Cannot save network

[wrmartin]

When attempting to save a network generated on a trajectory, I receive the following error:

Traceback (most recent call last):
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/groupby/groupby.py", line 1490, in array_func
    result = self.grouper._cython_operation(
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/groupby/ops.py", line 959, in _cython_operation
    return cy_op.cython_operation(
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/groupby/ops.py", line 657, in cython_operation
    return self._cython_op_ndim_compat(
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/groupby/ops.py", line 497, in _cython_op_ndim_compat
    return self._call_cython_op(
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/groupby/ops.py", line 541, in _call_cython_op
    func = self._get_cython_function(self.kind, self.how, values.dtype, is_numeric)
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/groupby/ops.py", line 173, in _get_cython_function
    raise NotImplementedError(
NotImplementedError: function is not implemented for this dtype: [how->mean,dtype->object]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/nanops.py", line 1692, in _ensure_numeric
    x = float(x)
ValueError: could not convert string to float: 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/nanops.py", line 1696, in _ensure_numeric
    x = complex(x)
ValueError: complex() arg is a malformed string

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pmg_tk/startup/ring-pymol/main_window.py", line 65, in <lambda>
    lambda: export_network_graph(self.get_selection(), self.temp_dir.name, self.log, self.disable_window,
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pmg_tk/startup/ring-pymol/utilities.py", line 434, in export_network_graph
    df = df.groupby('NodeId').mean()
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/groupby/groupby.py", line 1855, in mean
    result = self._cython_agg_general(
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/groupby/groupby.py", line 1507, in _cython_agg_general
    new_mgr = data.grouped_reduce(array_func)
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/internals/managers.py", line 1503, in grouped_reduce
    applied = sb.apply(func)
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/internals/blocks.py", line 329, in apply
    result = func(self.values, **kwargs)
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/groupby/groupby.py", line 1503, in array_func
    result = self._agg_py_fallback(values, ndim=data.ndim, alt=alt)
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/groupby/groupby.py", line 1457, in _agg_py_fallback
    res_values = self.grouper.agg_series(ser, alt, preserve_dtype=True)
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/groupby/ops.py", line 994, in agg_series
    result = self._aggregate_series_pure_python(obj, func)
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/groupby/ops.py", line 1015, in _aggregate_series_pure_python
    res = func(group)
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/groupby/groupby.py", line 1857, in <lambda>
    alt=lambda x: Series(x).mean(numeric_only=numeric_only),
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/generic.py", line 11556, in mean
    return NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/generic.py", line 11201, in mean
    return self._stat_function(
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/generic.py", line 11158, in _stat_function
    return self._reduce(
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/series.py", line 4666, in _reduce
    return op(delegate, skipna=skipna, **kwds)
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/nanops.py", line 96, in _f
    return f(*args, **kwargs)
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/nanops.py", line 158, in f
    result = alt(values, axis=axis, skipna=skipna, **kwds)
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/nanops.py", line 421, in new_func
    result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/nanops.py", line 727, in nanmean
    the_sum = _ensure_numeric(values.sum(axis, dtype=dtype_sum))
  File "/home0/martinw3/anaconda3/envs/pymol2/lib/python3.9/site-packages/pandas/core/nanops.py", line 1699, in _ensure_numeric
    raise TypeError(f"Could not convert {x} to numeric") from err
TypeError: Could not convert AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA to numeric

The plugin then becomes unresponsive.

[wrmartin]

I did some poking around and was able to get it to generate the .json file by modifying a few things in export_network_graph, but I'm thinking this is a pandas version issue. The changes feel like they aren't quite correct, though.

def export_network_graph(model, tmp_dir, log_f, disable_f, enable_f):
    disable_f()
    G = nx.MultiGraph()

    # Add the nodes to the graph
    file_pth = os.path.join(tmp_dir, model + ".cif_ringNodes")
    if not os.path.exists(file_pth):
        log_f("RING output files not found, run RING on the object first!", error=True)
        enable_f()
        return

    df = pd.read_csv(file_pth, sep='\t')
    if len(df) == 0:
        return IndexError
    df = df.groupby('NodeId')[['Position', 'Degree']].mean()

    for (nodeId, _, degree, *_) in df.itertuples(index=True):
        node = Node(str(nodeId))
        G.add_node(node, degree=round(degree, 3), chain=node.chain, resi=node.resi, resn=node.resn)

    # Add the edges to the graph
    file_pth = os.path.join(tmp_dir, model + ".cif_ringEdges")
    df = pd.read_csv(file_pth, sep='\t')

    distance_dict = dict()
    mean_distance = df.groupby(['NodeId1', 'NodeId2', 'Interaction'])[['Energy', 'Distance']].mean()
    for (nodeId, distance, *_) in mean_distance.itertuples(index=True, name='Distance'):
        nodeId1, nodeId2, interaction = nodeId
        intType = interaction.split(":")[0]
        node1 = Node(nodeId1)
        node2 = Node(nodeId2)
        edge = Edge(node1, node2)
        distance_dict.setdefault(intType, dict()).setdefault(edge, distance)

    conn_freq = get_freq(model, tmp_dir)

    sawn = set()
    df = df.groupby(["NodeId1", "Interaction", "NodeId2"]).sum()
    for (ids, *_) in df.itertuples(index=True):
        nodeId1, interaction, nodeId2 = ids
        intType = interaction.split(":")[0]
        node1 = Node(nodeId1)
        node2 = Node(nodeId2)
        edge = Edge(node1, node2)
        key = (edge, intType)
        if key not in sawn:
            G.add_edge(node1, node2, interaction=intType, frequency=round(conn_freq[intType][edge], 3),
                       distance=round(distance_dict[intType][edge], 3))
            sawn.add(key)

    with open("{}/{}.json".format(os.getcwd(), model), 'w+') as f:
        json.dump(nx.cytoscape_data(G), f)

    enable_f()

    log_f("Cytoscape network format saved as {}/{}.json".format(os.getcwd(), model))