import os
import warnings
import traceback
import pandas as pd
import numpy as np
from pathlib import Path
from collections import defaultdict
from .sheet import Sheet
from .objects import Epithelium
def _filter_columns(cols_hist, cols_in, element):
if not set(cols_hist).issubset(cols_in):
warnings.warn(
f"""
Columns {set(cols_hist).difference(cols_in)} are in the history
{element} dataframe but not in the sheet {element} dataframe.
These non existent columns will not be saved."""
)
cols_hist = set(cols_hist).intersection(cols_in)
return list(cols_hist)
[docs]class History:
""" This class handles recording and retrieving time series
of sheet objects.
"""
def __init__(self, sheet, save_every=None, dt=None, extra_cols=None, save_all=True):
"""Creates a `SheetHistory` instance.
Parameters
----------
sheet : a :class:`Sheet` object which we want to record
save_every : float, set every time interval to save the sheet
dt : float, time step
extra_cols : dictionnary with sheet.datasets as keys and list of
columns as values. Default None
save_all : bool
if True, saves all the data at each time point
"""
if extra_cols is None:
if save_all:
extra_cols = {
k: list(sheet.datasets[k].columns) for k in sheet.datasets
}
else:
extra_cols = defaultdict(list)
else:
extra_cols = defaultdict(list, **extra_cols)
self.sheet = sheet
self.time = 0.0
self.index = 0
if save_every is not None:
self.save_every = save_every
self.dt = dt
else:
self.save_every = None
self.datasets = {}
self.columns = {}
vcols = sheet.coords + extra_cols["vert"]
vcols = list(set(vcols))
self.vcols = _filter_columns(vcols, sheet.vert_df.columns, "vertex")
_vert_h = sheet.vert_df[self.vcols].reset_index(drop=False)
if not "time" in self.vcols:
_vert_h["time"] = 0
self.datasets["vert"] = _vert_h
self.columns["vert"] = self.vcols
fcols = extra_cols["face"]
self.fcols = _filter_columns(fcols, sheet.face_df.columns, "face")
_face_h = sheet.face_df[self.fcols].reset_index(drop=False)
if not "time" in self.fcols:
_face_h["time"] = 0
self.datasets["face"] = _face_h
self.columns["face"] = self.fcols
if sheet.cell_df is not None:
ccols = extra_cols["cell"]
self.ccols = _filter_columns(ccols, sheet.cell_df.columns, "cell")
_cell_h = sheet.cell_df[self.ccols].reset_index(drop=False)
if not "time" in self.ccols:
_cell_h["time"] = 0
self.datasets["cell"] = _cell_h
self.columns["cell"] = self.ccols
extra_cols["edge"].append("cell")
ecols = ["srce", "trgt", "face"] + extra_cols["edge"]
ecols = list(set(ecols))
self.ecols = _filter_columns(ecols, sheet.edge_df.columns, "edge")
_edge_h = sheet.edge_df[self.ecols].reset_index(drop=False)
if not "time" in self.ecols:
_edge_h["time"] = 0
self.datasets["edge"] = _edge_h
self.columns["edge"] = self.ecols
def __len__(self):
return self.time_stamps.__len__()
[docs] def to_archive(self, hf5file):
"""Saves the history to a HDF file
This file can later be accessed again with the `HistoryHdf5.from_archive`
class method
"""
with pd.HDFStore(hf5file, "a") as store:
for key, df in self.datasets.items():
kwargs = {"data_columns": ["time"]}
if "segment" in df.columns:
kwargs["min_itemsize"] = {"segment": 7}
store.append(key=key, value=df, **kwargs)
@property
def time_stamps(self):
return self.datasets["vert"]["time"].unique()
@property
def vert_h(self):
return self.datasets["vert"]
@property
def edge_h(self):
return self.datasets["edge"]
@property
def face_h(self):
return self.datasets["face"]
@property
def cell_h(self):
return self.datasets.get("cell", None)
[docs] def record(self, to_record=None, time_stamp=None):
"""Appends a copy of the sheet datasets to the history instance.
Parameters
----------
to_report : deprecated
"""
if to_record is not None:
warnings.warn("Deprecated all the data will be saved")
if time_stamp is not None:
self.time = time_stamp
else:
self.time += 1
if (self.save_every is None) or (
self.index % (int(self.save_every / self.dt)) == 0
):
for element in self.datasets:
hist = self.datasets[element]
cols = self.columns[element]
df = self.sheet.datasets[element][cols].reset_index(drop=False)
if not "time" in cols:
times = pd.Series(np.ones((df.shape[0],)) * self.time, name="time")
df = pd.concat([df, times], ignore_index=False, axis=1, sort=False)
if self.time in hist["time"]:
# erase previously recorded time point
hist = hist[hist["time"] != self.time]
hist = pd.concat([hist, df], ignore_index=True, axis=0, sort=False)
self.datasets[element] = hist
self.index += 1
[docs] def retrieve(self, time):
"""Return datasets at time `time`.
If a specific dataset was not recorded at time time, the closest record before that
time is used.
"""
if time > self.datasets["vert"]["time"].values[-1]:
warnings.warn(
"""
The time argument you requested is bigger than the maximum recorded time,
are you sure you pass time in parameter and not an index ?
"""
)
sheet_datasets = {}
for element in self.datasets:
hist = self.datasets[element]
cols = self.columns[element]
df = _retrieve(hist, time)
df = df.set_index(element)[cols]
sheet_datasets[element] = df
return type(self.sheet)(
f"{self.sheet.identifier}_{time:04.3f}", sheet_datasets, self.sheet.specs
)
def __iter__(self):
for t in self.time_stamps:
sheet = self.retrieve(t)
yield t, sheet
[docs]class HistoryHdf5(History):
""" This class handles recording and retrieving time series
of sheet objects.
