Intro to our Holoviz apps¶

In [5]:

import numpy as np
import xarray as xr

import panel as pn
pn.extension()

from labcore.testing.dispersive_qubit_readout_data import chevron_dataset
from labcore.data.datadict import datadict_to_meshgrid, dd2xr
from labcore.analysis import split_complex
from labcore.analysis.hvplotting import Node

import numpy as np import xarray as xr import panel as pn pn.extension() from labcore.testing.dispersive_qubit_readout_data import chevron_dataset from labcore.data.datadict import datadict_to_meshgrid, dd2xr from labcore.analysis import split_complex from labcore.analysis.hvplotting import Node

Nodes¶

let's have a look at what Nodes are. To work with them, we create some dummy data.

In [7]:

# generate some generic Rabi-like data
raw_data = chevron_dataset(
    Omega_0=1e6,
    Delta_vals=np.linspace(-1e6, 1e6, 20),
    t_vals=np.linspace(0, 3e-6, 20),
    n=100,  # number of shots
)

# convert directly to xarray.
# also: with the holoviz (or all pydata) tools, complex data isn't always the most convenient, so we split it immediately.
xrdata = split_complex(dd2xr(datadict_to_meshgrid(raw_data.expand())))

# generate some generic Rabi-like data raw_data = chevron_dataset( Omega_0=1e6, Delta_vals=np.linspace(-1e6, 1e6, 20), t_vals=np.linspace(0, 3e-6, 20), n=100, # number of shots ) # convert directly to xarray. # also: with the holoviz (or all pydata) tools, complex data isn't always the most convenient, so we split it immediately. xrdata = split_complex(dd2xr(datadict_to_meshgrid(raw_data.expand())))

In [ ]:

A trivial example¶

By default, nodes don't do anything but pass data around between them if they're connected correctly. In this example we just create a node and display it. Nothing is seen...

In [8]:

n = Node()
n

n = Node() n

Out[8]:

Note: there is no input data, so we have also no output data:

In [10]:

n.data_out

n.data_out

Now, let's set some data:

In [13]:

n.data_in = xrdata
n.data_out

n.data_in = xrdata n.data_out

Out[13]:

<xarray.Dataset>
Dimensions:     (repetition: 100, detuning: 20, time: 20)
Coordinates:
  * repetition  (repetition) int64 1 2 3 4 5 6 7 8 ... 93 94 95 96 97 98 99 100
  * detuning    (detuning) float64 -1e+06 -8.947e+05 ... 8.947e+05 1e+06
  * time        (time) float64 0.0 1.579e-07 3.158e-07 ... 2.842e-06 3e-06
Data variables:
    signal_Re   (repetition, detuning, time) float64 2.394 2.416 ... 1.473 2.239
    signal_Im   (repetition, detuning, time) float64 0.6124 0.5184 ... 0.7653

xarray.Dataset

• Dimensions:
  • repetition: 100
  • detuning: 20
  • time: 20
• Coordinates: (3)
  • repetition
    (repetition)
    int64
    1 2 3 4 5 6 ... 95 96 97 98 99 100

    array([  1,   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,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,
            43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,
            57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,
            71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,
            85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
            99, 100])

  • detuning
    (detuning)
    float64
    -1e+06 -8.947e+05 ... 1e+06

    array([-1000000.      ,  -894736.842105,  -789473.684211,  -684210.526316,
            -578947.368421,  -473684.210526,  -368421.052632,  -263157.894737,
            -157894.736842,   -52631.578947,    52631.578947,   157894.736842,
             263157.894737,   368421.052632,   473684.210526,   578947.368421,
             684210.526316,   789473.684211,   894736.842105,  1000000.      ])

  • time
    (time)
    float64
    0.0 1.579e-07 ... 2.842e-06 3e-06

    array([0.000000e+00, 1.578947e-07, 3.157895e-07, 4.736842e-07, 6.315789e-07,
           7.894737e-07, 9.473684e-07, 1.105263e-06, 1.263158e-06, 1.421053e-06,
           1.578947e-06, 1.736842e-06, 1.894737e-06, 2.052632e-06, 2.210526e-06,
           2.368421e-06, 2.526316e-06, 2.684211e-06, 2.842105e-06, 3.000000e-06])

