diff --git a/docs/func_modules/breaks.py b/docs/func_modules/breaks.py new file mode 100644 index 0000000000000000000000000000000000000000..115f1252ec53ea3cf96b805291a947171371afb6 --- /dev/null +++ b/docs/func_modules/breaks.py @@ -0,0 +1,73 @@ +""" +Detecting breakish changes in timeseries value courses. + +This module provides functions to detect and flag breakish changes in the data value course, like gaps +(:py:func:`flagMissing`), jumps/drops (:py:func:`flagJumps`) or isolated values (:py:func:`flagIsolated`). +""" +def flagMissing(field, nodata): + """ + The function flags all values indicating missing data. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. + nodata : any, default np.nan + A value that defines missing data. + """ + pass + + +def flagIsolated(field, gap_window, group_window): + """ + The function flags arbitrary large groups of values, if they are surrounded by sufficiently + large data gaps. + + A gap is a timespan containing either no data or invalid (usually `nan`) and flagged data only. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. + gap_window : str + The minimum size of the gap before and after a group of valid values, making this group considered an + isolated group. See condition (2) and (3) + group_window : str + The maximum temporal extension allowed for a group that is isolated by gaps of size 'gap_window', + to be actually flagged as isolated group. See condition (1). + + Notes + ----- + A series of values :math:`x_k,x_{k+1},...,x_{k+n}`, with associated timestamps :math:`t_k,t_{k+1},...,t_{k+n}`, + is considered to be isolated, if: + + 1. :math:`t_{k+1} - t_n <` `group_window` + 2. None of the :math:`x_j` with :math:`0 < t_k - t_j <` `gap_window`, is valid or unflagged (preceeding gap). + 3. None of the :math:`x_j` with :math:`0 < t_j - t_(k+n) <` `gap_window`, is valid or unflagged (succeding gap). + + See Also + -------- + :py:func:`flagMissing` + """ + pass + + +def flagJumps(field, thresh, winsz, min_periods): + """ + Flag datapoints, where the mean of the values significantly changes (where the value course "jumps"). + + Parameters + ---------- + field : str + The reference variable, the deviation from wich determines the flagging. + thresh : float + The threshold, the mean of the values have to change by, to trigger flagging. + winsz : str + The temporal extension, of the rolling windows, the mean values that are to be compared, + are obtained from. + min_periods : int, default 1 + Minimum number of periods that have to be present in a window of size `winsz`, so that + the mean value obtained from that window is regarded valid. + """ + pass + diff --git a/docs/func_modules/changepoints.py b/docs/func_modules/changepoints.py new file mode 100644 index 0000000000000000000000000000000000000000..e2513f0586b2730d4a705d9e9f6aca28b0d69d56 --- /dev/null +++ b/docs/func_modules/changepoints.py @@ -0,0 +1,107 @@ +""" + +""" +def flagChangePoints(field, stat_func, thresh_func, bwd_window, min_periods_bwd, fwd_window, min_periods_fwd, closed, reduce_window, reduce_func): + """ + Flag datapoints, where the parametrization of the process, the data is assumed to generate by, significantly + changes. + + The change points detection is based on a sliding window search. + + Parameters + ---------- + field : str + The reference variable, the deviation from wich determines the flagging. + stat_func : Callable[numpy.array, numpy.array] + A function that assigns a value to every twin window. Left window content will be passed to first variable, + right window content will be passed to the second. + thresh_func : Callable[numpy.array, numpy.array] + A function that determines the value level, exceeding wich qualifies a timestamps stat func value as denoting a + changepoint. + bwd_window : str + The left (backwards facing) windows temporal extension (freq-string). + min_periods_bwd : {str, int} + Minimum number of periods that have to be present in a backwards facing window, for a changepoint test to be + performed. + fwd_window : {None, str}, default None + The right (forward facing) windows temporal extension (freq-string). + min_periods_fwd : {None, str, int}, default None + Minimum number of periods that have to be present in a forward facing window, for a changepoint test to be + performed. + closed : {'right', 'left', 'both', 'neither'}, default 'both' + Determines the closure of the sliding windows. + reduce_window : {None, str}, default None + The sliding window search method is not an exact CP search method and usually there wont be + detected a single changepoint, but a "region" of change around a changepoint. + If `reduce_window` is given, for every window of size `reduce_window`, there + will be selected the value with index `reduce_func(x, y)` and the others will be dropped. + If `reduce_window` is None, the reduction window size equals the + twin window size, the changepoints have been detected with. + reduce_func : Callable[[numpy.array, numpy.array], np.array], default lambda x, y: x.argmax() + A function that must return an index value upon input of two arrays x and y. + First input parameter will hold the result from the stat_func evaluation for every + reduction window. Second input parameter holds the result from the thresh_func evaluation. + The default reduction function just selects the value that maximizes the stat_func. + + + Returns + ------- + """ + pass + + +def assignChangePointCluster(field, stat_func, thresh_func, bwd_window, min_periods_bwd, fwd_window, min_periods_fwd, closed, reduce_window, reduce_func, flag_changepoints, model_by_resids, assign_cluster): + """ + Assigns label to the data, aiming to reflect continous regimes of the processes the data is assumed to be + generated by. + The regime change points detection is based on a sliding window search. + + Note, that the cluster labels will be stored to the `field` field of the input data, so that the data that is + clustered gets overridden. + + Parameters + ---------- + field : str + The reference variable, the deviation from wich determines the flagging. + stat_func : Callable[[numpy.array, numpy.array], float] + A function that assigns a value to every twin window. Left window content will be passed to first variable, + right window content will be passed to the second. + thresh_func : Callable[numpy.array, numpy.array], float] + A function that determines the value level, exceeding wich qualifies a timestamps stat func value as denoting a + changepoint. + bwd_window : str + The left (backwards facing) windows temporal extension (freq-string). + min_periods_bwd : {str, int} + Minimum number of periods that have to be present in a backwards facing window, for a changepoint test to be + performed. + fwd_window : {None, str}, default None + The right (forward facing) windows temporal extension (freq-string). + min_periods_fwd : {None, str, int}, default None + Minimum number of periods that have to be present in a forward facing window, for a changepoint test to be + performed. + closed : {'right', 'left', 'both', 'neither'}, default 'both' + Determines the closure of the sliding windows. + reduce_window : {None, str}, default None + The sliding window search method is not an exact CP search method and usually there wont be + detected a single changepoint, but a "region" of change around a changepoint. + If `reduce_window` is given, for every window of size `reduce_window`, there + will be selected the value with index `reduce_func(x, y)` and the others will be dropped. + If `reduce_window` is None, the reduction window size equals the + twin window size, the changepoints have been detected with. + reduce_func : Callable[[numpy.array, numpy.array], numpy.array], default lambda x, y: x.argmax() + A function that must return an index value upon input of two arrays x and y. + First input parameter will hold the result from the stat_func evaluation for every + reduction window. Second input parameter holds the result from the thresh_func evaluation. + The default reduction function just selects the value that maximizes the stat_func. + flag_changepoints : bool, default False + If true, the points, where there is a change in data modelling regime detected get flagged bad. + model_by_resids : bool, default False + If True, the data is replaced by the stat_funcs results instead of regime labels. + assign_cluster : bool, default True + Is set to False, if called by function that oly wants to calculate flags. + + Returns + ------- + """ + pass + diff --git a/docs/func_modules/constants.py b/docs/func_modules/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..dcb4f796a28ab184346f12a1cc9c9592bc7e690d --- /dev/null +++ b/docs/func_modules/constants.py @@ -0,0 +1,53 @@ +""" + +""" +def flagConstants(field, thresh, window): + """ + This functions flags plateaus/series of constant values of length `window` if + their maximum total change is smaller than thresh. + + Function flags plateaus/series of constant values. Any interval of values y(t),..y(t+n) is flagged, if: + + (1) n > `window` + (2) |(y(t + i) - (t + j)| < `thresh`, for all i,j in [0, 1, ..., n] + + Flag values are (semi-)constant. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. + thresh : float + Upper bound for the maximum total change of an interval to be flagged constant. + window : str + Lower bound for the size of an interval to be flagged constant. + """ + pass + + +def flagByVariance(field, window, thresh, max_missing, max_consec_missing): + """ + Function flags plateaus/series of constant values. Any interval of values y(t),..y(t+n) is flagged, if: + + (1) n > `window` + (2) variance(y(t),...,y(t+n) < `thresh` + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. + window : str + Only intervals of minimum size "window" have the chance to get flagged as constant intervals + thresh : float + The upper bound, the variance of an interval must not exceed, if the interval wants to be flagged a plateau. + max_missing : {None, int}, default None + Maximum number of nan values tolerated in an interval, for retrieving a valid + variance from it. (Intervals with a number of nans exceeding "max_missing" + have no chance to get flagged a plateau!) + max_consec_missing : {None, int}, default None + Maximum number of consecutive nan values allowed in an interval to retrieve a + valid variance from it. (Intervals with a number of nans exceeding + "max_consec_missing" have no chance to get flagged a plateau!) + """ + pass + diff --git a/docs/func_modules/curvefit.py b/docs/func_modules/curvefit.py new file mode 100644 index 0000000000000000000000000000000000000000..4b67b225f1dfb7f017c9624d558f50389acf5f0e --- /dev/null +++ b/docs/func_modules/curvefit.py @@ -0,0 +1,67 @@ +""" + +""" +def fitPolynomial(field, winsz, polydeg, numba, eval_flags, min_periods, return_residues): + """ + Function fits a polynomial model to the data and returns the fitted data curve. + + The fit is calculated by fitting a polynomial of degree `polydeg` to a data slice + of size `winsz`, that has x at its center. + + Note, that the resulting fit is stored to the `field` field of the input data, so that the original data, the + polynomial is fitted to, gets overridden. + + Note, that, if data[field] is not alligned to an equidistant frequency grid, the window size passed, + has to be an offset string. Also numba boost options don`t apply for irregularly sampled + timeseries. + + Note, that calculating the residues tends to be quite costy, because a function fitting is perfomed for every + sample. To improve performance, consider the following possibillities: + + In case your data is sampled at an equidistant frequency grid: + + (1) If you know your data to have no significant number of missing values, or if you do not want to + calculate residues for windows containing missing values any way, performance can be increased by setting + min_periods=winsz. + + (2) If your data consists of more then around 200000 samples, setting numba=True, will boost the + calculations up to a factor of 5 (for samplesize > 300000) - however for lower sample sizes, + numba will slow down the calculations, also, up to a factor of 5, for sample_size < 50000. + By default (numba='auto'), numba is set to true, if the data sample size exceeds 200000. + + in case your data is not sampled at an equidistant frequency grid: + + (1) Harmonization/resampling of your data will have a noticable impact on polyfittings performance - since + numba_boost doesnt apply for irregularly sampled data in the current implementation. + + Note, that in the current implementation, the initial and final winsz/2 values do not get fitted. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-modelled. + winsz : {str, int} + The size of the window you want to use for fitting. If an integer is passed, the size + refers to the number of periods for every fitting window. If an offset string is passed, + the size refers to the total temporal extension. The window will be centered around the vaule-to-be-fitted. + For regularly sampled timeseries the period number will be casted down to an odd number if + even. + polydeg : int + The degree of the polynomial used for fitting + numba : {True, False, "auto"}, default "auto" + Wheather or not to apply numbas just-in-time compilation onto the poly fit function. This will noticably + increase the speed of calculation, if the sample size is sufficiently high. + If "auto" is selected, numba compatible fit functions get applied for data consisiting of > 200000 samples. + eval_flags : bool, default True + Wheather or not to assign new flags to the calculated residuals. If True, a residual gets assigned the worst + flag present in the interval, the data for its calculation was obtained from. + min_periods : {int, None}, default 0 + The minimum number of periods, that has to be available in every values fitting surrounding for the polynomial + fit to be performed. If there are not enough values, np.nan gets assigned. Default (0) results in fitting + regardless of the number of values present (results in overfitting for too sparse intervals). To automatically + set the minimum number of periods to the number of values in an offset defined window size, pass np.nan. + return_residues : bool, default False + Internal parameter. Makes the method return the residues instead of the fit. + """ + pass + diff --git a/docs/func_modules/drift.py b/docs/func_modules/drift.py new file mode 100644 index 0000000000000000000000000000000000000000..803bdec2579b697dbf20079aa4ba2c22611d8180 --- /dev/null +++ b/docs/func_modules/drift.py @@ -0,0 +1,362 @@ +""" + +""" +def flagDriftFromNorm(field, fields, segment_freq, norm_spread, norm_frac, metric, linkage_method): + """ + The function flags value courses that significantly deviate from a group of normal value courses. + + "Normality" is determined in terms of a maximum spreading distance, that members of a normal group must not exceed. + In addition, only a group is considered "normal" if it contains more then `norm_frac` percent of the + variables in "fields". + + See the Notes section for a more detailed presentation of the algorithm + + Parameters + ---------- + field : str + A dummy parameter. + fields : str + List of fieldnames in data, determining which variables are to be included into the flagging process. + segment_freq : str + An offset string, determining the size of the seperate datachunks that the algorihm is to be piecewise + applied on. + norm_spread : float + A parameter limiting the maximum "spread" of the timeseries, allowed in the "normal" group. See Notes section + for more details. + norm_frac : float, default 0.5 + Has to be in [0,1]. Determines the minimum percentage of variables, the "normal" group has to comprise to be the + normal group actually. The higher that value, the more stable the algorithm will be with respect to false + positives. Also, nobody knows what happens, if this value is below 0.5. + metric : Callable[[numpy.array, numpy.array], float] + A distance function. It should be a function of 2 1-dimensional arrays and return a float scalar value. + This value is interpreted as the distance of the two input arrays. The default is the averaged manhatten metric. + See the Notes section to get an idea of why this could be a good choice. + linkage_method : {"single", "complete", "average", "weighted", "centroid", "median", "ward"}, default "single" + The linkage method used for hierarchical (agglomerative) clustering of the timeseries. + See the Notes section for more details. + The keyword gets passed on to scipy.hierarchy.linkage. See its documentation to learn more about the different + keywords (References [1]). + See wikipedia for an introduction to hierarchical clustering (References [2]). + kwargs + + Notes + ----- + following steps are performed for every data "segment" of length `segment_freq` in order to find the + "abnormal" data: + + 1. Calculate the distances :math:`d(x_i,x_j)` for all :math:`x_i` in parameter `fields`. (with :math:`d` + denoting the distance function + passed to the parameter `metric`. + 2. Calculate a dendogram with a hierarchical linkage algorithm, specified by the parameter `linkage_method`. + 3. Flatten the dendogram at the level, the agglomeration costs exceed the value given by the parameter `norm_spread` + 4. check if there is a cluster containing more than `norm_frac` percentage of the variables in fields. + + 1. if yes: flag all the variables that are not in that cluster (inside the segment) + 2. if no: flag nothing + + The main parameter giving control over the algorithms behavior is the `norm_spread` parameter, that determines + the maximum spread of a normal group by limiting the costs, a cluster agglomeration must not exceed in every + linkage step. + For singleton clusters, that costs just equal half the distance, the timeseries in the clusters, have to + each other. So, no timeseries can be clustered together, that are more then + 2*`norm_spread` distanted from each other. + When timeseries get clustered together, this new clusters distance to all the other timeseries/clusters is + calculated according to the linkage method specified by `linkage_method`. By default, it is the minimum distance, + the members of the clusters have to each other. + Having that in mind, it is advisable to choose a distance function, that can be well interpreted in the units + dimension of the measurement and where the interpretation is invariant over the length of the timeseries. + That is, why, the "averaged manhatten metric" is set as the metric default, since it corresponds to the + averaged value distance, two timeseries have (as opposed by euclidean, for example). + + References + ---------- + Documentation of the underlying hierarchical clustering algorithm: + [1] https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html + Introduction to Hierarchical clustering: + [2] https://en.wikipedia.org/wiki/Hierarchical_clustering + """ + pass + + +def flagDriftFromReference(field, fields, segment_freq, thresh, metric): + """ + The function flags value courses that deviate from a reference course by a margin exceeding a certain threshold. + + The deviation is measured by the distance function passed to parameter metric. + + Parameters + ---------- + field : str + The reference variable, the deviation from wich determines the flagging. + fields : str + List of fieldnames in data, determining wich variables are to be included into the flagging process. + segment_freq : str + An offset string, determining the size of the seperate datachunks that the algorihm is to be piecewise + applied on. + thresh : float + The threshod by wich normal variables can deviate from the reference variable at max. + metric : Callable[(numpyp.array, numpy-array), float] + A distance function. It should be a function of 2 1-dimensional arrays and return a float scalar value. + This value is interpreted as the distance of the two input arrays. The default is the averaged manhatten metric. + See the Notes section to get an idea of why this could be a good choice. + kwargs + + Notes + ----- + it is advisable to choose a distance function, that can be well interpreted in the units + dimension of the measurement and where the interpretation is invariant over the length of the timeseries. + That is, why, the "averaged manhatten metric" is set as the metric default, since it corresponds to the + averaged value distance, two timeseries have (as opposed by euclidean, for example). + """ + pass + + +def flagDriftFromScaledNorm(field, fields_scale1, fields_scale2, segment_freq, norm_spread, norm_frac, metric, linkage_method): + """ + The function linearly rescales one set of variables to another set of variables with a different scale and then + flags value courses that significantly deviate from a group of normal value courses. + + The two sets of variables can be linearly scaled one to another and hence the scaling transformation is performed + via linear regression: A linear regression is performed on each pair of variables giving a slope and an intercept. + The transformation is then calculated a the median of all the calculated slopes and intercepts. + + Once the transformation is performed, the function flags those values, that deviate from a group of normal values. + "Normality" is determined in terms of a maximum spreading distance, that members of a normal group must not exceed. + In addition, only a group is considered "normal" if it contains more then `norm_frac` percent of the + variables in "fields". + + Parameters + ---------- + field : str + A dummy parameter. + fields_scale1 : str + List of fieldnames in data to be included into the flagging process which are scaled according to scaling + scheme 1. + fields_scale2 : str + List of fieldnames in data to be included into the flagging process which are scaled according to scaling + scheme 2. + segment_freq : str + An offset string, determining the size of the seperate datachunks that the algorihm is to be piecewise + applied on. + norm_spread : float + A parameter limiting the maximum "spread" of the timeseries, allowed in the "normal" group. See Notes section + for more details. + norm_frac : float, default 0.5 + Has to be in [0,1]. Determines the minimum percentage of variables, the "normal" group has to comprise to be the + normal group actually. The higher that value, the more stable the algorithm will be with respect to false + positives. Also, nobody knows what happens, if this value is below 0.5. + metric : Callable[(numpyp.array, numpy-array), float] + A distance function. It should be a function of 2 1-dimensional arrays and return a float scalar value. + This value is interpreted as the distance of the two input arrays. The default is the averaged manhatten metric. + See the Notes section to get an idea of why this could be a good choice. + linkage_method : {"single", "complete", "average", "weighted", "centroid", "median", "ward"}, default "single" + The linkage method used for hierarchical (agglomerative) clustering of the timeseries. + See the Notes section for more details. + The keyword gets passed on to scipy.hierarchy.linkage. See its documentation to learn more about the different + keywords (References [1]). + See wikipedia for an introduction to hierarchical clustering (References [2]). + kwargs + + References + ---------- + Documentation of the underlying hierarchical clustering algorithm: + [1] https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html + Introduction to Hierarchical clustering: + [2] https://en.wikipedia.org/wiki/Hierarchical_clustering + """ + pass + + +def correctExponentialDrift(field, maint_data_field, cal_mean, flag_maint_period): + """ + The function fits an exponential model to chunks of data[field]. + It is assumed, that between maintenance events, there is a drift effect shifting the meassurements in a way, that + can be described by the model M: + + M(t, a, b, c) = a + b(exp(c*t)) + + Where as the values y_0 and y_1, describing the mean value directly after the last maintenance event (y_0) and + directly before the next maintenance event (y_1), impose the following additional conditions on the drift model:. + + M(0, a, b, c) = y0 + M(1, a, b, c) = y1 + + Solving the equation, one obtains the one-parameter Model: + + M_drift(t, c) = y0 + [(y1 - y0)/(exp(c) - )] * (exp(c*t) - 1) + + For every datachunk in between maintenance events. + + After having found the optimal parameter c*, the correction is performed by bending the fitted curve M_drift(t, c*), + in a way that it matches y2 at t=1 (,with y2 being the mean value observed directly after the end of the next + maintenance event). + This bended curve is given by: + + M_shift(t, c*) = M(t, y0, [(y1 - y0)/(exp(c*) - )], c*) + + And the new values at t are computed via: + + new_vals(t) = old_vals(t) + M_shift(t) - M_drift(t) + + Parameters + ---------- + field : str + The fieldname of the data column, you want to correct. + maint_data_field : str + The fieldname of the datacolumn holding the maintenance information. + The maint data is to expected to have following form: + The series' timestamp itself represents the beginning of a + maintenance event, wheras the values represent the endings of the maintenance intervals. + cal_mean : int, default 5 + The number of values the mean is computed over, for obtaining the value level directly after and + directly before maintenance event. This values are needed for shift calibration. (see above description) + flag_maint_period : bool, default False + Wheather or not to flag BAD the values directly obtained while maintenance. + """ + pass + + +def correctRegimeAnomaly(field, cluster_field, model, regime_transmission, x_date): + """ + Function fits the passed model to the different regimes in data[field] and tries to correct + those values, that have assigned a negative label by data[cluster_field]. + + Currently, the only correction mode supported is the "parameter propagation." + + This means, any regime :math:`z`, labeled negatively and being modeled by the parameters p, gets corrected via: + + :math:`z_{correct} = z + (m(p^*) - m(p))`, + + where :math:`p^*` denotes the parameter set belonging to the fit of the nearest not-negatively labeled cluster. + + Parameters + ---------- + field : str + The fieldname of the data column, you want to correct. + cluster_field : str + A string denoting the field in data, holding the cluster label for the data you want to correct. + model : Callable + The model function to be fitted to the regimes. + It must be a function of the form :math:`f(x, *p)`, where :math:`x` is the ``numpy.array`` holding the + independent variables and :math:`p` are the model parameters that are to be obtained by fitting. + Depending on the `x_date` parameter, independent variable x will either be the timestamps + of every regime transformed to seconds from epoch, or it will be just seconds, counting the regimes length. + regime_transmission : {None, str}, default None: + If an offset string is passed, a data chunk of length `regime_transimission` right at the + start and right at the end is ignored when fitting the model. This is to account for the + unreliability of data near the changepoints of regimes. + x_date : bool, default False + If True, use "seconds from epoch" as x input to the model func, instead of "seconds from regime start". + + """ + pass + + +def correctOffset(): + """ + Parameters + ---------- + data : dios.DictOfSeries + A dictionary of pandas.Series, holding all the data. + field : str + The fieldname of the data column, you want to correct. + flagger : saqc.flagger + A flagger object, holding flags and additional Informations related to `data`. + max_mean_jump : float + when searching for changepoints in mean - this is the threshold a mean difference in the + sliding window search must exceed to trigger changepoint detection. + normal_spread : float + threshold denoting the maximum, regimes are allowed to abolutely differ in their means + to form the "normal group" of values. + search_winsz : str + Size of the adjacent windows that are used to search for the mean changepoints. + min_periods : int + Minimum number of periods a search window has to contain, for the result of the changepoint + detection to be considered valid. + regime_transmission : {None, str}, default None: + If an offset string is passed, a data chunk of length `regime_transimission` right from the + start and right before the end of any regime is ignored when calculating a regimes mean for data correcture. + This is to account for the unrelyability of data near the changepoints of regimes. + + """ + pass + + +def flagRegimeAnomaly(field, cluster_field, norm_spread, linkage_method, metric, norm_frac): + """ + A function to flag values belonging to an anomalous regime regarding modelling regimes of field. + + "Normality" is determined in terms of a maximum spreading distance, regimes must not exceed in respect + to a certain metric and linkage method. + + In addition, only a range of regimes is considered "normal", if it models more then `norm_frac` percentage of + the valid samples in "field". + + Note, that you must detect the regime changepoints prior to calling this function. + + Note, that it is possible to perform hypothesis tests for regime equality by passing the metric + a function for p-value calculation and selecting linkage method "complete". + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. + cluster_field : str + The name of the column in data, holding the cluster labels for the samples in field. (has to be indexed + equal to field) + norm_spread : float + A threshold denoting the valuelevel, up to wich clusters a agglomerated. + linkage_method : {"single", "complete", "average", "weighted", "centroid", "median", "ward"}, default "single" + The linkage method used for hierarchical (agglomerative) clustering of the variables. + metric : Callable[[numpy.array, numpy.array], float], default lambda x, y: np.abs(np.nanmean(x) - np.nanmean(y)) + A metric function for calculating the dissimilarity between 2 regimes. Defaults to just the difference in mean. + norm_frac : float + Has to be in [0,1]. Determines the minimum percentage of samples, + the "normal" group has to comprise to be the normal group actually. + """ + pass + + +def assignRegimeAnomaly(field, cluster_field, norm_spread, linkage_method, metric, norm_frac, set_cluster, set_flags): + """ + A function to detect values belonging to an anomalous regime regarding modelling regimes of field. + + The function changes the value of the regime cluster labels to be negative. + + "Normality" is determined in terms of a maximum spreading distance, regimes must not exceed in respect + to a certain metric and linkage method. + + In addition, only a range of regimes is considered "normal", if it models more then `norm_frac` percentage of + the valid samples in "field". + + Note, that you must detect the regime changepoints prior to calling this function. (They are expected to be stored + parameter `cluster_field`.) + + Note, that it is possible to perform hypothesis tests for regime equality by passing the metric + a function for p-value calculation and selecting linkage method "complete". + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. + cluster_field : str + The name of the column in data, holding the cluster labels for the samples in field. (has to be indexed + equal to field) + norm_spread : float + A threshold denoting the valuelevel, up to wich clusters a agglomerated. + linkage_method : {"single", "complete", "average", "weighted", "centroid", "median", "ward"}, default "single" + The linkage method used for hierarchical (agglomerative) clustering of the variables. + metric : Callable[[numpy.array, numpy.array], float], default lambda x, y: np.abs(np.nanmean(x) - np.nanmean(y)) + A metric function for calculating the dissimilarity between 2 regimes. Defaults to just the difference in mean. + norm_frac : float + Has to be in [0,1]. Determines the minimum percentage of samples, + the "normal" group has to comprise to be the normal group actually. + set_cluster : bool, default False + If True, all data, considered "anormal", gets assigned a negative clusterlabel. This option + is present for further use (correction) of the anomaly information. + set_flags : bool, default True + Wheather or not to flag abnormal values (do not flag them, if you want to correct them + afterwards, becasue flagged values usually are not visible in further tests.). + """ + pass + diff --git a/docs/func_modules/flagtools.py b/docs/func_modules/flagtools.py new file mode 100644 index 0000000000000000000000000000000000000000..9809d7a1c0b80679e12d29d5039213f1d739f5da --- /dev/null +++ b/docs/func_modules/flagtools.py @@ -0,0 +1,135 @@ +""" + +""" +def flagDummy(field): + """ + Function does nothing but returning data and flagger. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. + """ + pass + + +def flagForceFail(field): + """ + Function raises a runtime error. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. + """ + pass + + +def flagUnflagged(field, kwargs): + """ + Function sets the flagger.GOOD flag to all values flagged better then flagger.GOOD. + If there is an entry 'flag' in the kwargs dictionary passed, the + function sets the kwargs['flag'] flag to all values flagged better kwargs['flag'] + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. + kwargs : Dict + If kwargs contains 'flag' entry, kwargs['flag] is set, if no entry 'flag' is present, + 'flagger.UNFLAGGED' is set. + """ + pass + + +def flagGood(field): + """ + Function sets the flagger.GOOD flag to all values flagged better then flagger.GOOD. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. + """ + pass + + +def flagManual(field, mdata, mflag, method): + """ + Flag data by given, "manually generated" data. + + The data is flagged at locations where `mdata` is equal to a provided flag (`mflag`). + The format of mdata can be an indexed object, like pd.Series, pd.Dataframe or dios.DictOfSeries, + but also can be a plain list- or array-like. + How indexed mdata is aligned to data is specified via the `method` parameter. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. + mdata : {pd.Series, pd.Dataframe, DictOfSeries} + The "manually generated" data + mflag : scalar + The flag that indicates data points in `mdata`, of wich the projection in data should be flagged. + method : {'plain', 'ontime', 'left-open', 'right-open'}, default plain + Defines how mdata is projected on data. Except for the 'plain' method, the methods assume mdata to have an + index. + + * 'plain': mdata must have the same length as data and is projected one-to-one on data. + * 'ontime': works only with indexed mdata. mdata entries are matched with data entries that have the same index. + * 'right-open': mdata defines intervals, values are to be projected on. + The intervals are defined by any two consecutive timestamps t_1 and 1_2 in mdata. + the value at t_1 gets projected onto all data timestamps t with t_1 <= t < t_2. + * 'left-open': like 'right-open', but the projected interval now covers all t with t_1 < t <= t_2. + + Examples + -------- + An example for mdata + >>> mdata = pd.Series([1,0,1], index=pd.to_datetime(['2000-02', '2000-03', '2001-05'])) + >>> mdata + 2000-02-01 1 + 2000-03-01 0 + 2001-05-01 1 + dtype: int64 + + On *dayly* data, with the 'ontime' method, only the provided timestamnps are used. + Bear in mind that only exact timestamps apply, any offset will result in ignoring + the timestamp. + >>> _, fl = flagManual(data, field, flagger, mdata, mflag=1, method='ontime') + >>> fl.isFlagged(field) + 2000-01-31 False + 2000-02-01 True + 2000-02-02 False + 2000-02-03 False + .. .. + 2000-02-29 False + 2000-03-01 True + 2000-03-02 False + Freq: D, dtype: bool + + With the 'right-open' method, the mdata is forward fill: + >>> _, fl = flagManual(data, field, flagger, mdata, mflag=1, method='right-open') + >>> fl.isFlagged(field) + 2000-01-31 False + 2000-02-01 True + 2000-02-02 True + .. .. + 2000-02-29 True + 2000-03-01 False + 2000-03-02 False + Freq: D, dtype: bool + + With the 'left-open' method, backward filling is used: + >>> _, fl = flagManual(data, field, flagger, mdata, mflag=1, method='left-open') + >>> fl.isFlagged(field) + 2000-01-31 False + 2000-02-01 False + 2000-02-02 True + .. .. + 2000-02-29 True + 2000-03-01 True + 2000-03-02 False + Freq: D, dtype: bool + """ + pass + diff --git a/docs/func_modules/generic.py b/docs/func_modules/generic.py new file mode 100644 index 0000000000000000000000000000000000000000..693fde4fad7dd775ab5a0867151b1fc284fb264c --- /dev/null +++ b/docs/func_modules/generic.py @@ -0,0 +1,108 @@ +""" + +""" +def process(field, func, nodata): + """ + generate/process data with generically defined functions. + + The functions can depend on on any of the fields present in data. + + Formally, what the function does, is the following: + + 1. Let F be a Callable, depending on fields f_1, f_2,...f_K, (F = F(f_1, f_2,...f_K)) + Than, for every timestamp t_i that occurs in at least one of the timeseries data[f_j] (outer join), + The value v_i is computed via: + v_i = data([f_1][t_i], data[f_2][t_i], ..., data[f_K][t_i]), if all data[f_j][t_i] do exist + v_i = `nodata`, if at least one of the data[f_j][t_i] is missing. + 2. The result is stored to data[field] (gets generated if not present) + + Parameters + ---------- + field : str + The fieldname of the column, where you want the result from the generic expressions processing to be written to. + func : Callable + The data processing function with parameter names that will be + interpreted as data column entries. + See the examples section to learn more. + nodata : any, default np.nan + The value that indicates missing/invalid data + + Examples + -------- + Some examples on what to pass to the func parameter: + To compute the sum of the variables "temperature" and "uncertainty", you would pass the function: + + >>> lambda temperature, uncertainty: temperature + uncertainty + + You also can pass numpy and pandas functions: + + >>> lambda temperature, uncertainty: np.round(temperature) * np.sqrt(uncertainty) + """ + pass + + +def flag(field, func, nodata): + """ + a function to flag a data column by evaluation of a generic expression. + + The expression can depend on any of the fields present in data. + + Formally, what the function does, is the following: + + Let X be an expression, depending on fields f_1, f_2,...f_K, (X = X(f_1, f_2,...f_K)) + Than for every timestamp t_i in data[field]: + data[field][t_i] is flagged if X(data[f_1][t_i], data[f_2][t_i], ..., data[f_K][t_i]) is True. + + Note, that all value series included in the expression to evaluate must be labeled identically to field. + + Note, that the expression is passed in the form of a Callable and that this callables variable names are + interpreted as actual names in the data header. See the examples section to get an idea. + + Note, that all the numpy functions are available within the generic expressions. + + Parameters + ---------- + field : str + The fieldname of the column, where you want the result from the generic expressions evaluation to be projected + to. + func : Callable + The expression that is to be evaluated is passed in form of a callable, with parameter names that will be + interpreted as data column entries. The Callable must return an boolen array like. + See the examples section to learn more. + nodata : any, default np.nan + The value that indicates missing/invalid data + + Examples + -------- + Some examples on what to pass to the func parameter: + To flag the variable `field`, if the sum of the variables + "temperature" and "uncertainty" is below zero, you would pass the function: + + >>> lambda temperature, uncertainty: temperature + uncertainty < 0 + + There is the reserved name 'This', that always refers to `field`. So, to flag field if field is negative, you can + also pass: + + >>> lambda this: this < 0 + + If you want to make dependent the flagging from flags already present in the data, you can use the built-in + ``isflagged`` method. For example, to flag the 'temperature', if 'level' is flagged, you would use: + + >>> lambda level: isflagged(level) + + You can furthermore specify a flagging level, you want to compare the flags to. For example, for flagging + 'temperature', if 'level' is flagged at a level named 'doubtfull' or worse, use: + + >>> lambda level: isflagged(level, flag='doubtfull', comparator='<=') + + If you are unsure about the used flaggers flagging level names, you can use the reserved key words BAD, UNFLAGGED + and GOOD, to refer to the worst (BAD), best(GOOD) or unflagged (UNFLAGGED) flagging levels. For example. + + >>> lambda level: isflagged(level, flag=UNFLAGGED, comparator='==') + + Your expression also is allowed to include pandas and numpy functions + + >>> lambda level: np.sqrt(level) > 7 + """ + pass + diff --git a/docs/func_modules/interpolation.py b/docs/func_modules/interpolation.py new file mode 100644 index 0000000000000000000000000000000000000000..9009d87c8c244a5d619eac3804a81dcc2125fdaf --- /dev/null +++ b/docs/func_modules/interpolation.py @@ -0,0 +1,123 @@ +""" + +""" +def interpolateByRolling(field, winsz, func, center, min_periods, interpol_flag): + """ + Interpolates missing values (nan values present in the data) by assigning them the aggregation result of + a window surrounding them. + + Note, that in the current implementation, center=True can only be used with integer window sizes - furthermore + note, that integer window sizes can yield screwed aggregation results for not-harmonized or irregular data. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-interpolated. + winsz : int, str + The size of the window, the aggregation is computed from. Either counted in periods number (Integer passed), + or defined by a total temporal extension (offset String passed). + func : Callable + The function used for aggregation. + center : bool, default True + Wheather or not the window, the aggregation is computed of, is centered around the value to be interpolated. + min_periods : int + Minimum number of valid (not np.nan) values that have to be available in a window for its aggregation to be + computed. + interpol_flag : {'GOOD', 'BAD', 'UNFLAGGED', str}, default 'UNFLAGGED' + Flag that is to be inserted for the interpolated values. You can either pass one of the three major flag-classes + or specify directly a certain flag from the passed flagger. + """ + pass + + +def interpolateInvalid(field, method, inter_order, inter_limit, interpol_flag, downgrade_interpolation, not_interpol_flags): + """ + Function to interpolate nan values in the data. + + There are available all the interpolation methods from the pandas.interpolate method and they are applicable by + the very same key words, that you would pass to the ``pd.Series.interpolate``'s method parameter. + + Note, that the `inter_limit` keyword really restricts the interpolation to chunks, not containing more than + `inter_limit` successive nan entries. + + Note, that the function differs from ``proc_interpolateGrid``, in its behaviour to ONLY interpolate nan values that + were already present in the data passed. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-interpolated. + method : {"linear", "time", "nearest", "zero", "slinear", "quadratic", "cubic", "spline", "barycentric", + "polynomial", "krogh", "piecewise_polynomial", "spline", "pchip", "akima"}: string + The interpolation method you want to apply. + inter_order : int, default 2 + If there your selected interpolation method can be performed at different 'orders' - here you pass the desired + order. + inter_limit : int, default 2 + Maximum number of consecutive 'nan' values allowed for a gap to be interpolated. + interpol_flag : {'GOOD', 'BAD', 'UNFLAGGED', str}, default 'UNFLAGGED' + Flag that is to be inserted for the interpolated values. You can either pass one of the three major flag-classes + or specify directly a certain flag from the passed flagger. + downgrade_interpolation : bool, default False + If interpolation can not be performed at `inter_order` - (not enough values or not implemented at this order) - + automaticalyy try to interpolate at order `inter_order` :math:`- 1`. + not_interpol_flags : {None, str, List[str]}, default None + A list of flags or a single Flag, marking values, you want NOT to be interpolated. + """ + pass + + +def interpolateIndex(field, freq, method, inter_order, to_drop, downgrade_interpolation, empty_intervals_flag, grid_field, inter_limit, freq_check): + """ + Function to interpolate the data at regular (equidistant) timestamps (or Grid points). + + Note, that the interpolation will only be calculated, for grid timestamps that have a preceding AND a succeeding + valid data value within "freq" range. + + Note, that the function differs from proc_interpolateMissing, by returning a whole new data set, only containing + samples at the interpolated, equidistant timestamps (of frequency "freq"). + + Note, it is possible to interpolate unregular "grids" (with no frequencies). In fact, any date index + can be target of the interpolation. Just pass the field name of the variable, holding the index + you want to interpolate, to "grid_field". 'freq' is then use to determine the maximum gap size for + a grid point to be interpolated. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-interpolated. + freq : str + An Offset String, interpreted as the frequency of + the grid you want to interpolate your data at. + method : {"linear", "time", "nearest", "zero", "slinear", "quadratic", "cubic", "spline", "barycentric", + "polynomial", "krogh", "piecewise_polynomial", "spline", "pchip", "akima"}: string + The interpolation method you want to apply. + inter_order : integer, default 2 + If there your selected interpolation method can be performed at different 'orders' - here you pass the desired + order. + to_drop : {None, str, List[str]}, default None + Flags that refer to values you want to drop before interpolation - effectively excluding grid points from + interpolation, that are only surrounded by values having a flag in them, that is listed in drop flags. Default + results in the flaggers *BAD* flag to be the drop_flag. + downgrade_interpolation : bool, default False + If interpolation can not be performed at `inter_order` - (not enough values or not implemented at this order) - + automatically try to interpolate at order `inter_order` :math:`- 1`. + empty_intervals_flag : str, default None + A Flag, that you want to assign to those values in the resulting equidistant sample grid, that were not + surrounded by valid data in the original dataset, and thus were not interpolated. Default automatically assigns + ``flagger.BAD`` flag to those values. + grid_field : String, default None + Use the timestamp of another variable as (not necessarily regular) "grid" to be interpolated. + inter_limit : Integer, default 2 + Maximum number of consecutive Grid values allowed for interpolation. If set + to *n*, chunks of *n* and more consecutive grid values, where there is no value in between, wont be + interpolated. + freq_check : {None, 'check', 'auto'}, default None + + * ``None``: do not validate frequency-string passed to `freq` + * ``'check'``: estimate frequency and log a warning if estimate miss matchs frequency string passed to 'freq', or + if no uniform sampling rate could be estimated + * ``'auto'``: estimate frequency and use estimate. (Ignores `freq` parameter.) + """ + pass + diff --git a/docs/func_modules/module_dict.pkl b/docs/func_modules/module_dict.pkl new file mode 100644 index 0000000000000000000000000000000000000000..7fafca46813d0491babe42170df526c3842f39ab Binary files /dev/null and b/docs/func_modules/module_dict.pkl differ diff --git a/docs/func_modules/outliers.py b/docs/func_modules/outliers.py new file mode 100644 index 0000000000000000000000000000000000000000..183cfc24ac18da792553310fd9e9b942a89247b9 --- /dev/null +++ b/docs/func_modules/outliers.py @@ -0,0 +1,359 @@ +""" + +""" +def flagByStray(field, partition_freq, partition_min, iter_start, alpha): + """ + Flag outliers in 1-dimensional (score) data with the STRAY Algorithm. + + Find more information on the algorithm in References [1]. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. + partition_freq : {None, str, int}, default None + partition_freq : {np.inf, float, str}, default np.inf + Determines the segmentation of the data into partitions, the kNN algorithm is + applied onto individually. + + * ``np.inf``: Apply Scoring on whole data set at once + * ``x`` > 0 : Apply scoring on successive data chunks of periods length ``x`` + * Offset String : Apply scoring on successive partitions of temporal extension matching the passed offset + string + + partition_min : int, default 11 + Minimum number of periods per partition that have to be present for a valid outlier dettection to be made in + this partition. (Only of effect, if `partition_freq` is an integer.) Partition min value must always be + greater then the nn_neighbors value. + iter_start : float, default 0.5 + Float in [0,1] that determines which percentage of data is considered "normal". 0.5 results in the stray + algorithm to search only the upper 50 % of the scores for the cut off point. (See reference section for more + information) + alpha : float, default 0.05 + Level of significance by which it is tested, if a score might be drawn from another distribution, than the + majority of the data. + + References + ---------- + [1] Talagala, P. D., Hyndman, R. J., & Smith-Miles, K. (2019). Anomaly detection in high dimensional data. + arXiv preprint arXiv:1908.04000. + """ + pass + + +def flagMVScores(field, fields, trafo, alpha, n_neighbors, scoring_func, iter_start, stray_partition, stray_partition_min, trafo_on_partition, reduction_range, reduction_drop_flagged, reduction_thresh, reduction_min_periods): + """ + The algorithm implements a 3-step outlier detection procedure for simultaneously flagging of higher dimensional + data (dimensions > 3). + + In references [1], the procedure is introduced and exemplified with an application on hydrological data. + + See the notes section for an overview over the algorithms basic steps. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. (Here a dummy, for structural reasons) + fields : List[str] + List of fieldnames, corresponding to the variables that are to be included into the flagging process. + trafo : callable, default lambda x:x + Transformation to be applied onto every column before scoring. Will likely get deprecated soon. Its better + to transform the data in a processing step, preceeeding the call to ``flagMVScores``. + alpha : float, default 0.05 + Level of significance by which it is tested, if an observations score might be drawn from another distribution + than the majority of the observation. + n_neighbors : int, default 10 + Number of neighbors included in the scoring process for every datapoint. + scoring_func : Callable[numpy.array, float], default np.sum + The function that maps the set of every points k-nearest neighbor distances onto a certain scoring. + iter_start : float, default 0.5 + Float in [0,1] that determines which percentage of data is considered "normal". 0.5 results in the threshing + algorithm to search only the upper 50 % of the scores for the cut off point. (See reference section for more + information) + stray_partition : {None, str, int}, default None + Only effective when `threshing` = 'stray'. + Determines the size of the data partitions, the data is decomposed into. Each partition is checked seperately + for outliers. If a String is passed, it has to be an offset string and it results in partitioning the data into + parts of according temporal length. If an integer is passed, the data is simply split up into continous chunks + of `partition_freq` periods. if ``None`` is passed (default), all the data will be tested in one run. + stray_partition_min : int, default 11 + Only effective when `threshing` = 'stray'. + Minimum number of periods per partition that have to be present for a valid outlier detection to be made in + this partition. (Only of effect, if `stray_partition` is an integer.) + trafo_on_partition : bool, default True + Whether or not to apply the passed transformation on every partition the algorithm is applied on, separately. + reduction_range : {None, str}, default None + If not None, it is tried to reduce the stray result onto single outlier components of the input fields. + An offset string, denoting the range of the temporal surrounding to include into the MAD testing while trying + to reduce flags. + reduction_drop_flagged : bool, default False + Only effective when `reduction_range` is not ``None``. + Whether or not to drop flagged values other than the value under test from the temporal surrounding + before checking the value with MAD. + reduction_thresh : float, default 3.5 + Only effective when `reduction_range` is not ``None``. + The `critical` value, controlling wheather the MAD score is considered referring to an outlier or not. + Higher values result in less rigid flagging. The default value is widely considered apropriate in the + literature. + reduction_min_periods : int, 1 + Only effective when `reduction_range` is not ``None``. + Minimum number of meassurements necessarily present in a reduction interval for reduction actually to be + performed. + + Notes + ----- + The basic steps are: + + 1. transforming + + The different data columns are transformed via timeseries transformations to + (a) make them comparable and + (b) make outliers more stand out. + + This step is usually subject to a phase of research/try and error. See [1] for more details. + + Note, that the data transformation as an built-in step of the algorithm, will likely get deprecated soon. Its better + to transform the data in a processing step, preceeding the multivariate flagging process. Also, by doing so, one + gets mutch more control and variety in the transformation applied, since the `trafo` parameter only allows for + application of the same transformation to all of the variables involved. + + 2. scoring + + Every observation gets assigned a score depending on its k nearest neighbors. See the `scoring_method` parameter + description for details on the different scoring methods. Furthermore [1], [2] may give some insight in the + pro and cons of the different methods. + + 3. threshing + + The gaps between the (greatest) scores are tested for beeing drawn from the same + distribution as the majority of the scores. If a gap is encountered, that, with sufficient significance, can be + said to not be drawn from the same distribution as the one all the smaller gaps are drawn from, than + the observation belonging to this gap, and all the observations belonging to gaps larger then this gap, get flagged + outliers. See description of the `threshing` parameter for more details. Although [2] gives a fully detailed + overview over the `stray` algorithm. + """ + pass + + +def flagRaise(field, thresh, raise_window, intended_freq, average_window, mean_raise_factor, min_slope, min_slope_weight, numba_boost): + """ + The function flags raises and drops in value courses, that exceed a certain threshold + within a certain timespan. + + The parameter variety of the function is owned to the intriguing + case of values, that "return" from outlierish or anomalious value levels and + thus exceed the threshold, while actually being usual values. + + NOTE, the dataset is NOT supposed to be harmonized to a time series with an + equidistant frequency grid. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. + thresh : float + The threshold, for the total rise (thresh > 0), or total drop (thresh < 0), value courses must + not exceed within a timespan of length `raise_window`. + raise_window : str + An offset string, determining the timespan, the rise/drop thresholding refers to. Window is inclusively defined. + intended_freq : str + An offset string, determining The frequency, the timeseries to-be-flagged is supposed to be sampled at. + The window is inclusively defined. + average_window : {None, str}, default None + See condition (2) of the description linked in the references. Window is inclusively defined. + The window defaults to 1.5 times the size of `raise_window` + mean_raise_factor : float, default 2 + See second condition listed in the notes below. + min_slope : {None, float}, default None + See third condition listed in the notes below. + min_slope_weight : float, default 0.8 + See third condition listed in the notes below. + numba_boost : bool, default True + + Notes + ----- + The value :math:`x_{k}` of a time series :math:`x` with associated + timestamps :math:`t_i`, is flagged a raise, if: + + * There is any value :math:`x_{s}`, preceeding :math:`x_{k}` within `raise_window` range, so that: + + * :math:`M = |x_k - x_s | >` `thresh` :math:`> 0` + + * The weighted average :math:`\mu^{*}` of the values, preceding :math:`x_{k}` within `average_window` + range indicates, that :math:`x_{k}` does not return from an "outlierish" value course, meaning that: + + * :math:`x_k > \mu^* + ( M` / `mean_raise_factor` :math:`)` + + * Additionally, if `min_slope` is not `None`, :math:`x_{k}` is checked for being sufficiently divergent from its + very predecessor :max:`x_{k-1}`$, meaning that, it is additionally checked if: + + * :math:`x_k - x_{k-1} >` `min_slope` + * :math:`t_k - t_{k-1} >` `min_slope_weight` :math:`\times` `intended_freq` + """ + pass + + +def flagMAD(field, window): + """ + The function represents an implementation of the modyfied Z-score outlier detection method. + + See references [1] for more details on the algorithm. + + Note, that the test needs the input data to be sampled regularly (fixed sampling rate). + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. (Here a dummy, for structural reasons) + window : str + Offset string. Denoting the windows size that the "Z-scored" values have to lie in. + z: float, default 3.5 + The value the Z-score is tested against. Defaulting to 3.5 (Recommendation of [1]) + + References + ---------- + [1] https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm + """ + pass + + +def flagOffset(field, thresh, tolerance, window, numba_kickin): + """ + A basic outlier test that is designed to work for harmonized and not harmonized data. + + The test classifies values/value courses as outliers by detecting not only a rise in value, but also, + checking for a return to the initial value level. + + Values :math:`x_n, x_{n+1}, .... , x_{n+k}` of a timeseries :math:`x` with associated timestamps + :math:`t_n, t_{n+1}, .... , t_{n+k}` are considered spikes, if + + 1. :math:`|x_{n-1} - x_{n + s}| >` `thresh`, for all :math:`s \in [0,1,2,...,k]` + + 2. :math:`|x_{n-1} - x_{n+k+1}| <` `tolerance` + + 3. :math:`|t_{n-1} - t_{n+k+1}| <` `window` + + Note, that this definition of a "spike" not only includes one-value outliers, but also plateau-ish value courses. + + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. (Here a dummy, for structural reasons) + thresh : float + Minimum difference between to values, to consider the latter one as a spike. See condition (1) + tolerance : float + Maximum difference between pre-spike and post-spike values. See condition (2) + window : {str, int}, default '15min' + Maximum length of "spiky" value courses. See condition (3). Integer defined window length are only allowed for + regularly sampled timeseries. + numba_kickin : int, default 200000 + When there are detected more than `numba_kickin` incidents of potential spikes, + the pandas.rolling - part of computation gets "jitted" with numba. + Default value hast proven to be around the break even point between "jit-boost" and "jit-costs". + + + References + ---------- + The implementation is a time-window based version of an outlier test from the UFZ Python library, + that can be found here: + + https://git.ufz.de/chs/python/blob/master/ufz/level1/spike.py + """ + pass + + +def flagByGrubbs(field, winsz, alpha, min_periods): + """ + The function flags values that are regarded outliers due to the grubbs test. + + See reference [1] for more information on the grubbs tests definition. + + The (two-sided) test gets applied onto data chunks of size "winsz". The tests application will + be iterated on each data-chunk under test, till no more outliers are detected in that chunk. + + Note, that the test performs poorely for small data chunks (resulting in heavy overflagging). + Therefor you should select "winsz" so that every window contains at least > 8 values and also + adjust the min_periods values accordingly. + + Note, that the data to be tested by the grubbs test are expected to be distributed "normalish". + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. + winsz : {int, str} + The size of the window you want to use for outlier testing. If an integer is passed, the size + refers to the number of periods of every testing window. If a string is passed, it has to be an offset string, + and will denote the total temporal extension of every window. + alpha : float, default 0.05 + The level of significance, the grubbs test is to be performed at. (between 0 and 1) + min_periods : int, default 8 + The minimum number of values that have to be present in an interval under test, for a grubbs test result to be + accepted. Only makes sence in case `winsz` is an offset string. + check_lagged: boolean, default False + If True, every value gets checked twice for being an outlier. Ones in the initial rolling window and one more + time in a rolling window that is lagged by half the windows delimeter (winsz/2). Recommended for avoiding false + positives at the window edges. Only available when rolling with integer defined window size. + + References + ---------- + introduction to the grubbs test: + + [1] https://en.wikipedia.org/wiki/Grubbs%27s_test_for_outliers + """ + pass + + +def flagRange(field, min, max): + """ + Function flags values not covered by the closed interval [`min`, `max`]. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. + min : float + Lower bound for valid data. + max : float + Upper bound for valid data. + """ + pass + + +def flagCrossStatistic(field, fields, thresh, cross_stat): + """ + Function checks for outliers relatively to the "horizontal" input data axis. + + For `fields` :math:`=[f_1,f_2,...,f_N]` and timestamps :math:`[t_1,t_2,...,t_K]`, the following steps are taken + for outlier detection: + + 1. All timestamps :math:`t_i`, where there is one :math:`f_k`, with :math:`data[f_K]` having no entry at + :math:`t_i`, are excluded from the following process (inner join of the :math:`f_i` fields.) + 2. for every :math:`0 <= i <= K`, the value + :math:`m_j = median(\{data[f_1][t_i], data[f_2][t_i], ..., data[f_N][t_i]\})` is calculated + 2. for every :math:`0 <= i <= K`, the set + :math:`\{data[f_1][t_i] - m_j, data[f_2][t_i] - m_j, ..., data[f_N][t_i] - m_j\}` is tested for outliers with the + specified method (`cross_stat` parameter). + + Parameters + ---------- + field : str + A dummy parameter. + fields : str + List of fieldnames in data, determining wich variables are to be included into the flagging process. + thresh : float + Threshold which the outlier score of an value must exceed, for being flagged an outlier. + cross_stat : {'modZscore', 'Zscore'}, default 'modZscore' + Method used for calculating the outlier scores. + + * ``'modZscore'``: Median based "sigma"-ish approach. See Referenecs [1]. + * ``'Zscore'``: Score values by how many times the standard deviation they differ from the median. + See References [1] + + References + ---------- + [1] https://www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm + """ + pass + diff --git a/docs/func_modules/pattern.py b/docs/func_modules/pattern.py new file mode 100644 index 0000000000000000000000000000000000000000..13e769ac6bd8074ff168032e4fd113d049d84c82 --- /dev/null +++ b/docs/func_modules/pattern.py @@ -0,0 +1,38 @@ +""" + +""" +def flagPatternByDTW(field): + """ + Pattern recognition via wavelets. + + The steps are: + 1. work on chunks returned by a moving window + 2. each chunk is compared to the given pattern, using the wavelet algorithm as presented in [1] + 3. if the compared chunk is equal to the given pattern it gets flagged + + Parameters + ---------- + + field : str + The fieldname of the data column, you want to correct. + """ + pass + + +def flagPatternByWavelet(field): + """ + Pattern Recognition via Dynamic Time Warping. + + The steps are: + 1. work on chunks returned by a moving window + 2. each chunk is compared to the given pattern, using the dynamic time warping algorithm as presented in [1] + 3. if the compared chunk is equal to the given pattern it gets flagged + + Parameters + ---------- + + field : str + The fieldname of the data column, you want to correct. + """ + pass + diff --git a/docs/func_modules/resampling.py b/docs/func_modules/resampling.py new file mode 100644 index 0000000000000000000000000000000000000000..3338e65341bde15b3d0f78e4d6497d176275c6c8 --- /dev/null +++ b/docs/func_modules/resampling.py @@ -0,0 +1,304 @@ +""" + +""" +def aggregate(field, freq, value_func, flag_func, method, to_drop): + """ + A method to "regularize" data by aggregating (resampling) data at a regular timestamp. + + A series of data is considered "regular", if it is sampled regularly (= having uniform sampling rate). + + The data will therefor get aggregated with a function, specified by the `value_func` parameter and + the result gets projected onto the new timestamps with a method, specified by "method". + + The following method (keywords) are available: + + * ``'nagg'``: (aggreagtion to nearest) - all values in the range (+/- freq/2) of a grid point get aggregated with + `agg_func`. and assigned to it. Flags get aggregated by `flag_func` and assigned the same way. + * ``'bagg'``: (backwards aggregation) - all values in a sampling interval get aggregated with agg_func and the + result gets assigned to the last regular timestamp. Flags get aggregated by `flag_func` and assigned the same way. + * ``'fagg'``: (forward aggregation) - all values in a sampling interval get aggregated with agg_func and the result + gets assigned to the next regular timestamp. Flags get aggregated by `flag_func` and assigned the same way. + + Note, that, if there is no valid data (exisitng and not-na) available in a sampling interval assigned to a regular + timestamp by the selected method, nan gets assigned to this timestamp. The associated flag will be of value + ``flagger.UNFLAGGED``. + + Note: the method will likely and significantly alter values and shape of ``data[field]``. The original data is kept + in the data dios and assigned to the fieldname ``field + '_original'``. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-regularized. + freq : str + The sampling frequency the data is to be aggregated (resampled) at. + value_func : Callable + The function you want to use for aggregation. + flag_func : Callable + The function you want to aggregate the flags with. It should be capable of operating on the flags dtype + (usually ordered categorical). + method : {'fagg', 'bagg', 'nagg'}, default 'nagg' + Specifies which intervals to be aggregated for a certain timestamp. (preceeding, succeeding or + "surrounding" interval). See description above for more details. + to_drop : {List[str], str}, default None + Flagtypes you want to drop before aggregation - effectively excluding values that are flagged + with a flag in to_drop from the aggregation process. Default results in flagger.BAD + values being dropped initially. + """ + pass + + +def linear(field, freq, to_drop): + """ + A method to "regularize" data by interpolating linearly the data at regular timestamp. + + A series of data is considered "regular", if it is sampled regularly (= having uniform sampling rate). + + Interpolated values will get assigned the worst flag within freq-range. + + Note: the method will likely and significantly alter values and shape of ``data[field]``. The original data is kept + in the data dios and assigned to the fieldname ``field + '_original'``. + + Note, that the data only gets interpolated at those (regular) timestamps, that have a valid (existing and + not-na) datapoint preceeding them and one succeeding them within freq range. + Regular timestamp that do not suffice this condition get nan assigned AND The associated flag will be of value + ``flagger.UNFLAGGED``. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-regularized. + freq : str + An offset string. The frequency of the grid you want to interpolate your data at. + to_drop : {List[str], str}, default None + Flagtypes you want to drop before interpolation - effectively excluding values that are flagged + with a flag in to_drop from the interpolation process. Default results in flagger.BAD + values being dropped initially. + """ + pass + + +def interpolate(field, freq, method, order, to_drop): + """ + A method to "regularize" data by interpolating the data at regular timestamp. + + A series of data is considered "regular", if it is sampled regularly (= having uniform sampling rate). + + Interpolated values will get assigned the worst flag within freq-range. + + There are available all the interpolations from the pandas.Series.interpolate method and they are called by + the very same keywords. + + Note, that, to perform a timestamp aware, linear interpolation, you have to pass ``'time'`` as `method`, + and NOT ``'linear'``. + + Note: the `method` will likely and significantly alter values and shape of ``data[field]``. The original data is + kept in the data dios and assigned to the fieldname ``field + '_original'``. + + Note, that the data only gets interpolated at those (regular) timestamps, that have a valid (existing and + not-na) datapoint preceeding them and one succeeding them within freq range. + Regular timestamp that do not suffice this condition get nan assigned AND The associated flag will be of value + ``flagger.UNFLAGGED``. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-regularized. + freq : str + An offset string. The frequency of the grid you want to interpolate your data at. + method : {"linear", "time", "nearest", "zero", "slinear", "quadratic", "cubic", "spline", "barycentric", + "polynomial", "krogh", "piecewise_polynomial", "spline", "pchip", "akima"}: string + The interpolation method you want to apply. + order : int, default 1 + If your selected interpolation method can be performed at different *orders* - here you pass the desired + order. + to_drop : {List[str], str}, default None + Flagtypes you want to drop before interpolation - effectively excluding values that are flagged + with a flag in `to_drop` from the interpolation process. Default results in ``flagger.BAD`` + values being dropped initially. + """ + pass + + +def mapToOriginal(field, method, to_drop): + """ + The Function function "undoes" regularization, by regaining the original data and projecting the + flags calculated for the regularized data onto the original ones. + + Afterwards the regularized data is removed from the data dios and ``'field'`` will be associated + with the original data "again". + + Wherever the flags in the original data are "better" then the regularized flags projected on them, + they get overridden with this regularized flags value. + + Which regularized flags are to be projected on which original flags, is controlled by the "method" parameters. + + Generally, if you regularized with the method "X", you should pass the method "inverse_X" to the deharmonization. + If you regularized with an interpolation, the method "inverse_interpolation" would be the appropriate choice. + Also you should pass the same drop flags keyword. + + The deharm methods in detail: + ("original_flags" are associated with the original data that is to be regained, + "regularized_flags" are associated with the regularized data that is to be "deharmonized", + "freq" refers to the regularized datas sampling frequencie) + + * ``'inverse_nagg'``: all original_flags within the range *+/- freq/2* of a regularized_flag, get assigned this + regularized flags value. (if regularized_flags > original_flag) + * ``'inverse_bagg'``: all original_flags succeeding a regularized_flag within the range of "freq", get assigned this + regularized flags value. (if regularized_flag > original_flag) + * ``'inverse_fagg'``: all original_flags preceeding a regularized_flag within the range of "freq", get assigned this + regularized flags value. (if regularized_flag > original_flag) + + * ``'inverse_interpolation'``: all original_flags within the range *+/- freq* of a regularized_flag, get assigned this + regularized flags value (if regularized_flag > original_flag). + + * ``'inverse_nshift'``: That original_flag within the range +/- *freq/2*, that is nearest to a regularized_flag, + gets the regularized flags value. (if regularized_flag > original_flag) + * ``'inverse_bshift'``: That original_flag succeeding a source flag within the range freq, that is nearest to a + regularized_flag, gets assigned this regularized flags value. (if regularized_flag > original_flag) + * ``'inverse_nshift'``: That original_flag preceeding a regularized flag within the range freq, that is nearest to a + regularized_flag, gets assigned this regularized flags value. (if source_flag > original_flag) + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-deharmonized. + method : {'inverse_fagg', 'inverse_bagg', 'inverse_nagg', 'inverse_fshift', 'inverse_bshift', 'inverse_nshift', + 'inverse_interpolation'} + The method used for projection of regularized flags onto original flags. See description above for more + details. + to_drop : {List[str], str}, default None + Flagtypes you want to drop before interpolation - effectively excluding values that are flagged + with a flag in to_drop from the interpolation process. Default results in flagger.BAD + values being dropped initially. + """ + pass + + +def resample(field, freq, agg_func, max_invalid_total_d, max_invalid_consec_d, max_invalid_total_f, max_invalid_consec_f, flag_agg_func, empty_intervals_flag, to_drop, freq_check): + """ + Function to resample the data. Afterwards the data will be sampled at regular (equidistant) timestamps + (or Grid points). Sampling intervals therefor get aggregated with a function, specifyed by 'agg_func' parameter and + the result gets projected onto the new timestamps with a method, specified by "method". The following method + (keywords) are available: + + * ``'nagg'``: all values in the range (+/- `freq`/2) of a grid point get aggregated with agg_func and assigned to it. + * ``'bagg'``: all values in a sampling interval get aggregated with agg_func and the result gets assigned to the last + grid point. + * ``'fagg'``: all values in a sampling interval get aggregated with agg_func and the result gets assigned to the next + grid point. + + + Note, that. if possible, functions passed to agg_func will get projected internally onto pandas.resample methods, + wich results in some reasonable performance boost - however, for this to work, you should pass functions that have + the __name__ attribute initialised and the according methods name assigned to it. + Furthermore, you shouldnt pass numpys nan-functions + (``nansum``, ``nanmean``,...) because those for example, have ``__name__ == 'nansum'`` and they will thus not + trigger ``resample.func()``, but the slower ``resample.apply(nanfunc)``. Also, internally, no nans get passed to + the functions anyway, so that there is no point in passing the nan functions. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-resampled. + freq : str + An Offset String, that will be interpreted as the frequency you want to resample your data with. + agg_func : Callable + The function you want to use for aggregation. + method: {'fagg', 'bagg', 'nagg'}, default 'bagg' + Specifies which intervals to be aggregated for a certain timestamp. (preceding, succeeding or + "surrounding" interval). See description above for more details. + max_invalid_total_d : {None, int}, default None + Maximum number of invalid (nan) datapoints, allowed per resampling interval. If max_invalid_total_d is + exceeded, the interval gets resampled to nan. By default (``np.inf``), there is no bound to the number of nan + values in an interval and only intervals containing ONLY nan values or those, containing no values at all, + get projected onto nan + max_invalid_consec_d : {None, int}, default None + Maximum number of consecutive invalid (nan) data points, allowed per resampling interval. + If max_invalid_consec_d is exceeded, the interval gets resampled to nan. By default (np.inf), + there is no bound to the number of consecutive nan values in an interval and only intervals + containing ONLY nan values, or those containing no values at all, get projected onto nan. + max_invalid_total_f : {None, int}, default None + Same as `max_invalid_total_d`, only applying for the flags. The flag regarded as "invalid" value, + is the one passed to empty_intervals_flag (default=``flagger.BAD``). + Also this is the flag assigned to invalid/empty intervals. + max_invalid_consec_f : {None, int}, default None + Same as `max_invalid_total_f`, only applying onto flags. The flag regarded as "invalid" value, is the one passed + to empty_intervals_flag (default=flagger.BAD). Also this is the flag assigned to invalid/empty intervals. + flag_agg_func : Callable, default: max + The function you want to aggregate the flags with. It should be capable of operating on the flags dtype + (usually ordered categorical). + empty_intervals_flag : {None, str}, default None + A Flag, that you want to assign to invalid intervals. Invalid are those intervals, that contain nan values only, + or no values at all. Furthermore the empty_intervals_flag is the flag, serving as "invalid" identifyer when + checking for `max_total_invalid_f` and `max_consec_invalid_f patterns`. Default triggers ``flagger.BAD`` to be + assigned. + to_drop : {None, str, List[str]}, default None + Flags that refer to values you want to drop before resampling - effectively excluding values that are flagged + with a flag in to_drop from the resampling process - this means that they also will not be counted in the + the `max_consec`/`max_total evaluation`. `to_drop` = ``None`` results in NO flags being dropped initially. + freq_check : {None, 'check', 'auto'}, default None + + * ``None``: do not validate frequency-string passed to `freq` + * ``'check'``: estimate frequency and log a warning if estimate miss matchs frequency string passed to 'freq', or + if no uniform sampling rate could be estimated + * ``'auto'``: estimate frequency and use estimate. (Ignores `freq` parameter.) + """ + pass + + +def reindexFlags(field, method, source, freq, to_drop, freq_check): + """ + The Function projects flags of "source" onto flags of "field". Wherever the "field" flags are "better" then the + source flags projected on them, they get overridden with this associated source flag value. + + Which "field"-flags are to be projected on which source flags, is controlled by the "method" and "freq" + parameters. + + method: (field_flag in associated with "field", source_flags associated with "source") + + 'inverse_nagg' - all field_flags within the range +/- freq/2 of a source_flag, get assigned this source flags value. + (if source_flag > field_flag) + 'inverse_bagg' - all field_flags succeeding a source_flag within the range of "freq", get assigned this source flags + value. (if source_flag > field_flag) + 'inverse_fagg' - all field_flags preceeding a source_flag within the range of "freq", get assigned this source flags + value. (if source_flag > field_flag) + + 'inverse_interpolation' - all field_flags within the range +/- freq of a source_flag, get assigned this source flags value. + (if source_flag > field_flag) + + 'inverse_nshift' - That field_flag within the range +/- freq/2, that is nearest to a source_flag, gets the source + flags value. (if source_flag > field_flag) + 'inverse_bshift' - That field_flag succeeding a source flag within the range freq, that is nearest to a + source_flag, gets assigned this source flags value. (if source_flag > field_flag) + 'inverse_nshift' - That field_flag preceeding a source flag within the range freq, that is nearest to a + source_flag, gets assigned this source flags value. (if source_flag > field_flag) + + 'match' - any field_flag with a timestamp matching a source_flags timestamp gets this source_flags value + (if source_flag > field_flag) + + Note, to undo or backtrack a resampling/shifting/interpolation that has been performed with a certain method, + you can just pass the associated "inverse" method. Also you should pass the same drop flags keyword. + + Parameters + ---------- + field : str + The fieldname of the data column, you want to project the source-flags onto. + method : {'inverse_fagg', 'inverse_bagg', 'inverse_nagg', 'inverse_fshift', 'inverse_bshift', 'inverse_nshift'} + The method used for projection of source flags onto field flags. See description above for more details. + source : str + The source source of flags projection. + freq : {None, str},default None + The freq determines the projection range for the projection method. See above description for more details. + Defaultly (None), the sampling frequency of source is used. + to_drop : {None, str, List[str]}, default None + Flags referring to values that are to drop before flags projection. Relevant only when projecting with an + inverted shift method. Defaultly flagger.BAD is listed. + freq_check : {None, 'check', 'auto'}, default None + - None: do not validate frequency-string passed to `freq` + - 'check': estimate frequency and log a warning if estimate miss matchs frequency string passed to 'freq', or + if no uniform sampling rate could be estimated + - 'auto': estimate frequency and use estimate. (Ignores `freq` parameter.) + """ + pass + diff --git a/docs/func_modules/residues.py b/docs/func_modules/residues.py new file mode 100644 index 0000000000000000000000000000000000000000..ca621862127b3fe2d7cc8e1a934cb9a15a602d66 --- /dev/null +++ b/docs/func_modules/residues.py @@ -0,0 +1,65 @@ +""" + +""" +def calculatePolynomialResidues(field, winsz, polydeg, numba, eval_flags, min_periods): + """ + Function fits a polynomial model to the data and returns the residues. + + The residue for value x is calculated by fitting a polynomial of degree "polydeg" to a data slice + of size "winsz", wich has x at its center. + + Note, that the residues will be stored to the `field` field of the input data, so that the original data, the + polynomial is fitted to, gets overridden. + + Note, that, if data[field] is not alligned to an equidistant frequency grid, the window size passed, + has to be an offset string. Also numba boost options don`t apply for irregularly sampled + timeseries. + + Note, that calculating the residues tends to be quite costy, because a function fitting is perfomed for every + sample. To improve performance, consider the following possibillities: + + In case your data is sampled at an equidistant frequency grid: + + (1) If you know your data to have no significant number of missing values, or if you do not want to + calculate residues for windows containing missing values any way, performance can be increased by setting + min_periods=winsz. + + (2) If your data consists of more then around 200000 samples, setting numba=True, will boost the + calculations up to a factor of 5 (for samplesize > 300000) - however for lower sample sizes, + numba will slow down the calculations, also, up to a factor of 5, for sample_size < 50000. + By default (numba='auto'), numba is set to true, if the data sample size exceeds 200000. + + in case your data is not sampled at an equidistant frequency grid: + + (1) Harmonization/resampling of your data will have a noticable impact on polyfittings performance - since + numba_boost doesnt apply for irregularly sampled data in the current implementation. + + Note, that in the current implementation, the initial and final winsz/2 values do not get fitted. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-modelled. + winsz : {str, int} + The size of the window you want to use for fitting. If an integer is passed, the size + refers to the number of periods for every fitting window. If an offset string is passed, + the size refers to the total temporal extension. The window will be centered around the vaule-to-be-fitted. + For regularly sampled timeseries the period number will be casted down to an odd number if + even. + polydeg : int + The degree of the polynomial used for fitting + numba : {True, False, "auto"}, default "auto" + Wheather or not to apply numbas just-in-time compilation onto the poly fit function. This will noticably + increase the speed of calculation, if the sample size is sufficiently high. + If "auto" is selected, numba compatible fit functions get applied for data consisiting of > 200000 samples. + eval_flags : bool, default True + Wheather or not to assign new flags to the calculated residuals. If True, a residual gets assigned the worst + flag present in the interval, the data for its calculation was obtained from. + min_periods : {int, None}, default 0 + The minimum number of periods, that has to be available in every values fitting surrounding for the polynomial + fit to be performed. If there are not enough values, np.nan gets assigned. Default (0) results in fitting + regardless of the number of values present (results in overfitting for too sparse intervals). To automatically + set the minimum number of periods to the number of values in an offset defined window size, pass np.nan. + """ + pass + diff --git a/docs/func_modules/rolling.py b/docs/func_modules/rolling.py new file mode 100644 index 0000000000000000000000000000000000000000..9d06bcb804da0c2036b96a0043b30fd5d4d2ac71 --- /dev/null +++ b/docs/func_modules/rolling.py @@ -0,0 +1,36 @@ +""" + +""" +def roll(field, winsz, func, eval_flags, min_periods, center): + """ + Models the data with the rolling mean and returns the residues. + + Note, that the residues will be stored to the `field` field of the input data, so that the data that is modelled + gets overridden. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-modelled. + winsz : {int, str} + The size of the window you want to roll with. If an integer is passed, the size + refers to the number of periods for every fitting window. If an offset string is passed, + the size refers to the total temporal extension. + For regularly sampled timeseries, the period number will be casted down to an odd number if + center = True. + func : Callable[np.array, float], default np.mean + Function to apply on the rolling window and obtain the curve fit value. + eval_flags : bool, default True + Wheather or not to assign new flags to the calculated residuals. If True, a residual gets assigned the worst + flag present in the interval, the data for its calculation was obtained from. + Currently not implemented in combination with not-harmonized timeseries. + min_periods : int, default 0 + The minimum number of periods, that has to be available in every values fitting surrounding for the mean + fitting to be performed. If there are not enough values, np.nan gets assigned. Default (0) results in fitting + regardless of the number of values present. + center : bool, default True + Wheather or not to center the window the mean is calculated of around the reference value. If False, + the reference value is placed to the right of the window (classic rolling mean with lag.) + """ + pass + diff --git a/docs/func_modules/scores.py b/docs/func_modules/scores.py new file mode 100644 index 0000000000000000000000000000000000000000..765b667cfa3fc8f76902659590acd4f0ab57bfad --- /dev/null +++ b/docs/func_modules/scores.py @@ -0,0 +1,81 @@ +""" + +""" +def assignKNNScore(field, n_neighbors, trafo, trafo_on_partition, scoring_func, target_field, partition_freq, partition_min, kNN_algorithm, metric, p, radius): + """ + Score datapoints by an aggregation of the dictances to their k nearest neighbors. + + The function is a wrapper around the NearestNeighbors method from pythons sklearn library (See reference [1]). + + The steps taken to calculate the scores are as follows: + + 1. All the timeseries, named fields, are combined to one feature space by an *inner* join on their date time indexes. + thus, only samples, that share timestamps across all fields will be included in the feature space + 2. Any datapoint/sample, where one ore more of the features is invalid (=np.nan) will get excluded. + 3. For every data point, the distance to its `n_neighbors` nearest neighbors is calculated by applying the + metric `metric` at grade `p` onto the feature space. The defaults lead to the euclidian to be applied. + If `radius` is not None, it sets the upper bound of distance for a neighbor to be considered one of the + `n_neigbors` nearest neighbors. Furthermore, the `partition_freq` argument determines wich samples can be + included into a datapoints nearest neighbors list, by segmenting the data into chunks of specified temporal + extension and feeding that chunks to the kNN algorithm seperatly. + 4. For every datapoint, the calculated nearest neighbors distances get aggregated to a score, by the function + passed to `scoring_func`. The default, ``sum`` obviously just sums up the distances. + 5. The resulting timeseries of scores gets assigned to the field target_field. + + Parameters + ---------- + field : str + The reference variable, the deviation from wich determines the flagging. + n_neighbors : int, default 10 + The number of nearest neighbors to which the distance is comprised in every datapoints scoring calculation. + trafo : Callable[np.array, np.array], default lambda x: x + Transformation to apply on the variables before kNN scoring + trafo_on_partition : bool, default True + Weather or not to apply the transformation `trafo` onto the whole variable or onto each partition seperatly. + scoring_func : Callable[numpy.array, float], default np.sum + A function that assigns a score to every one dimensional array, containing the distances + to every datapoints `n_neighbors` nearest neighbors. + target_field : str, default 'kNN_scores' + Name of the field, where the resulting scores should be written to. + partition_freq : {np.inf, float, str}, default np.inf + Determines the segmentation of the data into partitions, the kNN algorithm is + applied onto individually. + + * ``np.inf``: Apply Scoring on whole data set at once + * ``x`` > 0 : Apply scoring on successive data chunks of periods length ``x`` + * Offset String : Apply scoring on successive partitions of temporal extension matching the passed offset + string + + partition_min : int, default 2 + The minimum number of periods that have to be present in a partition for the kNN scoring + to be applied. If the number of periods present is below `partition_min`, the score for the + datapoints in that partition will be np.nan. + kNN_algorithm : {'ball_tree', 'kd_tree', 'brute', 'auto'}, default 'ball_tree' + The search algorithm to find each datapoints k nearest neighbors. + The keyword just gets passed on to the underlying sklearn method. + See reference [1] for more information on the algorithm. + metric : str, default 'minkowski' + The metric the distances to any datapoints neighbors is computed with. The default of `metric` + together with the default of `p` result in the euclidian to be applied. + The keyword just gets passed on to the underlying sklearn method. + See reference [1] for more information on the algorithm. + p : int, default 2 + The grade of the metrice specified by parameter `metric`. + The keyword just gets passed on to the underlying sklearn method. + See reference [1] for more information on the algorithm. + radius : {None, float}, default None + If the radius is not None, only the distance to neighbors that ly within the range specified by `radius` + are comprised in the scoring aggregation. + The scoring method passed must be capable of handling np.nan values - since, for every point missing + within `radius` range to make complete the list of the distances to the `n_neighbors` nearest neighbors, + one np.nan value gets appended to the list passed to the scoring method. + The keyword just gets passed on to the underlying sklearn method. + See reference [1] for more information on the algorithm. + + References + ---------- + + [1] https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html + """ + pass + diff --git a/docs/func_modules/tools.py b/docs/func_modules/tools.py new file mode 100644 index 0000000000000000000000000000000000000000..88578e724722626a45e8d12ffbbd2d6949c7cf37 --- /dev/null +++ b/docs/func_modules/tools.py @@ -0,0 +1,128 @@ +""" + +""" +def copy(field): + """ + The function generates a copy of the data "field" and inserts it under the name field + suffix into the existing + data. + + Parameters + ---------- + field : str + The fieldname of the data column, you want to fork (copy). + """ + pass + + +def drop(field): + """ + The function drops field from the data dios and the flagger. + + Parameters + ---------- + field : str + The fieldname of the data column, you want to drop. + """ + pass + + +def rename(field, new_name): + """ + The function renames field to new name (in both, the flagger and the data). + + Parameters + ---------- + field : str + The fieldname of the data column, you want to rename. + new_name : str + String, field is to be replaced with. + """ + pass + + +def mask(field, mode, mask_var, period_start, period_end, include_bounds): + """ + This function realizes masking within saqc. + + Due to some inner saqc mechanics, it is not straight forwardly possible to exclude + values or datachunks from flagging routines. This function replaces flags with np.nan + value, wherever values are to get masked. Furthermore, the masked values get replaced by + np.nan, so that they dont effect calculations. + + Here comes a recipe on how to apply a flagging function only on a masked chunk of the variable field: + + 1. dublicate "field" in the input data (proc_copy) + 2. mask the dublicated data (modelling_mask) + 3. apply the tests you only want to be applied onto the masked data chunks (saqc_tests) + 4. project the flags, calculated on the dublicated and masked data onto the original field data + (proc_projectFlags or flagGeneric) + 5. drop the dublicated data (proc_drop) + + To see an implemented example, checkout flagSeasonalRange in the saqc.functions module + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-masked. + mode : {"periodic", "mask_var"} + The masking mode. + - "periodic": parameters "period_start", "period_end" are evaluated to generate a periodical mask + - "mask_var": data[mask_var] is expected to be a boolean valued timeseries and is used as mask. + mask_var : {None, str}, default None + Only effective if mode == "mask_var" + Fieldname of the column, holding the data that is to be used as mask. (must be moolean series) + Neither the series` length nor its labels have to match data[field]`s index and length. An inner join of the + indices will be calculated and values get masked where the values of the inner join are "True". + period_start : {None, str}, default None + Only effective if mode == "seasonal" + String denoting starting point of every period. Formally, it has to be a truncated instance of "mm-ddTHH:MM:SS". + Has to be of same length as `period_end` parameter. + See examples section below for some examples. + period_end : {None, str}, default None + Only effective if mode == "periodic" + String denoting starting point of every period. Formally, it has to be a truncated instance of "mm-ddTHH:MM:SS". + Has to be of same length as `period_end` parameter. + See examples section below for some examples. + include_bounds : boolean + Wheather or not to include the mask defining bounds to the mask. + + Examples + -------- + The `period_start` and `period_end` parameters provide a conveniant way to generate seasonal / date-periodic masks. + They have to be strings of the forms: "mm-ddTHH:MM:SS", "ddTHH:MM:SS" , "HH:MM:SS", "MM:SS" or "SS" + (mm=month, dd=day, HH=hour, MM=minute, SS=second) + Single digit specifications have to be given with leading zeros. + `period_start` and `seas on_end` strings have to be of same length (refer to the same periodicity) + The highest date unit gives the period. + For example: + + >>> period_start = "01T15:00:00" + >>> period_end = "13T17:30:00" + + Will result in all values sampled between 15:00 at the first and 17:30 at the 13th of every month get masked + + >>> period_start = "01:00" + >>> period_end = "04:00" + + All the values between the first and 4th minute of every hour get masked. + + >>> period_start = "01-01T00:00:00" + >>> period_end = "01-03T00:00:00" + + Mask january and february of evcomprosed in theery year. masking is inclusive always, so in this case the mask will + include 00:00:00 at the first of march. To exclude this one, pass: + + >>> period_start = "01-01T00:00:00" + >>> period_end = "02-28T23:59:59" + + To mask intervals that lap over a seasons frame, like nights, or winter, exchange sequence of season start and + season end. For example, to mask night hours between 22:00:00 in the evening and 06:00:00 in the morning, pass: + + >>> period_start = "22:00:00" + >>> period_end = "06:00:00" + + When inclusive_selection="season", all above examples work the same way, only that you now + determine wich values NOT TO mask (=wich values are to constitute the "seasons"). + """ + pass + diff --git a/docs/func_modules/transformation.py b/docs/func_modules/transformation.py new file mode 100644 index 0000000000000000000000000000000000000000..8ee5a5baeeb17c3607d208ae7e046a69105881ee --- /dev/null +++ b/docs/func_modules/transformation.py @@ -0,0 +1,25 @@ +""" + +""" +def transform(field, func, partition_freq): + """ + Function to transform data columns with a transformation that maps series onto series of the same length. + + Note, that flags get preserved. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-transformed. + func : Callable[{pd.Series, np.array}, np.array] + Function to transform data[field] with. + partition_freq : {None, float, str}, default None + Determines the segmentation of the data into partitions, the transformation is applied on individually + + * ``np.inf``: Apply transformation on whole data set at once + * ``x`` > 0 : Apply transformation on successive data chunks of periods length ``x`` + * Offset String : Apply transformation on successive partitions of temporal extension matching the passed offset + string + """ + pass + diff --git a/docs/intro_modules/AdvancedFlagging.py b/docs/intro_modules/AdvancedFlagging.py new file mode 100644 index 0000000000000000000000000000000000000000..22d84a7e0b5fcbbd76d3fefc0c0953c9cd0e6aad --- /dev/null +++ b/docs/intro_modules/AdvancedFlagging.py @@ -0,0 +1,66 @@ +""" + +""" +def flagPatternByDTW(field): + """ + Pattern recognition via wavelets. + + The steps are: + 1. work on chunks returned by a moving window + 2. each chunk is compared to the given pattern, using the wavelet algorithm as presented in [1] + 3. if the compared chunk is equal to the given pattern it gets flagged + + Parameters + ---------- + + field : str + The fieldname of the data column, you want to correct. + """ + pass + + +def flagOffset(field, thresh, tolerance, window, numba_kickin): + """ + A basic outlier test that is designed to work for harmonized and not harmonized data. + + The test classifies values/value courses as outliers by detecting not only a rise in value, but also, + checking for a return to the initial value level. + + Values :math:`x_n, x_{n+1}, .... , x_{n+k}` of a timeseries :math:`x` with associated timestamps + :math:`t_n, t_{n+1}, .... , t_{n+k}` are considered spikes, if + + 1. :math:`|x_{n-1} - x_{n + s}| >` `thresh`, for all :math:`s \in [0,1,2,...,k]` + + 2. :math:`|x_{n-1} - x_{n+k+1}| <` `tolerance` + + 3. :math:`|t_{n-1} - t_{n+k+1}| <` `window` + + Note, that this definition of a "spike" not only includes one-value outliers, but also plateau-ish value courses. + + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. (Here a dummy, for structural reasons) + thresh : float + Minimum difference between to values, to consider the latter one as a spike. See condition (1) + tolerance : float + Maximum difference between pre-spike and post-spike values. See condition (2) + window : {str, int}, default '15min' + Maximum length of "spiky" value courses. See condition (3). Integer defined window length are only allowed for + regularly sampled timeseries. + numba_kickin : int, default 200000 + When there are detected more than `numba_kickin` incidents of potential spikes, + the pandas.rolling - part of computation gets "jitted" with numba. + Default value hast proven to be around the break even point between "jit-boost" and "jit-costs". + + + References + ---------- + The implementation is a time-window based version of an outlier test from the UFZ Python library, + that can be found here: + + https://git.ufz.de/chs/python/blob/master/ufz/level1/spike.py + """ + pass + diff --git a/docs/intro_modules/BasicFlagging.py b/docs/intro_modules/BasicFlagging.py new file mode 100644 index 0000000000000000000000000000000000000000..0032a5c8fa61b9e2a517c04b1358891673a4e650 --- /dev/null +++ b/docs/intro_modules/BasicFlagging.py @@ -0,0 +1,32 @@ +""" + +""" +def flagRange(field, min, max): + """ + Function flags values not covered by the closed interval [`min`, `max`]. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. + min : float + Lower bound for valid data. + max : float + Upper bound for valid data. + """ + pass + + +def flagMissing(field, nodata): + """ + The function flags all values indicating missing data. + + Parameters + ---------- + field : str + The fieldname of the column, holding the data-to-be-flagged. + nodata : any, default np.nan + A value that defines missing data. + """ + pass + diff --git a/docs/intro_modules/module_dict.pkl b/docs/intro_modules/module_dict.pkl new file mode 100644 index 0000000000000000000000000000000000000000..23f577445c0f9414ab46ee4d171ebaeba97edd8d Binary files /dev/null and b/docs/intro_modules/module_dict.pkl differ diff --git a/saqc/funcs/breaks.py b/saqc/funcs/breaks.py index 3af127ecb3847e6f42a284e75640e537734156d5..8076bc6e462c230bc471591f1884e48b4d18770b 100644 --- a/saqc/funcs/breaks.py +++ b/saqc/funcs/breaks.py @@ -1,6 +1,12 @@ #! /usr/bin/env python # -*- coding: utf-8 -*- +"""Detecting breakish changes in timeseries value courses. + +This module provides functions to detect and flag breakish changes in the data value course, like gaps +(:py:func:`flagMissing`), jumps/drops (:py:func:`flagJumps`) or isolated values (:py:func:`flagIsolated`). +""" + from dios import DictOfSeries import numpy as np import pandas as pd @@ -52,14 +58,9 @@ def flagMissing(data: DictOfSeries, field: str, flagger: BaseFlagger, nodata: fl def flagIsolated(data: DictOfSeries, field: str, flagger: BaseFlagger, gap_window: str, group_window: str, **kwargs) -> Tuple[DictOfSeries, BaseFlagger]: """ The function flags arbitrary large groups of values, if they are surrounded by sufficiently - large data gaps. A gap is defined as group of missing and/or flagged values. - - A series of values x_k,x_(k+1),...,x_(k+n), with associated timestamps t_k,t_(k+1),...,t_(k+n), - is considered to be isolated, if: + large data gaps. - 1. t_(k+1) - t_n < `group_window` - 2. None of the x_j with 0 < t_k - t_j < `gap_window`, is valid or unflagged (preceeding gap). - 3. None of the x_j with 0 < t_j - t_(k+n) < `gap_window`, is valid or unflagged (succeding gap). + A gap is a timespan containing either no data or invalid (usually `nan`) and flagged data only. Parameters ---------- @@ -83,6 +84,19 @@ def flagIsolated(data: DictOfSeries, field: str, flagger: BaseFlagger, gap_windo flagger : saqc.flagger.BaseFlagger The flagger object, holding flags and additional Informations related to `data`. Flags values may have changed relatively to the flagger input. + + Notes + ----- + A series of values :math:`x_k,x_{k+1},...,x_{k+n}`, with associated timestamps :math:`t_k,t_{k+1},...,t_{k+n}`, + is considered to be isolated, if: + + 1. :math:`t_{k+1} - t_n <` `group_window` + 2. None of the :math:`x_j` with :math:`0 < t_k - t_j <` `gap_window`, is valid or unflagged (preceeding gap). + 3. None of the :math:`x_j` with :math:`0 < t_j - t_(k+n) <` `gap_window`, is valid or unflagged (succeding gap). + + See Also + -------- + :py:func:`flagMissing` """ gap_window = pd.tseries.frequencies.to_offset(gap_window) diff --git a/saqc/funcs/scores.py b/saqc/funcs/scores.py index 3d82942ed70a8029f992c0cfa77e950132a14e71..10b872ed9d332c30303f55dc485ab4ccb271f96c 100644 --- a/saqc/funcs/scores.py +++ b/saqc/funcs/scores.py @@ -30,7 +30,6 @@ def assignKNNScore( kNN_algorithm: Literal["ball_tree", "kd_tree", "brute", "auto"]='ball_tree', metric: str='minkowski', p: int=2, - radius: Optional[float]=None, **kwargs ) -> Tuple[DictOfSeries, BaseFlagger]: """ @@ -147,8 +146,7 @@ def assignKNNScore( sample_size = partition.shape[0] nn_neighbors = min(n_neighbors - 1, max(sample_size, 2)) - dist, *_ = ts_ops.kNN(partition.values, nn_neighbors, algorithm=kNN_algorithm, metric=metric, p=p, - radius=radius) + dist, *_ = ts_ops.kNN(partition.values, nn_neighbors, algorithm=kNN_algorithm, metric=metric, p=p) try: resids = getattr(dist, scoring_func.__name__)(axis=1) except AttributeError: diff --git a/saqc/lib/ts_operators.py b/saqc/lib/ts_operators.py index 5ae1ca7d0a97ff57a3e4dd9ffce5cb157357662a..2ee5a1aa049991af2023c526687737da7d52e113 100644 --- a/saqc/lib/ts_operators.py +++ b/saqc/lib/ts_operators.py @@ -113,22 +113,12 @@ def standardizeByIQR(ts): return (ts - np.median(ts)) / iqr(ts, nan_policy="omit") -def kNN(in_arr, n_neighbors, algorithm="ball_tree", metric='minkowski', p=2, radius=None): +def kNN(in_arr, n_neighbors, algorithm="ball_tree", metric='minkowski', p=2): # k-nearest-neighbor search nbrs = NearestNeighbors(n_neighbors=n_neighbors, algorithm=algorithm, metric=metric, p=p)\ .fit(in_arr.reshape(in_arr.shape[0], -1)) - if radius is None: - return nbrs.kneighbors() - - rad_nbrs = nbrs.radius_neighbors(radius=radius) - dist = np.zeros((in_arr.shape[0], n_neighbors)) - dist[:] = np.nan - i = 0 - for k in rad_nbrs[0]: - dist[i, 0:len(k)] = k - i += 1 - return dist, np.array([]) + return nbrs.kneighbors() def maxGap(in_arr): diff --git a/sphinx-doc/FlagFunctions.rst b/sphinx-doc/FlagFunctions.rst deleted file mode 100644 index 584d0dc5f48d0434a02d86ecbf884e47188d7236..0000000000000000000000000000000000000000 --- a/sphinx-doc/FlagFunctions.rst +++ /dev/null @@ -1,7 +0,0 @@ - -Functions -========= - -.. automodapi:: saqc.funcs - :skip: register - diff --git a/sphinx-doc/Makefile b/sphinx-doc/Makefile index efdfe91d931f29bf94f8b1f08c1b6f3d0661c8ab..f446fcdc9a98ffefc1205bd4cf788a4f4af3d754 100644 --- a/sphinx-doc/Makefile +++ b/sphinx-doc/Makefile @@ -18,8 +18,31 @@ help: clean: rm -rf _build _static _api rm -f *.automodsumm + rm -f func_modules/*.automodsumm + rm -f intro_modules/*.automodsumm mkdir _static +# trigger (saqc) customized documentation pipeline +doc: + # generate fake modules to b documented by sphinx + python make_doc_module.py -p "saqc/funcs" -t "docs/intro_modules" -sr ".." -mo "intro_doc" + python make_doc_module.py -p "saqc/funcs" -t "docs/func_modules" -sr ".." -mo "registered_doc" + # make rest files from fake modules + python make_doc_rst.py -p "docs/intro_modules" -t "sphinx-doc/intro_modules" -sr ".." + python make_doc_rst.py -p "docs/func_modules" -t "sphinx-doc/func_modules" -sr ".." + # make rest folders from markdown folders + python make_md_to_rst.py -p "sphinx-doc/getting_started_md" -sr ".." + python make_md_to_rst.py -p "sphinx-doc/how_to_doc_md" -sr ".." + # make the html build + @$(SPHINXBUILD) -M html "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + # format docstring module domain strings to correct adresses + python make_html_headings_proppa.py -b "sphinx-doc/_build/html/_api" -p "docs/func_modules" -sr ".." + python make_html_headings_proppa.py -b "sphinx-doc/_build/html/_api" -p "docs/intro_modules" -sr ".." + # clear fake modules/intermediate rest files +# rm -r ../docs + rm -r getting_started_md_m2r + rm -r how_to_doc_md_m2r + # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile diff --git a/sphinx-doc/conf.py b/sphinx-doc/conf.py index 77bdd67bc2a821b18f35e06557be2ea665b6a3c2..dd515fe4d853d2553bb87e17bcd31fb2e91affca 100644 --- a/sphinx-doc/conf.py +++ b/sphinx-doc/conf.py @@ -59,7 +59,7 @@ extensions = [ # https://recommonmark.readthedocs.io/en/latest/ 'recommonmark', # https://github.com/ryanfox/sphinx-markdown-tables - 'sphinx_markdown_tables', + 'sphinx_markdown_tables' ] @@ -75,6 +75,8 @@ automodapi_inheritance_diagram = False automodapi_toctreedirnm = '_api' autosectionlabel_prefix_document = True +autodoc_typehints = "none" + # -- Other options ----------------------------------------------------------- diff --git a/sphinx-doc/func_modules/breaks.rst b/sphinx-doc/func_modules/breaks.rst new file mode 100644 index 0000000000000000000000000000000000000000..309876aa1e9b54b24d9fb95d5ba2b61a8731c64b --- /dev/null +++ b/sphinx-doc/func_modules/breaks.rst @@ -0,0 +1,6 @@ + +breaks +====== + +.. automodapi:: docs.func_modules.breaks + :no-heading: diff --git a/sphinx-doc/func_modules/changepoints.rst b/sphinx-doc/func_modules/changepoints.rst new file mode 100644 index 0000000000000000000000000000000000000000..0c1baba6ff9c5633e02192684f323d49461d1ac1 --- /dev/null +++ b/sphinx-doc/func_modules/changepoints.rst @@ -0,0 +1,6 @@ + +changepoints +============ + +.. automodapi:: docs.func_modules.changepoints + :no-heading: diff --git a/sphinx-doc/func_modules/constants.rst b/sphinx-doc/func_modules/constants.rst new file mode 100644 index 0000000000000000000000000000000000000000..02d8041d170702f7d7589222351e9a47a3997bc7 --- /dev/null +++ b/sphinx-doc/func_modules/constants.rst @@ -0,0 +1,6 @@ + +constants +========= + +.. automodapi:: docs.func_modules.constants + :no-heading: diff --git a/sphinx-doc/func_modules/curvefit.rst b/sphinx-doc/func_modules/curvefit.rst new file mode 100644 index 0000000000000000000000000000000000000000..3f77cf70eff8b8d2ebf7c82338c7f4fe31946636 --- /dev/null +++ b/sphinx-doc/func_modules/curvefit.rst @@ -0,0 +1,6 @@ + +curvefit +======== + +.. automodapi:: docs.func_modules.curvefit + :no-heading: diff --git a/sphinx-doc/func_modules/drift.rst b/sphinx-doc/func_modules/drift.rst new file mode 100644 index 0000000000000000000000000000000000000000..eaab918ea2eef26e858a75cd09c923d4c2311d2f --- /dev/null +++ b/sphinx-doc/func_modules/drift.rst @@ -0,0 +1,6 @@ + +drift +===== + +.. automodapi:: docs.func_modules.drift + :no-heading: diff --git a/sphinx-doc/func_modules/flagtools.rst b/sphinx-doc/func_modules/flagtools.rst new file mode 100644 index 0000000000000000000000000000000000000000..e249cad636b8f020da8e0674a10f42577a5ff038 --- /dev/null +++ b/sphinx-doc/func_modules/flagtools.rst @@ -0,0 +1,6 @@ + +flagtools +========= + +.. automodapi:: docs.func_modules.flagtools + :no-heading: diff --git a/sphinx-doc/func_modules/generic.rst b/sphinx-doc/func_modules/generic.rst new file mode 100644 index 0000000000000000000000000000000000000000..7774afe1e77b7690f55e34ee7bae55dbdf1c4802 --- /dev/null +++ b/sphinx-doc/func_modules/generic.rst @@ -0,0 +1,6 @@ + +generic +======= + +.. automodapi:: docs.func_modules.generic + :no-heading: diff --git a/sphinx-doc/func_modules/interpolation.rst b/sphinx-doc/func_modules/interpolation.rst new file mode 100644 index 0000000000000000000000000000000000000000..3d59ea4d5397bad85e30e60b799ae8d7f3b9c01e --- /dev/null +++ b/sphinx-doc/func_modules/interpolation.rst @@ -0,0 +1,6 @@ + +interpolation +============= + +.. automodapi:: docs.func_modules.interpolation + :no-heading: diff --git a/sphinx-doc/func_modules/outliers.rst b/sphinx-doc/func_modules/outliers.rst new file mode 100644 index 0000000000000000000000000000000000000000..a4a888a57af550a3aaf7d5012c632a8b4da6bebe --- /dev/null +++ b/sphinx-doc/func_modules/outliers.rst @@ -0,0 +1,6 @@ + +outliers +======== + +.. automodapi:: docs.func_modules.outliers + :no-heading: diff --git a/sphinx-doc/func_modules/pattern.rst b/sphinx-doc/func_modules/pattern.rst new file mode 100644 index 0000000000000000000000000000000000000000..c8d13fb5565d6bacb844684e66471d81141dc8a3 --- /dev/null +++ b/sphinx-doc/func_modules/pattern.rst @@ -0,0 +1,6 @@ + +pattern +======= + +.. automodapi:: docs.func_modules.pattern + :no-heading: diff --git a/sphinx-doc/func_modules/resampling.rst b/sphinx-doc/func_modules/resampling.rst new file mode 100644 index 0000000000000000000000000000000000000000..b348b2849c2e60a9dea0bd39a88fd78311e7ff0f --- /dev/null +++ b/sphinx-doc/func_modules/resampling.rst @@ -0,0 +1,6 @@ + +resampling +========== + +.. automodapi:: docs.func_modules.resampling + :no-heading: diff --git a/sphinx-doc/func_modules/residues.rst b/sphinx-doc/func_modules/residues.rst new file mode 100644 index 0000000000000000000000000000000000000000..5e7133cf67c6cdbd5c51c0f933a32b0d3c355cb1 --- /dev/null +++ b/sphinx-doc/func_modules/residues.rst @@ -0,0 +1,6 @@ + +residues +======== + +.. automodapi:: docs.func_modules.residues + :no-heading: diff --git a/sphinx-doc/func_modules/rolling.rst b/sphinx-doc/func_modules/rolling.rst new file mode 100644 index 0000000000000000000000000000000000000000..17425a73909505ecf5f6e1c84d684b1eeab546c6 --- /dev/null +++ b/sphinx-doc/func_modules/rolling.rst @@ -0,0 +1,6 @@ + +rolling +======= + +.. automodapi:: docs.func_modules.rolling + :no-heading: diff --git a/sphinx-doc/func_modules/scores.rst b/sphinx-doc/func_modules/scores.rst new file mode 100644 index 0000000000000000000000000000000000000000..7be28b825177fd78ad20e9799637731a13bbaada --- /dev/null +++ b/sphinx-doc/func_modules/scores.rst @@ -0,0 +1,6 @@ + +scores +====== + +.. automodapi:: docs.func_modules.scores + :no-heading: diff --git a/sphinx-doc/func_modules/tools.rst b/sphinx-doc/func_modules/tools.rst new file mode 100644 index 0000000000000000000000000000000000000000..79c6408a4a8d878fd92b1169620cbba93d647c09 --- /dev/null +++ b/sphinx-doc/func_modules/tools.rst @@ -0,0 +1,6 @@ + +tools +===== + +.. automodapi:: docs.func_modules.tools + :no-heading: diff --git a/sphinx-doc/func_modules/transformation.rst b/sphinx-doc/func_modules/transformation.rst new file mode 100644 index 0000000000000000000000000000000000000000..cec1d2ea5654414175581f498505ea04ee6fec49 --- /dev/null +++ b/sphinx-doc/func_modules/transformation.rst @@ -0,0 +1,6 @@ + +transformation +============== + +.. automodapi:: docs.func_modules.transformation + :no-heading: diff --git a/sphinx-doc/getting_started_md/ConfigurationFiles.md b/sphinx-doc/getting_started_md/ConfigurationFiles.md new file mode 100644 index 0000000000000000000000000000000000000000..64743bb40d211db2a51adfb5ad07c64bbcbdd7be --- /dev/null +++ b/sphinx-doc/getting_started_md/ConfigurationFiles.md @@ -0,0 +1,98 @@ +# Configuration Files +The behaviour of SaQC can be completely controlled by a text based configuration file. + +## Format +SaQC expects configuration files to be semicolon-separated text files with a +fixed header. Each row of the configuration file lists +one variable and one or several test functions that are applied on the given variable. + + +### Header names + +The header names are basically fixed, but if you really insist in custom +configuration headers have a look [here](saqc/core/config.py). + +| Name | Data Type | Description | Required | +|---------|----------------------------------------------|------------------------|----------| +| varname | string | name of a variable | yes | +| test | [function notation](#test-function-notation) | test function | yes | +| plot | boolean (`True`/`False`) | plot the test's result | no | + + +### Test function notation +The notation of test functions follows the function call notation of Python and +many other programming languages and looks like this: +``` +flagRange(min=0, max=100) +``` +Here the function `flagRange` is called and the values `0` and `100` are passed +to the parameters `min` and `max` respectively. As we value readablity +of the configuration more than conciseness of the extrension language, only +keyword arguments are supported. That means that the notation `flagRange(0, 100)` +is not a valid replacement for the above example. + +## Examples +### Single Test +Every row lists one test per variable. If you want to call multiple tests on +a specific variable (and you probably want to), list them in separate rows: +``` +varname | test +#-------|---------------------------------- +x | flagMissing() +x | flagRange(min=0, max=100) +x | constants_flagBasic(window="3h") +y | flagRange(min=-10, max=40) +``` + +### Multiple Tests +A row lists multiple tests for a specific variable in separate columns. All test +columns need to share the common prefix `test`: + +``` +varname ; test_1 ; test_2 ; test_3 +#-------;----------------------------;---------------------------;--------------------------------- +x ; flagMissing() ; flagRange(min=0, max=100) ; constants_flagBasic(window="3h") +y ; flagRange(min=-10, max=40) ; ; +``` +The evaluation of such a configuration is in columns-major order, so the given +example is identical to the following: + +``` +varname ; test_1 +#-------;--------------------------------- +x ; flagMissing() +y ; flagRange(min=-10, max=40) +x ; flagRange(min=0, max=100) +x ; constants_flagBasic(window="3h") + +``` + +### Plotting +As the process of finding a good quality check setup is somewhat experimental, SaQC +provides a possibility to plot the results of the test functions. To use this feature, add the optional column `plot` and set it +to `True` for all results you want to plot. These plots are +meant to provide a quick and easy visual evaluation of the test. +``` +varname ; test ; plot +#-------;----------------------------------;----- +x ; flagMissing() ; +x ; flagRange(min=0, max=100) ; False +x ; constants_flagBasic(window="3h") ; True +y ; flagRange(min=-10, max=40)` ; +``` + +### Regular Expressions in `varname` column +Some of the tests (e.g. checks for missing values, range tests or interpolation +functions) are very likely to be used on all or at least several variables of +the processed dataset. As it becomes quite cumbersome to list all these +variables seperately, only to call the same functions with the same +parameters, SaQC supports regular expressions +within the `varname` column. Please not that a `varname` needs to be quoted +(with `'` or `"`) in order to be interpreted as a regular expression. + +``` +varname ; test +#----------;------------------------------ +'.*' ; harm_shift2Grid(freq="15Min") +'(x \| y)' ; flagMissing() +``` \ No newline at end of file diff --git a/sphinx-doc/getting_started_md/Customizations.md b/sphinx-doc/getting_started_md/Customizations.md new file mode 100644 index 0000000000000000000000000000000000000000..95a9f19b01f2acb0e5cf67065b4db9d6f5f1b74b --- /dev/null +++ b/sphinx-doc/getting_started_md/Customizations.md @@ -0,0 +1,65 @@ +# Customizations +SaQC comes with a continuously growing number of pre-implemented +[quality check and processing routines](sphinx-doc/getting_started_md/FunctionIndex.md) and +flagging schemes. +For any sufficiently large use case however it is very likely that the +functions provided won't fulfill all your needs and requirements. + +Acknowledging the impossibility to address all imaginable use cases, we +designed the system to allow for extensions and costumizations. The main extensions options, namely +[quality check routines](#custom-quality-check-routines) +and the [flagging scheme](#custom-flagging-schemes) +are described within this documents. + +## Custom quality check routines +In case you are missing quality check routines, you are of course very +welcome to file a feature request issue on the project's +[gitlab repository](https://git.ufz.de/rdm-software/saqc). However, if +you are more the "no-way-I-get-this-done-by-myself" type of person, +SaQC provides two ways to integrate custom routines into the system: +1. The [extension language](sphinx-doc/getting_started_md/GenericFunctions.md) +2. An [interface](#interface) to the evaluation machinery + +### Interface +In order to make a function usable within the evaluation framework of SaQC the following interface is needed: + +```python +def yourTestFunction( + data: pandas.DataFrame, + field: str, + flagger: saqc.flagger.BaseFlagger, + *args: Any, + **kwargs: Any + ) -> (dios.DictOfSeries, saqc.flagger.BaseFlagger) +``` + +#### Argument Descriptions + +| Name | Description | +|-----------|--------------------------------------------------------------------------------------------------| +| `data` | The actual dataset. | +| `field` | The field/column within `data`, that function is processing. | +| `flagger` | An instance of a flagger, responsible for the translation of test results into quality attributes. | +| `args` | Any other arguments needed to parameterize the function. | +| `kwargs` | Any other keyword arguments needed to parameterize the function. | + +### Integrate into SaQC +In order make your function available to the system it needs to be registered. We provide the decorator +[`register`](saqc/functions/register.py) in the module `saqc.functions.register` to integrate your +test functions into SaQC. Here is a complete dummy example: + +```python +from saqc.functions.register import register + +@register +def yourTestFunction(data, field, flagger, *args, **kwargs): + return data, flagger +``` + +### Example +The function [`flagRange`](saqc/funcs/functions.py) provides a simple, yet complete implementation of +a quality check routine. You might want to look into its implementation as a reference for your own. + + +## Custom flagging schemes +Sorry for the inconvenience! Coming soon... diff --git a/sphinx-doc/getting_started_md/FlaggingSchemes.md b/sphinx-doc/getting_started_md/FlaggingSchemes.md new file mode 100644 index 0000000000000000000000000000000000000000..edca5253ca8747048e2c4f3738dc9da522726222 --- /dev/null +++ b/sphinx-doc/getting_started_md/FlaggingSchemes.md @@ -0,0 +1,10 @@ +# DMP flagging scheme + +## Possible flags + +The DMP scheme produces the following flag constants: + +* "ok" +* "doubtfull" +* "bad" + diff --git a/sphinx-doc/getting_started_md/GenericFunctions.md b/sphinx-doc/getting_started_md/GenericFunctions.md new file mode 100644 index 0000000000000000000000000000000000000000..c2fa6654488728f72b43eef3188ff07f5d9baab8 --- /dev/null +++ b/sphinx-doc/getting_started_md/GenericFunctions.md @@ -0,0 +1,201 @@ +# Generic Functions + +## Generic Flagging Functions + +Generic flagging functions provide for cross-variable quality +constraints and to implement simple quality checks directly within the configuration. + +### Why? +In most real world datasets many errors +can be explained by the dataset itself. Think of a an active, fan-cooled +measurement device: no matter how precise the instrument may work, problems +are to be expected when the fan stops working or the power supply +drops below a certain threshold. While these dependencies are easy to +[formalize](#a-real-world-example) on a per dataset basis, it is quite +challenging to translate them into generic source code. + +### Specification +Generic flagging functions are used in the same manner as their +[non-generic counterparts](sphinx-doc/getting_started_md/FunctionIndex.md). The basic +signature looks like that: +```sh +flagGeneric(func=<expression>, flag=<flagging_constant>) +``` +where `<expression>` is composed of the [supported constructs](#supported-constructs) +and `<flag_constant>` is one of the predefined +[flagging constants](ParameterDescriptions.md#flagging-constants) (default: `BAD`). +Generic flagging functions are expected to return a boolean value, i.e. `True` or `False`. All other expressions will +fail during the runtime of `SaQC`. + + +### Examples + +#### Simple comparisons + +##### Task +Flag all values of `x` where `y` falls below 0. + +##### Configuration file +``` +varname ; test +#-------;------------------------ +x ; flagGeneric(func=y < 0) +``` + +#### Calculations + +##### Task +Flag all values of `x` that exceed 3 standard deviations of `y`. + +##### Configuration file +``` +varname ; test +#-------;--------------------------------- +x ; flagGeneric(func=x > std(y) * 3) +``` +#### Special functions + +##### Task +Flag all values of `x` where: `y` is flagged and `z` has missing values. + +##### Configuration file +``` +varname ; test +#-------;---------------------------------------------- +x ; flagGeneric(func=isflagged(y) & ismissing(z)) +``` + +#### A real world example +Let's consider the following dataset: + +| date | meas | fan | volt | +|------------------|------|-----|------| +| 2018-06-01 12:00 | 3.56 | 1 | 12.1 | +| 2018-06-01 12:10 | 4.7 | 0 | 12.0 | +| 2018-06-01 12:20 | 0.1 | 1 | 11.5 | +| 2018-06-01 12:30 | 3.62 | 1 | 12.1 | +| ... | | | | + +##### Task +Flag `meas` where `fan` equals 0 and `volt` +is lower than `12.0`. + +##### Configuration file +There are various options. We can directly implement the condition as follows: +``` +varname ; test +#-------;----------------------------------------------- +meas ; flagGeneric(func=(fan == 0) \| (volt < 12.0)) +``` +But we could also quality check our independent variables first +and than leverage this information later on: +``` +varname ; test +#-------;---------------------------------------------------- +'.*' ; flagMissing() +fan ; flagGeneric(func=fan == 0) +volt ; flagGeneric(func=volt < 12.0) +meas ; flagGeneric(func=isflagged(fan) \| isflagged(volt)) +``` + +## Generic Processing + +Generic processing functions provide a way to evaluate mathmetical operations +and functions on the variables of a given dataset. + +### Why +In many real-world use cases, quality control is embedded into a larger data +processing pipeline and it is not unusual to even have certain processing +requirements as a part of the quality control itself. Generic processing +functions make it easy to enrich a dataset through the evaluation of a +given expression. + +### Specification +The basic signature looks like that: +```sh +procGeneric(func=<expression>) +``` +where `<expression>` is composed of the [supported constructs](#supported-constructs). + + +## Variable References +All variables of the processed dataset are available within generic functions, +so arbitrary cross references are possible. The variable of interest +is furthermore available with the special reference `this`, so the second +[example](#calculations) could be rewritten as: +``` +varname ; test +#-------;------------------------------------ +x ; flagGeneric(func=this > std(y) * 3) +``` + +When referencing other variables, their flags will be respected during evaluation +of the generic expression. So, in the example above only values of `x` and `y`, that +are not already flagged with `BAD` will be used the avaluation of `x > std(y)*3`. + + +## Supported constructs + +### Operators + +#### Comparison + +The following comparison operators are available: + +| Operator | Description | +|----------|----------------------------------------------------------------------------------------------------| +| `==` | `True` if the values of the operands are equal | +| `!