def plotter(field, resistance, fitted_line):
fig, ax = MakePlot().create()
ax.plot(field, 1/resistance)
if field[0]>0:
ax.plot(np.linspace(0,14,200),1/np.poly1d(fitted_line)(np.linspace(0,14,200)))
else:
ax.plot(np.linspace(-14, 0, 200), 1 / np.poly1d(fitted_line)(np.linspace(-14, 0, 200)))
ax.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
ax.set_ylabel(r'Conductance $(\frac{2 e^2}{h})$', fontsize=12, fontname='arial')
ax.set_xlabel(r'Magnetic Field (T)', fontsize=12, fontname='arial')
ax.set_title(r'Checking Fit of Step', fontsize=14, fontname='arial')
ax.set_xlim()
ax.set_ylim()
ax.minorticks_on()
ax.tick_params('both', which='both', direction='in',
bottom=True, top=True, left=True, right=True)
fig.show()
temps_c = ['1.75 K +', '1.75 K -', '2.00 K +', '2.00 K -', '2.25 K +','2.25 K -',
'2.50 K +', '2.50 K -']
temps_d = ['1.75 K +', '1.75 K -', '2.00 K +', '2.00 K -', '2.25 K +','2.25 K -']
currents = [r'800 $\mu A$', r'900 $\mu A$']
data_sets = [curr_800, curr_900]
sample = 'VT64'
res_up_plus, res_down_plus, res_up_minus, res_down_minus = [], [], [], []
b_up_plus, b_down_plus, b_up_minus, b_down_minus = [], [], [], []
fig, axs = MakePlot(nrows=2,ncols=1,figsize=(9,12)).create()
axes = [axs[0],axs[1]]
for ind, curr_data_name in enumerate(data_sets):
temps = temps_a
res_lst, field_lst = [], []
label_lst = []
c = 0
for idx, current_temp_data_name in enumerate(curr_data_name):
dat = load_matrix(main_path+current_temp_data_name).T
yerr_neg_900 = np.std(np.array([dG_900_neg_up, dG_900_neg_down]), axis=0)
B900_neg = np.average(np.array([B_900_neg_up, B_900_neg_down]), axis=0)
xerr_neg_900 = np.std(np.array([B_900_neg_up, B_900_neg_down]), axis=0)
line_pos800 = np.poly1d(np.polyfit(B800_pos, dG800_pos, 1))
line_neg800 = np.poly1d(np.polyfit(B800_neg, dG800_neg, 1))
line_pos900 = np.poly1d(np.polyfit(B900_pos, dG900_pos, 1))
line_neg900 = np.poly1d(np.polyfit(B900_neg, dG900_neg, 1))
linspace_pos = np.linspace(0, 14, 10)
linspace_neg = np.linspace(-14, 0, 10)
# Need to put in xy error bars on the points. STD of the above averages.
fig, ax = MakePlot(figsize=(16, 9)).create()
ax.scatter(B800_pos, dG800_pos, c='r', label=r'800 $\mu$A')
ax.scatter(B800_neg, dG800_neg, c='r')
ax.errorbar(B800_pos,
dG800_pos,
xerr=xerr_pos_800,
yerr=yerr_pos_800,
fmt='none',
c='r',
alpha=0.5)
ax.errorbar(B800_neg,
dG800_neg,
xerr=xerr_neg_800,
yerr=yerr_neg_800,
cap_no_outliers = remove_outliers2(capacitance, 2)
cap_interpolated = np.copy(cap_no_outliers)
nans, x = nan_helper(cap_interpolated)
cap_interpolated[nans] = np.interp(x(nans), x(~nans), cap_interpolated[~nans])
cap_interpolated = savgol_filter(cap_interpolated, get_loess_window(field, 2.2), 2)
field = field[15:]
cap_interpolated = cap_interpolated[15:]
R = R[15:]
ref_C0 = cap_interpolated[0]
fig, axs = MakePlot(nrows=1, ncols=2).create()
fig2, ax2 = MakePlot().create()
fig3, ax3 = MakePlot().create()
fig_together, axs_together = MakePlot(nrows=1, ncols=2).create()
for ind, filename_open in enumerate(filenames):
d = {}
