In [1]:

    

from lib import transient
from imp import reload
reload(transient)

import os
import numpy as np

import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.colorbar as colorbar
import matplotlib.cm as cmx
from mpl_toolkits.axes_grid1 import make_axes_locatable
%matplotlib inline

import pandas as pd


    

In [31]:

    

tr = transient.Transient()

filepath = 'E:/steinn/new data/2017-11-28/'
files = os.listdir(filepath)
ax = []
trs = []
k_parameters = []
cutFreq = 0.01 # THz
t0 = 76
key_parameter = 'pump_power'
description = ''
for i in range(len(files)):
    tr = transient.Transient()
    tr.import_file(filepath + files[i],cleanData=False, key_parameter = key_parameter, description=description)
#     print(tr.pump_power)
    tr.crop_time_scale()
    tr.shift_time(t0)
    tr.filter_low_pass(cutFreq)
#     tr.flip_trace()
    tr.remove_DC_offset()
    tr.flip_time()
    k_parameters.append(float(tr.pump_power))
    
    tr.trace = np.divide(tr.trace,max(tr.trace))
#     tr.trace = np.divide(tr.trace,tr.pump_power)

    trs.append(tr)
    
print('Imported {0} scan(s) as {1} dependence'.format(len(trs),trs[0].key_parameter))


fig = plt.figure(figsize=(16,9))
ax = fig.add_subplot(111)
jet = cm = plt.get_cmap('jet') 
cNormLog = colors.LogNorm(vmin=min(k_parameters), vmax=max(k_parameters))
cNorm  = colors.Normalize(vmin=min(k_parameters), vmax=max(k_parameters))
scalarMap = cmx.ScalarMappable(norm=cNormLog, cmap=jet)
lines = []
for idx in range(len(trs)):
    colorVal = scalarMap.to_rgba(trs[idx].pump_power)
    retLine, = ax.plot(trs[idx].time, trs[idx].trace, color=colorVal)
    lines.append(retLine)
# make labels
    labels = []
for i in range(len(lines)):
    labels.append((lines[i],k_parameters[i]))
labels = sorted(labels, key=lambda kpar: float(kpar[1]))
lbLines=[]
lbVals=[]
for i in range(len(labels)):
    lbLines.append(labels[i][0])
    lbVals.append(labels[i][1])
ax.legend(lbLines, lbVals, loc='upper right')
# plt.xlim((-5,5))
# plt.ylim((-0.0002,0.0008))
ax.grid()


    

    




0.1
0.2
0.4
0.8
1.2
1.6
3.2
6.4
9.0
Imported 9 scan(s) as pump_power dependence






    

In [34]:

    

tr = transient.Transient()

filepath = 'E:/steinn/RuCl3/Low Fluence_temperature/'
files = os.listdir(filepath)
ax = []
trs = []
k_parameters = []
cutFreq = 0.01 # THz
t0 = 76
key_parameter = 'pump_power'
description = ''
for i in range(len(files)):
    tr = transient.Transient()
    tr.import_file(filepath + files[i],cleanData=False, key_parameter = key_parameter, description=description)
    print(tr.pump_power)
    tr.crop_time_scale()
    tr.shift_time(t0)
    tr.filter_low_pass(cutFreq)
#     tr.flip_trace()
    tr.remove_DC_offset()
    tr.flip_time()
    k_parameters.append(float(tr.temperature))
    
#     tr.trace = np.divide(tr.trace,max(tr.trace))
#     tr.trace = np.divide(tr.trace,tr.pump_power)

    trs.append(tr)
    
print('Imported {0} scan(s) as {1} dependence'.format(len(trs),trs[0].key_parameter))


fig = plt.figure(figsize=(16,9))
ax = fig.add_subplot(111)
jet = cm = plt.get_cmap('jet') 
cNormLog = colors.LogNorm(vmin=min(k_parameters), vmax=max(k_parameters))
cNorm  = colors.Normalize(vmin=min(k_parameters), vmax=max(k_parameters))
scalarMap = cmx.ScalarMappable(norm=cNormLog, cmap=jet)
lines = []
for idx in range(len(trs)):
    colorVal = scalarMap.to_rgba(trs[idx].temperature)
    retLine, = ax.plot(trs[idx].time, trs[idx].trace, color=colorVal)
    lines.append(retLine)
# make labels
    labels = []
for i in range(len(lines)):
    labels.append((lines[i],k_parameters[i]))
labels = sorted(labels, key=lambda kpar: float(kpar[1]))
lbLines=[]
lbVals=[]
for i in range(len(labels)):
    lbLines.append(labels[i][0])
    lbVals.append(labels[i][1])
ax.legend(lbLines, lbVals, loc='upper right')
# plt.xlim((-5,5))
# plt.ylim((-0.0002,0.0008))
ax.grid()


