### dLGN_{species}_scRNAseq_counts.h5
```
+--- data
|       +--- exon
|       |       +--- dims
|       |       +--- i
|       |       +--- p
|       |       +--- x (float)
|       +--- intron
|       |       +--- dims
|       |       +--- i
|       |       +--- p
|       |       +--- x
|       +--- t_exon
|       |       +--- dims
|       |       +--- i
|       |       +--- p
|       |       +--- x
|       +--- t_intron
|       |       +--- dims
|       |       +--- i
|       |       +--- p
|       |       +--- x
|       +--- total_exon_counts
|       +--- total_intron_counts
|       +--- gene_names
|       +--- sample_names
```
 - `exon`, `intron`: `x` contains read counts in sparse format. `i` and `p` are the cell and gene indices corresponding to each value in `x` of type float. `dim` specifies dimensions of the cell x gene matrix.
 - `t_exon`,`t_intron`: ??
 - `total_exon_counts`: total exon reads per cell (same as)
 - `total_intron_counts`: total exon reads per cell


#### Walk through h5 directory structure

```py
fname_ss = './dLGN_mouse_scRNAseq_counts.h5'

def print_attrs(name, obj):
    print(name)
    for key, val in obj.attrs.items():
        print("    %s: %s" % (key, val))

f_ss = h5py.File(fname_ss, mode='r')
f_ss.visititems(print_attrs)
```

#### Read sparse matrices from h5 file
```py
import scipy.sparse as ss
import h5py

def extract_sparse_mat(h5f, data_path):

    data = h5f[data_path]
    x = data['x']
    i = data['i']
    p = data['p']
    dims = data['dims']
    
    sparse_mat = ss.csc_matrix((x[0:x.len()],
                               i[0:i.len()],
                               p[0:p.len()]),
                               shape = (dims[0],dims[1]))
    return sparse_mat

fname = './dLGN_mouse_scRNAseq_counts.h5'
h5f = h5py.File(fname,'r')
exons = extract_sparse_mat(h5f,'/data/exon/')
introns = extract_sparse_mat(h5f,'/data/intron/')
```