation_utils.normalize_size(test_data['x2'][:,:2],test_data['size2'])
x1,x2=np.concatenate([norm_x1,test_data['x1'][:,2,np.newaxis]],axis=-1),np.concatenate([norm_x2,test_data['x2'][:,2,np.newaxis]],axis=-1)
feed_data={'x1':torch.from_numpy(x1[np.newaxis]).cuda().float(),
'x2':torch.from_numpy(x2[np.newaxis]).cuda().float(),
'desc1':torch.from_numpy(test_data['desc1'][np.newaxis]).cuda().float(),
'desc2':torch.from_numpy(test_data['desc2'][np.newaxis]).cuda().float()}
with torch.no_grad():
res=self.model(feed_data,test_mode=True)
p=res['p']
index1,index2=self.match_p(p[0,:-1,:-1])
corr1,corr2=test_data['x1'][:,:2][index1.cpu()],test_data['x2'][:,:2][index2.cpu()]
if len(corr1.shape)==1:
corr1,corr2=corr1[np.newaxis],corr2[np.newaxis]
return corr1,corr2
def match_p(self,p):#p N*M
score,index=torch.topk(p,k=1,dim=-1)
_,index2=torch.topk(p,k=1,dim=-2)
mask_th,index,index2=score[:,0]>self.p_th,index[:,0],index2.squeeze(0)
mask_mc=index2[index] == torch.arange(len(p)).cuda()
mask=mask_th&mask_mc
index1,index2=torch.nonzero(mask).squeeze(1),index[mask]
return index1,index2
class NN_Matcher(object):
def __init__(self,config):
config=namedtuple('config',config.keys())(*config.values())
self.mutual_check=config.mutual_check
self.ratio_th=config.ratio_th
def run(self,test_data):
desc1,desc2,x1,x2=test_data['desc1'],test_data['desc2'],test_data['x1'],test_data['x2']
desc_mat=np.sqrt(abs((desc1**2).sum(-1)[:,np.newaxis]+(desc2**2).sum(-1)[np.newaxis]-2*desc1@desc2.T))
nn_index=np.argpartition(desc_mat,kth=(1,2),axis=-1)
dis_value12=np.take_along_axis(desc_mat,nn_index, axis=-1)
ratio_score=dis_value12[:,0]/dis_value12[:,1]
nn_index1=nn_index[:,0]
nn_index2=np.argmin(desc_mat,axis=0)
mask_ratio,mask_mutual=ratio_score<self.ratio_th,np.arange(len(x1))==nn_index2[nn_index1]
corr1,corr2=x1[:,:2],x2[:,:2][nn_index1]
if self.mutual_check:
mask=mask_ratio&mask_mutual
else:
mask=mask_ratio
corr1,corr2=corr1[mask],corr2[mask]
return corr1,corr2
| import torch
import numpy as np
import os
from collections import OrderedDict,namedtuple
import sys
ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
sys.path.insert(0, ROOT_DIR)
from sgmnet import matcher as SGM_Model
from superglue import matcher as SG_Model
from utils import evaluation_utils
class GNN_Matcher(object):
def __init__(self,config,model_name):
assert model_name=='SGM' or model_name=='SG'
config=namedtuple('config',config.keys())(*config.values())
self.p_th=config.p_th
self.model = SGM_Model(config) if model_name=='SGM' else SG_Model(config)
self.model.cuda(),self.model.eval()
checkpoint = torch.load(os.path.join(config.model_dir, 'model_best.pth'))
#for ddp model
if list(checkpoint['state_dict'].items())[0][0].split('.')[0]=='module':
new_stat_dict=OrderedDict()
for key,value in checkpoint['state_dict'].items():
new_stat_dict[key[7:]]=value
checkpoint['state_dict']=new_stat_dict
self.model.load_state_dict(checkpoint['state_dict'])
def run(self,test_data):
norm_x1,norm_x2=evaluation_utils.normalize_size(test_data['x1'][:,:2],test_data['size1']),\
evaluation_utils.normalize_size(test_data['x2'][:,:2],test_data['size2'])
x1,x2=np.concatenate([norm_x1,test_data['x1'][:,2,np.newaxis]],axis=-1),np.concatenate([norm_x2,test_data['x2'][:,2,np.newaxis]],axis=-1)
feed_data={'x1':torch.from_numpy(x1[np.newaxis]).cuda().float(),
'x2':torch.from_numpy(x2[np.newaxis]).cuda().float(),
'desc1':torch.from_numpy(test_data['desc1'][np.newaxis]).cuda().float(),
'desc2':torch.from_numpy(test_data['desc2'][np.newaxis]).cuda().float()}
with torch.no_grad():
res=self.model(feed_data,test_mode=True)
p=res['p']
index1,index2=self.match_p(p[0,:-1,:-1])
corr1,corr2=test_data['x1'][:,:2][index1.cpu()],test_data['x2'][:,:2][index2.cpu()]
if len(corr1.shape)==1:
corr1,corr2=corr1[np.newaxis],corr2[np.newaxis]
return corr1,corr2
def match_p(self,p):#p N*M
score,index=torch.topk(p,k=1,dim=-1)
_,index2=torch.topk(p,k=1,dim=-2)
mask_th,index,index2=score[:,0]>self.p_th,index[:,0],index2.squeeze(0)
mask_mc=index2[index] == torch.arange(len(p)).cuda()
mask=mask_th&mask_mc
index1,index2=torch.nonzero(mask).squeeze(1),index[mask]
return index1,index2
class NN_Matcher(object):
def __init__(self,config):
config=namedtuple('config',config.keys())(*config.values())
self.mutual_check=config.mutual_check
self.ratio_th=config.ratio_th
def run(self,test_data):
desc1,desc2,x1,x2=test_data['desc1'],test_data['desc2'],test_data['x1'],test_data['x2']
desc_mat=np.sqrt(abs((desc1**2).sum(-1)[:,np.newaxis]+(desc2**2).sum(-1)[np.newaxis]-2*desc1@desc2.T))
nn_index=np.argpartition(desc_mat,kth=(1,2),axis=-1)
dis_value12=np.take_along_axis(desc_mat,nn_index, axis=-1)
ratio_score=dis_value12[:,0]/dis_value12[:,1]
nn_index1=nn_index[:,0]
nn_index2=np.argmin(desc_mat,axis=0)
mask_ratio,mask_mutual=ratio_score<self.ratio_th,np.arange(len(x1))==nn_index2[nn_index1]
corr1,corr2=x1[:,:2],x2[:,:2][nn_index1]
if self.mutual_check:
mask=mask_ratio&mask_mutual
else:
mask=mask_ratio
corr1,corr2=corr1[mask],corr2[mask]
return corr1,corr2
| en | 0.204195 | #for ddp model #p N*M | 1.946727 | 2 |
evolocity/preprocessing/__init__.py | samsledje/evolocity | 25 | 6613750 | from .featurize_seqs import featurize_fasta, featurize_seqs, get_model
from .neighbors import pca, neighbors, remove_duplicate_nodes
| from .featurize_seqs import featurize_fasta, featurize_seqs, get_model
from .neighbors import pca, neighbors, remove_duplicate_nodes
| none | 1 | 1.159764 | 1 |