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diff --git a/figures/software/PyCMDS/ideal axis positions/delay steps.pdf b/figures/software/PyCMDS/ideal axis positions/delay steps.pdf
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index 0000000..1472e0c
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+++ b/figures/software/PyCMDS/ideal axis positions/delay steps.pdf
diff --git a/figures/software/PyCMDS/ideal axis positions/delay steps.tex b/figures/software/PyCMDS/ideal axis positions/delay steps.tex
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+% document
+\documentclass[11 pt]{report}
+\usepackage[letterpaper, margin=0.75in]{geometry}  % 1 inch margins required
+\usepackage{setspace}
+\usepackage{afterpage}
+\usepackage{color}
+\usepackage{soul}
+\usepackage{array}
+
+% text
+\usepackage[utf8]{inputenc}
+\setlength\parindent{0pt}
+\setlength{\parskip}{1em}
+\usepackage{enumitem}
+\renewcommand{\familydefault}{\sfdefault}
+\newcommand{\RomanNumeral}[1]{\textrm{\uppercase\expandafter{\romannumeral #1\relax}}}
+\usepackage{etoolbox}
+\AtBeginEnvironment{verse}{\singlespacing}
+\AtBeginEnvironment{tabular}{\singlespacing}
+
+% graphics
+\usepackage{graphics}
+\usepackage{graphicx}
+\usepackage{epsfig}
+\usepackage{epstopdf}
+\usepackage{etoc}
+\usepackage{tikz}
+
+% math
+\usepackage{amssymb}
+\usepackage{amsmath}
+\usepackage[cm]{sfmath}
+\DeclareMathOperator{\me}{e}
+
+% hyperref
+\usepackage[colorlinks=true, linkcolor=black, urlcolor=blue, citecolor=black, anchorcolor=black]{hyperref}
+\usepackage[all]{hypcap}  % helps hyperref work properly
+
+% date (http://tex.stackexchange.com/a/237251)
+\def\twodigits#1{\ifnum#1<10 0\fi\the#1}
+\def\mydate{\leavevmode\hbox{\the\year-\twodigits\month-\twodigits\day}}
+
+\begin{document}
+
+{\Huge{delay space stepping strategy}}
+
+Blaise Thompson \hfill last modified \mydate
+
+\dotfill
+
+Linear stepping is more expensive than it needs to be.
+
+Want to capture the dynamic range of the data as quickly as possible.
+
+Typically have exponential decay dynamics (perhaps multi-exponential)\dots we can capitalize on this. We want to take high resolution data at early delays and low resolution data at late delays.
+
+Of course, we don't want to throw away any information we would otherwise be entitled to.
+
+Conceptually we want to 'linearize' the data, so that each subsequent delay step accounts for the same change in signal.
+
+Signal goes exponentially...
+
+\begin{eqnarray}
+S &=& \me^{-\frac{t}{\tau}} \\
+\log{(S)} &=& -\frac{t}{\tau} \\
+t &=& -\tau\log{(S)}
+\end{eqnarray}
+
+So to step linearly in $t$, my step size has to go as $-\tau\log{(S)}$.
+
+We want to go linearly in signal, meaning that we want to divide $S$ into even sections. If $S$ goes from 0 to 1 and we choose to acquire $N$ points,
+
+\begin{eqnarray}
+t_n &=& -\tau\log{\left(\frac{n}{N}\right)}.
+\end{eqnarray}
+
+Note that $t_n$ starts at long times and approaches zero delay. So the first $t_1$ is the smallest signal and $t_N$ is the largest.
+
+Now we can start to consider realistic cases, like where $\tau$ is not quite known and where some other longer dynamics persist (manifested as a static offset). Since these values are not separable in a general system, I'll keep $S$ normalized between 0 and 1.
