Corwinpro

from pygmsh.built_in.geometry import Geometry
from pygmsh import generate_mesh
import meshio
import dolfin

geom = Geometry()

lc = .1
p0 = geom.add_point([0, 0, 0], lcar=lc)
p1 = geom.add_point([1, 0, 0], lcar=lc)

Corwinpro

import abc
import asyncio


class MessageBox:
    """ A class that can
    - send messages of a certain type with some context (== put a message into
    an internal queue),
    - receive messages from the internal queue
    """

Corwinpro

import asyncio
import time
import random
from collections import defaultdict

NOF_AGENTS = 20
NOF_RECIPIENTS = 3


class Message:

Corwinpro

import dolfin as dolf
import numpy as np

LOG_LEVEL = 30
dolf.set_log_level(LOG_LEVEL)

mesh = dolf.UnitIntervalMesh(100)
n = dolf.FacetNormal(mesh)
wall_left = "near(x[0], 0)"
wall_right = "near(x[0], 1.)"

Corwinpro

from sklearn.base import BaseEstimator
from sklearn.model_selection import cross_val_score
from sklearn.metrics import roc_auc_score

class RandomForestClassifierCustom(BaseEstimator):
    def __init__(self, n_estimators=10, max_depth=10, max_features=10, 
                 random_state=SEED):
        self.n_estimators = n_estimators
        self.max_depth = max_depth
        self.max_features = max_features

Corwinpro

train_df['year_month'] = train_df['time1'].apply(lambda t: 100 * t.year + t.month).values.reshape(-1, 1)
test_df['year_month'] = test_df['time1'].apply(lambda t: 100 * t.year + t.month).values.reshape(-1, 1)
print(train_df['year_month'].loc[y_train == 1].describe())

train_df = train_df[(train_df['year_month'] >= 201401)]#201309
print(train_df['year_month'].loc[y_train == 1].describe())


### Output

Corwinpro

train_times, test_times = train_df[times], test_df[times]
train_session_start_hour = train_times['time1'].apply(lambda ts: ts.hour).values
test_session_start_hour = test_times['time1'].apply(lambda ts: ts.hour).values

def add_day_features(df):
    session_start_hour = df['time1'].apply(lambda ts: ts.hour)
    morning = ((session_start_hour >= 7) & (session_start_hour <= 11)).astype('int').values.reshape(-1, 1)
    day = ((session_start_hour >= 12) & (session_start_hour <= 18)).astype('int').values.reshape(-1, 1)
    evening = ((session_start_hour >= 19) & (session_start_hour <= 23)).astype('int').values.reshape(-1, 1)
    df['morning'] = morning

Corwinpro

    petrkungurtsev@:~$ sudo pip install ROL
Password:
The directory '/Users/petrkungurtsev/Library/Caches/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
The directory '/Users/petrkungurtsev/Library/Caches/pip' or its parent directory is not owned by the current user and caching wheels has been disabled. check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
Collecting ROL
  Downloading ROL-0.0.9.tar.gz (7.9MB)
    100% |████████████████████████████████| 7.9MB 167kB/s 
Installing collected packages: ROL
Running setup.py install for ROL ... error

Corwinpro

import numpy as np
import matplotlib.pyplot as plt
import math

def add_subplot_axes(ax,rect,axisbg='w'):
	fig = plt.gcf()
	box = ax.get_position()
	width = box.width
	height = box.height
	inax_position  = ax.transAxes.transform(rect[0:2])

Corwinpro

from dolfin import *
import numpy as np
import math
from petsc4py import PETSc


#BCSType = 'Dirichlet'
#BCSType = 'Neumann'
BCSType = 'Mixed'
print BCSType