cogeotiff/rio-tiler

User friendly Rasterio plugin to read raster datasets.

rio-tiler's Issues

version 1

We just published rio-tiler 1.0a0.
This version includes some new features like:

• Landsat 8 Thermal bands support
• CBERS data Support

Here are some next point I'd like to focus before releasing a final v1:

• create mask inside the tile functions (instead of defining it in array_to_img). Basically the tile function would return a tile data and a mask which could be used in array_to_img. This will able to rescale data between 0 and 255 (instead of 1 -> 255, 0 being used as nodata).

• Automatic color correction instead of linear rescaling (using rio-color).

• better support for main(local or https) and s3 files.

Sentinel2 L1C requester pays

Hi @vincentsarago 👋

It happened, AWS now requires a requester pays header for Sentinel2 L1C 😢 . How do we move forward with rio tiler from here?

This is the message I get when running the first example in the README

from rio_tiler import sentinel2
tile, mask = sentinel2.tile('S2A_tile_20170729_19UDP_0', 77, 89, 8)
# rasterio._err.CPLE_AWSAccessDeniedError: Access Denied

Interestingly when I run the command again I get this

# rasterio._err.CPLE_OpenFailedError: '/vsis3/sentinel-s2-l1c/tiles/19/U/DP/2017/7/29/0/preview.jp2' does not exist in the file system, and is not recognized as a supported dataset name.

which is more inline with what my (previously working) lambda sentinel2 tiler is now saying..

nodata is not set when calling `utils.tile_read` with a path instead of a DatasetReader

rename `rgb` option to `indexes` or `bands`

in .main we use rgb option to pass band indexes. If we want to use the same terminology as in rasterio we should use indexes.

For .sentinel2, .landsat8 and cbers I feel we should use the word bands (!= indexes, because we read individual band files) instead of rgb.

cc @samsammurphy

Add tools to return buffer instead of base64

can be useful

Remove caching

Right now every bounds/metadata/tile functions have a @lru_cache() decorator from cachetools.

This was made to have control over caching in the lambda function, but rio-tiler could be use somewhere else than in a lambda function so we should remote it. Not really sure if there will be a performance improvement ro decrease, I'll try to do some benchmark.

Explore DHI-GRAS/terracotta and bring change over rio-tiler

https://github.com/DHI-GRAS/terracotta is a new lib inspired by rio-tiler made by @dionhaefner et all.
This lib has plenty of nice tricks which could be useful in rio-tiler
eg:

• different resampling method for up and down sampling: https://github.com/DHI-GRAS/terracotta/blob/7aa0751a105d1a804f0c5389e0abaab4399ffdee/terracotta/drivers/raster_base.py#L431-L436
• check tile coverage https://github.com/DHI-GRAS/terracotta/blob/7aa0751a105d1a804f0c5389e0abaab4399ffdee/terracotta/drivers/raster_base.py#L428-L429
• prevent interpolation artefact ? https://github.com/DHI-GRAS/terracotta/blob/7aa0751a105d1a804f0c5389e0abaab4399ffdee/terracotta/drivers/raster_base.py#L396-L411

Allow TIRS band

rio-tiler landsat8 should be able to return TIR Brightness Temperature

use overview level instead of shape for min/max histogram retrieval

in https://github.com/mapbox/rio-tiler/blob/e8caf27e7f3ad299c1523581173bb6d4d4be8977/rio_tiler/utils.py#L43 or https://github.com/mapbox/rio-tiler/blob/e8caf27e7f3ad299c1523581173bb6d4d4be8977/rio_tiler/utils.py#L99 we use input width=1024, height=1024 to tell rasterio to read the data overviews. While this work it can result in fetching more data than we really need.

add instructions for AWS access key

HI Vincent,

Thanks again for your awesome medium article. It has inspired me to check out AWS. The first link in that article sent me here. Following the example to get a Landsat tile

>>> from rio_tiler import landsat8
>>> tile = landsat8.tile('LC08_L1TP_016037_20170813_20170814_01_RT', 71, 102, 8)

Looks like I need to set my AWS access key.

