.. _ts2img: ====== ts2img ====== Introduction ============ Conversion of time series data to images (ts2img) is a general problem that we have to deal with often for all kinds of datasets. Although most of the external datasets we use come in image format most of them are converted into a time series format for analysis. This is fairly straightforward for image stacks but gets more complicated for orbit data. Orbit data mostly comes as several data values accompanied by latitude, longitude information and must often be resampled to fit on a grid that lends itself for time series analysis. A more or less general solution for this already exists in the img2ts module. Possible steps involved in the conversion ========================================= The steps that a ts2img program might have to perform are: 1. Read time series in a geographic region - constrained by memory 2. Aggregate time series in time - methods for doing this aggregation can vary for each dataset so the method is best specified by the user - resolution in time has to be chosen and is probably also best specified by the user - after aggregation every point in time and in space must have a value which can of course be NaN - time series might have to be split into separate images during conversion, e.g. ASCAT time series are routinely split into images for ascending and descending satellite overpasses. **This means that we can not assume that the output dataset has the same number or names of variables as the input dataset.** 3. Put the now uniform time series of equal length into a 2D array per variable 4. A resampling step could be performed here but since only a part of the dataset is available edge cases would not be resolved correctly. A better solution would be to develop a good resampling tool which might already exist in pyresample and pytesmo functions that use it. 5. write this data into a file - this can be a netCDF file with dimensions of the grid into which the data is written - this could be any other file format, the interface to this format just has to make sure that in the end a consistent image dataset is built out of the parts that are written. Solution ======== The chosen first solution uses netCDF as an output format. The output will be a stack of images in netCDF format. This format can easily be converted into substacks or single images if that is needed for a certain user or project. The chosen solution will **not** do resampling since this is better and easier done using the whole converted dataset. This also means that if the input dataset is e.g. a dataset defined over land only then the resulting "image" will also not contain land points. I think it is best to let this be decided by the input dataset. The output of the resulting netCDF can have one of two possible "shapes": - 2D variables with time on one axis and gpi on the other. This is kind of how SWI time series are stored already. - 3D variables with latitude, longitude and time as the three dimensions. The decision of which it will be is dependent on the grid on which the input data is stored. If the grid has a 2D shape then the 3D solution will be chosen. If the input grid has only a 1D shape then only the 2D solution is possible. Time Series aggregation ----------------------- The chosen solution will use a custom function for each dataset to perform the aggregation if necessary. A simple example of a function that gets a time time series and aggregates it to a monthly time series could look like *agg\_tsmonthly* Simple example of a aggregation function .. code:: python def agg_tsmonthly(ts, **kwargs): """ Parameters ---------- ts : pandas.DataFrame time series of a point kwargs : dict any additional keyword arguments that are given to the ts2img object during initialization Returns ------- ts_agg : pandas.DataFrame aggregated time series, they all must have the same length otherwise it can not work each column of this DataFrame will be a layer in the image """ # very simple example # aggregate to monthly timestamp # should also make sure that the output has a certain length return ts.asfreq("M") Time series iteration --------------------- The function ``agg_tsmonthly`` will be called for every time series in the input dataset. The input dataset must have a ``iter_ts`` iterator that iterates over the grid points in a sensible order. Interface to the netCDF writer ------------------------------ The netCDF writer will be initialized outside the *ts2img* class with a filename and other attributes it needs. So the *ts2img* class only gets a writer object. This writer object already knows about the start and end date of the time series as well as the target grid and has initialized the correct dimensions in the netCDF file. This object must have a method ``write_ts`` which takes a array of gpi's and a 2D array containing the time series for these gpis. This should be enough to write the gpi's into the correct position of the netCDF file. This approach should also work if another output format is supposed to be used. Implementation of the main ts2img class --------------------------------------- The ts2img class will automatically use a the function given in ``agg_ts2img`` if no custom ``agg_ts2img`` function is provided. If the tsreader implements a method called ``agg_ts2img`` this function will be used instead. .. code:: python class Ts2Img(object): """ Takes a time series dataset and converts it into an image dataset. A custom aggregate function should be given otherwise a daily mean will be used Parameters ---------- tsreader: object object that implements a iter_ts method which iterates over pandas time series and has a grid attribute that is a pytesmo BasicGrid or CellGrid imgwriter: object writer object that implements a write_ts method that takes a list of grid point indices and a 2D array containing the time series data agg_func: function function that takes a pandas DataFrame and returns an aggregated pandas DataFrame ts_buffer: int how many time series to read before writing to disk, constrained by the working memory the process should use. """ def __init__(self, tsreader, imgwriter, agg_func=None, ts_buffer=1000): self.agg_func = agg_func if self.agg_func is None: try: self.agg_func = tsreader.agg_ts2img except AttributeError: self.agg_func = agg_tsmonthly self.tsreader = tsreader self.imgwriter = imgwriter self.ts_buffer = ts_buffer def calc(self, **tsaggkw): """ does the conversion from time series to images """ for gpis, ts in self.tsbulk(**tsaggkw): self.imgwriter.write_ts(gpis, ts) def tsbulk(self, gpis=None, **tsaggkw): """ iterator over gpi and time series arrays of size self.ts_buffer Parameters ---------- gpis: iterable, optional if given these gpis will be used, can be practical if the gpis are managed by an external class e.g. for parallel processing tsaggkw: dict Keywords to give to the time series aggregation function Returns ------- gpi_array: numpy.array numpy array of gpis in this batch ts_bulk: dict of numpy arrays for each variable one numpy array of shape (len(gpi_array), len(ts_aggregated)) """ # have to use the grid iteration as long as iter_ts only returns # data frame and no time series object including relevant metadata # of the time series i = 0 gpi_bulk = [] ts_bulk = {} ts_index = None if gpis is None: gpis, _, _, _ = self.tsreader.grid.grid_points() for gpi in gpis: gpi_bulk.append(gpi) ts = self.tsreader.read_ts(gpi) ts_agg = self.agg_func(ts, **tsaggkw) for column in ts_agg.columns: try: ts_bulk[column].append(ts_agg[column].values) except KeyError: ts_bulk[column] = [] ts_bulk[column].append(ts_agg[column].values) if ts_index is None: ts_index = ts_agg.index i += 1 if i >= self.ts_buffer: for key in ts_bulk: ts_bulk[key] = np.vstack(ts_bulk[key]) gpi_array = np.hstack(gpi_bulk) yield gpi_array, ts_bulk ts_bulk = {} gpi_bulk = [] i = 0 if i > 0: for key in ts_bulk: ts_bulk[key] = np.vstack(ts_bulk[key]) gpi_array = np.hstack(gpi_bulk) yield gpi_array, ts_bulk