Overview

The library

All functionality in magali is available from the base namespace of the magali package. This means that you can access all of them with a single import:

# magali is usually imported as mg
import magali as mg

import ensaio
import matplotlib.pyplot as plt

Load some data:

fname = ensaio.fetch_morroco_speleothem_qdm(version=1, file_format="matlab")
print(fname)

/home/runner/.cache/ensaio/v1/morroco-speleothem-qdm.mat

data = mg.read_qdm_harvard(fname)
data

<xarray.DataArray 'bz' (y: 600, x: 960)> Size: 5MB
array([[ 352.40587477,   94.8913792 ,   41.61924299, ...,  470.18833933,
         129.20055397,   18.50120941],
       [ 525.04809649,  624.84659897,   53.45418   , ...,  450.42515609,
         240.12455308,  -73.61367693],
       [ 105.0939369 ,  638.76559489,  307.60736872, ...,  236.91326522,
         386.8498122 ,  -86.44215589],
       ...,
       [ -83.74367957,   32.98078244, -411.75073652, ...,  745.99373583,
        1036.20033954, -140.64317643],
       [ 171.17113661, -214.47801235,  159.23437984, ...,  124.58138395,
         258.54331931,  -90.3376945 ],
       [  80.60950354,  273.08367487,  118.23499313, ...,   -4.19572521,
         -53.55728012,    2.10335918]], shape=(600, 960))
Coordinates:
  * x        (x) float64 8kB 0.0 2.35 4.7 7.05 ... 2.249e+03 2.251e+03 2.254e+03
  * y        (y) float64 5kB 0.0 2.35 4.7 7.05 ... 1.403e+03 1.405e+03 1.408e+03
    z        (y, x) float64 5MB 5.0 5.0 5.0 5.0 5.0 5.0 ... 5.0 5.0 5.0 5.0 5.0
Attributes:
    long_name:  vertical magnetic field
    units:      nT

xarray.DataArray

'bz'

• y: 600
• x: 960

• 352.4 94.89 41.62 288.4 404.5 ... 101.0 -186.6 -4.196 -53.56 2.103

  array([[ 352.40587477,   94.8913792 ,   41.61924299, ...,  470.18833933,
           129.20055397,   18.50120941],
         [ 525.04809649,  624.84659897,   53.45418   , ...,  450.42515609,
           240.12455308,  -73.61367693],
         [ 105.0939369 ,  638.76559489,  307.60736872, ...,  236.91326522,
           386.8498122 ,  -86.44215589],
         ...,
         [ -83.74367957,   32.98078244, -411.75073652, ...,  745.99373583,
          1036.20033954, -140.64317643],
         [ 171.17113661, -214.47801235,  159.23437984, ...,  124.58138395,
           258.54331931,  -90.3376945 ],
         [  80.60950354,  273.08367487,  118.23499313, ...,   -4.19572521,
           -53.55728012,    2.10335918]], shape=(600, 960))

• Coordinates: (3)
  • x
    (x)
    float64
    0.0 2.35 ... 2.251e+03 2.254e+03

    units :

    µm

    array([   0.  ,    2.35,    4.7 , ..., 2248.95, 2251.3 , 2253.65], shape=(960,))

  • y
    (y)
    float64
    0.0 2.35 ... 1.405e+03 1.408e+03

    units :

    µm

    array([   0.  ,    2.35,    4.7 , ..., 1402.95, 1405.3 , 1407.65], shape=(600,))

  • z
    (y, x)
    float64
    5.0 5.0 5.0 5.0 ... 5.0 5.0 5.0 5.0

    long_name :

    sensor sample distance

    units :

    µm

    array([[5., 5., 5., ..., 5., 5., 5.],
           [5., 5., 5., ..., 5., 5., 5.],
           [5., 5., 5., ..., 5., 5., 5.],
           ...,
           [5., 5., 5., ..., 5., 5., 5.],
           [5., 5., 5., ..., 5., 5., 5.],
           [5., 5., 5., ..., 5., 5., 5.]], shape=(600, 960))

• Indexes: (2)
  • x
    PandasIndex

    PandasIndex(Index([               0.0,               2.35,                4.7,
            7.050000000000001,                9.4,              11.75,
           14.100000000000001,              16.45,               18.8,
           21.150000000000002,
           ...
                       2232.5,            2234.85, 2237.2000000000003,
                      2239.55,             2241.9,            2244.25,
                       2246.6, 2248.9500000000003,             2251.3,
                      2253.65],
          dtype='float64', name='x', length=960))

  • y
    PandasIndex

    PandasIndex(Index([               0.0,               2.35,                4.7,
            7.050000000000001,                9.4,              11.75,
           14.100000000000001,              16.45,               18.8,
           21.150000000000002,
           ...
                       1386.5, 1388.8500000000001,             1391.2,
                      1393.55,             1395.9,            1398.25,
           1400.6000000000001,            1402.95,             1405.3,
                      1407.65],
          dtype='float64', name='y', length=600))

• Attributes: (2)

  long_name :

  vertical magnetic field

  units :

  nT

fig, ax = plt.subplots(1, 1, figsize=(9, 4.8), layout="constrained")
scale = 2500
data.plot.imshow(ax=ax, cmap="RdBu_r", vmin=-scale, vmax=scale)
ax.set_aspect("equal")
plt.show()