import elflab.analysis.signal_processing as signal
import elflab.datasets as datasets
import matplotlib.pyplot as plt
fn = r"raw\20141215j_sb14_MI_0.2T_40K_2K_71kHz_1mA_300ms_ph0.csv"
out = r"0degree\0.2T_cool.csv"
degree = 0.
if __name__ == '__main__':
d = datasets.load_csv(fn,
column_mapping={
0: "T",
1: "X",
2: "Y"
},
has_header=False)
# Rotate
d["X2"] = -d["X"]
d["Y2"] = -d["Y"]
# Averaging methods take into account of correlation within 10 x time constant
averager, err_est = signal.decorrelate_neighbour_averager(
30, np.nanmean, estimate_error=True)
d = datasets.downsample(d, 240, method=averager, error_est=err_est)
fig, ax1 = plt.subplots()
transform=sub.transAxes,
ha="center",
va="center",
size="large")
#sub.text(0.05, 0.6, r"$20 \mathrm{mT}$", transform=sub.transAxes, ha="left", va="center")
sub.set_xlim(xlim)
sub.set_xticks(xticks)
sub.xaxis.set_minor_locator(xminor_locator)
sub.set_yticks(np.arange(-0.5, 1, 0.5))
yminor_locator = AutoMinorLocator(5)
sub.yaxis.set_minor_locator(yminor_locator)
fn1 = r"mi\0degree\0T_warm.csv"
data = datasets.load_csv(fn1, mi_columns, has_header=True)
data = data.mask(data["T"] < 42)
sub.errorbar(data["T"],
data["Y"] / unit_re - offset_re,
yerr=data["err_Y"] * NSE / unit_re,
label="$\chi'$",
color=pal.mi_re,
marker="x",
markersize=markersize,
markerfacecolor="none",
linestyle="None",
linewidth=lw,
zorder=1)
sub.errorbar(data["T"],
data["X"] / unit_re - offset_im,
yerr=data["err_X"] * NSE / unit_re,
direction="in",
pad=1,
bottom=True,
top=True,
left=True,
right=False)
sub_b.set_xlabel(r"$T$ (K)", labelpad=0.1)
sub_b.set_ylabel(r"$M_z$ (arb. unit)", labelpad=1.0)
xminor_locator = AutoMinorLocator(5)
sub_b.xaxis.set_minor_locator(xminor_locator)
yminor_locator = AutoMinorLocator(5)
sub_b.yaxis.set_minor_locator(yminor_locator)
fn1 = r"squid\MvT.dat"
data = datasets.load_csv(fn1, SQUID_COLUMNS)
sub_b.errorbar(data["T"],
data["M"] / M_unit,
yerr=NSE * data["err_M"] / M_unit,
color=pal.squid_T,
marker="x",
markersize=markersize,
markerfacecolor="none",
linestyle="None",
linewidth=1,
zorder=1)
fn1 = r"squid\curie.dat"
data = datasets.load_csv(fn1, {0: "x", 1: "y"})
sub_b.plot(data["x"],
data["y"] / M_unit,
fig = plt.figure(dpi=1200)
fig.set_size_inches([0.7 * paperwidth, 0.3/0.4*0.7 * paperwidth])
#fig.set_size_inches([3.375, 2.1])
marker_size = 6
# Sub figure layout
gs1 = gridspec.GridSpec(2, 2)
gs1.update(wspace=0.0, hspace=0.0)
sub_a = fig.add_subplot(gs1[0, 0])
sub_b = fig.add_subplot(gs1[0, 1])
sub_c = fig.add_subplot(gs1[1, 0])
sub_d = fig.add_subplot(gs1[1, 1])
fn1 = r"mr/quadraticVar.dat"
q = datasets.load_csv(fn1, columns, has_header=False)
xlim = [-0.6, 0.6]
# Sub fig a
sub = sub_a
sub.text(0.2, 0.8, "(a)", transform=sub.transAxes, ha="right", va="center", size="large")
sub.tick_params(axis='both', which='both', direction="in", bottom=False, top=True, left=True, right=False, labelleft=True, labelright=False, labeltop=True, labelbottom=False)
sub.set_xlabel(r"$H$ (T $/\mu_0$)")
sub.set_ylabel(r"$\Delta{}R(H) / R(0) / 10^{-2}$")
sub.xaxis.set_label_position("top")
sub.yaxis.set_label_position("left")
sub.yaxis.set_label_coords(-0.20, 0.5)
sub.set_xlim(xlim)
yunit = 0.01
sub.set_xlim(xlim)
sub.set_xticks(xticks)
xminor_locator = AutoMinorLocator(2)
sub.xaxis.set_minor_locator(xminor_locator)
sub.set_yscale("log")
sub.set_yticks([0.01, 0.1, 1])
#yminor_locator = AutoMinorLocator(2)
#sub.yaxis.set_minor_locator(yminor_locator)
#sub.set_xticks(xticks)
#sub.xaxis.set_minor_locator(xminor_locator)
fn1 = r"rvt\s1.dat"
data = datasets.load_csv(fn1, rvt_columns, has_header=True)
mask = np.logical_and(data["T"] > xlim[0], data["T"] < xlim[1])
sub.plot(data["T"][mask], data["R"][mask], color=pal.rvt1, marker="x", markersize=markersize, markerfacecolor="none", linestyle="None", linewidth=lw, zorder=1)
sub.axvline(x=14.5, linestyle="--", color=pal.aid_lines, linewidth=lw, zorder=2)
sub.axvline(x=31, linestyle="--", color=pal.aid_lines, linewidth=lw, zorder=2)
sub.axvline(x=59, linestyle="--", color=pal.aid_lines, linewidth=lw, zorder=2)
#legend = sub.legend(handlelength=0.0)
# Sub fig rvt2
sub = sub_rvt2
sub.text(0.87, 0.8, "(b)", transform=sub.transAxes, ha="center", va="center", size="large")
sub_a.xaxis.set_minor_locator(xminor_locator)
sub_a.set_yscale("log")
sub_a.set_ylim([10**2.9, 10**4.3])
#yminor_locator = AutoMinorLocator(5)
#sub_a.yaxis.set_minor_locator(yminor_locator)
column_map = {0: "2theta", 1: "counts"}
# EuS
fn1 = r"xrd\20170706_EuS12_Si_100_5d120d_hybrid.ASC"
data = post_process(
datasets.load_csv(fn1,
column_map,
has_header=False,
delimiter=' ',
skipinitialspace=True))
sub_a.plot(data["2theta"],
data["counts"] * 10.0**1.0,
color=pal.xrd_eus,
marker=None,
linewidth=lw,
label="EuS only",
zorder=2)
# S4 / SB02
fn1 = r"xrd\20140319_Sb2Te3_EuS_Si100_SB02_xpert2_5d120d.ASC"
data = post_process(
datasets.load_csv(fn1,
column_map,
xticks = range(8)
#xlabels = ["I", "II", "III", "IV", "V", "VI", "VII", "VIII"]
xlabels = ["A", "B", "C", "D", "E", "F", "G", "H"]
#sub.set_xlim(xlim)
sub.set_xticks(xticks)
sub.set_xticklabels(xlabels)
#sub.xaxis.set_minor_locator(xminor_locator)
sub.set_yscale("log")
sub.set_ylim([10**-3.5, 10**2.4])
#sub.set_yticks([1.e-2, 1.e-1, 1, 1.e1])
trend_columns = {1: "source", 2: "WL", 3: "R"}
fn1 = r"WL_trend\trend.csv"
data = datasets.load_csv(fn1, trend_columns, has_header=True)
mask = data["WL"] == 1
d = data.mask(mask)
sub.plot(d["source"],
d["R"],
label="negative magnetoresistance",
color=pal.wl_yes,
marker="o",
markersize=6.0,
markerfacecolor="none",
linestyle="None",
linewidth=lw,
zorder=3)
mask = data["WL"] == -1