"""
def __init__(
self,
sheet=None,
save_every=None,
dt=None,
extra_cols=None,
hf5file="",
overwrite=False,
):
"""Creates a `SheetHistory` instance.
Parameters
----------
sheet : a :class:`Sheet` object which we want to record
save_every : float, set every time interval to save the sheet
dt : float, time step
extra_cols : dictionnary with sheet.datasets as keys and list of
columns as values. Default None
hf5file : string, define the path of the HDF5 file
overwrite : bool, Overwrite or not the file if it is already exist. Default False
"""
if not hf5file:
warnings.warn(
"No directory is given. The HDF5 file will be saved in the working directory as out.hf5."
)
self.hf5file = Path(os.getcwd()) / "out.hf5"
else:
self.hf5file = Path(hf5file)
if self.hf5file.exists():
if overwrite:
tb = traceback.extract_stack(limit=2)
if not "from_archive" in tb[0].name:
warnings.warn(
"The file already exist and will be overwritten."
" This is normal if you reopened an archive"
)
else:
expand = 0
while True:
new_hf5file = self.hf5file.parent / self.hf5file.name.replace(
self.hf5file.suffix, f"{expand}{self.hf5file.suffix}"
)
expand += 1
if new_hf5file.exists():
continue
else:
self.hf5file = new_hf5file
warnings.warn(
"The file already exist and the new filename is {}".format(
new_hf5file
)
)
break
if sheet is None:
last = self.time_stamps[-1]
with pd.HDFStore(self.hf5file, "r") as file:
keys = file.keys()
if "\cell" in keys:
sheet = Epithelium
History.__init__(self, sheet, save_every, dt, extra_cols)
self.dtypes = {
k: df[self.columns[k]].dtypes for k, df in sheet.datasets.items()
}
[docs] @classmethod
def from_archive(cls, hf5file, columns=None, eptm_class=None):
datasets = {}
settings = {}
hf5file = Path(hf5file)
with pd.HDFStore(hf5file, "r") as store:
keys = [k.strip("/") for k in store.keys()]
if columns is None:
# read everything
columns = {k: None for k in keys}
if eptm_class is None:
eptm_class = Epithelium if "cell" in keys else Sheet
last = store.select("vert", columns=["time"]).iloc[-1]["time"]
for key in keys:
if key == "settings":
settings = store[key]
continue
df = store.select(key, where=f"time == {last}", columns=columns[key])
datasets[key] = df
eptm = eptm_class(hf5file.name, datasets)
eptm.settings.update(settings)
return cls(sheet=eptm, hf5file=hf5file, overwrite=True)
@property
def time_stamps(self):
with pd.HDFStore(self.hf5file, "r") as file:
times = file.select("vert", columns=["time"])["time"].unique()
return times
[docs] def record(self, to_record=None, time_stamp=None, sheet=None):
"""Appends a copy of the sheet datasets to the history HDF file.
Parameters
----------
to_report : Deprecated - list of strings
the datasets from self.sheet to be saved
sheet: a :class:`Sheet` object which we want to record. This argument can
be used if the sheet object is different at each time point.
"""
if to_record is not None:
warnings.warn("Deprecated, all the datasets will be saved anyway")
if sheet is not None:
self.sheet = sheet
if time_stamp is not None:
self.time = time_stamp
else:
self.time += 1.0
dtypes_ = {k: df.dtypes for k, df in self.sheet.datasets.items()}
for element, df in self.sheet.datasets.items():
diff_col = set(dtypes_[element].keys()).difference(
set(self.dtypes[element].keys())
)
if diff_col:
warnings.warn(
"New columns {} will not be saved in the {} table".format(
diff_col, element
)
)
else:
old_types = self.dtypes[element].to_dict()
new_types = dtypes_[element].to_dict()
if new_types != old_types:
changed_type = {
k: old_types[k]
for k in old_types
if k in new_types and old_types[k] != new_types[k]
}
raise ValueError(
"There is a change of datatype in {} table in {} columns".format(
element, changed_type
)
)
if (self.save_every is None) or (
self.index % (int(self.save_every / self.dt)) == 0
):
for element, df in self.sheet.datasets.items():
times = pd.Series(np.ones((df.shape[0],)) * self.time, name="time")
df = df[self.columns[element]]
df = pd.concat([df, times], ignore_index=False, axis=1, sort=False)
kwargs = {"data_columns": ["time"]}
if "segment" in df.columns:
kwargs["min_itemsize"] = {"segment": 8}
with pd.HDFStore(self.hf5file, "a") as file:
file.append(key=element, value=df, **kwargs)
self.index += 1
[docs] def retrieve(self, time):
"""Return datasets at time `time`.
If a specific dataset was not recorded at time time, the closest record before that
time is used.
"""
times = self.time_stamps
if time > times[-1]:
warnings.warn(
"The time argument you passed is bigger than the maximum recorded time, are you sure you pass time in parameter and not an index ? "
)
time = times[np.argmin(np.abs(times - time))]
with pd.HDFStore(self.hf5file, "r") as store:
sheet_datasets = {}
for element in self.datasets:
sheet_datasets[element] = store.select(element, where=f"time == {time}")
return type(self.sheet)(
f"{self.sheet.identifier}_{time:04.3f}", sheet_datasets, self.sheet.specs
)
def _retrieve(dset, time):
times = dset["time"].values
t = times[np.argmin(np.abs(times - time))]
return dset[dset["time"] == t]