    

    




1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
Imported 38 scan(s) as pump_power dependence






    

In [36]:

    

trs[1].pump_energy


    

    
Out[36]:





0.143

In [26]:

    

tr.filter_low_pass(cutHigh=0.05)


    

In [3]:

    

filepath = 'E:/steinn/new data/2017-11-28/'
files = os.listdir(filepath)
pump_spot = 80
probe_spot = 50
cutFreq = 0.01 # THz
t0 = 80
key_parameter = 'pump_power'
description = ''

###########################
# init lsits:
scanList = []
k_parameters = []

for i in range(len(files)):
    tr = transient.Transient()
    tr.import_file(filepath + files[i],cleanData=False, key_parameter = key_parameter, description=description)
    tr.pump_spot = pump_spot
    tr.probe_spot = probe_spot
    tr.calc_energy_densities()
    tr.crop_time_scale()
    tr.shift_time(t0)
    tr.filter_low_pass(cutFreq)
#     tr.flip_trace()
    tr.remove_DC_offset()
    tr.flip_time()
    k_parameters.append(float(getattr(tr,key_parameter)))
    tr.trace = np.divide(tr.trace,tr.pump_energy)
    scanList.append(tr)    
print('Imported {0} scan(s) as {1} dependence'.format(len(scanList),scanList[0].key_parameter))
data1 = scanList
fig = plt.figure(figsize=(16,9))
ax = fig.add_subplot(111)
for idx in range(len(scanList)):
    ax.plot(scanList[idx].time, scanList[idx].trace)
# plt.xlim((-2,5))
# plt.ylim((-0.0003,0.002))
plt.grid()


    

    




0.014
0.108
0.028
0.108
0.056
0.108
0.112
0.108
0.169
0.108
0.225
0.108
0.45
0.108
0.9
0.108
1.265
0.108
Imported 9 scan(s) as pump_power dependence






    

In [74]:

    

data3 = data1[:4]
for i in data2:
    data3.append(i)
list(data3)


    

    
Out[74]:





[<lib.transient.Transient at 0x1f835d4d668>,
 <lib.transient.Transient at 0x1f83473b320>,
 <lib.transient.Transient at 0x1f835b3da20>,
 <lib.transient.Transient at 0x1f833f592b0>,
 <lib.transient.Transient at 0x1f835da7e10>,
 <lib.transient.Transient at 0x1f8360e64a8>,
 <lib.transient.Transient at 0x1f835dd2198>,
 <lib.transient.Transient at 0x1f837511128>,
 <lib.transient.Transient at 0x1f835da0f28>,
 <lib.transient.Transient at 0x1f835c44160>,
 <lib.transient.Transient at 0x1f8360390b8>,
 <lib.transient.Transient at 0x1f835da0438>,
 <lib.transient.Transient at 0x1f83604d9e8>]

In [44]:

    

dataFrame = {}
key_parameter = 'pump_energy'
for tr in scanList:
    # tr.calc_energy_densities()
    name = str(getattr(tr,key_parameter)) + ' ' + str(tr.get_unit(key_parameter))
    dataFrame[name] = tr.trace
dataFrame = pd.DataFrame(dataFrame, index = scanList[0].time)
#     dataFrame[] = tr.trace
#     tr.trace = np.divide(tr.trace,max(tr.trace))
#     tr.trace = np.divide(tr.trace,tr.pump_power)


# fig = plt.figure(figsize=(16,9))
# ax = fig.add_subplot(111)
# jet = cm = plt.get_cmap('jet') 
# cNormLog = colors.LogNorm(vmin=min(k_parameters), vmax=max(k_parameters))
# cNorm  = colors.Normalize(vmin=min(k_parameters), vmax=max(k_parameters))
# scalarMap = cmx.ScalarMappable(norm=cNormLog, cmap=jet)
# lines = []
# for idx in range(len(trs)):
#     colorVal = scalarMap.to_rgba(trs[idx].temperature)
#     retLine, = ax.plot(trs[idx].time, trs[idx].trace, color=colorVal)
#     lines.append(retLine)
# # make labels
#     labels = []
# for i in range(len(lines)):
#     labels.append((lines[i],k_parameters[i]))
# labels = sorted(labels, key=lambda kpar: float(kpar[1]))
# lbLines=[]
# lbVals=[]
# for i in range(len(labels)):
#     lbLines.append(labels[i][0])
#     lbVals.append(labels[i][1])
# ax.legend(lbLines, lbVals, loc='upper right')
# # plt.xlim((-5,5))
# # plt.ylim((-0.0002,0.0008))
# ax.grid()


    

    




