code: def merge(
      datasets,
      bounds=None,
      res=None,
      nodata=None,
      dtype=None,
      precision=None,
      indexes=None,
      output_count=None,
      resampling=Resampling.nearest,
      method="first",
      target_aligned_pixels=False,
      dst_path=None,
      dst_kwds=None,
  ):
      """Copy valid pixels from input files to an output file.
  
      All files must have the same number of bands, data type, and
      coordinate reference system.
  
      Input files are merged in their listed order using the reverse
      painter's algorithm (default) or another method. If the output file exists,
      its values will be overwritten by input values.
  
      Geospatial bounds and resolution of a new output file in the
      units of the input file coordinate reference system may be provided
      and are otherwise taken from the first input file.
  
      Parameters
      ----------
      datasets : list of dataset objects opened in 'r' mode, filenames or PathLike objects
          source datasets to be merged.
      bounds: tuple, optional
          Bounds of the output image (left, bottom, right, top).
          If not set, bounds are determined from bounds of input rasters.
      res: tuple, optional
          Output resolution in units of coordinate reference system. If not set,
          the resolution of the first raster is used. If a single value is passed,
          output pixels will be square.
      nodata: float, optional
          nodata value to use in output file. If not set, uses the nodata value
          in the first input raster.
      dtype: numpy.dtype or string
          dtype to use in outputfile. If not set, uses the dtype value in the
          first input raster.
      precision: int, optional
          This parameters is unused, deprecated in rasterio 1.3.0, and
          will be removed in version 2.0.0.
      indexes : list of ints or a single int, optional
          bands to read and merge
      output_count: int, optional
          If using callable it may be useful to have additional bands in the output
          in addition to the indexes specified for read
      resampling : Resampling, optional
          Resampling algorithm used when reading input files.
          Default: `Resampling.nearest`.
      method : str or callable
          pre-defined method:
              first: reverse painting
              last: paint valid new on top of existing
              min: pixel-wise min of existing and new
              max: pixel-wise max of existing and new
          or custom callable with signature:
              merged_data : array_like
                  array to update with new_data
              new_data : array_like
                  data to merge
                  same shape as merged_data
              merged_mask, new_mask : array_like
                  boolean masks where merged/new data pixels are invalid
                  same shape as merged_data
              index: int
                  index of the current dataset within the merged dataset collection
              roff: int
                  row offset in base array
              coff: int
                  column offset in base array
  
      target_aligned_pixels : bool, optional
          Whether to adjust output image bounds so that pixel coordinates
          are integer multiples of pixel size, matching the ``-tap``
          options of GDAL utilities.  Default: False.
      dst_path : str or PathLike, optional
          Path of output dataset
      dst_kwds : dict, optional
          Dictionary of creation options and other paramters that will be
          overlaid on the profile of the output dataset.
  
      Returns
      -------
      tuple
  
          Two elements:
  
              dest: numpy.ndarray
                  Contents of all input rasters in single array
  
              out_transform: affine.Affine()
                  Information for mapping pixel coordinates in `dest` to another
                  coordinate system
  
      """
      if precision is not None:
          warnings.warn(
              "The precision parameter is unused, deprecated, and will be removed in 2.0.0.",
              RasterioDeprecationWarning,
          )
  
      if method in MERGE_METHODS:
          copyto = MERGE_METHODS[method]
      elif callable(method):
          copyto = method
      else:
          raise ValueError(
              "Unknown method {0}, must be one of {1} or callable".format(
                  method, list(MERGE_METHODS.keys())
              )
          )
  
      # Create a dataset_opener object to use in several places in this function.
      if isinstance(datasets[0], (str, os.PathLike)):
          dataset_opener = rasterio.open
      else:
  
          @contextmanager
          def nullcontext(obj):
              try:
                  yield obj
              finally:
                  pass
  
          dataset_opener = nullcontext
  
      with dataset_opener(datasets[0]) as first:
          first_profile = first.profile
          first_crs = first.crs
          first_res = first.res
          nodataval = first.nodatavals[0]
          dt = first.dtypes[0]
  
          if indexes is None:
              src_count = first.count
          elif isinstance(indexes, int):
              src_count = indexes
          else:
              src_count = len(indexes)
  
          try:
              first_colormap = first.colormap(1)
          except ValueError:
              first_colormap = None
  
      if not output_count:
          output_count = src_count
  
      # Extent from option or extent of all inputs
      if bounds:
          dst_w, dst_s, dst_e, dst_n = bounds
      else:
          # scan input files
          xs = []
          ys = []
          for dataset in datasets:
              with dataset_opener(dataset) as src:
                  left, bottom, right, top = src.bounds
              xs.extend([left, right])
              ys.extend([bottom, top])
          dst_w, dst_s, dst_e, dst_n = (
              min(xs),
              min(ys),
              max(xs),
              max(ys),
          )
  
      # Resolution/pixel size
      if not res:
          res = first_res
      elif not np.iterable(res):
          res = (res, res)
      elif len(res) == 1:
          res = (res[0], res[0])
  
      if target_aligned_pixels:
          dst_w = math.floor(dst_w / res[0]) * res[0]
          dst_e = math.ceil(dst_e / res[0]) * res[0]
          dst_s = math.floor(dst_s / res[1]) * res[1]
          dst_n = math.ceil(dst_n / res[1]) * res[1]
  
