def write_grid(
df,
title="MolGrid",
fn="molgrid.html",
drop=[],
keep=[],
smiles_col="Smiles",
mol_col="Mol",
id_col=None,
b64_col=None,
fp_col=None,
**kwargs,
):
"""Write the html_grid to file and return the link."""
tbl = html_grid(
df,
title=title,
drop=drop,
keep=keep,
smiles_col=smiles_col,
mol_col=mol_col,
id_col=id_col,
b64_col=b64_col,
fp_col=fp_col,
size=IMG_GRID_SIZE,
**kwargs,
)
header = kwargs.get("header", None)
summary = kwargs.get("summary", None)
page = templ.page(tbl, title=title, header=header, summary=summary)
write(page, fn)
if IPYTHON:
return HTML('<a href="{}">{}</a>'.format(fn, title))
def report(
self,
columns: List[str] = ["Compound_Id", "Smiles"],
title: str = "Cluster Report",
intro: str = "Clusters in their order in the dataset.",
summary: str = "",
):
"""Write a HTML report. `Cluster_No` and `IsRepr` have to be present in the DataFrame.
Writes the clusters in the order as they are in the dataset.
Arguments:
columns: List of columns to include.
title: The report title.
intro: Some text used for introduction of the report.
summary: text displayed at the end of the report. By default the configuration will be shown.
"""
def add_cluster(cl_no):
sim_cluster = cl_info.query(
"Cluster_No == @cl_no")["Similar_To"].values[0]
if sim_cluster > 0:
sim_text = (
f" (Similar to Cluster {sim_cluster})"
)
else:
sim_text = ""
mf_cl = mf.MolFrame(df.query("Cluster_No == @cl_no")[columns])
mf_cl = mf_cl.add_mols()
html.append(
f"<br><h2>Cluster {cl_no} ({len(mf_cl.data)} Members){sim_text}</h2><br>"
)
grid = mfv.html_grid(mf_cl.data, id_col=id_col, mols_per_row=6)
html.append(grid)
df = self.molf.data.copy()
id_col = self.molf.id_col
cl_info = self.cl_info
if id_col not in columns:
columns = [id_col] + columns
if "Smiles" not in columns:
columns.append("Smiles")
if summary == "":
summary = f"<p><b>Configuration:</b> {str(self.config)}</p>"
html = [f"<h1>{title}</h1><br>{intro}<br><br>"]
clusters = df["Cluster_No"].unique()
for cl_no in clusters:
add_cluster(cl_no)
html.append(summary)
mfht.write(mfht.page("\n".join(html)), "Clusters.html")
def report(
df: pd.DataFrame,
id_col: str = "Compound_Id",
columns: List[str] = ["Compound_Id", "Smiles"],
title: str = "Cluster Report",
intro: str = "Large clusters first, similar clusters together.",
):
"""Write a HTML report. `Cluster_No` and `IsRepr` have to be present in the DataFrame.
In the current setting, the largest clusters are at the top of the report,
with similar clusters (determind by the chemical similarities of the representative structures)
are grouped together.
Writes the report to disk as `Clusters.html`.
Used in `projects/paint3_anal/190328_cpd_clustering.ipynb`.
Arguments:
df: The input DataFrame containing the structures as Smiles.
id_col: The name of the column to use for identity. Default is `Compound_Id`.
columns: List of columns to include.
title: The report title.
intro: Some text used for introduction of the report.
"""
def add_cluster(cl_no, sim_to=None):
if sim_to is None:
sim_to = ""
html.append("<hr>")
else:
sim_to = f"(similar to {sim_to})"
mf_cl = mf.MolFrame(df.query("Cluster_No == @cl_no")[columns])
mf_cl = mf_cl.add_mols()
html.append(
f"<br><h2>Cluster {cl_no} ({len(mf_cl.data)} Members) {sim_to}</h2><br>"
)
grid = mfv.html_grid(mf_cl.data, id_col="Compound_Id")
html.append(grid)
if id_col not in columns:
columns = [id_col] + columns
if "Smiles" not in columns:
columns.append("Smiles")
df_repr = df.query("IsRepr == 'Yes'").reset_index().drop("index", axis=1)
chem_sim = {}
for idx, rec0 in df_repr.iterrows():
for _, rec1 in df_repr.iloc[idx + 1:].iterrows():
cl0 = rec0["Cluster_No"]
cl1 = rec1["Cluster_No"]
sim = mf.chem_sim(rec0["Smiles"], rec1["Smiles"])
chem_sim[(cl0, cl1)] = sim
chem_sim[(cl1, cl0)] = sim
cl_sizes = (df[["Cluster_No", "Compound_Id"
]].groupby(by="Cluster_No").count().reset_index().rename(
columns={"Compound_Id": "Size"}))
cl_sizes = cl_sizes.sort_values("Size", ascending=False)
cl_order = {x: True for x in cl_sizes["Cluster_No"].values}
html = [f"<h1>{title}</h1><br>{intro}<br><br>"]
while len(cl_order) > 0:
cl_no = list(cl_order.keys())[0]
add_cluster(cl_no)
cl_order.pop(cl_no)
to_remove = []
for sim_cl in cl_order:
if chem_sim[(cl_no, sim_cl)] > 0.45:
add_cluster(sim_cl, cl_no)
to_remove.append(sim_cl)
for x in to_remove:
cl_order.pop(x)
mfht.write(mfht.page("\n".join(html)), "Clusters.html")