---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-44-eba52c30ddd5> in <module>()
      5     name = str(getattr(tr,key_parameter)) + ' ' + str(tr.get_unit(key_parameter))
      6     dataFrame[name] = tr.trace
----> 7 dataFrame = pd.DataFrame(dataFrame, index = scanList[0].time)
      8 #     dataFrame[] = tr.trace
      9 #     tr.trace = np.divide(tr.trace,max(tr.trace))

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py in __init__(self, data, index, columns, dtype, copy)
    273                                  dtype=dtype, copy=copy)
    274         elif isinstance(data, dict):
--> 275             mgr = self._init_dict(data, index, columns, dtype=dtype)
    276         elif isinstance(data, ma.MaskedArray):
    277             import numpy.ma.mrecords as mrecords

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py in _init_dict(self, data, index, columns, dtype)
    409             arrays = [data[k] for k in keys]
    410 
--> 411         return _arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
    412 
    413     def _init_ndarray(self, values, index, columns, dtype=None, copy=False):

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py in _arrays_to_mgr(arrays, arr_names, index, columns, dtype)
   5602     axes = [_ensure_index(columns), _ensure_index(index)]
   5603 
-> 5604     return create_block_manager_from_arrays(arrays, arr_names, axes)
   5605 
   5606 

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\internals.py in create_block_manager_from_arrays(arrays, names, axes)
   4312         return mgr
   4313     except ValueError as e:
-> 4314         construction_error(len(arrays), arrays[0].shape, axes, e)
   4315 
   4316 

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\internals.py in construction_error(tot_items, block_shape, axes, e)
   4274     implied = tuple(map(int, [len(ax) for ax in axes]))
   4275     if passed == implied and e is not None:
-> 4276         raise e
   4277     if block_shape[0] == 0:
   4278         raise ValueError("Empty data passed with indices specified.")

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\internals.py in create_block_manager_from_arrays(arrays, names, axes)
   4307 
   4308     try:
-> 4309         blocks = form_blocks(arrays, names, axes)
   4310         mgr = BlockManager(blocks, axes)
   4311         mgr._consolidate_inplace()

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\internals.py in form_blocks(arrays, names, axes)
   4371     blocks = []
   4372     if len(float_items):
-> 4373         float_blocks = _multi_blockify(float_items)
   4374         blocks.extend(float_blocks)
   4375 

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\internals.py in _multi_blockify(tuples, dtype)
   4448     for dtype, tup_block in grouper:
   4449 
-> 4450         values, placement = _stack_arrays(list(tup_block), dtype)
   4451 
   4452         block = make_block(values, placement=placement)

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\internals.py in _stack_arrays(tuples, dtype)
   4493     stacked = np.empty(shape, dtype=dtype)
   4494     for i, arr in enumerate(arrays):
-> 4495         stacked[i] = _asarray_compat(arr)
   4496 
   4497     return stacked, placement

ValueError: could not broadcast input array from shape (6330) into shape (6329)

In [39]:

    

dataFrame[].plot(colormap='jet',)


    

    
Out[39]:





<matplotlib.axes._subplots.AxesSubplot at 0x1f83474e198>






    

In [69]:

    

def plotScanList(data,key_parameter,
                 xlim=(0,0),ylim=(0,0),
                 norm=None, 
                 save = True,
                 saveDir='e:/Steinn/Data/RuCl3/figs/', 
                 title=None):
    kParList = []
    for scan in data:
        kParList.append(getattr(scan,key_parameter))
        if norm is not None:
            if norm == 'peak':
                scan.trace = np.divide(scan.trace,max(scan.trace))
            else:
                scan.trace = np.divide(scan.trace,getattr(scan,norm))

            

    fig = plt.figure(figsize=(16,9))
    ax = fig.add_subplot(111)
    jet = cm = plt.get_cmap('jet') 
    cNormLog = colors.LogNorm(vmin=min(kParList), vmax=max(kParList))
    cNorm  = colors.Normalize(vmin=min(kParList), vmax=max(kParList))
    scalarMap = cmx.ScalarMappable(norm=cNormLog, cmap=jet)
    lines = []
    for scan in data:
        colorVal = scalarMap.to_rgba(getattr(scan,key_parameter))
        retLine, = ax.plot(scan.time, scan.trace, color=colorVal)
        lines.append(retLine)
    # make labels
        labels = []
    for i in range(len(lines)):
        labels.append((lines[i],kParList[i]))
    labels = sorted(labels, key=lambda kpar: float(kpar[1]))
    lbLines=[]
    lbVals=[]
    for i in range(len(labels)):
        lbLines.append(labels[i][0])
        lbVals.append(labels[i][1])
    ax.legend(lbLines, lbVals, loc='upper right')
    if xlim == (0,0):
        pass
    else:
        plt.xlim(xlim)
    if ylim == (0,0):
        pass
    else:
        plt.ylim(ylim)
    
    ax.grid()
    plt.title(title)
    plt.ylabel = 'dr/r'
    if norm is not None:
        plt.ylabel = 'dr/r [norm to {}]'.format(norm)
    plt.xlabel = 'Time [ps]'
    if save:
        if not os.path.isdir(saveDir):
            os.makedirs(saveDir)
        plt.savefig(saveDir+title)


    

In [80]:

    

filepath = 'E:/steinn/new data/asdf2/'
files = os.listdir(filepath)
pump_spot = 80
probe_spot = 50
cutFreq = 0.01 # THz
t0 = 80
key_parameter = 'pump_power'
description = ''

###########################
# init lsits:
scanList = []
k_parameters = []

for i in range(len(files)):
    tr = transient.Transient()
    tr.import_file(filepath + files[i],cleanData=False, key_parameter = key_parameter, description=description)
    tr.pump_spot = pump_spot
    tr.probe_spot = probe_spot
    tr.calc_energy_densities()
    tr.crop_time_scale()
    tr.shift_time(t0)
    tr.filter_low_pass(cutFreq)
#     tr.flip_trace()
    tr.remove_DC_offset()
    tr.flip_time()
    k_parameters.append(float(getattr(tr,key_parameter)))
    tr.trace = np.divide(tr.trace,max(tr.trace))
    scanList.append(tr)    
print('Imported {0} scan(s) as {1} dependence'.format(len(scanList),scanList[0].key_parameter))
data1 = scanList
fig = plt.figure(figsize=(16,9))
ax = fig.add_subplot(111)
for idx in range(len(scanList)):
    ax.plot(scanList[idx].time, scanList[idx].trace)
# plt.xlim((-2,5))
# plt.ylim((-0.0003,0.002))
plt.grid()


    

    




0.028
0.108
0.028
0.108
Imported 2 scan(s) as pump_power dependence






    

In [101]:

    

filepath = 'E:/steinn/RuCl3/temperature dependence/p0,1mW/'
files = os.listdir(filepath)
pump_spot = 80
probe_spot = 50
cutFreq = 0.01 # THz
t0 = 80.5
key_parameter = 'pump_power'
description = ''

###########################
# init lsits:
scanList = []
k_parameters = []

for i in range(len(files)):
    tr = transient.Transient()
    tr.import_file(filepath + files[i],cleanData=False, key_parameter = key_parameter, description=description)
    tr.pump_spot = pump_spot
    tr.probe_spot = probe_spot
    tr.calc_energy_densities()
    tr.crop_time_scale()
    tr.shift_time(t0)
    tr.filter_low_pass(cutFreq)
#     tr.flip_trace()
    tr.remove_DC_offset()
    tr.flip_time()
    k_parameters.append(float(getattr(tr,key_parameter)))
    tr.temperature = 3.8
    tr.description = str(tr.temperature) + 'K'
    tr.give_name()
#     tr.trace = np.divide(tr.trace,tr.pump_energy)
    scanList.append(tr)    
print('Imported {0} scan(s) as {1} dependence'.format(len(scanList),scanList[0].key_parameter))
data1 = scanList
fig = plt.figure(figsize=(16,9))
ax = fig.add_subplot(111)
for idx in range(len(scanList)):
    ax.plot(scanList[idx].time, scanList[idx].trace)
# plt.xlim((-4,8))
# plt.ylim((-0.0003,0.006))
plt.grid()


    

    




0.014
0.108
0.014
0.108
0.014
0.108
0.014
0.108
0.014
0.108
0.014
0.108
0.014
0.108
Imported 7 scan(s) as pump_power dependence






    

In [30]:

    

csvDir = 'e:/Steinn/Data/RuCl3/'
if not os.path.isdir(csvDir):
    os.makedirs(csvDir)
for scan in scanList:
    scan.export_file_csv(csvDir)


    

    




Exporting Ru3Cl_3.8K_0.1_mW
Exporting Ru3Cl_3.8K_0.1_mW

In [59]:

    

csvScanList = []
for csv in os.listdir(csvDir):
    filePath = csvDir + csv
    if not os.path.isdir(filePath):
        tr = transient.Transient()
        tr.import_file(filePath,cleanData=False)
#         tr.filter_low_pass(cutHigh=0.01)
        csvScanList.append(tr)


    

In [90]:

    

for scan in data1:
    print(scan.pump_energy)


    

    




0.007
0.014
0.028
0.056
0.112
0.225
1.645
0.45
0.9

In [ ]:

    




    

In [102]:

    

title = '0,1mW-temperature-noNorm selected-01-12'
plotScanList(data1,'temperature',norm=None,title=title, save=True)


    

In [ ]: