thomasaarholt

# Make signal starting point and length equal
signal_min = bss_spectra[0].axes_manager[-1].axis.min() # This is a float
signal_shortest_len = len(bss_spectra[0].axes_manager[-1].axis) # This is an int

# Get largest value of s
for s in mlls_objects:
    if s.axes_manager[0].axis.min() > signal_min:
        signal_min = s.axes_manager[0].axis.min()

# Crop all the spectra to start at the same energy index

thomasaarholt

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-8-d6ad06492590> in <module>()
----> 1 bss_spectra[0].plot()

/Users/thomas/Dropbox/0_Git/GitHub Desktop/hyperspy/hyperspy/signal.py in plot(self, navigator, axes_manager, **kwargs)
   2029 
   2030         self._plot.plot(**kwargs)
-> 2031         self.events.data_changed.connect(self.update_plot, [])
   2032         if self._plot.signal_plot:

thomasaarholt

function [out,h] = read_bytes(fid, location, num_bytes, ttype, reverse_bytes, b8)

fseek(fid,location,'bof');
h=fread(fid,num_bytes,ttype);

if reverse_bytes 
    % when data have been stored under Windows, the bytes are reversed
    h=h(end:-1:1);
end;

thomasaarholt

function [im,pp,p]=read_im_file(ff,ask_for_planes,load_accumulated)

% Read the binary im file produced by the NanoSIMS 50L machine, and output
% the raw images as well as the additional parameters characterizing the
% dataset.
% NOTE: I don't know why, but data in some binary files are stored as uint8
% (e.g. from MPI Bremen), and in some others they are stored as ushort
% (e.g., IoW Warnemuende). It seems that this difference can be "detected"
% based on the value of the pixel_size in the Header_image structure.
% Therefore, the below coding relies on this to automatically find out how

thomasaarholt

>>> s = hs.load(...)
>>> m = s.create_model()
...

>>> m.store()
>>> s.models()
└── a
    ├── components
    │   ├── C_K
    │   ├── O_K

thomasaarholt

def mybin(s, xbin=1, ybin=1, sbin=1):
    """Return spectrum s binned by factors xbin, ybin, sbin"""
    if (xbin < 1) or (ybin < 1) or (sbin < 1):
        raise ValueError("One of your binnings is smaller than 1. For signal axis to remain constant, leave as 1")

    if all(n==1 for n in (xbin, ybin, sbin)):
        print("Not binning because all binnings == 1")
        return s # End Early

    # Get signal channels

thomasaarholt

# In model.py
        from numpy.linalg import lstsq as linear_lstsq
(...)
          elif fitter == "linear_lstsq":
                signal_axis = self.axis.axis[np.where(self.channel_switches)]
                component_data = np.array([component.function(signal_axis) for component in self])
                print(weights)
                output = linear_lstsq(component_data.T, self.signal()[np.where(self.channel_switches)], **kwargs)
                self.p0 = tuple([old*factor for old, factor in zip(self.p0, output[0])])
                self.fit_output = output

thomasaarholt

def ROI_Rect(s, roi_XY=[], hide=False):
    """
    Plots a hyperspy signal and draws an interactive ROI on it on the top left tenth of the image.
    Can take a list of [x1, y1, x2, y2] to set a known intial ROI.
    Returns a tuple of (roi, roi_signal). Use hide=True to not show the plot.
    Please report bugs/improvements to [email protected]
    """
    import hyperspy.api as hs

    if s.axes_manager.navigation_dimension < 2:

thomasaarholt

>>> m = sc.create_model(auto_add_lines=False)
>>> samf = m.create_samfire()

[0:apply]: 
---------------------------------------------------------------------------AttributeError                            Traceback (most recent call last)<string> in <module>()
/Users/thomas/Dropbox/0_Git/GitHub/hyperspy_aar/hyperspy/samfire_utils/samfire_pool.py in <lambda>(worker, m_dict)
    148             self.rworker = ipp_Reference('worker')
    149             direct_view.apply(lambda worker, m_dict:
--> 150                               worker.create_model(m_dict, 'z'), self.rworker,
    151                               m_dict)

thomasaarholt

>>> m = sc.create_model()
>>> m.components
   # |      Attribute Name |      Component Name |      Component Type
---- | ------------------- | ------------------- | -------------------
   0 |  background_order_6 |  background_order_6 |          Polynomial
   1 |               Al_Ka |               Al_Ka |            Gaussian
   2 |               Al_Kb |               Al_Kb |            Gaussian
   3 |                C_Ka |                C_Ka |            Gaussian
   4 |               Ca_Ka |               Ca_Ka |            Gaussian
...