Processing ERRORR Files with Andalus#

This notebook demonstrates how to create an andalus.Covariance object from ERRORR output files and prepare the data for database storage.

Background#

We use the sandy library to parse ERRORR files and combine covariance matrices. Currently, andalus supports reading covariance information from the following sections:

File 31 (MF31): \(\bar{\nu}\) (nubar)
File 33 (MF33): Cross sections
File 34 (MF34): Angular distributions
File 35 (MF35): Energy distributions

Note on Identifiers: Since MT numbers in MF34 and MF35 may overlap with those in MF33, we ensure uniqueness in MF34/35 by defining the identifier as:

Identifier = (MF * 1000) + MT

1. Setup and Imports#

We import andalus to handle the covariance objects. Internally, andalus leverages sandy to parse the raw text into structured pandas DataFrames.

[ ]:

import matplotlib.pyplot as plt

import andalus

plt.style.use("ggplot")

2. Reading ERRORR Output#

Raw ERRORR files use a specific nuclear data format that is difficult to parse manually. Below is a snippet of the raw input data:

[2]:

!head -10 "../data/u235.errorr31"

                                                                     1 0  0    0
 9.223500+4 2.330248+2          6          0        -11          09228 1451    1
 0.000000+0 0.000000+0         33          0         34          09228 1451    2
 1.000000-5 1.000000-1 5.400000-1 4.000000+0 8.315290+0 1.370960+19228 1451    3
 2.260330+1 4.016900+1 6.790400+1 9.166090+1 1.486250+2 3.043250+29228 1451    4
 4.539990+2 7.485180+2 1.234100+3 2.034680+3 3.354630+3 5.530840+39228 1451    5
 9.118820+3 1.503440+4 2.478750+4 4.086770+4 6.737950+4 1.110900+59228 1451    6
 1.831560+5 3.019740+5 4.978710+5 8.208500+5 1.353350+6 2.231300+69228 1451    7
 3.678790+6 6.065310+6 1.000000+7 1.964030+7                      9228 1451    8
                                                                  9228 1  099999

The Covariance.from_errorr method simplifies this process. You provide a dictionary mapping the error types (keys) to their file paths, specify the ZAI (isotope identifier), and optionally provide a list of MT numbers to extract.

[3]:

files = {
    "errorr33": "../data/u235.errorr33",
    "errorr31": "../data/u235.errorr31",
    "errorr34": "../data/u235.errorr34",
    "errorr35": "../data/u235.errorr35",
}

cov = andalus.Covariance.from_errorr(files, zai=922350, mts=[1, 2, 4, 18, 102, 456, 34251, 35018])

3. Exploring the Covariance Object#

The resulting object inherits from pandas.DataFrame, meaning you can use standard data manipulation tools. However, it also contains nuclear-specific metadata like zai, nuclide, and optional physics parameters like temperature or tolerance.

[4]:

print(f"ZAI: {cov.zai}")
print(f"Nuclide: {cov.nuclide}")
?cov

ZAI: 922350
Nuclide: U235
Type:        Covariance
String form:
MT                                      1                                 \
           E_min_eV              <...>  0.003955
           1.000000e+07 1.964030e+07     0.003955     0.019607

           [264 rows x 264 columns]
Length:      264
File:        c:\users\dhouben\documents\andalus\andalus\covariance.py
Docstring:
Subclass of pd.DataFrame for handling individual nuclide covariance data.

Attributes
----------
zai : int
    Nuclide identifier (ZAI).
temperature : float, optional
    Temperature at which the covariance was generated.
err : float, optional
    Associated error or tolerance.

4. Correlation Matrix Visualization#

We can easily derive a correlation matrix from our covariance data and visualize it using a heat map.

[14]:

corr = cov.correlation()

fig, ax = plt.subplots(figsize=(8, 6))
im = ax.imshow(corr.values, vmin=-1, vmax=1, cmap="bwr", aspect="auto")

cbar = fig.colorbar(im, ax=ax)
cbar.set_label("Correlation Coefficient")

ax.set_title("Correlation Matrix")
ax.set_xlabel("Index")
ax.set_ylabel("Index")

lvl0 = corr.index.get_level_values(0)

# find contiguous MT blocks
blocks = []
start = 0
for i in range(1, len(lvl0)):
    if lvl0[i] != lvl0[i - 1]:
        blocks.append((lvl0[i - 1], start, i - 1))
        start = i
blocks.append((lvl0[-1], start, len(lvl0) - 1))

# tick at block centers
tick_pos = [(s + e) / 2 for _, s, e in blocks]
tick_labels = [str(mt) for mt, _, _ in blocks]

ax.set_xticks(tick_pos)
ax.set_xticklabels(tick_labels)
ax.set_yticks(tick_pos)
ax.set_yticklabels(tick_labels)

# optional separators between MT blocks
for _, _, e in blocks[:-1]:
    ax.axvline(e + 0.5, color="white", lw=0.5, alpha=0.6)
    ax.axhline(e + 0.5, color="white", lw=0.5, alpha=0.6)

ax.set_xlabel("MT")
ax.set_ylabel("MT")

plt.tight_layout()

../../_images/_collections_examples_covariance_from_errorr_10_0.png

5. Exporting Data#

Once the data is processed, it can be exported to a format optimized for use in ANDALUS.

[53]:

cov.to_hdf5("u235_cov.h5")

And it can then be easily read.

[66]:

andalus.Covariance.from_hdf5("u235_cov.h5", zai=922350)

[66]:

		MT	456										...	35018
		E_min	1.00000e-05	1.00000e-01	5.40000e-01	4.00000e+00	8.31529e+00	1.37096e+01	2.26033e+01	4.01690e+01	6.79040e+01	9.16609e+01	...	1.11090e+05	1.83156e+05	3.01974e+05	4.97871e+05	8.20850e+05	1.35335e+06	2.23130e+06	3.67879e+06	6.06531e+06	1.00000e+07
		E_max	1.00000e-01	5.40000e-01	4.00000e+00	8.31529e+00	1.37096e+01	2.26033e+01	4.01690e+01	6.79040e+01	9.16609e+01	1.48625e+02	...	1.83156e+05	3.01974e+05	4.97871e+05	8.20850e+05	1.35335e+06	2.23130e+06	3.67879e+06	6.06531e+06	1.00000e+07	1.96403e+07
MT	E_min	E_max
456	1.00000e-05	1.00000e-01	2.05760e-05	2.05760e-05	1.79154e-05	1.76400e-05	1.76400e-05	1.76400e-05	1.76400e-05	1.76400e-05	1.76400e-05	1.76400e-05	...	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00
	1.00000e-01	5.40000e-01	2.05760e-05	2.05760e-05	1.79154e-05	1.76400e-05	1.76400e-05	1.76400e-05	1.76400e-05	1.76400e-05	1.76400e-05	1.76400e-05	...	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00
	5.40000e-01	4.00000e+00	1.79154e-05	1.79154e-05	2.00769e-05	2.03007e-05	1.84709e-05	1.76400e-05	1.76400e-05	1.76400e-05	1.76400e-05	1.76400e-05	...	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00
	4.00000e+00	8.31529e+00	1.76400e-05	1.76400e-05	2.03007e-05	2.05760e-05	1.85570e-05	1.76400e-05	1.76400e-05	1.76400e-05	1.76400e-05	1.76400e-05	...	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00
	8.31529e+00	1.37096e+01	1.76400e-05	1.76400e-05	1.84709e-05	1.85570e-05	2.71709e-05	1.95905e-05	1.76400e-05	1.76400e-05	1.76400e-05	1.76400e-05	...	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
35018	1.35335e+06	2.23130e+06	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	...	-8.24541e-04	-6.39354e-04	-3.54139e-04	-1.32770e-04	4.91341e-05	2.42313e-04	1.20013e-04	4.55191e-06	-9.22334e-05	-2.44922e-06
	2.23130e+06	3.67879e+06	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	...	-7.69174e-04	-6.44898e-04	-4.72111e-04	-2.48225e-04	-3.08282e-05	1.20013e-04	2.92605e-04	2.06564e-04	4.78707e-05	-3.31639e-06
	3.67879e+06	6.06531e+06	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	...	-6.33792e-04	-5.84774e-04	-4.77911e-04	-3.00563e-04	-8.00144e-05	4.55191e-06	2.06564e-04	5.42362e-04	6.37521e-04	7.84379e-04
	6.06531e+06	1.00000e+07	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	...	-5.45683e-04	-5.36010e-04	-4.44288e-04	-2.73286e-04	-1.02590e-04	-9.22334e-05	4.78707e-05	6.37521e-04	2.10911e-03	3.95538e-03
	1.00000e+07	1.96403e+07	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	0.00000e+00	...	-1.32210e-03	-8.83098e-04	-4.94075e-04	-3.63020e-04	-2.68487e-04	-2.44922e-06	-3.31639e-06	7.84379e-04	3.95538e-03	1.96065e-02

231 rows × 231 columns