ipashchenko

# place this file in ~/.config/terminator/config
[global_config]
  title_transmit_bg_color = "#d30102"
  focus = system
[keybindings]
  reset_clear = <Ctrl>R
  new_tab = <Ctrl>T
  split_horiz = <Ctrl><Shift>E
  split_vert = <Ctrl>E
  close_term = <Ctrl><Shift>W

ipashchenko

"""
Functions for converting dates to/from JD and MJD. Assumes dates are historical
dates, including the transition from the Julian calendar to the Gregorian
calendar in 1582. No support for proleptic Gregorian/Julian calendars.

:Author: Matt Davis
:Website: http://github.com/jiffyclub

"""

ipashchenko

<?xml version="1.0" encoding="UTF-8"?>
<scheme name="Solarized Dark" version="1" parent_scheme="Default">
  <option name="LINE_SPACING" value="1.0" />
  <option name="EDITOR_FONT_SIZE" value="14" />
  <option name="EDITOR_FONT_NAME" value="Consolas" />
  <colors>
    <option name="ADDED_LINES_COLOR" value="" />
    <option name="ANNOTATIONS_COLOR" value="2b36" />
    <option name="ANNOTATIONS_MERGED_COLOR" value="" />
    <option name="CARET_COLOR" value="dc322f" />

ipashchenko

diff -r 96a023de3ddf make/sun/font/Makefile
--- a/make/sun/font/Makefile	Wed Jun 06 18:39:46 2012 -0700
+++ b/make/sun/font/Makefile	Fri Jun 08 12:52:01 2012 +0900
@@ -128,7 +128,7 @@
   ifeq ($(USING_SYSTEM_FT_LIB), false)
     FREETYPE_LIB = $(LIB_LOCATION)/$(LIB_PREFIX)freetype.$(LIBRARY_SUFFIX).6
   endif
-  OTHER_LDLIBS += -L$(FREETYPE_LIB_PATH) -lfreetype
+  OTHER_LDLIBS += -L$(FREETYPE_LIB_PATH) -lfreetype -lfontconfig
 endif 

ipashchenko

from numpy.fft import fft, ifft, fft2, ifft2, fftshift
import numpy as np

def fft_convolve2d(x,y):
    """ 2D convolution, using FFT"""
    fr = fft2(x)
    fr2 = fft2(np.flipud(np.fliplr(y)))
    m,n = fr.shape
    cc = np.real(ifft2(fr*fr2))
    cc = np.roll(cc, -m/2+1,axis=0)

ipashchenko

import numpy as np
import scipy.ndimage as ndimage

# The array you gave above
data = np.array( 
        [
           [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 

ipashchenko

import joblib
import os

cachedir = 'cache'
if not os.path.isdir(cachedir): os.mkdir(cachedir)
mem = joblib.Memory(cachedir=cachedir, verbose=True)

@mem.cache
def my_long_function(i):
	return i + i

ipashchenko

import pandas as pd
import numpy as np
from sklearn.feature_extraction import DictVectorizer
 
def encode_onehot(df, cols):
    """
    One-hot encoding is applied to columns specified in a pandas DataFrame.
    
    Modified from: https://gist.github.com/kljensen/5452382
    

ipashchenko

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

from keras.layers import Input, Dense, merge, ELU, Dropout
from keras.models import Model
from keras.regularizers import l2
from keras import backend as K
from keras.optimizers import rmsprop, adam

ipashchenko

#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential