From 9d89c09dfe49aba4c68b6911600715add419babd Mon Sep 17 00:00:00 2001
From: Blaise Thompson <blaise@untzag.com>
Date: Tue, 27 Feb 2018 23:58:32 -0600
Subject: 2018-02-27 23:58

---
 software/PyCMDS/ideal axis positions/steps.py | 83 ---------------------------
 1 file changed, 83 deletions(-)
 delete mode 100644 software/PyCMDS/ideal axis positions/steps.py

(limited to 'software/PyCMDS/ideal axis positions/steps.py')

diff --git a/software/PyCMDS/ideal axis positions/steps.py b/software/PyCMDS/ideal axis positions/steps.py
deleted file mode 100644
index 13419c3..0000000
--- a/software/PyCMDS/ideal axis positions/steps.py	
+++ /dev/null
@@ -1,83 +0,0 @@
-### 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')
-- 
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