• Data variables: (2)
  • signal_Re
    (repetition, detuning, time)
    float64
    2.394 2.416 2.011 ... 1.473 2.239

    array([[[ 2.39429204,  2.41584085,  2.01070382, ...,  0.6793216 ,
              2.40363714,  1.84911351],
            [ 1.20058654,  0.38661186, -0.00937807, ...,  1.75357879,
              1.54847952,  2.35827275],
            [ 2.41412335,  0.45049803,  0.08999442, ..., -0.05222153,
             -0.05433743,  2.42819182],
            ...,
            [ 1.43617869,  2.47237218,  0.49377027, ...,  0.45416915,
              0.77174222,  1.61199389],
            [ 2.72212855,  0.96724549, -0.07665558, ...,  0.55679343,
             -0.13133571,  2.69979919],
            [ 1.37780344,  2.39873711,  0.6753374 , ...,  2.84024508,
              1.83308746,  1.35408574]],
    
           [[ 1.61140291,  1.99985761,  1.71308173, ...,  1.12535782,
              1.95211842,  0.04698709],
            [ 1.38891653, -0.30882788, -0.68283045, ..., -0.41126179,
              1.63722469,  2.20281887],
            [ 1.85195278,  0.98095402,  2.86235175, ...,  2.85720975,
              1.67642489,  2.02264347],
    ...
            [ 2.45424413,  1.97817553, -0.16036014, ...,  2.37403284,
              2.1342944 ,  1.88157219],
            [ 1.87642135,  1.44188519, -0.32405311, ...,  1.49219097,
              2.01843329,  2.7472376 ],
            [ 2.05309515,  2.81978425,  2.57091351, ...,  1.5781437 ,
              1.94156871,  2.0546083 ]],
    
           [[ 1.42775298,  1.63658385,  2.08598928, ...,  0.09562478,
              2.36919346,  1.55970098],
            [ 1.95123319,  0.09412897,  0.21869851, ...,  0.04774279,
              1.94256876,  2.23526748],
            [ 2.62100716,  2.13166133, -0.06878885, ...,  2.34982587,
              1.69033609,  2.99005151],
            ...,
            [ 2.51083966,  2.14627053,  2.11965435, ..., -0.04852749,
              1.82382828,  1.1774061 ],
            [ 2.86878792,  2.27512861,  0.919346  , ..., -0.42102062,
              2.25091241,  1.65840419],
            [ 2.08326324,  1.61226336, -0.32004031, ...,  0.11225661,
              1.47272199,  2.23852907]]])

  • signal_Im
    (repetition, detuning, time)
    float64
    0.6124 0.5184 ... 0.01058 0.7653

    array([[[ 6.12439108e-01,  5.18371782e-01,  5.37610698e-01, ...,
              2.79891003e+00,  4.70894072e-01, -2.66921560e-01],
            [ 6.08095583e-02,  2.14812469e+00,  2.51789045e+00, ...,
             -5.32404394e-01, -2.21957557e-01,  1.32042513e-01],
            [-3.56678744e-01,  1.55751874e-01,  2.55762606e+00, ...,
              1.84020202e+00,  1.56732843e+00,  4.43405943e-01],
            ...,
            [-7.73251510e-01,  7.84068620e-01,  1.61664216e+00, ...,
              2.53640404e+00,  2.42237481e+00,  3.89334112e-01],
            [ 3.72180998e-01,  8.95917906e-01,  2.81327267e+00, ...,
              2.34984038e+00,  1.87400679e+00,  3.01958223e-01],
            [ 9.71940778e-01, -8.12839349e-02,  2.26240358e+00, ...,
             -5.54946405e-01,  7.25710090e-01,  4.61186834e-01]],
    
           [[ 2.30062557e-01, -5.39251607e-01, -1.49084643e-01, ...,
              6.83342128e-02, -1.47124004e-01,  2.22704947e+00],
            [-5.02726635e-01,  2.10082407e+00,  1.56912321e+00, ...,
              2.24352756e+00, -5.49738010e-01, -4.31328129e-01],
            [-2.28589141e-01, -8.25650541e-02, -2.82386289e-01, ...,
             -2.54353554e-01, -6.60173598e-01,  1.86845278e-01],
    ...
            [-6.34680441e-02,  5.53216266e-01,  2.61957215e+00, ...,
             -1.46180957e-01, -1.31822609e-01,  3.10733970e-02],
            [-1.01504979e-01, -5.95690263e-01,  1.86810551e+00, ...,
             -3.00842301e-03, -5.60020614e-01, -1.15270200e-01],
            [ 6.96537377e-02, -2.65448933e-01, -8.67848943e-02, ...,
              1.88832992e-02, -6.97018424e-02,  5.51572531e-01]],
    
