def median(data, var = None, interval):
# filter the data with a median filter, per (interval) in (var)
# if var == None, interval is the number of data point
if var is None:
return datasets.downsample(dataset, size, averaging=np.nanmedian , error_est=None)
else:
filtered_lists = {}
for key in data:
filtered_lists[key] = []
sorted = data.sort(var)
i1 = 0
while i1 < sorted.length:
i0 = i1
v0 = sorted[var][i0]
v1 = v0
while (i1 < soorted.length) and (v1 - v0 < interval):
i1 += 1
v1 = sorted[var][i1]
for key in sorted:
med = np.nanmedian(sorted[key][v0:v1])
filtered_listes[key].append(nan)
def median(data, var=None, interval):
# filter the data with a median filter, per (interval) in (var)
# if var == None, interval is the number of data point
if var is None:
return datasets.downsample(dataset,
size,
averaging=np.nanmedian,
error_est=None)
else:
filtered_lists = {}
for key in data:
filtered_lists[key] = []
sorted = data.sort(var)
i1 = 0
while i1 < sorted.length:
i0 = i1
v0 = sorted[var][i0]
v1 = v0
while (i1 < soorted.length) and (v1 - v0 < interval):
i1 += 1
v1 = sorted[var][i1]
for key in sorted:
med = np.nanmedian(sorted[key][v0:v1])
filtered_listes[key].append(nan)
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()
ax1.set_xlabel("$T$ / K")
plot1 = ax1.errorbar(d["T"],
d["X2"] / 1.e-6,
2.0 * d.errors["X2"] / 1.e-6,
fmt='go',
label="Re($V$)")
ax1.set_ylabel('Re($V$) $\mu$V', color='g')
ax2 = ax1.twinx()
plot2 = ax2.errorbar(d["T"],
d["Y2"] / 1.e-6,
2.0 * d.errors["Y2"] / 1.e-6,
fmt='bx',
def mean_filter(data):
data = datasets.downsample(data, 9, method=np.nanmean)
return data
def plot(fn, co, cos, ls, mk, label, subs):
sub_a, sub_b, sub_c, sub_d = subs
data = ppmsana.import_dc(fn)[ch]
data = datasets.downsample(data,
3,
np.nanmedian,
error_est=errors.combined)
corrected = tdrift.linear(data, "R", data["time"][0], data["R"][0],
data["time"][-1], data["R"][-1])
down, up = transport.split_MR_down_up(corrected)
upsym = transport.symmetrize_MR(up, down)
downsym = transport.symmetrize_MR(down, up)
d = datasets.merge(upsym, downsym)
d = d.sort("H")
d0 = d.mask(np.abs(d["H"]) < zero_H)
n = d0["R"].shape[0]
#print(n)
R0 = np.mean(d0["R"])
err2 = errors.combined(d0["R"], d0.errors["R"])
d["mr"] = d["R"] / R0 - 1.0
d.errors["mr"] = (d.errors["R"] + err2) / R0
d1 = datasets.consolidate(d,
"H",
dH1,
np.nanmean,
error_est=errors.combined)
sub_a.plot(d1["H"] / H_unit,
d1["mr"] / mr_unit,
marker=None,
color=co,
linestyle=ls,
lw=lw,
label=label)
sub_c.plot(d1["H"] / H_unit * cos,
d1["mr"] / mr_unit,
marker=None,
color=co,
linestyle=ls,
lw=lw,
label=label)
d = d1.mask(np.abs(d1["H"]) < H2)
sub_b.errorbar(d["H"] / H_unit,
d["mr"] / mr_unit,
yerr=d.errors["mr"] / mr_unit,
marker=mk,
markerfacecolor="none",
color=co,
linestyle="none",
lw=lw2,
mew=mew2,
label=label)
d = d1.mask(np.abs(d1["H"] * cos) < H2)
sub_d.errorbar(d["H"] / H_unit * cos,
d["mr"] / mr_unit,
yerr=d.errors["mr"] / mr_unit,
marker=mk,
markerfacecolor="none",
color=co,
linestyle="none",
lw=lw2,
mew=mew2,
label=label)
ch = 1
zero_H = 200.0
if __name__ == '__main__':
plt.xlabel("$H$ / Oe")
plt.ylabel("$\Delta R(H) / R(0)$")
# 0d
fn = r"20130516_13_RvH_4K_0D_Ch1.temp_Ch2.11P.EuS11.Al2O3.xx_Ch3.11P.EuS11.Al2O3.xy.dat"
data = ppmsana.import_dc(fn)[ch]
data = datasets.downsample(data, 3, np.nanmedian, error_est=errors.combined)
corrected = tdrift.linear(data, "R", data["time"][0], data["R"][0], data["time"][-1], data["R"][-1])
down, up = transport.split_MR_down_up(corrected)
upsym = transport.symmetrize_MR(up, down)
downsym = transport.symmetrize_MR(down, up)
d = datasets.merge(upsym, downsym)
d = d.sort("H")
d0 = d.mask(np.abs(d["H"]) < zero_H)
n = d0["R"].shape[0]
#print(n)
R0 = np.mean(d0["R"])