Use hpstat for yli.turnbull to enable computation of confidence intervals
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@ -45,7 +45,7 @@ The mandatory dependencies of this library are:
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Optional dependencies are:
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* [hpstat](https://yingtongli.me/git/hpstat), for *IntervalCensoredCox*
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* [hpstat](https://yingtongli.me/git/hpstat), for *turnbull* and *IntervalCensoredCox*
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* [matplotlib](https://matplotlib.org/) and [seaborn](https://seaborn.pydata.org/), for plotting functions
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* [mpmath](https://mpmath.org/), for *beta_ratio* and *beta_oddsratio*
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* [PyCryptodome](https://www.pycryptodome.org/), for *pickle_write_encrypted* and *pickle_read_encrypted*
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@ -72,7 +72,7 @@ Relevant statistical functions are all directly available from the top-level *yl
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* Survival analysis:
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* *kaplanmeier*: Kaplan–Meier plot
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* *logrank*: Log-rank test
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* *turnbull*: Turnbull estimator plot for interval-censored data
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* *turnbull*: Turnbull estimator plot, including pointwise confidence intervals, for interval-censored data
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* Input/output:
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* *pickle_write_compressed*, *pickle_read_compressed*: Pickle a pandas DataFrame and compress using LZMA
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* *pickle_write_encrypted*, *pickle_read_encrypted*: Pickle a pandas DataFrame, compress using LZMA, and encrypt
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138
yli/survival.py
138
yli/survival.py
@ -15,9 +15,14 @@
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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import numpy as np
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import pandas as pd
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from scipy import stats
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import statsmodels.api as sm
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import io
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import json
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import subprocess
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from .config import config
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from .sig_tests import ChiSquaredResult
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from .utils import Estimate, check_nan
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@ -130,25 +135,27 @@ def calc_survfunc_kaplanmeier(time, status, ci, transform_x=None, transform_y=No
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return xpoints, ypoints, None, None
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def turnbull(df, time_left, time_right, by=None, *, step_loc=0.5, transform_x=None, transform_y=None, nan_policy='warn'):
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def turnbull(df, time_left, time_right, by=None, *, ci=True, step_loc=0.5, transform_x=None, transform_y=None, nan_policy='warn', fig=None, ax=None):
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"""
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Generate a Turnbull estimator plot, which extends the Kaplan–Meier estimator to interval-censored observations
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The intervals are assumed to be half-open intervals, (*left*, *right*]. *right* == *np.inf* implies the event was right-censored. Unlike :func:`yli.kaplanmeier`, times must be given as numeric dtypes and not as pandas timedelta.
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The intervals are assumed to be half-open intervals, (*left*, *right*]. *right* == *np.inf* implies the event was right-censored.
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By default, the survival function is drawn as a step function at the midpoint of each Turnbull interval.
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Uses the Python *lifelines* and *matplotlib* libraries.
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Uses the hpstat *turnbull* command.
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:param df: Data to generate plot for
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:type df: DataFrame
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:param time_left: Column in *df* for the time to event, left interval endpoint (numeric)
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:param time_left: Column in *df* for the time to event, left interval endpoint (numeric or timedelta)
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:type time_left: str
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:param time_right: Column in *df* for the time to event, right interval endpoint (numeric)
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:param time_right: Column in *df* for the time to event, right interval endpoint (numeric or timedelta)
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:type time_right: str
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:param by: Column in *df* to stratify by (categorical)
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:type by: str
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:param step_loc: Proportion along the length of each Turnbull interval to step down the survival function, e.g. 0 for left bound, 1 for right bound, 0.5 for interval midpoint (numeric)
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:param ci: Whether to plot confidence intervals around the survival function
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:type ci: bool
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:param step_loc: Proportion along the length of each Turnbull interval to step down the survival function, e.g. 0 for left bound, 1 for right bound, 0.5 for interval midpoint
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:type step_loc: float
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:param transform_x: Function to transform x axis by
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:type transform_x: callable
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@ -168,7 +175,11 @@ def turnbull(df, time_left, time_right, by=None, *, step_loc=0.5, transform_x=No
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else:
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df = check_nan(df[[time_left, time_right]], nan_policy)
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fig, ax = plt.subplots()
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# Covert timedelta to numeric
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df, time_units = survtime_to_numeric(df, time_left, time_right)
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if ax is None:
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fig, ax = plt.subplots()
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if by is not None:
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# Group by independent variable
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@ -176,13 +187,16 @@ def turnbull(df, time_left, time_right, by=None, *, step_loc=0.5, transform_x=No
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for group in groups.groups:
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subset = groups.get_group(group)
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handle = plot_survfunc_turnbull(ax, subset[time_left], subset[time_right], step_loc, transform_x, transform_y)
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handle = plot_survfunc_turnbull(ax, subset[time_left], subset[time_right], ci, step_loc, transform_x, transform_y)
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handle.set_label('{} = {}'.format(by, group))
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else:
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# No grouping
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plot_survfunc_turnbull(ax, df[time_left], df[time_right], step_loc, transform_x, transform_y)
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plot_survfunc_turnbull(ax, df[time_left], df[time_right], ci, step_loc, transform_x, transform_y)
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ax.set_xlabel('Analysis time')
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if time_units:
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ax.set_xlabel('{} + {} ({})'.format(time_left, time_right, time_units))
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else:
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ax.set_xlabel('{} + {}'.format(time_left, time_right))
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ax.set_ylabel('Survival probability')
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ax.set_xlim(left=0)
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ax.set_ylim(0, 1)
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@ -192,29 +206,39 @@ def turnbull(df, time_left, time_right, by=None, *, step_loc=0.5, transform_x=No
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return fig, ax
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def plot_survfunc_turnbull(ax, time_left, time_right, step_loc=0.5, transform_x=None, transform_y=None):
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xpoints, ypoints = calc_survfunc_turnbull(time_left, time_right, step_loc, transform_x, transform_y)
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def plot_survfunc_turnbull(ax, time_left, time_right, ci, step_loc=0.5, transform_x=None, transform_y=None):
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xpoints, ypoints, ypoints0, ypoints1 = calc_survfunc_turnbull(time_left, time_right, ci, step_loc, transform_x, transform_y)
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handle = ax.plot(xpoints, ypoints)[0]
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if ci:
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ax.fill_between(xpoints, ypoints0, ypoints1, alpha=0.3, label='_')
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return handle
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def calc_survfunc_turnbull(time_left, time_right, step_loc=0.5, transform_x=None, transform_y=None):
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from lifelines.fitters.npmle import npmle
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EPSILON = 1e-10
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# TODO: Support left == right => failure was exactly observed
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followup_left = time_left + EPSILON # Add epsilon to make interval half-open
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followup_right = time_right
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def calc_survfunc_turnbull(time_left, time_right, ci, step_loc=0.5, transform_x=None, transform_y=None):
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# Estimate the survival function
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#sf = lifelines.KaplanMeierFitter().fit_interval_censoring(followup_left, followup_right)
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# Call lifelines.fitters.npmle.npmle directly so we can compute midpoints, etc.
