Remove unused functions for swp2
tforest
parent
89813468b5
commit
6a6d4bf6f9
71
swp2.py
71
swp2.py
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@ -3,13 +3,11 @@ import os
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import numpy as np
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import math
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import json
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import io
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from scipy.special import gammaln
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from matplotlib.backends.backend_pdf import PdfPages
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from matplotlib.ticker import MaxNLocator
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from mpl_toolkits.axes_grid1.inset_locator import inset_axes
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from matplotlib.ticker import MultipleLocator
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def log_facto(k):
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"""
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Using the Stirling's approximation
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"""
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k = int(k)
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if k > 1e6:
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return k * np.log(k) - k + np.log(2*math.pi*k)/2
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@ -18,14 +16,6 @@ def log_facto(k):
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val += np.log(i)
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return val
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def log_facto_1(k):
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startf = 1 # start of factorial sequence
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stopf = int(k+1) # end of of factorial sequence
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q = gammaln(range(startf+1, stopf+1)) # n! = G(n+1)
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return q[-1]
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def return_x_y_from_stwp_theta_file(stwp_theta_file, breaks, mu, tgen, relative_theta_scale = False):
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with open(stwp_theta_file, "r") as swp_file:
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# Read the first line
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@ -128,51 +118,6 @@ def return_x_y_from_stwp_theta_file(stwp_theta_file, breaks, mu, tgen, relative_
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# # x[i] = x[i]/N0
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return x,y,likelihood,thetas,sfs,L
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def return_x_y_from_stwp_theta_file_as_is(stwp_theta_file, breaks, mu, tgen, relative_theta_scale = False):
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with open(stwp_theta_file, "r") as swp_file:
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# Read the first line
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line = swp_file.readline()
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L = float(line.split()[2])
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rands = swp_file.readline()
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line = swp_file.readline()
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# skip empty lines before SFS
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while line == "\n":
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line = swp_file.readline()
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sfs = np.array(line.split()).astype(float)
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# Process lines until the end of the file
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while line:
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# check at each line
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if line.startswith("dim") :
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dim = int(line.split()[1])
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if dim == breaks+1:
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likelihood = line.split()[5]
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groups = line.split()[6:6+dim]
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theta_site = line.split()[6+dim:6+dim+1+dim]
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elif dim < breaks+1:
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line = swp_file.readline()
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continue
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elif dim > breaks+1:
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break
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#return 0,0,0
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# Read the next line
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line = swp_file.readline()
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#### END of parsing
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# quit this file if the number of dimensions is incorrect
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if dim < breaks+1:
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return 0,0
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# get n, the last bin of the last group
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# revert the list of groups as the most recent times correspond
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# to the closest and last leafs of the coal. tree.
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groups = groups[::-1]
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theta_site = theta_site[::-1]
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thetas = {}
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for i in range(len(groups)):
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groups[i] = groups[i].split(',')
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# print(groups[i], len(groups[i]))
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thetas[i] = [float(theta_site[i]), groups[i], likelihood]
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return thetas, sfs
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def plot_k_epochs_thetafolder(folder_path, mu, tgen, breaks = 2, title = "Title", theta_scale = True):
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scenari = {}
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@ -180,7 +125,7 @@ def plot_k_epochs_thetafolder(folder_path, mu, tgen, breaks = 2, title = "Title"
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for file_name in os.listdir(folder_path):
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if os.path.isfile(os.path.join(folder_path, file_name)):
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# Perform actions on each file
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x,y,likelihood,sfs,L = return_x_y_from_stwp_theta_file(folder_path+file_name, breaks = breaks,
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x, y, likelihood, theta, sfs, L = return_x_y_from_stwp_theta_file(folder_path+file_name, breaks = breaks,
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tgen = tgen,
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mu = mu, relative_theta_scale = theta_scale)
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if x == 0 or y == 0:
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@ -396,7 +341,7 @@ def save_k_theta(folder_path, mu, tgen, title = "Title", theta_scale = True,
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cpt +=1
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if os.path.isfile(os.path.join(folder_path, file_name)):
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for k in range(breaks_max):
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thetas,sfs = return_x_y_from_stwp_theta_file_as_is(folder_path+file_name, breaks = k,
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x,y,likelihood,thetas,sfs,L = return_x_y_from_stwp_theta_file(folder_path+file_name, breaks = k,
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tgen = tgen,
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mu = mu, relative_theta_scale = theta_scale)
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if thetas == 0:
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@ -591,7 +536,7 @@ def plot_test_theta(folder_path, mu, tgen, title = "Title", theta_scale = True,
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cpt +=1
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if os.path.isfile(os.path.join(folder_path, file_name)):
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for k in range(breaks_max):
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thetas,sfs = return_x_y_from_stwp_theta_file_as_is(folder_path+file_name, breaks = k,
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x, y, likelihood, theta, sfs, L = return_x_y_from_stwp_theta_file(folder_path+file_name, breaks = k,
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tgen = tgen,
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mu = mu, relative_theta_scale = theta_scale)
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if thetas == 0:
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@ -751,7 +696,7 @@ def combined_plot(folder_path, mu, tgen, breaks, title = "Title", theta_scale =
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# # plot_all_epochs_thetafolder(folder_path, mu, tgen, title, theta_scale, ax = None)
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# # plot_test_theta(folder_path, mu, tgen, title, theta_scale, breaks_max = breaks, ax = None)
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# # plt.clf()
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# save_k_theta(folder_path, mu, tgen, title, theta_scale, breaks_max = breaks, output = title+"_plotdata.json")
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save_k_theta(folder_path, mu, tgen, title, theta_scale, breaks_max = breaks, output = title+"_plotdata.json")
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with open(title+"_plotdata.json", 'r') as json_file:
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loaded_data = json.load(json_file)
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# plot page 1 of summary
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