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Fixing repr #267

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Apr 16, 2024
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Fixing repr #267

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4553274
Fixing repr
gviejo Apr 16, 2024
99449de
fixing docs
gviejo Apr 16, 2024
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45 changes: 21 additions & 24 deletions docs/examples/tutorial_pynapple_process.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,44 +36,41 @@
# Discrete correlograms
# ---------------------
#
# The function to compute cross-correlogram is [*cross_correlogram*](https://peyrachelab.github.io/pynapple/process.correlograms/#pynapple.process.correlograms.cross_correlogram).
# First let's generate some data. Here we have two neurons recorded together. We can group them in a `TsGroup`.
#
#
# The function is compiled with [numba](https://numba.pydata.org/) to improve performances. This means it only accepts pure numpy arrays as input arguments.

ts1 = nap.Ts(t=np.sort(np.random.uniform(0, 1000, 2000)), time_units="s")
ts2 = nap.Ts(t=np.sort(np.random.uniform(0, 1000, 1000)), time_units="s")
epoch = nap.IntervalSet(start=0, end=1000, time_units="s")
ts_group = nap.TsGroup({0: ts1, 1: ts2}, time_support=epoch)

ts1 = nap.Ts(t=np.sort(np.random.uniform(0, 1000, 1000)), time_units="s")
ts2 = nap.Ts(t=np.sort(np.random.uniform(0, 1000, 10)), time_units="s")
print(ts_group)

ts1_time_array = ts1.as_units("s").index.values
ts2_time_array = ts2.as_units("s").index.values
# %%
# First we can compute their autocorrelograms meaning the number of spikes of a neuron observed in a time windows centered around its own spikes.
# For this we can use the function `compute_autocorrelogram`.
# We need to specifiy the `binsize` and `windowsize` to bin the spike train.

binsize = 0.1 # second
cc12, xt = nap.process.correlograms.cross_correlogram(
t1=ts1_time_array, t2=ts2_time_array, binsize=binsize, windowsize=1 # second
autocorrs = nap.compute_autocorrelogram(
group=ts_group, binsize=100, windowsize=1000, time_units="ms", ep=epoch # ms
)

plt.figure(figsize=(10, 6))
plt.bar(xt, cc12, binsize)
plt.xlabel("Time t1 (us)")
plt.ylabel("CC")
print(autocorrs, "\n")

# %%
# To simplify converting to a numpy.ndarray, pynapple provides wrappers for computing autocorrelogram and crosscorrelogram for TsGroup. The function is then called for each unit or each pairs of units. It returns directly a pandas.DataFrame holding all the correlograms. In this example, autocorrelograms and cross-correlograms are computed for the same TsGroup.

epoch = nap.IntervalSet(start=0, end=1000, time_units="s")
ts_group = nap.TsGroup({0: ts1, 1: ts2}, time_support=epoch)
# The variable `autocorrs` is a pandas DataFrame with the center of the bins for the index and each columns is a neuron.
#
# Similarly, we can compute crosscorrelograms meaning how many spikes of neuron 1 do I observe whenever neuron 0 fires. Here the function
# is called `compute_crosscorrelogram` and takes a `TsGroup` as well.

autocorrs = nap.compute_autocorrelogram(
group=ts_group, binsize=100, windowsize=1000, time_units="ms", ep=epoch # ms # ms
)
crosscorrs = nap.compute_crosscorrelogram(
group=ts_group, binsize=100, windowsize=1000, time_units="ms" # ms # ms
group=ts_group, binsize=100, windowsize=1000, time_units="ms" # ms
)

print(autocorrs, "\n")
print(crosscorrs, "\n")

# %%
# Column name (0, 1) is read as cross-correlogram of neuron 0 and 1 with neuron 0 being the reference time.

# %%
# ***
# Peri-Event Time Histogram (PETH)
Expand Down
42 changes: 37 additions & 5 deletions pynapple/core/interval_set.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,6 +53,7 @@
from .config import nap_config
from .time_index import TsIndex
from .utils import (
_get_terminal_size,
_IntervalSetSliceHelper,
_jitfix_iset,
convert_to_numpy,
Expand Down Expand Up @@ -172,11 +173,39 @@ def __repr__(self):
headers = ["start", "end"]
bottom = "shape: {}, time unit: sec.".format(self.shape)

return (
tabulate(self.values, headers=headers, showindex="always", tablefmt="plain")
+ "\n"
+ bottom
)
rows = _get_terminal_size()[1]
max_rows = np.maximum(rows - 10, 6)

if len(self) > max_rows:
n_rows = max_rows // 2
with warnings.catch_warnings():
warnings.simplefilter("ignore")
return (
tabulate(
self.values[0:n_rows],
headers=headers,
showindex=self.index[0:n_rows],
tablefmt="plain",
)
+ "\n\n...\n"
+ tabulate(
self.values[-n_rows:],
headers=[" " * 5, " " * 3], # To align properly the columns
showindex=self.index[-n_rows:],
tablefmt="plain",
)
+ "\n"
+ bottom
)

else:
return (
tabulate(
self.values, headers=headers, showindex="always", tablefmt="plain"
)
+ "\n"
+ bottom
)

def __str__(self):
return self.__repr__()
Expand All @@ -203,6 +232,9 @@ def __getitem__(self, key, *args, **kwargs):
elif isinstance(key, (list, slice, np.ndarray)):
output = self.values.__getitem__(key)
return IntervalSet(start=output[:, 0], end=output[:, 1])
elif isinstance(key, pd.Series):
output = self.values.__getitem__(key)
return IntervalSet(start=output[:, 0], end=output[:, 1])
elif isinstance(key, tuple):
if len(key) == 2:
if isinstance(key[1], Number):
Expand Down
137 changes: 80 additions & 57 deletions pynapple/core/time_series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,6 +41,7 @@
from .time_index import TsIndex
from .utils import (
_concatenate_tsd,
_get_terminal_size,
_split_tsd,
_TsdFrameSliceHelper,
convert_to_numpy,
Expand Down Expand Up @@ -620,42 +621,39 @@ def __repr__(self):
headers = ["Time (s)", ""]
bottom = "dtype: {}".format(self.dtype) + ", shape: {}".format(self.shape)

max_rows = 2
rows = _get_terminal_size()[1]
max_rows = np.maximum(rows - 10, 2)

if len(self):

def create_str(array):
if array.ndim == 1:
if len(array) > 2:
return (
"["
+ array[0].__repr__()
+ " ... "
+ array[-1].__repr__()
+ "]"
)
elif len(array) == 2:
return (
"[" + array[0].__repr__() + "," + array[1].__repr__() + "]"
return np.array2string(
np.array([array[0], array[-1]]),
precision=6,
separator=" ... ",
)
elif len(array) == 1:
return "[" + array[0].__repr__() + "]"
else:
return "[]"
return np.array2string(array, precision=6, separator=", ")
else:
return "[" + create_str(array[0]) + " ...]"

_str_ = []
if self.shape[0] < 100:
for i, array in zip(self.index, self.values):
_str_.append([i.__repr__(), create_str(array)])
else:
for i, array in zip(self.index[0:5], self.values[0:5]):
if self.shape[0] > max_rows:
n_rows = max_rows // 2
for i, array in zip(self.index[0:n_rows], self.values[0:n_rows]):
_str_.append([i.__repr__(), create_str(array)])
_str_.append(["...", ""])
for i, array in zip(
self.index[-5:],
self.values[self.values.shape[0] - 5 : self.values.shape[0]],
self.index[-n_rows:],
self.values[self.values.shape[0] - n_rows : self.values.shape[0]],
):
_str_.append([i.__repr__(), create_str(array)])
else:
for i, array in zip(self.index, self.values):
_str_.append([i.__repr__(), create_str(array)])

return tabulate(_str_, headers=headers, colalign=("left",)) + "\n" + bottom

Expand Down Expand Up @@ -818,40 +816,52 @@ def __repr__(self):
headers = ["Time (s)"] + [str(k) for k in self.columns]
bottom = "dtype: {}".format(self.dtype) + ", shape: {}".format(self.shape)

max_cols = 5
try:
max_cols = os.get_terminal_size()[0] // 16
except Exception:
import shutil

max_cols = shutil.get_terminal_size().columns // 16
else:
pass
cols, rows = _get_terminal_size()
max_cols = np.maximum(cols // 100, 5)
max_rows = np.maximum(rows - 10, 2)

if self.shape[1] > max_cols:
headers = headers[0 : max_cols + 1] + ["..."]