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sf_probs, turnbull_intervals = npmle(followup_left, followup_right)
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# Prepare arguments
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# TODO: Pass through other arguments
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hpstat_args = [config.hpstat_path, 'turnbull', '-', '--output', 'json']
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xpoints = [i.left*(1-step_loc) + i.right*step_loc for i in turnbull_intervals]
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ypoints = 1 - np.cumsum(sf_probs)
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# Export data to CSV
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csv_buf = io.StringIO()
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pd.DataFrame({'LeftTime': time_left, 'RightTime': time_right}).to_csv(csv_buf, index=False)
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csv_str = csv_buf.getvalue()
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# Run hpstat binary
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proc = subprocess.run(hpstat_args, input=csv_str, stdout=subprocess.PIPE, stderr=None, encoding='utf-8', check=True)
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raw_result = json.loads(proc.stdout)
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survival_prob = np.array(raw_result['survival_prob'])
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from IPython.display import clear_output
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clear_output(wait=True)
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xpoints = [i[0]*(1-step_loc) + i[1]*step_loc for i in raw_result['failure_intervals'] if i[1]]
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ypoints = survival_prob
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# Draw straight lines
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# np.concatenate(...) to force starting drawing from time 0, survival 100%
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@ -226,9 +250,27 @@ def calc_survfunc_turnbull(time_left, time_right, step_loc=0.5, transform_x=None
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if transform_y:
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ypoints = transform_y(ypoints)
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return xpoints, ypoints
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if ci:
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zstar = -stats.norm.ppf(config.alpha/2)
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survival_prob_se = np.array(raw_result['survival_prob_se'])
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# Get confidence intervals
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ci0 = survival_prob - zstar * survival_prob_se
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ci1 = survival_prob + zstar * survival_prob_se
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# Plot confidence intervals
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ypoints0 = np.concatenate([[1], ci0]).repeat(2)[:-1]
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ypoints1 = np.concatenate([[1], ci1]).repeat(2)[:-1]
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if transform_y:
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ypoints0 = transform_y(ypoints0)
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ypoints1 = transform_y(ypoints1)
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return xpoints, ypoints, ypoints0, ypoints1
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return xpoints, ypoints, None, None
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def survtime_to_numeric(df, time):
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def survtime_to_numeric(df, time, time2=None):
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"""
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Convert pandas timedelta dtype to float64, auto-detecting the best time unit to display
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@ -236,6 +278,8 @@ def survtime_to_numeric(df, time):
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:type df: DataFrame
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:param time: Column to check for pandas timedelta dtype
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:type df: DataFrame
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:param time: Second column, if any, to check for pandas timedelta dtype
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:type df: DataFrame
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:return: (*df*, *time_units*)
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@ -243,28 +287,42 @@ def survtime_to_numeric(df, time):
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* **time_units** (*str*) – Human-readable description of the time unit, or *None* if not converted
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"""
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max_time = None
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if df[time].dtype == '<m8[ns]':
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df[time] = df[time].dt.total_seconds()
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max_time = df[time].max()
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if df[time2].dtype == '<m8[ns]':
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df[time2] = df[time2].dt.total_seconds()
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max_time = max(max_time or 0, df[time2].max())
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if max_time is not None:
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# Auto-detect best time units
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if df[time].max() > 365.24*24*60*60:
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df[time] = df[time] / (365.24*24*60*60)
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if max_time > 365.24*24*60*60:
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time_divider = 365.24*24*60*60
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time_units = 'years'
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elif df[time].max() > 7*24*60*60 / 12:
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df[time] = df[time] / (7*24*60*60)
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elif max_time > 7*24*60*60 / 12:
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time_divider = 7*24*60*60
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time_units = 'weeks'
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elif df[time].max() > 24*60*60:
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df[time] = df[time] / (24*60*60)
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elif max_time > 24*60*60:
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time_divider = 24*60*60
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time_units = 'days'
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elif df[time].max() > 60*60:
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df[time] = df[time] / (60*60)
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elif max_time > 60*60:
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time_divider = 60*60
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time_units = 'hours'
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elif df[time].max() > 60:
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df[time] = df[time] / 60
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elif max_time > 60:
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time_divider = 60
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time_units = 'minutes'
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else:
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time_divider = 1
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time_units = 'seconds'
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df[time] /= time_divider
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if time2:
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df[time2] /= time_divider
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return df, time_units
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else:
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return df, None
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