AttributeError: 'NoneType' object has no attribute 'access_key'

I'm a complete newb at AWS so don't know how to do that. Could you provide instructions in the README.md?

(if I find out I'll make a PR)

Thanks! ^_^

Very slow performance metadata

I use python 3.6 and installed rio-tiler from repo.
And script run time is too high.

from aws_sat_api.search import landsat
from rio_tiler import landsat8
from datetime import datetime

startTime = datetime.now()

l8_path = 178
l8_row = 21
full_search = False
l8_meta = landsat(l8_path, l8_row, full_search)

metadata = landsat8.metadata('LC81780212013117LGN01', 5, 95)
print(metadata)

print(datetime.now() - startTime)

Output:

{'sceneid': 'LC81780212013117LGN01', 'bounds': [36.11826, 54.83456, 40.03735, 56.95001], 'rgbMinMax': {'1': [1489, 7031], '2': [1238, 7094], '3': [922, 6769], '4': [777, 7117], '5': [1278, 7794], '6': [811, 6038], '7': [474, 4849], '9': [17, 336], '10': [264, 285], '11': [261, 281]}}
0:03:00.593763

Please help.

Add notebook tutorial

Include support for new CBERS instruments (AWFI, PAN10M and PAN5M)

These new datasets are being experimentally ingested into CBERS on AWS repository.

`main.tile` and `utils.tile_band_worker` should accept `DatasetReader` object or address path

rio-tiler should not be design to open a raster file every time it need to returns a tile (lambda function case) but we should be able to pass an open dataset and get the tile data.

Where

• rio_tiler.main.tile: https://github.com/mapbox/rio-tiler/blob/91a63d5df0731e17a134e6595abb1ced4c7a3e29/rio_tiler/main.py#L39
• rio_tiler.utils.tile_band_worker https://github.com/mapbox/rio-tiler/blob/91a63d5df0731e17a134e6595abb1ced4c7a3e29/rio_tiler/utils.py#L129

Next!

Features to develop for next versions:

• Landsat / Sentinel bands ratio (e.g NDVI)
• Improve Landsat pan-sharpening
• Add Modis support (https://aws.amazon.com/public-datasets/modis/)
• Add SRTM support (https://aws.amazon.com/public-datasets/terrain/)

CURL error

Hey Vincent,

I hit a CURL error (already fixed) but thought I should mention it here. This is for Linux Mint 18.1 Serena.

This worked

>>> from rio_tiler import landsat8
>>> bounds = landsat8.bounds('LC08_L1TP_016037_20170813_20170814_01_RT')
# {'sceneid': 'LC08_L1TP_016037_20170813_20170814_01_RT', 'bounds': [-81.30836, 32.10539, -78.82045, 34.22818]}

However getting metadata and tile both required curl (it seems), I hit this error:

rasterio._err.CPLE_HttpResponseError: CURL error: error setting certificate verify locations:   CAfile: /etc/pki/tls/certs/ca-bundle.crt   CApath: none

I made sure that I had the ca-certificates package

apt-get install ca-certificates

(the latest version was already installed). Then I fixed the CURL certificates path in .bashrc

export CURL_CA_BUNDLE=/etc/ssl/certs/ca-certificates.crt

Everything now seems to work fine =D !

It was a bit weird (for me) that curl was looking in the wrong place (i.e. /etc/pki/tls/certs/ca-bundle.crt), not sure how to proceed (e.g. note in docs, fix in code, ignore)?

Move dataset related tools to each sub-module

When we first started to work on rio-tiler it was mainly to access Landsat-8, but since we added Sentinel, CBERS and main for any other COGs.

