Commit 8ee92168 authored by Ryan Gutenkunst's avatar Ryan Gutenkunst
Browse files

Move citation information

parent 8ff10ef3
......@@ -4,7 +4,3 @@ Fitting Distributions of Fitness Effects to population genomic data, both single
from dadi.DFE.Cache1D_mod import Cache1D
from dadi.DFE.Cache2D_mod import Cache2D, mixture, mixture_symmetric_point_pos
from dadi.DFE import PDFs, DemogSelModels, Plotting
print("If you publish with the dadi DFE code, please cite Gutenkunst et al. (2009) PLoS "
"Genetics and Kim, Huber, and Lohmueller (2017) Genetics "
"(https://doi.org/10.1534/genetics.116.197145).")
\ No newline at end of file
......@@ -6,9 +6,6 @@ import numpy
from dadi import Inference
from dadi.Spectrum_mod import Spectrum
print("""If you use the Godambe methods in your published research, please cite Coffman et al. (2016) in addition to the main dadi paper Gutenkunst et al. (2009).
AJ Coffman, P Hsieh, S Gravel, RN Gutenkunst "Computationally efficient composite likelihood statistics for demographic inference" Molecular Biology and Evolution 33:591-593 (2016)""")
def hessian_elem(func, f0, p0, ii, jj, eps, args=(), one_sided=None):
"""
Calculate element [ii][jj] of the Hessian matrix, a matrix
......
......@@ -63,4 +63,25 @@ def RAM_to_pts(RAM, P):
pts: Grid points setting
P: Number of populations
"""
return int((RAM*1024**3/(8*4))**(1./P))
\ No newline at end of file
return int((RAM*1024**3/(8*4))**(1./P))
def citation():
"""
Print citation information for dadi codebase.
"""
print("""
If you find dadi useful in your research, please cite:
RN Gutenkunst, RD Hernandez, SH Williamson, CD Bustamante "Inferring the joint demographic history of multiple populations from multidimensional SNP data" PLoS Genetics 5:e1000695 (2009)
If you find the Godambe Information Matrix methods useful, please cite:
AJ Coffman, P Hsieh, S Gravel, RN Gutenkunst "Computationally efficient composite likelihood statistics for demographic inference" Molecular Biology and Evolution 33:591 (2016)
If you find the DFE inference methods useful, please cite:
BY Kim, CD Huber, KE Lohmueller "Inference of the Distribution of Selection Coefficients for New Nonsynonymous Mutations Using Large Samples" Genetics 206:345 (2017)
If you find the triallelic methods useful, please cite:
AP Ragsdale, AJ Coffman, P Hsieh, TJ Struck, RN Gutenkunst "Triallelic population genomics for inferring correlated fitness effects of same site nonsynonymous mutations" Genetics 203:513 (2016)
If you find the two-locus methods useful, please cite:
AP Ragsdale, RN Gutenkunst "Inferring demographic history using two-locus statistics" Genetics 206:1037 (2017)
""")
\ No newline at end of file
......@@ -9,11 +9,6 @@ over two months, hence I moved the code into the dadi distribution itself.
import ctypes, platform, sys
from string import Template
#(base) PS C:\Users\rgute\Desktop\dadi-devel\tests> python test_CUDA.py -v C:\Users\rgute\anaconda3\lib\site-packages\skcuda\cublas.py:284: UserWarning: creating CUBLAS context to get version number
# warnings.warn('creating CUBLAS context to get version number')
#test_2d_const_params (__main__.CUDATestCase) ... C:\Users\rgute\anaconda3\lib\site-packages\pycuda\gpuarray.py:183: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead
# s = np.asscalar(s)
# Load library:
_linux_version_list = [10.2, 10.1, 10.0, 9.2, 9.1, 9.0, 8.0, 7.5, 7.0, 6.5, 6.0, 5.5, 5.0, 4.0]
_win32_version_list = [10, 10, 100, 92, 91, 90, 80, 75, 70, 65, 60, 55, 50, 40]
......
......@@ -12,6 +12,23 @@ Please join the `dadi-user` Google group: [https://groups.google.com/group/dadi-
As we do our own research, dadi is constantly improving. Our philosophy is to include in dadi any code we develop for our own projects that may useful to others. Similarly, if you develop dadi-related code that you think might be useful to others, please let us know so we can include it with the main distribution. If you have particular needs that modification to dadi may fulfill, please contact the developers and we may be able to help.
### Citations
If you find dadi useful in your research, please cite:
RN Gutenkunst, RD Hernandez, SH Williamson, CD Bustamante "Inferring the joint demographic history of multiple populations from multidimensional SNP data" PLoS Genetics 5:e1000695 (2009)
If you find the Godambe Information Matrix methods useful, please cite:
AJ Coffman, P Hsieh, S Gravel, RN Gutenkunst "Computationally efficient composite likelihood statistics for demographic inference" Molecular Biology and Evolution 33:591 (2016)
If you find the DFE inference methods useful, please cite:
BY Kim, CD Huber, KE Lohmueller "Inference of the Distribution of Selection Coefficients for New Nonsynonymous Mutations Using Large Samples" Genetics 206:345 (2017)
If you find the triallelic methods useful, please cite:
AP Ragsdale, AJ Coffman, P Hsieh, TJ Struck, RN Gutenkunst "Triallelic population genomics for inferring correlated fitness effects of same site nonsynonymous mutations" Genetics 203:513 (2016)
If you find the two-locus methods useful, please cite:
AP Ragsdale, RN Gutenkunst "Inferring demographic history using two-locus statistics" Genetics 206:1037 (2017)
### Suggested workflow
One of Python’s major strengths is its interactive nature. This is very useful in the ex-ploratory stages of a project: for examining data and testing models. If you intend to use dadi’s plotting commands, which rely on `matplotlib`, they you’ll almost certainly want to install IPython, an enhanced Python shell that fixes several difficulties with interactive plotting using `matplotlib`.
......
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