In python, create your figure and save it as .eps
. This will generate a vector image of your figure:
fig, ax = plt.subplots()
ax.plot(range(10))
fig.savefig('straightLine.eps', format='eps')
################################################################################################# | |
#### This is a simulation to demonstrate how real populations reach Hardy Weinburg equilibrium | |
#### under random mating. | |
#### Author: Corey Chivers, 2011 | |
################################################################################################# | |
cross<-function(parents) | |
{ | |
offspring<-c('d','d') #initiate a child object | |
offspring[1]<-sample(parents[1,],1) |
Author: Corey Chivers
def Weierstrass(x, reps=10): | |
res = np.zeros(x.shape[0]) | |
for i in range(reps): | |
num = x*(3**i)*np.pi | |
denom = 2.0**i | |
res = res + np.cos(num)/denom | |
return res | |
title = '$f(x) = {cos(3x\pi)}/{2} + {cos(3^2x\pi)}/{2^2} + {cos(3^3x\pi)}/{2^3} ...$' | |
delta = 0.5 |
from scipy import stats | |
import numpy as np | |
import matplotlib as plt | |
def beta_errors(num, denom): | |
return stats.beta.interval(.95, num+1, denom-num+1) | |
def calibration_curve_error_bars(a, p, n_bins=10): | |
pmin, pmax = p.min(), p.max() |
def cdf_diff(df, var, grp='label', col=None, rm_outlier=None, hard_lim=None, ax=None, xlim=None): | |
'''Plot cummulative distributions of multiple groups for comparison. | |
Arguments: | |
df: DataFrame | |
var: string, name of column to be plotted | |
grp: string, grouping variable | |
col: list, colors to use for each group | |
rm_outlier: None|float, remove datapoints beyond this many sigma. | |
ax: axis on which to plot. Default none will return a new figure |
import scipy as sp | |
def beta_errors(num, denom): | |
return sp.stats.beta.interval(0.95, num+1, denom-num+1) | |
def plot_km(df, threshold=0.5, max_days=365, y_text_shrink=1, ax=None): | |
days = range(max_days) | |
idb_above = df['Pred']>threshold | |
survival_series = df['survival_time_days'] |