Rename package

Author: guilhermebene

README.md

@@ -25,7 +25,7 @@ They should be placed inside the folder `lib` in the project's root as follows.
 │   └───avaspec3
 │   │   └───avaspecx64.dll
 ├───scripts
-├───singlepixel
+├───spas
 ├───requirements.txt
 ├───setup.py
 ├───stats
@@ -40,7 +40,7 @@ They should be placed inside the folder `lib` in the project's root as follows.
 In python:
 
 ``` python
-import singlepixel as sp
+import spas as sp
 sp.walsh_patterns(save_data=True)
 ```
 By default the patterns are 1024x768 PNG images saved in `./Walsh_64_64/`
@@ -81,7 +81,7 @@ conda install pywin32
 
 * Initilization (just once, two consecutively returns an error)
 ``` python 
-from singlepixel import *
+from spas import *
 spectrometer, DMD, DMD_initial_memory = init() 
 ```
 
@@ -140,7 +140,7 @@ disconnect(spectrometer, DMD)
 Reconstruct the measurements contained in the variable `meas` 
 ``` python 
 import spyrit.misc.walsh_hadamard as wh
-from singlepixel import reconstruction_hadamard
+from spas import reconstruction_hadamard
 H = wh.walsh2_matrix(64)
 rec = reconstruction_hadamard(acquisition_parameters.patterns, 'walsh', H,
                                 meas)
@@ -149,14 +149,14 @@ rec = reconstruction_hadamard(acquisition_parameters.patterns, 'walsh', H,
 Bin the reconstructed hypercube in 8 bins between 530 and 730 nm
 
 ``` python
-from singlepixel import spectral_binning
+from spas import spectral_binning
 rec_bin, wavelengths_bin, _ = spectral_binning(
                             rec.T, acquisition_parameters.wavelengths, 530, 730, 8)
 ```
 
 Plot the 8 bins
 ``` python
-from singlepixel import plot_color
+from spas import plot_color
 plot_color(rec_bin, wavelengths_bin)
 ```
 
@@ -173,7 +173,7 @@ meas = file['spectral_data']
 
 Read the metadata (we need the get the acquisition order of the patterns)
 ``` python
-from singlepixel import read_metadata, reconstruction_hadamard
+from spas import read_metadata, reconstruction_hadamard
 _, acquisition_parameters, _, _ = read_metadata('../meas/my_first_measurement' + '_metadata.json')
 
 ```
@@ -201,7 +201,7 @@ meas = file['spectral_data']
 
 Read the metadata (we need the get the acquisition order of the patterns)
 ``` python
-from singlepixel import read_metadata, reconstruction_hadamard
+from spas import read_metadata, reconstruction_hadamard
 _, acquisition_parameters, _, _ = read_metadata('../meas/my_first_measurement' + '_metadata.json')
 
 ```
@@ -211,7 +211,7 @@ _, acquisition_parameters, _, _ = read_metadata('../meas/my_first_measurement' +
 An example network can be downloaded [here](https://www.creatis.insa-lyon.fr/~ducros/spyritexamples/2021_ISTE/NET_c0mp_N0_50.0_sig_0.0_Denoi_N_64_M_1024_epo_40_lr_0.001_sss_20_sdr_0.2_bs_256_reg_1e-07.pth) , which you can save in `./models/`. It allows to reconstruction images from only 1'024 hadamard coefficients (i.e., 2'048 raw measurements)
 
 ``` python
-from singlepixel import ReconstructionParameters, setup_reconstruction
+from spas import ReconstructionParameters, setup_reconstruction
 network_params = ReconstructionParameters(
     img_size=64,
     CR=1024,
@@ -237,18 +237,18 @@ model, device = setup_reconstruction(cov_path, mean_path, '../stats/H.npy', mode
 
 Load noise calibration parameters (provided with the data or computed using `/noise-calibration/noise_modeling.py`)
 ``` python
-from singlepixel import load_noise
+from spas import load_noise
 noise = load_noise('../noise-calibration/fit_model.npz')
 ```
 
 Bin before reconstruction and plot
 
 ``` python
-from singlepixel import spectral_binning
+from spas import spectral_binning
 meas_bin, wavelengths_bin, _, noise_bin = spectral_binning(meas.T, acquisition_parameters.wavelengths, 530, 730, 8, noise)
 ```
 ``` python
-from singlepixel import reconstruct, plot_color
+from spas import reconstruct, plot_color
 rec = reconstruct(model, device, meas_bin[0:8192//4,:], 1, noise_bin)           
 plot_color(rec, wavelengths_bin)
 

noise-calibration/noise-analysis.ipynb

@@ -18,7 +18,7 @@
     "import numpy as np\n",
     "from matplotlib import pyplot as plt\n",
     "\n",
-    "from singlepixel import read_metadata"
+    "from spas import read_metadata"
    ]
   },
   {

noise-calibration/noise_modeling.py

@@ -10,7 +10,7 @@
     import numpy as np
     from matplotlib import pyplot as plt
 
-    from singlepixel import *
+    from spas import *
 
     #%% Init
     spectrometer, DMD, DMD_initial_memory = init()

scripts/acquisition-recon-multiprocessing.py

@@ -4,7 +4,7 @@
 in "real-time", using multiprocessing.
 """
 
-from singlepixel import *
+from spas import *
 
 if __name__ == '__main__':
 

scripts/acquisition-recon-sequential.py

@@ -5,7 +5,7 @@
 performed after the acquisition and not in "real-time".
 """
 
-from singlepixel import *
+from spas import *
 
 import numpy as np
 import spyrit.misc.walsh_hadamard as wh

scripts/acquisiton_script.py

@@ -1,4 +1,4 @@
-from singlepixel import *
+from spas import *
 
 #%% Init
 spectrometer, DMD, DMD_initial_memory = init() # just once, two consecutively returns an error

scripts/raster_scan.py

@@ -5,7 +5,7 @@
 from matplotlib import pyplot as plt
 from scipy.io import loadmat
 
-from singlepixel import *
+from spas import *
 
 def reconstruction_hadamard(acquisition_metadata, Q, M, N=64):
     ind_opt = acquisition_metadata.patterns[1::2]

scripts/reconstruction_script.py

@@ -6,7 +6,7 @@
 from matplotlib import pyplot as plt
 from spyrit.learning.model_Had_DCAN import Hadamard_Transform_Matrix
 
-from singlepixel import read_metadata, reconstruction_hadamard
+from spas import read_metadata, reconstruction_hadamard
 
 # Matlab patterns
 #file = loadmat('../data/matlab.mat')

setup.py

@@ -1,7 +1,7 @@
 from setuptools import setup, find_packages
 
 setup(
-    name='singlepixel',
+    name='spas',
     version='0.0.1',
     author='Guilherme Beneti Martins',
     packages=find_packages())

singlepixel/__init__.py spas/__init__.py

@@ -21,7 +21,7 @@
 from spyrit.learning.model_Had_DCAN import compNet, noiCompNet, DenoiCompNet 
 from spyrit.learning.nets import load_net
 
-from singlepixel.noise import noiseClass
+from spas.noise import noiseClass
 
 class netType(IntEnum):
     """Possible model architectures.