From 49de5fe553c2066520162cccd7d5dd586efc934d Mon Sep 17 00:00:00 2001
From: Blaise Thompson <blaise@untzag.com>
Date: Mon, 16 Oct 2017 21:05:48 -0500
Subject: structure

---
 software/PyCMDS/ideal axis positions/steps.py | 83 +++++++++++++++++++++++++++
 1 file changed, 83 insertions(+)
 create 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
new file mode 100644
index 0000000..13419c3
--- /dev/null
+++ b/software/PyCMDS/ideal axis positions/steps.py	
@@ -0,0 +1,83 @@
+### 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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