field, cap_interpolated, R, idx2, raw_cap = process_data(main_path, filename_open)
if idx2.shape[0] != 0:
b_sign_change_idx = np.argmin(cap_interpolated)
from tools.MakePlot import * import matplotlib.pyplot as plt import scipy.interpolate import scipy.ndimage import matplotlib.colors as mcolors from matplotlib.ticker import FormatStrFormatter main_path = '/Volumes/GoogleDrive/My Drive/Heatmap Cooldowns/' save_path = '/Volumes/GoogleDrive/My Drive/Thesis Figures/Transport/' possible_currents1 = np.array([10,20,50,100,200,500,1000,1500]) fig, ax = MakePlot(figsize=(32,18)).create() # scan is at 1 mA # VT1 # df = load_matrix(main_path+'RvsT_FeSb2-VT1&26-cooldown.dat') # df = df[['Temperature (K)', 'Bridge 1 Resistance (Ohms)']] # # resistance = scipy.ndimage.median_filter(np.array(df['Bridge 1 Resistance (Ohms)']),size=3) # temperature = np.array(df['Temperature (K)']) # VT26 # df = load_matrix(main_path+'RvsT_FeSb2-VT1&26-cooldown.dat') # df = df[['Temperature (K)', 'Bridge 2 Resistance (Ohms)']] # # resistance = scipy.ndimage.median_filter(np.array(df['Bridge 2 Resistance (Ohms)']),size=3)
#currents_log = np.logspace(-3,np.log10(1.5),10000)
currents_log = np.linspace(0.01,1.5,10000)
temperoos, cureentoos = [], []
temp_lst = []
xx, yy = np.meshgrid(temps, possible_currents1 / 1000)
data_grid_og = np.array([np.ravel(xx), np.ravel(yy)]).T
temperatures = np.linspace(2, temps[-1], 10000)
grid_x, grid_y = np.meshgrid(temperatures, currents_log)
sample_lst = []
fig, axs = MakePlot(figsize=(32,18),nrows=5, ncols=4).create()
axs = [axs[0,0], axs[0,1], axs[0,2],axs[0,3],
axs[1,0], axs[1,1], axs[1,2],axs[1,3],
axs[2,0], axs[2,1], axs[2,2],axs[2,3],
axs[3,0], axs[3,1], axs[3,2],axs[3,3],
axs[4,0], axs[4,1], axs[4,2],axs[4,3]]
for i, temp in enumerate(temps):
temp_path = main_path + 'VT26/' + str(temp) + '/'
current_lst = []
ax = axs[i]
for temp in temps:
temp_path = main_path + sample + '/' + str(temp) + '/'
resistance_lst, current_lst, field_curr, tempatu_lst = [], [], [], []
for current in possible_currents1:
resistance, field = load_r_and_h(temp_path, current)
#nans, x = nan_helper(resistance)
#resistance[nans] = np.interp(x(nans), x(~nans), resistance[~nans])
max_resistance_loc = np.argmax(resistance)
fig, ax = MakePlot().create()
plt.axvline(field[max_resistance_loc])
plt.scatter(x=field, y=resistance)
plt.title('Temp = ' + str(temp) + 'current' + str(current) +
'sample:' + sample)
plt.show()
field_curr.append(field[max_resistance_loc])
tempatu_lst.append(temp)
current_lst.append(current / 1000)
fields_lst.append(field_curr)
temperoos.append(tempatu_lst)
cureentoos.append(current_lst)
df = pd.DataFrame({
r_no_hall, r_hall, field = extract_n_remove_hall(dat)
small_df = pd.DataFrame({
r"Magnetic Field (T)":
field,
r"Transverse Resistance $(\Omega)$":
r_hall,
r"Longitudinal Resistance $(\Omega)$":
r_no_hall,
r"Rotation Angle (degrees)":
[filename.split('.')[-3].split('_')[-1]] * len(field)
})
dat_lst.append(small_df)
df = pd.concat(dat_lst)
fig, ax = MakePlot(nrows=1, ncols=2).create()
sns.lineplot(x="Magnetic Field (T)",
y=r"Longitudinal Resistance $(\Omega)$",
hue='Rotation Angle (degrees)',
data=df,
ax=ax[0],
legend=False)
sns.lineplot(x="Magnetic Field (T)",
y=r"Transverse Resistance $(\Omega)$",
hue='Rotation Angle (degrees)',
data=df,
ax=ax[1])
plt.suptitle(
'VT49 Transverse and Longitudinal Resistance by Field and Angle (1.8 K)')
plt.savefig(main_path + 'resistance_all_same_ax.png', dpi=600)
from tools.MakePlot import * import matplotlib.pyplot as plt import scipy.interpolate import scipy.ndimage import matplotlib.colors as mcolors from matplotlib.ticker import FormatStrFormatter main_path = '/Volumes/GoogleDrive/My Drive/Heatmap Cooldowns/' save_path = '/Volumes/GoogleDrive/My Drive/Thesis Figures/Transport/' possible_currents1 = np.array([10,20,50,100,200,500,1000,1500]) fig, ax = MakePlot(figsize=(32,18)).create() # scan is at 1 mA # VT1 df = load_matrix(main_path+'RvsT_FeSb2-VT1&26-cooldown.dat') df = df[['Temperature (K)', 'Bridge 1 Resistance (Ohms)']] resistance_vt1 = scipy.ndimage.median_filter(np.array(df['Bridge 1 Resistance (Ohms)']),size=3) temperature_vt1 = np.array(df['Temperature (K)']) # VT26 df = load_matrix(main_path+'RvsT_FeSb2-VT1&26-cooldown.dat') df = df[['Temperature (K)', 'Bridge 2 Resistance (Ohms)']] resistance_vt26 = scipy.ndimage.median_filter(np.array(df['Bridge 2 Resistance (Ohms)']),size=3)
temp_lst.append(flatten(resistance_lst))
fields_lst.append(flatten(field_curr))
temperoos.append(flatten(tempatu_lst))
cureentoos.append(flatten(current_lst))
df = pd.DataFrame({
'Magnetoresistive Ratio': flatten(temp_lst),
'Temperature (K)': flatten(temperoos),
'Current (mA)': flatten(cureentoos),
'Magnetic Field (T)': flatten(fields_lst)
})
groupers = df.groupby('Current (mA)')
fig, ax = MakePlot(nrows=2, ncols=5).create()
axs = [
ax[0, 0], ax[0, 1], ax[0, 2], ax[0, 3], ax[0, 4], ax[1, 0], ax[1, 1],
ax[1, 2], ax[1, 3], ax[1, 4]
]
for ind, key in enumerate(groupers.groups.keys()):
if ind != 7:
sns.scatterplot(x='Magnetic Field (T)',
y='Magnetoresistive Ratio',
hue='Temperature (K)',
data=df[df['Current (mA)'] == key],
palette="bright",
legend=False,
ax=axs[ind]) #style='Temperature (K)',
if ind == 7:
for current in possible_currents1:
resistance, field = load_r_and_h(load_path, current)
resistances.append(resistance)
fields.append(field)
currents.append([current] * len(resistance))
df = pd.DataFrame({
r'Resistance $(\Omega)$': flatten(resistances),
'Current (mA)': flatten(currents),
'Magnetic Field (T)': flatten(fields)
})
groupers = df.groupby('Current (mA)')
fig, ax = MakePlot(nrows=2, ncols=5).create()
axs = [
ax[0, 0], ax[0, 1], ax[0, 2], ax[0, 3], ax[0, 4], ax[1, 0], ax[1, 1],
ax[1, 2], ax[1, 3], ax[1, 4]
]
for ind, key in enumerate(groupers.groups.keys()):
sns.scatterplot(x='Magnetic Field (T)',
y=r'Resistance $(\Omega)$',
data=df[df['Current (mA)'] == key],
legend=False,
ax=axs[ind]) # style='Temperature (K)',
if ind < 5:
axs[ind].set_xlabel('')
if ind != 0:
sample = 'VT64'
colours = sns.color_palette('muted')
plot = 'long'
for ind, curr_data_name in enumerate(data_sets):
if ind <= 5:
temps = temps_a
else:
temps = temps_b
res_lst, field_lst = [], []
label_lst = []
fig_long, ax_long = MakePlot().create()
fig_trans, ax_trans = MakePlot().create()
c = 0
for idx, current_temp_data_name in enumerate(curr_data_name):
dat = load_matrix(main_path + current_temp_data_name).T
res_lst.append(dat[0])
field_lst.append(dat[1])
label_lst.append([temps[idx]] * len(dat[0]))
start_loc = 0
max_loc = np.argmax(dat[1])
min_loc = np.argmin(dat[1])
mid_loc = (min_loc - max_loc) / 2
mid_loc = 1 + max_loc + int(mid_loc.round(0))
temps_c = ['1.75 K +', '1.75 K -', '2.00 K +', '2.00 K -', '2.25 K +','2.25 K -',
'2.50 K +', '2.50 K -']
temps_d = ['1.75 K +', '1.75 K -', '2.00 K +', '2.00 K -', '2.25 K +','2.25 K -']
currents = [r'500 $\mu A$', r'600 $\mu A$',r'700 $\mu A$',r'800 $\mu A$', r'900 $\mu A$', r'1000 $\mu A$', r'1100 $\mu A$', r'1200 $\mu A$', r'1500 $\mu A$']
data_sets = [curr_500, curr_600, curr_700, curr_800, curr_900, curr_1000, curr_1100, curr_1200, curr_1500]
sample = 'VT64'
res_up_plus, res_down_plus, res_up_minus, res_down_minus = [], [], [], []
b_up_plus, b_down_plus, b_up_minus, b_down_minus = [], [], [], []
fig, axs = MakePlot(nrows=3,ncols=3,figsize=(16,9)).create()
axes = [axs[0,0], axs[0,1], axs[0,2], axs[1,0], axs[1,1], axs[1,2], axs[2,0], axs[2,1], axs[2,2]]
for ind, curr_data_name in enumerate(data_sets):
if ind == 0 or 2<ind<=5:
temps = temps_a
elif ind == 1 :
temps = temps_c
elif ind == 2:
temps = temps_d
else:
temps = temps_b
diff_down_neg = median_filter(
np.abs(1 / ffit_down_neg(field_down_neg[small_locs]) /
conductance_quantum -
1 / resistance_down_neg[small_locs] / conductance_quantum),
4)
down_minus.append(diff_down_neg)
vec_down_minus.append(field_down_neg[small_locs])
max_down_minus.append(np.max(diff_down_neg))
max_down_minus_field.append(
field_down_neg[small_locs][np.argmax(diff_down_neg)])
sns.set_context('paper')
sns.set_context('paper')
fig, ax = MakePlot().create()
clrs = sns.color_palette('husl', n_colors=10)
line_up_plus = np.poly1d(np.polyfit(max_up_plus_field, max_up_plus, 1))
line_down_plus = np.poly1d(
np.polyfit(max_down_plus_field, max_down_plus, 1))
line_up_minus = np.poly1d(np.polyfit(max_up_minus_field, max_up_minus, 1))
line_down_minus = np.poly1d(
np.polyfit(max_down_minus_field, max_down_minus, 1))
for i in range(len(up_minus)):
plt.plot(vec_up_plus[i],
up_plus[i],
label=temps[i],
color=clrs[i],
temps2 = (2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0,
15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0
) #,22.0,23.0,24.0,25.0,26.0,27.0,28.0,29.0)
temp_lab1 = [
2, 5
] #,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21]#,22,23,24,25,26,27,28,29]#,19,20,21]#,22]
temp_lab2 = [
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21
] #,22,23,24,25,26,27,28,29]#,19,20,21]#,22]
possible_currents1 = np.array([10, 20, 50, 100, 200, 500, 1000, 1500])
possible_currents2 = np.array([50, 1000])
fig, axs = MakePlot(figsize=(32, 18), nrows=2, ncols=2).create()