In https://github.com/cogeotiff/rio-tiler/blob/master/rio_tiler/utils.py we have plenty of utility functions which are specific to AWS PDS dataset and I feel each of this should be with their own parent
e.g
rio_tiler.utils.landsat_min_max_worker -> rio_tiler.landsat8.landsat_min_max_worker
rio_tiler.utils.sentinel_parse_scene_id -> rio_tiler.sentinel2.parse_scene_id

This will be a breaking change so if no one raise a hand, I'll move forward with this and add it to 1.0.0 Milestone

`Failed to connect` error

Hello,

Newbie here. I've just installed rio-tiler successfully, and I'm just trying out the examples on the README.md:

(sat) [~] $ ipython
Python 3.7.1 | packaged by conda-forge | (default, Nov 13 2018, 18:15:35)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.2.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: from rio_tiler import sentinel2

In [2]: tile, mask = sentinel2.tile('S2A_tile_20170729_19UDP_0', 77, 89, 8)
---------------------------------------------------------------------------
CPLE_AppDefinedError                      Traceback (most recent call last)
rasterio/_base.pyx in rasterio._base.DatasetBase.__init__()

rasterio/_shim.pyx in rasterio._shim.open_dataset()

rasterio/_err.pyx in rasterio._err.exc_wrap_pointer()

CPLE_AppDefinedError: Failed to connect to 169.254.169.254 port 80: Connection refused

During handling of the above exception, another exception occurred:

RasterioIOError                           Traceback (most recent call last)
<ipython-input-2-23a3f4efe0e1> in <module>
----> 1 tile, mask = sentinel2.tile('S2A_tile_20170729_19UDP_0', 77, 89, 8)

~/miniconda3/envs/sat/lib/python3.7/site-packages/cachetools/func.py in wrapper(*args, **kwargs)
     74                 except KeyError:
     75                     stats[1] += 1
---> 76             v = func(*args, **kwargs)
     77             try:
     78                 with lock:

~/miniconda3/envs/sat/lib/python3.7/site-packages/rio_tiler/sentinel2.py in tile(sceneid, tile_x, tile_y, tile_z, rgb, r_bds, g_bds, b_bds, tilesize)
    126
    127     sentinel_preview = '{}/preview.jp2'.format(sentinel_address)
--> 128     with rasterio.open(sentinel_preview) as src:
    129         wgs_bounds = transform_bounds(
    130             *[src.crs, 'epsg:4326'] + list(src.bounds), densify_pts=21)

~/miniconda3/envs/sat/lib/python3.7/site-packages/rasterio/env.py in wrapper(*args, **kwds)
    419
    420         with env_ctor(session=session):
--> 421             return f(*args, **kwds)
    422
    423     return wrapper

~/miniconda3/envs/sat/lib/python3.7/site-packages/rasterio/__init__.py in open(fp, mode, driver, width, height, count, crs, transform, dtype, nodata, sharing, **kwargs)
    214         # None.
    215         if mode == 'r':
--> 216             s = DatasetReader(path, driver=driver, **kwargs)
    217         elif mode == 'r+':
    218             s = get_writer_for_path(path)(path, mode, driver=driver, **kwargs)

rasterio/_base.pyx in rasterio._base.DatasetBase.__init__()

RasterioIOError: Failed to connect to 169.254.169.254 port 80: Connection refused

In [3]:

Obviously, it has problem connecting to something but sure why. As I read it from the usage, one doesn't need to provide any credentials of any sort, correct? Any help would be most appreciated, thanks!!!

Option to apply vrt colormap to png

There is an example of how to convert it and apply it via pillow here. Happy to submit a PR if there is a generic approach that works.

Looks like one way to do it is make it an option on array_to_img and then return the vrt source from tile_band_worker and then aws.tile. Then add a color_table_to_palette helper function to utils. That way users that want to use the vrt color table can extract it and pass it to array_to_img?

I guess another option is to use rasterio's write_colormap but it looks like that would require writing the data out to vsimem and reading it back? Would that be better than applying it to the PNG directly?

(Also thanks for this repo :) I was struggling to get my own WarpedVRT code to work, then I found this)

add pre-commit

let's add pre-commit and black

Sentinel RasterIO Errors

Reopening issue #53 because this has not been fixed and is an issue with rio-tiler.

Running the following:

from rio_tiler import sentinel2
import os
os.environ["CURL_CA_BUNDLE"] = "/etc/ssl/certs/ca-certificates.crt"
os.environ["AWS_REQUEST_PAYER"] = "requester"
tile, mask = sentinel2.tile("S2B_tile_20180813_23MQQ_0", 781, 1052, 11, (4, 3, 2), tilesize=256)

gives the error:

RasterioIOError: '/vsis3/sentinel-s2-l1c/tiles/23/M/QQ/2018/8/13/0/B4.jp2' does not exist in the file system, and is not recognized as a supported dataset name.

running gdalinfo on this path gives the error:

ERROR 4: /vsis3/sentinel-s2-l1c/tiles/23/M/QQ/2018/8/13/0/B4.jp2: No such file or directory
gdalinfo failed - unable to open '/vsis3/sentinel-s2-l1c/tiles/23/M/QQ/2018/8/13/0/B4.jp2'.

This path is being generated by rio-tiler. This is only happening on Sentinel; Landsat works fine.

Error adding mask as alpha channel

Problem

Calling array_to_image using the array and mask returned from tile using a 4 band tiff returns an error: WEBP driver doesn't support 5 bands. Must be 3 (RGB) or 4 (RGBA) bands.

Relevant code

# image at URL is 4 band geotiff
tile, mask = main.tile(url, int(tile_x), int(tile_y), int(tile_z), tilesize=256, nodata=None)
options = img_profiles["webp"]
buffer = utils.array_to_image(tile, mask=mask, img_format="webp", color_map=None, **options)

Possible Cause

It looks like utils:392 is just adding a band to the array without checking to see whether an alpha band already exists

Add elevation data to rgb encoded png function

ref: https://github.com/vincentsarago/terrain-tiler

mapbox-gl-js (mapbox/mapbox-gl-js#5286) or mapzen-tangram support rgb encoded elevation values.

here is how to create a mapbox-gl compatible rgb values:
https://github.com/vincentsarago/terrain-tiler/blob/master/app/elevation.py#L16-L21

and for mapzen:
https://github.com/tilezen/joerd/blob/85ed04e7c2267880fe4272015ac37f396d91e864/joerd/output/terrarium.py#L31-L91

Allow sentinel 8A band in expression

There is a bug in rio_tiler.utils.expression that is not allowing band 8A https://github.com/mapbox/rio-tiler/blob/d8a3581158a06511d23d58c9401244ea8ec6ee33/rio_tiler/utils.py#L552

performance issue with CBERS

with rio-tiler==1.0a2 retrieving CBERS tiles is way slower and need more memory.

I wonder if reading a mask while we know it's not needed could be the reason.
https://github.com/mapbox/rio-tiler/blob/6100616a6b4a56e7778cf616da4c909530cc6126/rio_tiler/utils.py#L165-L168

Note: For Landsat/Sentinel we don't need to read the masks because there is no internal nodata and need to create the mask manually.

rio-tiler incompatible with rasterio 1.0b2

It looks like the rasterio release from today is breaking rio-tiler's functionality on my geotifs (which used to work before): https://github.com/mapbox/rasterio/releases/tag/1.0b2 Here is the error:

/tmp/rio/.env/lib/python3.5/site-packages/rio_tiler/utils.py:162: RasterioDeprecationWarning: dst_crs will
 be removed after 1.0, use crs
  with WarpedVRT(source, **vrt_params) as vrt:

Traceback (most recent call last):
  File "/tmp/rio/tiler.py", line 49, in <module>
    main()
  File "/tmp/rio/tiler.py", line 41, in main
    data, _ = tiler.tile(args.path, x, y, z)
  File "/tmp/rio/.env/lib/python3.5/site-packages/rio_tiler/main.py", line 75, in tile
    nodata=nodata, alpha=alpha)
  File "/tmp/rio/.env/lib/python3.5/site-packages/rio_tiler/utils.py", line 168, in tile_read
    indexes=indexes)
  File "rasterio/_warp.pyx", line 979, in rasterio._warp.WarpedVRTReaderBase.read
ValueError: WarpedVRT does not permit boundless reads

Workaround

pip install 'rasterio==1.0b1' 'rio-tiler==1.0a7'

Small self-contained reproducible program

https://gist.github.com/daniel-j-h/69a967d63d833f74a123e2540bd146b9

Geotifs are from OpenAerialMap, e.g.

https://oin-hotosm.s3.amazonaws.com/5ac7745591b5310010e0d49a/0/5ac7745591b5310010e0d49b.tif

Let me know if I'm doing something wrong here or if this is an issue with rasterio.

cc @dnomadb @sgillies @vincentsarago

Performance issue with `main.tile` mask creation

There is a bug in rasterio (mentioned in rasterio/rasterio#1191, rasterio/rasterio#1266) WrappedVrt which might be linked in poor performance when reading mask withing a file.

For sentinel, landsat and CBERS we don't read the mask because we are creating one using a nodata value.

update `rio_tiler.utils.expression` "expr" input type to keyword

Each tile function takes scene, x, y, z as required inputs but rio_tiler.utils.expression need expr as well.

I think changing the type to keyword might ease the usability and enable to seamlessly create application that can use either expression or {module}.tile e.g

Add band's name check for Sentinel, Landsat and CBERS

as mentioned in #54 (comment), rio-tiler should raise an error when the band names are not valid

add resampling option for `rio_tiler.utils.tile_read`

Allow custom `max_workers` for multithreading

https://github.com/cogeotiff/rio-tiler/search?q=max_workers&unscoped_q=max_workers

We have different values set in the code, but I think this is unnecessary and should only set one value and it should be customizable!

delete mapbox_elevation_rgb

there is no need to provide mapbox_elevation_rgb and because rasterio has already a module to do so https://github.com/mapbox/rio-rgbify/blob/master/rio_rgbify/encoders.py#L4

Retrieving metadata doesnt work for me

Import works normal, package is installed correctly, but landsat8.metadata('LC08_L1TP_016037_20170813_20170814_01_RT', pmin=5, pmax=95) gives me the following error.

Traceback (most recent call last):
File "", line 1, in
File "/usr/local/lib/python2.7/dist-packages/cachetools/func.py", line 58, in wrapper
v = func(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/rio_tiler/landsat8.py", line 71, in metadata
meta_data = utils.landsat_get_mtl(sceneid).get('L1_METADATA_FILE')
File "/usr/local/lib/python2.7/dist-packages/rio_tiler/utils.py", line 217, in landsat_get_mtl
raise Exception('Could not retrieve {} metadata'.format(sceneid))
Exception: Could not retrieve LC08_L1TP_016037_20170813_20170814_01_RT metadata

I am using Linux Ubuntu, Python 2.7.12.

Inverse "grid_square" and "latitude_band" order in S2 Key

The Sentinel-2 sceneid as defined in sat-api in in forme of S2A_tile_20171203_55MGS_0 where:

• 55 is the utm_zone
• M is the latitude_band
• GS is the grid_square

in https://github.com/mapbox/rio-tiler/blob/master/rio_tiler/utils.py#L293 the sceneid is defined as:
https://github.com/mapbox/rio-tiler/blob/f1c3d5f86650158707fad5843d736d3e9edb082f/rio_tiler/utils.py#L308-L310

More Docs

This project needs better documentation

Release 1.1.0

Changes from #86 #85 #84 #83

add histogram "bins" options for `rio_tiler.utils._stats`

from numpy.histogram docs:

bins : int or sequence of scalars or str, optional
If bins is an int, it defines the number of equal-width bins in the given range (10, by default). If bins is a sequence, it defines a monotonically increasing array of bin edges, including the rightmost edge, allowing for non-uniform bin widths.
New in version 1.11.0.
If bins is a string, it defines the method used to calculate the optimal bin width, as defined by histogram_bin_edges.

[Explore] Replace PIL image encoding with rasterio/gdal

GDAL/Rasterio is more efficient for image encoding than Pillow.

