Ilya ipashchenko
ipashchenko
/ gist:5266179
Created
March 28, 2013 19:40
— forked from olistik/gist:3894072
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| # 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
/ jdutil.py
Created
October 29, 2013 22:10
— forked from jiffyclub/jdutil.py
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| """ | |
| 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
/ Solarized Dark.xml
Created
November 16, 2013 09:31
— forked from brianly/Solarized Dark.xml
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| <?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
/ fontfix.patch
Created
February 8, 2014 21:02
— forked from aleksandara/fontfix.patch
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| 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
/ fft_convolution.py
Last active
August 29, 2015 14:06
— forked from thearn/fft_convolution.py
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| 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
/ geom_vs_poisson.py
Last active
May 11, 2020 11:16
Model selection via Bayes factor estimation with ``emcee`` (see http://stronginference.com/bayes-factors-pymc.html for ``pymc`` version of model probabilities calculation)
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| import numpy as np | |
| from scipy import stats | |
| from emcee import PTSampler, EnsembleSampler | |
| try: | |
| import matplotlib.pyplot as plt | |
| except: | |
| plt = None | |
| def lnlike_geom(p, y): |
ipashchenko
/ infer_num_trials_binomial.py
Last active
August 29, 2015 14:21
Bayesian estimation of number of trials in a Binomial distribution
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| import numpy as np | |
| from scipy import stats | |
| try: | |
| import matplotlib.pyplot as plt | |
| except: | |
| plt = None | |
| def posterior_trials(n_max, data, alpha, beta, delta=0.005): | |
| """ |
ipashchenko
/ discrete_emcee_hack.py
Last active
August 29, 2015 14:21
discrete parameters sampling with ``emcee`` using @lfloeer's hack
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| import emcee | |
| import numpy as np | |
| from scipy import stats | |
| try: | |
| import matplotlib.pyplot as plt | |
| except: | |
| plt = None | |
| vround = np.vectorize(round) |
ipashchenko
/ grid_dm_search_info.py
Last active
August 29, 2015 14:22
parameters of grid DM search for FRB
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| def delta_dm_max(nu_max, nu_min, dt): | |
| """ | |
| Return difference in DM that corresponds to arrival time shift between | |
| highest and lowest frequency channels equals to time resolution. | |
| :param nu_max: | |
| Frequency of highest frequency channel [MHz]. | |
| :param nu_min: | |
| Frequency of lowest frequency channel [MHz]. | |
| :param dt: |
ipashchenko
/ gist:e42c685ee1238dc166ce
Last active
August 29, 2015 14:22
— forked from joferkington/gist:5074650
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| 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], |
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