=` | `True` if the values of the operands are not equal | +| `>` | `True` if the values of the left operand are greater than the values of the right operand | +| `<` | `True` if the values of the left operand are smaller than the values of the right operand | +| `>=` | `True` if the values of the left operand are greater or equal than the values of the right operand | +| `<=` | `True` if the values of the left operand are smaller or equal than the values of the right operand | + +#### Arithmetics +The following arithmetic operators are supported: + +| Operator | Description | +|----------|----------------| +| `+` | addition | +| `-` | subtraction | +| `*` | multiplication | +| `/` | division | +| `**` | exponentiation | +| `%` | modulus | + +#### Bitwise +The bitwise operators also act as logical operators in comparison chains + +| Operator | Description | +|----------|-------------------| +| `&` | binary and | +| `\|` | binary or | +| `^` | binary xor | +| `~` | binary complement | + +### Functions +All functions expect a [variable reference](#variable-references) +as the only non-keyword argument (see [here](#special-functions)) + +#### Mathematical Functions + +| Name | Description | +|-------------|-----------------------------------| +| `abs` | absolute values of a variable | +| `max` | maximum value of a variable | +| `min` | minimum value of a variable | +| `mean` | mean value of a variable | +| `sum` | sum of a variable | +| `std` | standard deviation of a variable | +| `len` | the number of values for variable | + +#### Special Functions + +| Name | Description | +|-------------|-----------------------------------| +| `ismissing` | check for missing values | +| `isflagged` | check for flags | + +### Constants +Generic functions support the same constants as normal functions, a detailed +list is available [here](ParameterDescriptions.md#constants). diff --git a/sphinx-doc/getting_started_md/GettingStarted.md b/sphinx-doc/getting_started_md/GettingStarted.md new file mode 100644 index 0000000000000000000000000000000000000000..e1474bfe042c37160a6f5499ab369e197e8f8277 --- /dev/null +++ b/sphinx-doc/getting_started_md/GettingStarted.md @@ -0,0 +1,295 @@ +# Getting started with SaQC + +Requirements: this tutorial assumes that you have Python version 3.6.1 or newer +installed, and that both your operating system and Python version are in 64-bit. + +## Contents + +1. [Set up your environment](#1-set-up-your-environment) +2. [Get SaQC](#2-get-saqc) +3. [Training tour](#3-training-tour) + * [3.1 Get toy data and configuration](#get-toy-data-and-configuration) + * [3.2 Run SaQC](#run-saqc) + * [3.3 Configure SaQC](#configure-saqc) + * [Change test parameters](#change-test-parameters) + * [3.4 Explore the functionality](#explore-the-functionality) + * [Process multiple variables](#process-multiple-variables) + * [Data harmonization and custom functions](#data-harmonization-and-custom-functions) + * [Save outputs to file](#save-outputs-to-file) + + +## 1. Set up your environment + +SaQC is written in Python, so the easiest way to set up your system to use SaQC +for your needs is using the Python Package Index (PyPI). Following good Python +practice, you will first want to create a new virtual environment that you +install SaQC into by typing the following in your console: + + +##### On Unix/Mac-systems + +```sh +# if you have not installed venv yet, do so: +python3 -m pip install --user virtualenv + +# move to the directory where you want to create your virtual environment +cd YOURDIR + +# create virtual environment called "env_saqc" +python3 -m venv env_saqc + +# activate the virtual environment +source env_saqc/bin/activate +``` + +##### On Windows-systems + +```sh +# if you have not installed venv yet, do so: +py -3 -m pip install --user virtualenv + +# move to the directory where you want to create your virtual environment +cd YOURDIR + +# create virtual environment called "env_saqc" +py -3 -m venv env_saqc + +# move to the Scripts directory in "env_saqc" +cd env_saqc/Scripts + +# activate the virtual environment +./activate +``` + +## 2. Get SaQC + +### Via PyPI + +Type the following: + +##### On Unix/Mac-systems + + +```sh +python3 -m pip install saqc +``` + +##### On Windows-systems + + +```sh +py -3 -m pip install saqc +``` + + +### From Gitlab repository + +Download SaQC directly from the [GitLab-repository](https://git.ufz.de/rdm/saqc) to make sure you use the most recent version: + +```sh +# clone gitlab - repository +git clone https://git.ufz.de/rdm-software/saqc + +# switch to the folder where you installed saqc +cd saqc + +# install all required packages +pip install -r requirements.txt + +# install all required submodules +git submodule update --init --recursive +``` + + +## 3. Training tour + +The following passage guides you through the essentials of the usage of SaQC via +a toy dataset and a toy configuration. + +### Get toy data and configuration + +If you take a look into the folder `saqc/ressources/data` you will find a toy +dataset `data.csv` which contains the following: + + Date,Battery,SM1,SM2 + 2016-04-01 00:05:48,3573,32.685,29.3157 + 2016-04-01 00:20:42,3572,32.7428,29.3157 + 2016-04-01 00:35:37,3572,32.6186,29.3679 + 2016-04-01 00:50:32,3572,32.736999999999995,29.3679 + ... + +These are two timeseries of soil moisture (SM1+2) and the battery voltage of the +measuring device over time. Generally, this is the way that your data should +look like to run saqc. Note, however, that you do not necessarily need a series +of dates to reference to and that you are free to use more columns of any name +that you like. + +Now create your our own configuration file `saqc/ressources/data/myconfig.csv` +and paste the following lines into it: + + varname;test;plot + SM2;flagRange(min=10, max=60);False + SM2;flagMad(window="30d", z=3.5);True + +These lines illustrate how different quality control tests can be specified for +different variables by following the pattern: + +*varname*|;| *testname (testparameters)*|;| *plotting option*| +:---------------|:------|:------|:----|:--| + +In this case, we define a range-test that flags all values outside the range +[10,60] and a test to detect spikes using the MAD-method. You can find an +overview of all available quality control tests in the +[documentation](FunctionIndex.md). Note that the tests are +_executed in the order that you define in the configuration file_. The quality +flags that are set during one test are always passed on to the subsequent one. + +### Run SaQC + +Remember to have your virtual environment activated: + +##### On Unix/Mac-systems + +```sh +source env_saqc/bin/activate +``` + +##### On Windows + +```sh +cd env_saqc/Scripts +./activate +``` + + +Via your console, move into the folder you downloaded saqc into: +```sh +cd saqc +``` + +From here, you can run saqc and tell it to run the tests from the toy +config-file on the toy dataset via the `-c` and `-d` options: +##### On Unix/Mac-systems +```sh +python3 -m saqc -c ressources/data/myconfig.csv -d ressources/data/data.csv +``` +##### On Windows +```sh +py -3 -m saqc -c ressources/data/myconfig.csv -d ressources/data/data.csv +``` + +If you installed saqc via PYPi, you can omit ```sh python -m```. + +The command will output this plot: + + + +So, what do we see here? + +* The plot shows the data as well as the quality flags that were set by the + tests for the variable `SM2`, as defined in the config-file +* Following our definition in the config-file, first the `flagRange`-test that flags + all values outside the range [10,60] was executed and after that, + the `flagMad`-test to identify spikes in the data +* In the config, we set the plotting option to `True` for `flagMad`, + only. Thus, the plot aggregates all preceeding tests (here: `range`) to black + points and highlights the flags of the selected test as red points. + + +#### Save outputs to file +If you want the final results to be saved to a csv-file, you can do so by the +use of the `-o` option: + +```sh +saqc -c ressources/data/config.csv -d ressources/data/data.csv -o ressources/data/out.csv +``` + +Which saves a dataframe that contains both the original data and the quality +flags that were assigned by SaQC for each of the variables: + + Date,SM1,SM1_flags,SM2,SM2_flags + 2016-04-01 00:05:48,32.685,OK,29.3157,OK + 2016-04-01 00:20:42,32.7428,OK,29.3157,OK + 2016-04-01 00:35:37,32.6186,OK,29.3679,OK + 2016-04-01 00:50:32,32.736999999999995,OK,29.3679,OK + ... + + +### Configure SaQC + +#### Change test parameters +Now you can start to change the settings in the config-file and investigate the +effect that has on how many datapoints are flagged as "BAD". When using your +own data, this is your way to configure the tests according to your needs. For +example, you could modify your `myconfig.csv` and change the parameters of the +range-test: + + varname;test;plot + SM2;flagRange(min=-20, max=60);False + SM2;flagMad(window="30d", z=3.5);True +Rerunning SaQC as above produces the following plot: + + + +You can see that the changes that we made to the parameters of the range test +take effect so that only the values > 60 are flagged by it (black points). This, +in turn, leaves more erroneous data that is then identified by the proceeding +spike-test (red points). + +### Explore the functionality +#### Process multiple variables +You can also define multiple tests for multiple variables in your data. These +are then executed sequentially and can be plotted seperately. E.g. you could do +something like this: + + varname;test;plot + SM1;flagRange(min=10, max=60);False + SM2;flagRange(min=10, max=60);False + SM1;flagMad(window="15d", z=3.5);True + SM2;flagMad(window="30d", z=3.5);True + +which gives you separate plots for each line where the plotting option is set to +`True` as well as one summary "data plot" that depicts the joint flags from all +tests: + +|SM1 | SM2 | +|:-------------------------:|:-------------------------:| +|here | there| + + + +|SM1 | SM2 | +|:-------------------------:|:-------------------------:| +|  | | +|  | bumm| + + + +#### Data harmonization and custom functions + +SaQC includes functionality to harmonize the timestamps of one or more data +series. Also, you can write your own tests using a python-based +[extension language](sphinx-doc/getting_started_md/GenericFunctions.md). This would look like this: + + varname;test;plot + SM2;shiftToFreq(freq="15Min");False + SM2;generic(func=(SM2 < 30));True + +The above executes an internal framework that harmonizes the timestamps of SM2 +to a 15min-grid (see data below). Further information about this routine can be +found in the :ref:`Flagging Functions Overview <flaggingFunctions>`. + + Date,SM1,SM1_flags,SM2,SM2_flags + 2016-04-01 00:00:00,,,29.3157,OK + 2016-04-01 00:05:48,32.685,OK,, + 2016-04-01 00:15:00,,,29.3157,OK + 2016-04-01 00:20:42,32.7428,OK,, + ... + +Also, all values where SM2 is below 30 are flagged via the custom function (see +plot below). You can learn more about the syntax of these custom functions +[here](sphinx-doc/getting_started_md/GenericFunctions.md). + + + +:py:func:`lala <docs.func_modules.outliers.flagRange>` \ No newline at end of file diff --git a/sphinx-doc/getting_started_md/ParameterDescriptions.md b/sphinx-doc/getting_started_md/ParameterDescriptions.md new file mode 100644 index 0000000000000000000000000000000000000000..8fcfa0511100177240701bb9338174bf4dfde27a --- /dev/null +++ b/sphinx-doc/getting_started_md/ParameterDescriptions.md @@ -0,0 +1,35 @@ +## Offset Strings +All the [pandas offset aliases](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases) ars supported by SaQC. The following table lists some of the more relevant options: + +| Alias | Description | +| ----- | ----------- | +| `"S"`, `"s"` | second | +| `"T"`, `"Min"`, `"min"` | minute | +| `"H"`, `"h"` | hour | +| `"D"`, `"d"` | calendar day | +| `"W"`, `"w"` | week | +| `"M"`, `"m"` | month | +| `"Y"`, `"y"` | year | + +Multiples are build by preceeding the alias with the desired multiply (e.g `"5Min"`, `"4W"`) + + +## Constants + +### Flagging Constants +The following flag constants are available and can be used to mark the quality of a data point: + +| Alias | Description | +| ---- | ---- | +| `GOOD` | A value did pass all the test and is therefore considered to be valid | +| `BAD` | At least on test failed on the values and is therefore considered to be invalid | +| `UNFLAGGED` | The value has not got a flag yet. This might mean, that all tests passed or that no tests ran | + +How these aliases will be translated into 'real' flags (output of SaQC) dependes on the [flagging scheme](FlaggingSchemes.md) +and might range from numerical values to string constants. + +### Numerical Constants +| Alias | Description | +| ---- | ---- | +| `NAN` | Not a number | +| `NODATA` | Missing data | diff --git a/sphinx-doc/how_to_doc_md/HowToDoc.md b/sphinx-doc/how_to_doc_md/HowToDoc.md new file mode 100644 index 0000000000000000000000000000000000000000..a7536175293fcd61245eaa07a2ae3ca630bb5675 --- /dev/null +++ b/sphinx-doc/how_to_doc_md/HowToDoc.md @@ -0,0 +1,215 @@ +# Documentation Guide + +We document our code via docstrings in numpy-style. +Features, install and usage instructions and other more text intense stuff, +is written in extra documents. +The documents and the docstrings then are collected and rendered using [sphinx](https://www.sphinx-doc.org/). + + +## Documentation Strings + + +- Write docstrings for all public modules, functions, classes, and methods. + Docstrings are not necessary for non-public methods, + but you should have a comment that describes what the method does. + This comment should appear after the def line. + [[PEP8](https://www.python.org/dev/peps/pep-0008/#documentation-strings)] + +- Note that most importantly, the `"""` that ends a multiline docstring should be on a line by itself [[PEP8](https://www.python.org/dev/peps/pep-0008/#documentation-strings)] : + ```python + """Return a foobang + + Optional plotz says to frobnicate the bizbaz first. + """ + ``` + +- For one liner docstrings, please keep the closing `"""` on the same line. + [[PEP8](https://www.python.org/dev/peps/pep-0008/#documentation-strings)] + +### Numpy Style + +We use NumPy-style docstrings. Like this: + +```python +def diag(v, k=0): + """ + Extract a diagonal or construct a diagonal array. + + See the more detailed documentation for ``numpy.diagonal`` if you use this + function to extract a diagonal and wish to write to the resulting array; + whether it returns a copy or a view depends on what version of numpy you + are using. + + Parameters + ---------- + v : array_like + If `v` is a 2-D array, return a copy of its `k`-th diagonal. + If `v` is a 1-D array, return a 2-D array with `v` on the `k`-th + diagonal. + k : int, optional + Diagonal in question. The default is 0. Use `k>0` for diagonals + above the main diagonal, and `k<0` for diagonals below the main + diagonal. + + Returns + ------- + out : ndarray + The extracted diagonal or constructed diagonal array. + + See Also + -------- + diagonal : Return specified diagonals. + diagflat : Create a 2-D array with the flattened input as a diagonal. + trace : Sum along diagonals. + triu : Upper triangle of an array. + tril : Lower triangle of an array. + + Examples + -------- + >>> x = np.arange(9).reshape((3,3)) + >>> x + array([[0, 1, 2], + [3, 4, 5], + [6, 7, 8]]) + >>> np.diag(x) + array([0, 4, 8]) + >>> np.diag(x, k=1) + array([1, 5]) + >>> np.diag(x, k=-1) + array([3, 7]) + >>> np.diag(np.diag(x)) + array([[0, 0, 0], + [0, 4, 0], + [0, 0, 8]]) + """ +``` + + +For a description of the sections read the [numpydoc docstring guide](https://numpydoc.readthedocs.io/en/latest/format.html). +For a more descriptive, fully fledged example see [here](https://numpydoc.readthedocs.io/en/latest/example.html#example). Please use the official type hints, as defined in the [standard library module](https://docs.python.org/3/library/typing.html) `typing` wherever data type information is given. +But mostly the following sections are sufficient: +1. **Always give a *One-line summary*** +2. optionally use *Extended summary* +2. **Always give the *Parameters* Section** with `typing` conform type descriptions +3. **Always give the *Returns* Section** with `typing` conform type descriptions +2. optionally use *See Also* +2. optionally use *Notes* +2. optionally use *Examples* +3. every other Section is even more optional :P +5. And **always check if the `-----` has the same length** as the word it underlines. Seriously, otherwise sphinx will mock, and its really no fun to find and correct these !. + ``` + See Also + -------- + ``` + That's is ok, but following is **not** + + ``` + See Also + --------- + ^ + ``` + +## Flagger, data, field, etc. + +use this: +```py +def foo(data, field, flagger): + """ + data : dios.DictOfSeries + A saqc-data object. + + field : str + A field denoting a column in data. + + flagger : saqc.flagger.BaseFlagger + A saqc-flagger object. + """ +``` + + +### IDE helper + +In pycharm one can activate autogeneration of numpy doc style like so: +1. `File->Settings...` +2. `Tools->Python Integrated Tools` +3. `Docstrings->Docstring format` +4. Choose `NumPy` + + +### Docstring formatting pitfalls + +* Latex is included via :math:\`latex_code\` + + * note, the backticks surrounding the actual code + * Latex commands need to be signified with **double** backlash! (``\\mu`` instead of ``\mu``) + +* Nested lists need to be all of the same kind (either numbered or marked - otherwise result is salad) +* List items covering several lines in the docstring have to be all aligned - (so, not only the superfluent ones, but ALL, including the first one - otherwise result is salad) +* Start of a list has to be seperated from preceding docstring code by *one blank line* - (otherwise list items get just chained in one line and result is salad) +* Most formatting signifiers are not allowed to start or end with a space. (so no :math: \`1+1 \`, \` var2\`, \`\` a=1 \`\`, ...) +* Do not include lines *only* containing two or more `-` signs, except it is the underscore line of the section heading (otherwise resulting html representation could be messed up) + +## Adding Markdown content to the Documentation + +* If you generate cookbooks and/or tutorials in markdown and want them to be integrated in the sphinx doc - there are some obstaclish thingies to care for + +- You will have to gather all markdown files in subfolders of "sphinx-doc". + +- To include a folder named 'foo_md' of markdown files in the documentation, you will have to add the following line to the Makefile: + +```python +python make_md_to_rst.py -p "sphinx-doc/gfoo_md" +``` + +- The markdown files must be in that subfolders - they cant be gathered in nested subfolders. + +- You can not link to sections in other markdown files, that contain the `-` character. + +- The Section structure/ordering must be consistent in the ReST sence (otherwise they wont appear) + +- You can link to ressources - like pictures and include them in the markdown, if the pictures are in (possibly another) folder in `\sphinx-doc` and the pathes to this ressources are given relatively! + +- You can include a markdown file in a rest document, by appending '_m2r' to the folder name. So to include the markdown file 'foo_md/bar.md' in a toc tree for example - you would do something like: +```python +.. toctree:: + :hidden: + :maxdepth: 1 + + foo_md_m2r/bar +``` + +- If you want to hyperlink/include other sources from the sphinx documentation that are rest-files, you will not be able to include them in a way, that they will appear in you markdown rendering. - however - there is the slightly hacky possibillity to just include the respective rest directives. This will mess up your markdown code - meaning that you will have those rest snippets flying around, but when the markdown file gets converted to the rest file and build into the sphinx html build, the linked sources will be integrated properly. The syntax is as follows: + +- to include the link to the rest source `functions.rst` in the folder `foo`, under the name `bar`, you would need to insert: +```python +:doc:`foo <rel_path/functions>` +``` + +- to link to a section with name `foo` in a rest source named `bumm.rst`, under the name `bar`, you would just insert: +```python +:ref:`bar <relative/path/from/sphinx/root/bumm:foo>` +``` + +- in that manner you might be able to smuggle most rest directives through into the resulting html build. Especially if you want to link to the docstrings of certain (domain specific) objects. Lets say you want to link to the *function* `saqc.funcs.flagRange` under the name `ranger` - you just include: + +```python +:py:func:`Ranger <saqc.funcs.flagRange>` +``` + +whereas the `:func:` part determines the role, the object is documented as. See [this page](https://www.sphinx-doc.org/en/master/#ref-role) for an overview of the available roles + +## Refering to documented Functions + +* Since the documentation generates an own module structure to document the functions, linking to the documented functions is a bit hacky: + +- Functions: to link to any function in the 'saqc.funcs' module - you will have to link the rest file it is documented in. All functions from the function module can be linked via replacing the 'saqc.funcs' part of the module path by 'docs.func_modules': + +- For example, 'saqc.funcs.outliers.flagRange' is linked via: +```python +:py:func:`docs.func_modules.outliers.flagRange` +``` + +To hide the temporal module structure and/or make transparent the intended module structure, use named links, like so: +```python +:py:func:`saqc.outliers.flagRange <docs.func_modules.outliers.flagRange>` +``` diff --git a/sphinx-doc/index.rst b/sphinx-doc/index.rst index c8d40fbd90e200486bdc243c67445ccd0e690015..cfe2a734f3c55e98e15385f6991cfcea7dbb863a 100644 --- a/sphinx-doc/index.rst +++ b/sphinx-doc/index.rst @@ -1,27 +1,51 @@ -.. SaQC documentation master file, created by - sphinx-quickstart on Mon Aug 17 12:11:29 2020. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to SaQC's documentation! -================================ +================== +SaQC documentation +================== Saqc is a great tool to clean data from rubbish. .. toctree:: :hidden: + :maxdepth: 1 Repository <https://git.ufz.de/rdm-software/saqc> + Documentation Guide <how_to_doc_m2r/HowToDoc.rst> + + +Getting Started +=============== .. toctree:: :maxdepth: 2 - flagger + getting_started_md_m2r/GettingStarted + + +Flagging Functions +================== + +Flagging Functions .. toctree:: :maxdepth: 2 + :glob: + :titlesonly: + + intro_modules/* + + +Func Modules +============ + +Function modules + +.. toctree:: + :maxdepth: 3 + :glob: + :titlesonly: + + func_modules/* - FlagFunctions Indices and tables ================== diff --git a/sphinx-doc/intro_modules/AdvancedFlagging.rst b/sphinx-doc/intro_modules/AdvancedFlagging.rst new file mode 100644 index 0000000000000000000000000000000000000000..3fc3e5f13968f542df5a449c0bac962014a95888 --- /dev/null +++ b/sphinx-doc/intro_modules/AdvancedFlagging.rst @@ -0,0 +1,6 @@ + +AdvancedFlagging +================ + +.. automodapi:: docs.intro_modules.AdvancedFlagging + :no-heading: diff --git a/sphinx-doc/intro_modules/BasicFlagging.rst b/sphinx-doc/intro_modules/BasicFlagging.rst new file mode 100644 index 0000000000000000000000000000000000000000..265498330e2f3881778dcf8d167adefaeb58eb81 --- /dev/null +++ b/sphinx-doc/intro_modules/BasicFlagging.rst @@ -0,0 +1,6 @@ + +BasicFlagging +============= + +.. automodapi:: docs.intro_modules.BasicFlagging + :no-heading: diff --git a/sphinx-doc/make_doc_module.py b/sphinx-doc/make_doc_module.py new file mode 100644 index 0000000000000000000000000000000000000000..b2f8ff48462dca6456abb4f993f8874ac2860628 --- /dev/null +++ b/sphinx-doc/make_doc_module.py @@ -0,0 +1,194 @@ +import ast +import os +import click +import pkgutil +import shutil +import re +from collections import OrderedDict +import pickle + +new_line_re = "(\r\n|[\r\n])" + +doc_mod_structure = {'BasicFlagging': ['outliers.flagRange', + 'breaks.flagMissing'], + 'BasicFlagging_dcstring': '', + 'AdvancedFlagging': ['pattern.flagPatternByDTW', + 'outliers.flagOffset'], + 'AdvancedFlagging_dcstring': ''} + + +def rm_section(dcstring, section, _return_section=False): + """ + Detects a section in a docstring and (default) removes it, or (_return_section=True) returns it + """ + section_re = (f'{new_line_re}(?P<s_name>[^\n\r]{{2,}}){new_line_re}(?P<s_dash>-{{2,}}){new_line_re}') + triggers = re.finditer(section_re, dcstring) + matches = [(trigger.groupdict()['s_name'], trigger.span()) for trigger in triggers if + len(trigger.groupdict()['s_name']) == len(trigger.groupdict()['s_dash'])] + \ + [(None, (len(dcstring), None))] + sections = [m[0] for m in matches] + starts = ends = 0 + if section in sections: + i = sections.index(section) + starts = matches[i][1][0] + ends = matches[i + 1][1][0] + + if _return_section: + return dcstring[starts:ends] + else: + return dcstring[:starts] + dcstring[ends:] + + +def rm_parameter(dcstring, parameter): + """ + remove a parameters documentation from a function docstring + """ + paramatches = _get_paramatches(dcstring) + start = end = 0 + for p in paramatches: + if parameter == p.groupdict()['paraname']: + start = re.search(p[0], dcstring).span()[0] + try: + end = dcstring.find(next(paramatches)[0]) + except(StopIteration): + end = len(re.sub(new_line_re + '$', '', dcstring)) + + return dcstring[0:start] + dcstring[end:] + + +def get_parameter(dcstr): + """ + returns the list of parameters and their defaults, documented in a docstrings Parameters section + """ + paramatches = _get_paramatches(dcstr) + return [(p.groupdict()['paraname'], p.groupdict()['paradefaults']) for p in paramatches] + + +def _get_paramatches(dcstr): + parastr = rm_section(dcstr, 'Parameters', _return_section=True) + match_re = f"{new_line_re}(?P<paraname>[\S]+) : [^\n\r]*(default (?P<paradefaults>[^\n\r]*))?" + return re.finditer(match_re, parastr) + + +def parse_func_dcstrings(m_paths): + func_dict = {} + for m in m_paths: + with open(m) as f: + lines = f.readlines() + module_ast = ast.parse(''.join(lines)) + funcs = [node for node in module_ast.body if isinstance(node, ast.FunctionDef)] + for func in funcs: + dcstr = ast.get_docstring(func) + if func.name[0] == '_' or (dcstr is None): + continue + dcstr = rm_section(dcstr, 'Returns') + dcstr = rm_parameter(dcstr, 'data') + dcstr = rm_parameter(dcstr, 'flagger') + parameters = get_parameter(dcstr) + parameters = [f"{p[0]}={p[1]}" if p[1] else p[0] for p in parameters] + signature = f"def {func.name}({', '.join(parameters)}):" + # get @register module registration if present + reg_module = None + r = [d for d in func.decorator_list if d.func.id == 'register'] + if r: + rm = [kw.value.s for kw in r[0].keywords if kw.arg == 'module'] + if rm: + reg_module = rm[0] + + func_dict[f"{os.path.splitext(os.path.basename(m))[0]}.{func.name}"] = (signature, dcstr, reg_module) + + return func_dict + + + +def parse_module_dcstrings(m_paths): + mod_dict = {} + for m in m_paths: + with open(m) as f: + lines = f.readlines() + + mod_docstr = ast.get_docstring(ast.parse(''.join(lines))) + mod_dict[f"{os.path.splitext(os.path.basename(m))[0]}"] = mod_docstr or '' + return mod_dict + + +def make_doc_module(targetpath, func_dict, doc_mod_structure): + for doc_mod in [d for d in doc_mod_structure.keys() if not re.search('_dcstring$', d)]: + with open(os.path.join(targetpath, f"{doc_mod}.py"), 'w+') as f: + mod_string = ['"""\n' + doc_mod_structure[doc_mod + '_dcstring'] + '\n"""'] + mod_funcs = doc_mod_structure[doc_mod] + for func in mod_funcs: + mod_string.append(func_dict[func][0]) + mod_string.append(' """') + # indent the docstring: + indented_doc_string = '\n'.join([f" {l}" for l in func_dict[func][1].splitlines()]) + mod_string.append(indented_doc_string) + mod_string.append(' """') + mod_string.append(' pass') + mod_string.append('') + mod_string.append('') + f.write('\n'.join(mod_string)) + + with open(os.path.join(targetpath, 'module_dict.pkl'), 'wb+') as file: + pickle.dump(doc_mod_structure, file) + + return 0 + + +@click.command() +@click.option( + "-p", "--pckpath", type=str, required=True, default="saqc/funcs", + help="Relative path to the package to be documented (relative to sphinx root)." +) +@click.option( + "-t", "--targetpath", type=str, required=True, default="docs/intro_modules", + help="Output folder path (relative to sphinx root). Will be overridden if already existent." +) +@click.option( + "-sr", "--sphinxroot", type=str, required=True, default='..', help="Relative path to the sphinx root." +) +@click.option( + "-mo", "--mode", type=str, required=True, default='intro_doc', help="either 'intro_doc' or 'module_doc'." +) + +def main(pckpath, targetpath, sphinxroot, mode): + root_path = os.path.abspath(sphinxroot) + pkg_path = os.path.join(root_path, pckpath) + targetpath = os.path.join(root_path, targetpath) + modules = [] + # collect modules + for _, modname, _ in pkgutil.walk_packages(path=[pkg_path], onerror=lambda x: None): + modules.append(modname) + + # clear target dir + if os.path.isdir(targetpath): + shutil.rmtree(targetpath) + os.makedirs(targetpath, exist_ok=True) + + # parse all the functions + module_paths = [os.path.join(pkg_path, f'{m}.py') for m in modules] + mod_dict = parse_module_dcstrings(module_paths) + func_dict = parse_func_dcstrings(module_paths) + if mode == 'intro_doc': + make_doc_module(targetpath, func_dict, doc_mod_structure) + if mode == 'registered_doc': + doc_struct = {} + for dm in func_dict.keys(): + module = func_dict[dm][2] + if module: + if module in doc_struct.keys(): + doc_struct[module].append(dm) + else: + doc_struct[module] = [dm] + doc_struct[module + '_dcstring'] = mod_dict[module] + make_doc_module(targetpath, func_dict, doc_struct) + if mode == 'module_doc': + doc_struct = {m:[] for m in modules} + for dm in func_dict.keys(): + module = re.search('([^ .]*)\.[^ ]*$', dm).group(1) + doc_struct[module].append(dm) + make_doc_module(targetpath, func_dict, doc_struct) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/sphinx-doc/make_doc_rst.py b/sphinx-doc/make_doc_rst.py new file mode 100644 index 0000000000000000000000000000000000000000..4271c324107b2d685b9c5e6ca49844643f9a88ff --- /dev/null +++ b/sphinx-doc/make_doc_rst.py @@ -0,0 +1,58 @@ +import os +import click +import pkgutil +import ast +import shutil + +def parse_imports(path): + modules = [] + file = open(path) + lines = file.readlines() + for node in ast.iter_child_nodes(ast.parse(''.join(lines))): + if isinstance(node, ast.ImportFrom) | isinstance(node, ast.Import): + modules += [x.name for x in node.names] + [x.asname for x in node.names if x.asname is not None] + file.close() + return modules + +@click.command() +@click.option( + "-p", "--pckpath", type=str, required=True, default="saqc/funcs", + help="Relative path to the package to be documented (relative to sphinx root)." +) +@click.option( + "-t", "--targetpath", type=str, required=True, default="sphinx-doc/internal_doc_rst", + help="Output folder path (relative to sphinx root). Will be overridden if already existent." +) +@click.option( + "-sr", "--sphinxroot", type=str, required=True, default='..', help="Relative path to the sphinx root." +) + +def main(pckpath, targetpath, sphinxroot): + root_path = os.path.abspath(sphinxroot) + targetpath = os.path.join(root_path, targetpath) + pkg_path = os.path.join(root_path, pckpath) + modules = [] + for _, modname, _ in pkgutil.walk_packages(path=[pkg_path], onerror=lambda x: None): + modules.append(modname) + + emptyline = [""] + + # clear target directory: + if os.path.isdir(targetpath): + shutil.rmtree(targetpath) + os.mkdir(targetpath) + + for module in modules: + imports = parse_imports(os.path.join(pkg_path, f'{module}.py')) + skiplist = [f'\t:skip: {k}' for k in imports] + section = [module] + ["="*len(module)] + automodapi_directive = [".. automodapi:: " + pckpath.replace('/', '.') + '.' + module] + no_heading = [f'\t:no-heading:'] + to_write = emptyline + section + emptyline + automodapi_directive + skiplist + no_heading + to_write = "".join([f'{k}\r\n' for k in to_write]) + with open(os.path.join(targetpath, f'{module}.rst'), 'w+') as f: + f.write(to_write) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/sphinx-doc/make_html_headings_proppa.py b/sphinx-doc/make_html_headings_proppa.py new file mode 100644 index 0000000000000000000000000000000000000000..6e4d6b7daf106c5e0ff393e376f2e8343877ec0d --- /dev/null +++ b/sphinx-doc/make_html_headings_proppa.py @@ -0,0 +1,52 @@ + +import os +import click +import re +import pickle + + +@click.command() +@click.option( + "-b", "--buildpath", type=str, required=True, default="sphinx-doc/_build/html/_api", + help="Relative path to the html api files to be manipulated (relative to sphinx root)." +) +@click.option( + "-sr", "--sphinxroot", type=str, required=True, default='..', help="Relative path to the sphinx root." +) +@click.option( + "-p", "--pckpath", type=str, required=True, default="docs/doc_modules/func_modules", + help="Relative path to the documented package (relative to sphinx root)." +) + +def main(buildpath, sphinxroot, pckpath): + root_path = os.path.abspath(sphinxroot) + buildpath = os.path.join(root_path, buildpath) + pckpath = os.path.join(root_path, pckpath) + files = os.listdir(buildpath) + # gather all files from the doc module + files = [f for f in files if re.search('^docs\.',f)] + with open(os.path.join(pckpath, 'module_dict.pkl'), 'rb') as file_: + doc_mod_structure = pickle.load(file_) + + + for key in doc_mod_structure.keys(): + # search for all function files assigned to the module + mod_f = [f for f in files if re.search(f'(^|[.]){key}\.[^.]*\.html',f)] + for file_ in mod_f: + parts = file_.split('.') + func = parts[-2] + module_domain = '.'.join(parts[:-2]) + + with open(os.path.join(buildpath, file_), 'r') as wf: + code = wf.read() + + old_domain_str = f'<code class="sig-prename descclassname">{module_domain}' + new_domain = [f.split('.')[0] for f in doc_mod_structure[key] if f.split('.')[1] == func][0] + new_domain_str = f'<code class="sig-prename descclassname">{new_domain}' + code = code.replace(old_domain_str, new_domain_str) + with open(os.path.join(buildpath, file_), 'w+') as wf: + wf.write(code) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/sphinx-doc/make_md_to_rst.py b/sphinx-doc/make_md_to_rst.py new file mode 100644 index 0000000000000000000000000000000000000000..7c1663d76a951632fa5f1f3ee826cd2d614d9118 --- /dev/null +++ b/sphinx-doc/make_md_to_rst.py @@ -0,0 +1,153 @@ + +""" +The script generates a folder of rest files from a folder of markdown files. +Markdown Hyperlinks between the files in the folder get converted to rest links so that they function properly in a +sphinx generated html build obtained from the resulting rest folder. +""" + +import os +import click +import shutil +from m2r import parse_from_file +import re + +new_line_re = "(\r\n|[\r\n])" + +def rebaseAbsRoot(path, target, root): + """ + If path and target intersect at root, return relative path from path to target + Functionality is limited. + path and target must be path strings pointing at FILES! + root is only allowed to appear once in every path + you cant root to os.sep (no folder seperators allowed in the root string) + """ + + p = path.find(root) + t = target.find(root) + if (p == -1) or (t == -1) or ('..' in path): + return target + + path = path[path.find(root):].split(os.sep) + target = target[target.find(root):].split(os.sep) + # remove common path chunks: + while path[0] == target[0]: + del path[0] + del target[0] + + up_steps = (len(path) - 1)*f'..{os.sep}' + down_steps = os.sep.join(target) + new_path = os.path.join(up_steps, down_steps) + return new_path + +def fixTables(f_rst): + body_re = f'((.+){new_line_re})*{new_line_re}((.+){new_line_re})*' + tables = list(re.finditer(f'\.\. list-table::{new_line_re}' + body_re, f_rst)) + for t in tables: + tab = t[0] + def pic_repl(match): + leading = match.groupdict()['list_level'] + pic_dir = match.groupdict()['pic_directive'] + pic_pad = re.match('^[ ]*', pic_dir).span()[1] + pic_dir = re.sub(f'{" " * pic_pad}', " " * len(leading), pic_dir) + pic_dir = leading + pic_dir[len(leading):] + end_space = re.search(f'{new_line_re}[ ]*$', match[0]) + if end_space: + pic_dir = re.sub(f'{new_line_re}[ ]*$', end_space[0], pic_dir) + return pic_dir + messy_re = f'(?P<list_level>.*){new_line_re}(?P<pic_directive>[ ]*.. image::[^*-]*)' + # using while loop cause messed pic patterns overlap + tab, repnum = re.subn(messy_re, pic_repl, tab, 1) + while repnum: + tab, repnum = re.subn(messy_re, pic_repl, tab, 1) + + bullets = tab.split(' *')[1:] + items = [bullet.split(' -') for bullet in bullets] + last_items = items[-1] + item_num = len(items[0]) + last_item_num = len(last_items) + if item_num > last_item_num: + has_content = len([content for content in last_items if re.search('[^\s-]', content)]) > 0 + if has_content: + # append empty cells + tab = tab + (' - \n'*(item_num - last_item_num)) + else: + # delete last row (using replace to avoid false meta char interpretation + tab = tab.replace(bullets[-1][0], '') + + bullet_num = len(list(re.finditer(f' \*(?P<items>([ ]+-.*{new_line_re})*)', tab))) + if bullet_num == 1: + #fix empty body table error: + tab = re.sub(':header-rows: [0-9]', ':header-rows: 0', tab) + + if tab != t[0]: + f_rst = f_rst.replace(t[0], tab) + + return f_rst + + +def fixLinks(f_rst, f ,targetpath): + md_links = list( + re.finditer('(?P<numbered>\. )?`(?P<link_name>[^<`]*) <(?P<md_link>\S*.md)?(#)?(?P<section>[^>]*)?>`_?', f_rst)) + for link in md_links: + # change directory: + link_path = link.groupdict()['md_link'] + if not link_path: + link_path = f + # change directory to point at temporal rest dir (if link isnt relative): + if os.path.dirname(link_path) is not '': + link_path = os.path.join(os.path.dirname(link_path) + '_m2r', os.path.basename(link_path)) + # rebase the link to relative link if its not + link_path = rebaseAbsRoot(os.path.join(targetpath, f), link_path, 'sphinx-doc') + # remove extension name (rst syntax) + link_path = re.sub('\.md$', '', link_path) + if link.groupdict()['section']: + # while document links have to be relative - section links have to be absolute from sphinx doc dir - + # markdown space representation by dash has to be removed... + abs_path = os.path.basename(os.path.abspath('')) + abs_path = targetpath[targetpath.find(abs_path) + len(abs_path) + 1:] + link_path = os.path.join(abs_path, os.path.basename(link_path)) + role = ':ref:' + section = ':' + link.groupdict()['section'].replace('-', ' ') + # one more regex spell for the sake of numbered section linking: + if link.groupdict()['numbered']: + section = re.sub('(:[0-9]+)', '\g<1>.', section) + else: + role = ':doc:' + section = '' + + f_rst = re.sub(f'`(?P<link_name>{link.groupdict()["link_name"]}) ' + f'<({link.groupdict()["md_link"]})?(#[^>]*)?>`(_)?', + r'{}`\g<link_name> <{}{}>`'.format(role, link_path, section), f_rst) + return f_rst + + +@click.command() +@click.option( + "-p", "--mdpath", type=str, required=True, default="sphinx-doc/getting_started_md", + help="Relative path to the folder containing the .md files to be converted (relative to sphinx root)." +) +@click.option( + "-sr", "--sphinxroot", type=str, required=True, default='..', help="Relative path to the sphinx root." +) +def main(mdpath, sphinxroot): + root_path = os.path.abspath(sphinxroot) + mdpath = os.path.join(root_path, mdpath) + targetpath = mdpath + "_m2r" + + # clear target directory: + if os.path.isdir(targetpath): + shutil.rmtree(targetpath) + os.mkdir(targetpath) + + mdfiles = [f for f in os.listdir(mdpath) if os.path.splitext(f)[1] == '.md'] + for f in mdfiles: + f_rst = parse_from_file(os.path.join(mdpath, f)) + # regex magic- replace invalid links: + f_rst = fixLinks(f_rst, f, targetpath) + f_rst = fixTables(f_rst) + with open(os.path.join(targetpath, f.replace('.md', '.rst')), 'w+') as file_: + file_.write(f_rst) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/sphinx-doc/ressources/data/config.csv b/sphinx-doc/ressources/data/config.csv new file mode 100644 index 0000000000000000000000000000000000000000..c8f7f803de8e32aafba102654ee9112aaa3e659d --- /dev/null +++ b/sphinx-doc/ressources/data/config.csv @@ -0,0 +1,6 @@ +varname ; test ; plot +#----------;-------------------------------------;------ +SM2 ; shift(freq="15Min") ; False +SM2 ; flagMissing(nodata=NAN) ; False +'SM(1|2)+' ; flagRange(min=10, max=60) ; False +SM2 ; flagMAD(window="30d", z=3.5) ; True diff --git a/sphinx-doc/ressources/data/config_ci.csv b/sphinx-doc/ressources/data/config_ci.csv new file mode 100644 index 0000000000000000000000000000000000000000..74ddfbae40943f3bbcc75e58fbab4379e39815c4 --- /dev/null +++ b/sphinx-doc/ressources/data/config_ci.csv @@ -0,0 +1,7 @@ +varname;test;plot +SM2;shift(freq="15Min");False +'.*';flagRange(min=10, max=60);False +SM2;flagMissing(nodata=NAN);False +SM2;flagRange(min=10, max=60);False +SM2;flagMAD(window="30d", z=3.5);False +Dummy;flag(func=(isflagged(SM1) | isflagged(SM2))) diff --git a/sphinx-doc/ressources/data/data.csv b/sphinx-doc/ressources/data/data.csv new file mode 100644 index 0000000000000000000000000000000000000000..bb853b10bcffe98dbfc8d11348bcfe3fdf47eb9a --- /dev/null +++ b/sphinx-doc/ressources/data/data.csv @@ -0,0 +1,14694 @@ +Date,Battery,SM1,SM2 +2016-04-01 00:05:48,3573,32.685,29.3157 +2016-04-01 00:20:42,3572,32.7428,29.3157 +2016-04-01 00:35:37,3572,32.6186,29.3679 +2016-04-01 00:50:32,3572,32.736999999999995,29.3679 +2016-04-01 01:05:26,3572,32.736999999999995,29.3131 +2016-04-01 01:20:21,3571,32.6186,29.3157 +2016-04-01 01:35:16,3571,32.736999999999995,29.3157 +2016-04-01 01:50:11,3571,32.736999999999995,29.4727 +2016-04-01 02:05:05,3571,32.6186,29.5252 +2016-04-01 02:20:00,3571,32.911,29.5252 +2016-04-01 02:34:55,3570,32.85,29.5226 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