Note: Also removing Pillow will make lambda function lighter ;-)

Landsat 8 pansharpening returns bad data

There is something strange going on with the pan option in rio-tiler.landsat8.tile
https://github.com/cogeotiff/rio-tiler/blob/master/rio_tiler/landsat8.py#L397-L417

with pan-sharpening

without pan-sharpening

https://gist.github.com/vincentsarago/0ba79fbcfd9de7218a793e1e2c7c5823

Understanding the min-max metadata call

Hey @vincentsarago

Hoping you can help me out by sharing a bit of knowledge (you're very good at it!)

I'm trying to tidy up a few loose ends on my geotiff-server and one of the things that's given me a bit of grief is colour correction, building my own version of this repo has been a good crash course in imagery & colours!

I think I understand your approach but was hoping you could validate that I vaguely understand

The process as I understand it

• Before making any calls to get tiles the web app sends a request to the server to get some generic information that'll be used for subsequent tiling

  • This involves sending the scene id to metadata endpoint

• During this metadata call the server retrieves a 1024px x 1024px overview of each band for the entire geographic range of the scene

  • the purpose of this is to get a feel for the range of values that might be across the scene (a rough way of generating a histogram) so that you can then colour balance the tiles accordingly

• You then get the percentiles for each band and sends them back to the web app (defaults to 2% and 98% to remove the extremes)

  • You only use values that are greater than 0 because 0 are your nodata pixels

Am I vaguely on track with my understanding?

Thanks,
Rowan

More colormap options!

With #90 @DanSchoppe added a third colormap to rio-tiler. While this is awesome, I think rio-tiler could do more for allowing user to define custom colormap.

colormap are used when translating a one band data array to the image format using the new array_to_image(using GDAL using a simple translation:

rio-tiler/rio_tiler/utils.py

Line 386 in e512d84

arr = np.transpose(color_map[arr][0], [2, 0, 1]).astype(np.uint8)

So here ☝️ a rio_tiler.utils.array_to_image compatible colormap object is usually an array of shape (256, 3)

c = get_colormap(format="gdal")
print(c.shape)
 (256, 3)
print(c)
[
  [255 255 255]
  [250 250 250]
  [246 246 246]
  [242 242 242]
  ...
  [255   1 191]
  [255   1 207]
  [255   1 223]
  [255   1 239]
]

I first though we could have used a custom dict like

{
    0: [255, 255, 255],
    100: [255,   1, 191],
    ...
}

to create a colormap fordiscrete values, but dict are not hashable so the current code in rio_tiler.utils.array_to_image won't work.

I do love the dict option for colormap (like Rasterio use https://github.com/mapbox/rasterio/blob/960a906dad2a4e426387ce048a52c6e90afdcd2b/docs/topics/color.rst#writing-colormaps) so I first check if we can maybe change the rio_tiler.utils.get_colormap function to have to return a dictionary colormap (because it seems to be the default format for GDAL) and in the same time adapt the rio_tiler.utils.array_to_image to work with dict.

In a second time we could add another utility function to create colormaps, it seems that matplotlib has some functionality but I'm not sure we should had more required module to rio-tiler.

Resampling artifacts for Int16 datasets on CENTOS

I'm seing some resampling artifacts on tile bottom border but only when the tile cover area with nodata.

The artifacts disappear when using nearest resampling.

rio-tiler/rio_tiler/utils.py

Lines 234 to 256 in 9618f13

	vrt_params = dict(
	add_alpha=True, crs="epsg:3857", resampling=Resampling[resampling_method]
	)
	vrt_transform, vrt_width, vrt_height = get_vrt_transform(src_dst, bounds)
	vrt_params.update(dict(transform=vrt_transform, width=vrt_width, height=vrt_height))
	indexes = indexes if indexes is not None else src_dst.indexes
	out_shape = (len(indexes), tilesize, tilesize)
	nodata = nodata if nodata is not None else src_dst.nodata
	if nodata is not None:
	vrt_params.update(dict(nodata=nodata, add_alpha=False, src_nodata=nodata))
	if has_alpha_band(src_dst):
	vrt_params.update(dict(add_alpha=False))
	with WarpedVRT(src_dst, **vrt_params) as vrt:
	data = vrt.read(
	out_shape=out_shape,
	indexes=indexes,
	resampling=Resampling[resampling_method],
	)