+
+\begin{eqnarray}
+S &=& (1-c)\me^{-\frac{t}{\tau_{\mathrm{actual}}}} + c \\
+S_n &=& (1-c)\me^{-\frac{-\tau_{\mathrm{step}}\log{\left(\frac{n}{N}\right)}}{\tau_{\mathrm{actual}}}} + c \\
+S_n &=& (1-c)\me^{-\frac{\tau_{\mathrm{step}}}{\tau_{\mathrm{actual}}} \log{\left(\frac{N}{n}\right)}} + c \\
+S_n &=& (1-c)\left(\frac{N}{n}\right)^{-\frac{\tau_{\mathrm{step}}}{\tau_{\mathrm{actual}}}} + c \\
+S_n &=& (1-c)\left(\frac{n}{N}\right)^{\frac{\tau_{\mathrm{step}}}{\tau_{\mathrm{actual}}}} + c
+\end{eqnarray}
+
+
+
+\begin{figure}[!htb]
+	\centering
+	\includegraphics[scale=0.5]{"out"}
+	\caption{}
+\end{figure}
+
+
+
+\end{document}
+\ No newline at end of file
diff --git a/figures/software/PyCMDS/ideal axis positions/exponential.png b/figures/software/PyCMDS/ideal axis positions/exponential.png
new file mode 100644
index 0000000..7ad27f3
--- /dev/null
+++ b/figures/software/PyCMDS/ideal axis positions/exponential.png
diff --git a/figures/software/PyCMDS/ideal axis positions/steps.py b/figures/software/PyCMDS/ideal axis positions/steps.py
new file mode 100644
index 0000000..13419c3
--- /dev/null
+++ b/figures/software/PyCMDS/ideal axis positions/steps.py
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+### import ####################################################################
+
+
+import matplotlib.pyplot as plt
+plt.close('all')
+
+import numpy as np
+
+import WrightTools as wt
+
+
+### define ####################################################################
+
+
+def get_signal(d, tau, pulsewidth=10, offset=0):
+    # pulse
+    pulse = np.exp((-d**2)/(pulsewidth**2))
+    # signal
+    sig = np.zeros(d.shape)
+    sig[d<=0] = np.exp(d[d<=0]/tau)
+    sig[d<=0] += offset
+    sig /= sig.max()
+    # finish
+    #sig = np.convolve(sig, pulse, mode='same')
+    return sig
+
+
+def logarithmic_stepping(p_tau, p_npts, n_tau, n_npts):
+    # positive
+    p_xi = np.arange(0, p_npts)
+    p_delays = p_tau * np.log((p_xi.size+1)/(p_xi+1))
+    # negative
+    n_xi = np.arange(0, n_npts)
+    n_delays = -n_tau * np.log((n_xi.size+1)/(n_xi+1))
+    return np.hstack((n_delays, [0], p_delays))
+
+
+tau = 200
+
+
+d = logarithmic_stepping(50, 3, 200, 15)
+
+
+### workspace #################################################################
+
+
+if True:
+    fig, gs = wt.artists.create_figure(width=13, cols=[1, 1], nrows=1)
+    # delay space
+    ax = plt.subplot(gs[0, 0]) 
+    ds = np.linspace(-1500, 1500, 1000)
+    sig = get_signal(ds, tau)
+    plt.plot(ds, sig, c='b', lw=2, alpha=0.5)
+    sig = get_signal(ds, tau, offset=0.5)
+    plt.plot(ds, sig, c='r', lw=2, alpha=0.5)
+    sig = get_signal(ds, tau*2, offset=0)
+    plt.plot(ds, sig, c='g', lw=2, alpha=0.5)
+    plt.xlim(-1250, 100)
+    plt.ylim(-0.1, 1.1)
+    for x in d:
+        plt.axvline(x, c='k', zorder=0)
+    plt.axvline(0, lw=3, c='k')
+    ax.set_xlabel('delay', fontsize=18)
+    ax.set_ylabel('signal', fontsize=18)
+    plt.grid(ls=':')
+    # index space
+    ax = plt.subplot(gs[0, 1])
+    d = logarithmic_stepping(50, 3, 200, 15)
+    sig = get_signal(d, tau)
+    plt.scatter(np.arange(sig.size), sig, c='b', edgecolor='none', s=50, alpha=0.5)
+    sig = get_signal(d, tau, offset=0.5)
+    plt.scatter(np.arange(sig.size), sig, c='r', edgecolor='none', s=50, alpha=0.5)
+    sig = get_signal(d, tau*2, offset=0)
+    plt.scatter(np.arange(sig.size), sig, c='g', edgecolor='none', s=50, alpha=0.5) 
+    i = np.argmin(np.abs(d))
+    plt.axvline(i, lw=3, c='k')
+    plt.grid(ls=':')
+    plt.ylim(-0.1, 1.1)
+    plt.setp(ax.get_yticklabels(), visible=False)
+    ax.set_xlim(0-1, sig.size)
+    ax.set_xlabel('index', fontsize=18)
+    # finish
+    wt.artists.savefig('exponential.png')