axs_temp = [axs[0, 0], axs[0, 1]]
axs_curr = [axs[1, 0], axs[1, 1]]
for i, temp in enumerate(temps):
temp_path = main_path + 'VT1/' + str(temp) + '/'
ax = axs_temp[i]
if i == 1:
possible_currents1 = possible_currents1[:-1]
def fit_wo_step2(resistance,
field,
window_length=5,
polyorder=3,
deg=3,
return_field=False,
plot=False,
num_pts=6,
return_area=False,
return_shortened_locs=False):
resistance_smooth = scipy.signal.savgol_filter(x=resistance,
window_length=window_length,
polyorder=polyorder)
first_diff = np.diff(resistance_smooth)
B1, B2 = get_B1_and_B2(first_diff)
step_locs = np.arange(B1, B2)
# select a region with 6 points before and 6 points after step
# add in functionality here to ensure num_pts not greater/less than total
num_left, num_right = num_pts, num_pts
if B1 - num_left < 0:
num_left = 0
if B2 + num_right > field.shape[0]:
num_right = field.shape[0]
smaller_region = np.arange(B1 - num_left, B2 + num_right)
B1 = field[B1]
B2 = field[B2]
region_to_fit = np.setdiff1d(smaller_region, step_locs)
fitted_line = np.polyfit(x=field[region_to_fit],
y=resistance[region_to_fit],
deg=deg)
if plot:
fig, ax = MakePlot().create()
ax.plot(field, 1 / resistance)
ax.plot(np.linspace(0, 14, 200),
1 / np.poly1d(fitted_line)(np.linspace(0, 14, 200)))
plt.show()
if return_shortened_locs:
step_locs = smaller_region
if return_area:
area = integrate_the_step(field[smaller_region],
np.poly1d(fitted_line),
resistance[smaller_region])
if return_field:
return np.poly1d(fitted_line), B1, B2, area, step_locs
else:
return np.poly1d(fitted_line), area, step_locs
else:
if return_field:
return np.poly1d(fitted_line), B1, B2, step_locs
else:
return np.poly1d(fitted_line), step_locs
else:
if return_area:
area = integrate_the_step(field[smaller_region],
np.poly1d(fitted_line),
resistance[smaller_region])
if return_field:
return np.poly1d(fitted_line), B1, B2, area
else:
return np.poly1d(fitted_line), area
else:
if return_field:
return np.poly1d(fitted_line), B1, B2
else:
return np.poly1d(fitted_line)
currents_c = [
r'500 $\mu A$', '', '', r'800 $\mu A$', r'900 $\mu A$', r'1000 $\mu A$',
r'1100 $\mu A$', r'1200 $\mu A$', r'1500 $\mu A$'
]
data_sets = [
temp_1p75, temp_2p00, temp_2p25, temp_2p5, temp_2p75, temp_3p00, temp_3p25,
temp_3p5, temp_3p75
]
sample = 'VT64'
res_up_plus, res_down_plus, res_up_minus, res_down_minus = [], [], [], []
b_up_plus, b_down_plus, b_up_minus, b_down_minus = [], [], [], []
fig, axs = MakePlot(nrows=3, ncols=3).create()
axes = [
axs[0, 0], axs[0, 1], axs[0, 2], axs[1, 0], axs[1, 1], axs[1, 2],
axs[2, 0], axs[2, 1], axs[2, 2]
]
for ind, curr_data_name in enumerate(data_sets):
if ind < 3:
currents = currents_a
elif ind == 3:
currents = currents_b
else:
currents = currents_c
res_lst, field_lst = [], []
angles = [
0, 0, 10, 20, 25, 27.5, 30, 30, 32.5, 35, 35, 37.5, 40, 50, 60, 70, 80
]
filenames_2ndsweep = [
'033.txt', '034.txt', '035.txt', '040.txt', '036.txt', '037.txt',
'038.txt', '039.txt'
]
angles_2 = [0, 10, 20, 26, 30, 40, 50, 60]