def round_if_float(x):
if isinstance(x, float):
return np.round(x, 5)
else:
return x

with warnings.catch_warnings():
warnings.simplefilter("ignore")
if len(self):
table = []
end = ["..."] if self.shape[1] > max_cols else []
if len(self) > 51:
for i, array in zip(self.index[0:5], self.values[0:5, 0:max_cols]):
table.append([i] + [k for k in array] + end)
if len(self) > max_rows:
n_rows = max_rows // 2
for i, array in zip(
self.index[0:n_rows], self.values[0:n_rows, 0:max_cols]
):
table.append([i] + [round_if_float(k) for k in array] + end)
table.append(["..."])
for i, array in zip(
self.index[-5:],
self.index[-n_rows:],
self.values[
self.values.shape[0] - 5 : self.values.shape[0], 0:max_cols
self.values.shape[0] - n_rows : self.values.shape[0],
0:max_cols,
],
):
table.append([i] + [k for k in array] + end)
return tabulate(table, headers=headers) + "\n" + bottom
table.append([i] + [round_if_float(k) for k in array] + end)
return (
tabulate(table, headers=headers, colalign=("left",))
+ "\n"
+ bottom
)
else:
for i, array in zip(self.index, self.values[:, 0:max_cols]):
table.append([i] + [k for k in array] + end)
return tabulate(table, headers=headers) + "\n" + bottom
table.append([i] + [round_if_float(k) for k in array] + end)
return (
tabulate(table, headers=headers, colalign=("left",))
+ "\n"
+ bottom
)
else:
return tabulate([], headers=headers) + "\n" + bottom

Expand Down Expand Up @@ -1053,27 +1063,24 @@ def __repr__(self):
headers = ["Time (s)", ""]
bottom = "dtype: {}".format(self.dtype) + ", shape: {}".format(self.shape)

max_rows = 2
rows = _get_terminal_size()[1]
max_rows = np.maximum(rows - 10, 2)

with warnings.catch_warnings():
warnings.simplefilter("ignore")
if len(self):
if len(self) < 51:
return (
tabulate(
np.vstack((self.index, self.values)).T,
headers=headers,
colalign=("left",),
)
+ "\n"
+ bottom
)
else:
if len(self) > max_rows:
n_rows = max_rows // 2
table = []
for i, v in zip(self.index[0:5], self.values[0:5]):
for i, v in zip(self.index[0:n_rows], self.values[0:n_rows]):
table.append([i, v])
table.append(["..."])
for i, v in zip(
self.index[-5:],
self.values[self.values.shape[0] - 5 : self.values.shape[0]],
self.index[-n_rows:],
self.values[
self.values.shape[0] - n_rows : self.values.shape[0]
],
):
table.append([i, v])

Expand All @@ -1082,6 +1089,16 @@ def __repr__(self):
+ "\n"
+ bottom
)
else:
return (
tabulate(
np.vstack((self.index, self.values)).T,
headers=headers,
colalign=("left",),
)
+ "\n"
+ bottom
)
else:
return tabulate([], headers=headers) + "\n" + bottom

Expand Down Expand Up @@ -1357,14 +1374,20 @@ def __init__(self, t, time_units="s", time_support=None):

def __repr__(self):
upper = "Time (s)"
if len(self) < 50:
_str_ = "\n".join([i.__repr__() for i in self.index])
else:

max_rows = 2
rows = _get_terminal_size()[1]
max_rows = np.maximum(rows - 10, 2)

if len(self) > max_rows:
n_rows = max_rows // 2
_str_ = "\n".join(
[i.__repr__() for i in self.index[0:5]]
[i.__repr__() for i in self.index[0:n_rows]]
+ ["..."]
+ [i.__repr__() for i in self.index[-5:]]
+ [i.__repr__() for i in self.index[-n_rows:]]
)
else:
_str_ = "\n".join([i.__repr__() for i in self.index])

bottom = "shape: {}".format(len(self.index))
return "\n".join((upper, _str_, bottom))
Expand Down
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