      # Compute output array shape. We guarantee it will cover the output
      # bounds completely
      output_width = int(round((dst_e - dst_w) / res[0]))
      output_height = int(round((dst_n - dst_s) / res[1]))
  
      output_transform = Affine.translation(
          dst_w, dst_n
      ) * Affine.scale(res[0], -res[1])
  
      if dtype is not None:
          dt = dtype
          logger.debug("Set dtype: %s", dt)
  
      out_profile = first_profile
      out_profile.update(**(dst_kwds or {}))
  
      out_profile["transform"] = output_transform
      out_profile["height"] = output_height
      out_profile["width"] = output_width
      out_profile["count"] = output_count
      out_profile["dtype"] = dt
      if nodata is not None:
          out_profile["nodata"] = nodata
  
      # create destination array
      dest = np.zeros(
          (output_count, output_height, output_width), dtype=dt
      )
  
      if nodata is not None:
          nodataval = nodata
          logger.debug("Set nodataval: %r", nodataval)
  
      if nodataval is not None:
          # Only fill if the nodataval is within dtype's range
          inrange = False
          if np.issubdtype(dt, np.integer):
              info = np.iinfo(dt)
              inrange = info.min <= nodataval <= info.max
          elif np.issubdtype(dt, np.floating):
              if math.isnan(nodataval):
                  inrange = True
              else:
                  info = np.finfo(dt)
                  inrange = info.min <= nodataval <= info.max
          if inrange:
              dest.fill(nodataval)
          else:
              warnings.warn(
                  "The nodata value, %s, is beyond the valid "
                  "range of the chosen data type, %s. Consider overriding it "
                  "using the --nodata option for better results."
                  % (nodataval, dt)
              )
      else:
          nodataval = 0
  
      for idx, dataset in enumerate(datasets):
          with dataset_opener(dataset) as src:
              # Real World (tm) use of boundless reads.
              # This approach uses the maximum amount of memory to solve the
              # problem. Making it more efficient is a TODO.
  
              # 0. Precondition checks
              #    - Check that source is within destination bounds
              #    - Check that CRS is same
  
              if disjoint_bounds(
                  (dst_w, dst_s, dst_e, dst_n), src.bounds
              ):
                  logger.debug(
                      "Skipping source: src=%r, bounds=%r",
                      src,
                      (dst_w, dst_s, dst_e, dst_n),
                  )
                  continue
  
              if first_crs != src.crs:
                  raise RasterioError(
                      f"CRS mismatch with source: {dataset}"
                  )
  
              # 1. Compute spatial intersection of destination and source
              src_w, src_s, src_e, src_n = src.bounds
  
              int_w = src_w if src_w > dst_w else dst_w
              int_s = src_s if src_s > dst_s else dst_s
              int_e = src_e if src_e < dst_e else dst_e
              int_n = src_n if src_n < dst_n else dst_n
  
              # 2. Compute the source window
              src_window = windows.from_bounds(
                  int_w, int_s, int_e, int_n, src.transform
              )
  
              # 3. Compute the destination window
              dst_window = windows.from_bounds(
                  int_w, int_s, int_e, int_n, output_transform
              )
  
              # 4. Read data in source window into temp
              src_window_rnd_shp = src_window.round_lengths()
              dst_window_rnd_shp = dst_window.round_lengths()
              dst_window_rnd_off = dst_window_rnd_shp.round_offsets()
  
              temp_height, temp_width = (
                  int(dst_window_rnd_off.height),
                  int(dst_window_rnd_off.width),
              )
              temp_shape = (src_count, temp_height, temp_width)
  
              temp_src = src.read(
                  out_shape=temp_shape,
                  window=src_window_rnd_shp,
                  boundless=False,
                  masked=True,
                  indexes=indexes,
                  resampling=resampling,
              )
  
          # 5. Copy elements of temp into dest
          roff, coff = (
              max(0, int(dst_window_rnd_off.row_off)),
              max(0, int(dst_window_rnd_off.col_off)),
          )
          region = dest[
              :, roff : roff + temp_height, coff : coff + temp_width
          ]
  
          if math.isnan(nodataval):
              region_mask = np.isnan(region)
          elif np.issubdtype(region.dtype, np.floating):
              region_mask = np.isclose(region, nodataval)
          else:
              region_mask = region == nodataval
  
          # Ensure common shape, resolving issue #2202.
          temp = temp_src[:, : region.shape[1], : region.shape[2]]
          temp_mask = np.ma.getmask(temp)
          copyto(
              region,
              temp,
              region_mask,
              temp_mask,
              index=idx,
              roff=roff,
              coff=coff,
          )
  
      if dst_path is None:
          return dest, output_transform
  
      else:
          with rasterio.open(dst_path, "w", **out_profile) as dst:
              dst.write(dest)
              if first_colormap:
                  dst.write_colormap(1, first_colormap)
  
file_path: rasterio/rasterio/merge.py
imports: from contextlib import contextmanager
  import logging
  import os
  import math
  import warnings
  import numpy
  import rasterio
  from rasterio.coords import disjoint_bounds
  from rasterio.enums import Resampling
  from rasterio.errors import RasterioDeprecationWarning, RasterioError
  from rasterio import windows
  from rasterio.transform import Affine
label_desc: Use image node_id 7 for calling a global function with example usage: merge(datasets, bounds, res, nodata, dtype, precision, indexes, output_count, resampling, method, target_aligned_pixels, dst_path, dst_kwds) and returns: dest,  output_transform