To Do

• check which resampling is responsible (WarpVRT at L235 or read at L255)
• check if the artifact is present when using gdalwarp
• create Notebook

also need to re-check #78

cc @sgillies (FYI)

Add a note on why using rio_tiler.sentinel2 might not be a great idea

ref: https://medium.com/@_VincentS_/do-you-really-want-people-using-your-data-ec94cd94dc3f

RasterIO errors

I'm getting inconsistent and unhelpful rasterio errors whenever I try to use this library. Running the following code:

landsat8.tile("LC08_L1TP_221078_20180625_20180626_01_RT", 11825, 18900, 15)

first gives me the error:

rio_tiler/utils.py in tile_read(source, bounds, tilesize, indexes, nodata)
222
223 else:
--> 224 with rasterio.open(source) as src:
225 vrt_transform, vrt_width, vrt_height = get_vrt_transform(src,
226 bounds)

CPLE_HttpResponseError: CURL error: error setting certificate verify locations: CAfile: /etc/pki/tls/certs/ca-bundle.crt CApath: none

Running the same command again gives me

rio_tiler/utils.py in tile_read(source, bounds, tilesize, indexes, nodata)
222
223 else:
--> 224 with rasterio.open(source) as src:
225 vrt_transform, vrt_width, vrt_height = get_vrt_transform(src,
226 bounds)

RasterioIOError: '/vsis3/landsat-pds/c1/L8/221/078/LC08_L1TP_221078_20180625_20180626_01_RT/LC08_L1TP_221078_20180625_20180626_01_RT_B4.TIF' does not exist in the file system, and is not recognized as a supported dataset name.

I get this behavior on both Sentilel and Landsat. I get this on all versions of rio_tiler, ranging from 0.0.3 to 1.0rc1

I've already tried the solutions in #52 and rasterio/rasterio#1289; neither helps.

Add utility function that fetch image statistic

We could maybe deprecate

rio-tiler/rio_tiler/utils.py

Line 107 in 8c442e5

def band_min_max_worker(address, pmin=2, pmax=98, width=1024, height=1024):

and create a complete function (image_statistic?) which in addition to percentiles, could also return min, max, mean ...

Nodata problem !

The mask seems to be off and there might be something going on here https://github.com/mapbox/rio-tiler/blob/8ab4273dd5cd09af82e34f1e7bc37a89ef29d167/rio_tiler/utils.py#L197-L201

WarpedVRT does not permit boundless reads

"WarpedVRT does not permit boundless reads"
This is the error message I am getting with 1.0a7
If I change all boundless=True to boundless=False in utils.py, everything works again.

Move color correction outside the `tile` function ?

Right now we apply linear color correction in the Landsat and Sentinel tile functions

https://github.com/mapbox/rio-tiler/blob/f33930f4da0493c6941e5c3e617364eb9caca86a/rio_tiler/landsat8.py#L181-L188
and
https://github.com/mapbox/rio-tiler/blob/f33930f4da0493c6941e5c3e617364eb9caca86a/rio_tiler/sentinel2.py#L150-L158

IMO, the rescaling should be either in array_to_img or handle in the handler function (e.g https://github.com/mapbox/landsat-tiler/blob/27d8e870567ffa37727ea6287c4c4fcf0d4b7462/app/landsat.py#L80)

Doing this could able to create more high level functions.

add zoom range and resolution in metadata output

Having those could be useful IMO

Performance issues after rasterio > 1.013 for 16bits dataset

I'm seeing a huge performance downgrade for the WarpedVRT read after updating rasterio to version 1.0.18.
I've narrowed down, and the performance downgrade seems to be happening between version 1.0.13 and 1.0.14

I've tested with this dataset https://s3-us-west-2.amazonaws.com/remotepixel-pub/cog/LC08_L1TP_232064_20170806_20170813_01_T1_RGB654_cogeo.tif

Note: when using a dataset in `EPSG:4326: I'm not seeing the performance downgraded.

cc @sgillies