C0_a = find_C0(main_path, '033.txt')
C0_b = C0_a
print(C0_a)
fig, axs = MakePlot(nrows=1, ncols=2).create()
axins = axs[0].inset_axes([0.1, 0.375, 0.25, 0.25])
for ind, filename_open in enumerate(filenames):
d = {}
field, cap_interpolated, R, idx2, raw_cap = process_data(main_path,
filename_open,
ref_C0=C0_a)
ax = axs[0]
ax.scatter(field,
raw_cap,
c=plt.cm.jet(ind / len(filenames)),
from matplotlib.cm import get_cmap
from tools.DataFile import DataFile
from tools.MakePlot import *
import matplotlib.pyplot as plt
import scipy.interpolate
import scipy.ndimage
import matplotlib.colors as mcolors
from matplotlib.ticker import FormatStrFormatter
main_path = '/Volumes/GoogleDrive/My Drive/Heatmap Cooldowns/'
save_path = '/Volumes/GoogleDrive/My Drive/Thesis Figures/Transport/'
possible_currents1 = np.array([10, 20, 50, 100, 200, 500, 1000, 1500])
fig, ax = MakePlot(figsize=(32, 18)).create()
# scan is at 1 mA
df = load_matrix(main_path + 'RvsT_FeSb2-VT1&26-cooldown.dat')
df = df[['Temperature (K)', 'Bridge 1 Resistance (Ohms)']]
resistance = scipy.ndimage.median_filter(np.array(
df['Bridge 1 Resistance (Ohms)']),
size=3)
temperature = np.array(df['Temperature (K)'])
ax.plot(temperature, resistance, linewidth=5, c='purple', label='VT1')
df = load_matrix(main_path + 'RvsT_FeSb2-VT1&26-cooldown.dat')
df = df[['Temperature (K)', 'Bridge 2 Resistance (Ohms)']]
resistance = scipy.ndimage.median_filter(np.array(
main_path = '/Users/npopiel/Documents/MPhil/Data/'
save_path = '/Volumes/GoogleDrive/My Drive/Thesis Figures/Transport/'
samples = ['VT11']
temps = [
10.0
] #,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,12.0,13.0,14.0,15.0,16.0,17.0,18.0,19.0,20.0,21.0)#,22.0,23.0,24.0,25.0,26.0,27.0,28.0,29.0)
temp_lab1 = [
2
] #,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21]#,22,23,24,25,26,27,28,29]#,19,20,21]#,22]
possible_currents1 = np.array([10, 20, 50, 100, 200, 500, 1000, 1500])
fig, axs = MakePlot(figsize=(32, 18), nrows=2, ncols=4).create()
axs = [
axs[0, 0], axs[0, 1], axs[0, 2], axs[0, 3], axs[1, 0], axs[1, 1],
axs[1, 2], axs[1, 3]
]
for i, temp in enumerate(temps):
temp_path = main_path + 'VT1/' + str(temp) + '/'
for ind, current in enumerate(possible_currents1):
ax = axs[ind]
resistance, field = load_r_and_h(temp_path, current)
lab = str(current) + r' $\mu \mathrm{A}$'
import matplotlib.colors as mcolors
from matplotlib.ticker import FormatStrFormatter
main_path = '/Users/npopiel/Documents/MPhil/Data/'
save_path = '/Volumes/GoogleDrive/My Drive/Thesis Figures/Transport/'
data = np.loadtxt(
'/Volumes/GoogleDrive/My Drive/Thesis Figures/NJM_Nov19_sweep.130.dat',
delimiter='\t',
skiprows=10)
field = data[:, 0]
resistance = data[:, 1] * 1e5
fig, ax = MakePlot(figsize=(16, 9), nrows=1, ncols=1).create()
ax.plot(field, resistance, linewidth=1.8, c='k')
ax.ticklabel_format(style='sci', axis='y', scilimits=(0, 0), useMathText=True)
ax.minorticks_on()
ax.tick_params('both',
which='major',
direction='in',
length=6,
width=2,
bottom=True,
top=True,
left=True,
right=True)