           [[ 5.77733932e-02,  4.49966193e-01, -3.17608069e-01, ...,
              1.48937500e+00, -1.83142237e-01, -3.61452923e-01],
            [-7.17090804e-01,  1.73602557e+00,  1.66805554e+00, ...,
              2.81057648e+00, -2.38979670e-01, -1.40718115e-01],
            [ 1.31608837e-01,  7.38725888e-02,  2.17430591e+00, ...,
             -1.21735869e-01, -1.54676491e-01,  2.60753912e-01],
            ...,
            [-2.24798920e-01, -4.30408288e-01,  2.20729028e-02, ...,
              1.31371250e+00,  1.70229825e-01, -2.13814390e-01],
            [-7.78459016e-02,  3.62070825e-01,  2.51885359e+00, ...,
              1.14211801e+00,  9.91125450e-02, -5.20870902e-01],
            [-5.15084296e-01, -3.80074990e-01,  1.40922337e+00, ...,
              2.06647356e+00,  1.05807313e-02,  7.65276896e-01]]])

• Indexes: (3)
  • repetition
    PandasIndex

    PandasIndex(Index([  1,   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,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,
            43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,
            57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,
            71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,
            85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,
            99, 100],
          dtype='int64', name='repetition'))

  • detuning
    PandasIndex

    PandasIndex(Index([         -1000000.0,  -894736.8421052631,  -789473.6842105263,
            -684210.5263157894,  -578947.3684210526, -473684.21052631584,
             -368421.052631579, -263157.89473684214, -157894.73684210528,
            -52631.57894736843,   52631.57894736831,  157894.73684210517,
              263157.894736842,   368421.0526315789,   473684.2105263157,
             578947.3684210526,   684210.5263157894,   789473.6842105263,
             894736.8421052631,           1000000.0],
          dtype='float64', name='detuning'))

  • time
    PandasIndex

    PandasIndex(Index([                   0.0, 1.5789473684210527e-07, 3.1578947368421055e-07,
            4.736842105263158e-07,  6.315789473684211e-07,  7.894736842105264e-07,
            9.473684210526316e-07, 1.1052631578947369e-06, 1.2631578947368422e-06,
           1.4210526315789475e-06, 1.5789473684210528e-06,  1.736842105263158e-06,
           1.8947368421052632e-06, 2.0526315789473687e-06, 2.2105263157894738e-06,
           2.3684210526315793e-06, 2.5263157894736844e-06, 2.6842105263157895e-06,
            2.842105263157895e-06,                  3e-06],
          dtype='float64', name='time'))

• Attributes: (0)

OK, not very exciting yet. But internally stuff has happened -- the data went through the processing routine, and the output of that routine is now available as output data.

Note: we always look at the data and plot it; that holds for every node. When we embed the .plot object into a viewable (like a panel layout) we get our custom plot app we can use to slice our data.

In [17]:

pn.Column(
    n.plot
)

pn.Column( n.plot )

Out[17]:

In [ ]:

A basic loader node¶

Loaders are specific nodes that load data. That means, they do not have any input data, only output data. Every loader node must have a method load_data that returns a DataDict.

The base class for loaders already comes with a set of preprocessing options: averaging over dimension that can be specified (you have to type in the name, and it'll average over it if it's there), auto-gridding, and auto-refresh.

In the example below, you can either load a freshly generated set of data by pressing the Load Data button, or repeatedly by setting Auto-refresh to some value (disabling will stop it).

TODO Stuff that's been set on the dimension sliders is currently being reset everytime the data is reloaded.

In [18]:

from labcore.data.datadict import DataDict
from labcore.analysis.hvplotting import LoaderNodeBase

class RabiSimulation(LoaderNodeBase):
    def load_data(self) -> DataDict:
        Omega_0 = 1
        Delta_vals = np.linspace(-2, 2, 20)
        t_vals = np.linspace(0, 3, 20)
        nreps = 10
        dd = chevron_dataset(Omega_0, Delta_vals, t_vals, nreps).expand()
        return dd

s = RabiSimulation(name='sim')
app = pn.Column(
    s,
    s.plot,
)
app

from labcore.data.datadict import DataDict from labcore.analysis.hvplotting import LoaderNodeBase class RabiSimulation(LoaderNodeBase): def load_data(self) -> DataDict: Omega_0 = 1 Delta_vals = np.linspace(-2, 2, 20) t_vals = np.linspace(0, 3, 20) nreps = 10 dd = chevron_dataset(Omega_0, Delta_vals, t_vals, nreps).expand() return dd s = RabiSimulation(name='sim') app = pn.Column( s, s.plot, ) app

Out[18]:

In [ ]: