specifies the parameter (coverage) the query shall be applied on.
#
#
# >> Example: temp2m for ERA-interim 2m air temperature
#
#
# > * [...]: specifies the data or subset of data to work with. You can subset any axis of the coverage.
#
#
# >> Example: the coverage temp2m has three axes: Lat, Long, ansi, and subsets of all three axes can be defined as: [Lat(-10.0:10.0), Long(-10.0:10.0), ansi("1999-01-01T00:00":"1999-01-31T18:00")].
#
#
# > * format: the implied output format can be specified here.
#
#
# >> Possible format encodings are:
# * csv
# * netcdf or
# * png
#
# ***
# ## Basic WCPS requests
# The following examples show a variety of WCPS queries and mathematical operations that can be applied to the data of a WCS Server. Examples base on ERA-interim 2m air temperature global fields, 6-hourly values from 1 January 1979 to 31 December 2015
# * Retrieve **2m air temperature in Kelvin for Europe for January 1999** encoded as **csv**
#
# http://earthserver.ecmwf.int/rasdaman/ows?service=WCS&version=2.0.1&request=ProcessCoverages&query=for c in (temp2m) return encode(c[Lat(-35.0:65), Long(35.0:80.0),ansi("1999-01-01T00:00":"1999-01-31T18:00")],"csv")
# In[6]:
import urllib2
url = "http://earthserver.ecmwf.int/rasdaman/ows?service=WCS&version=2.0.1&request=ProcessCoverages&query=for%20c%20in%20(temp2m)%20return%20encode(c[Lat(-35.0:65.0),%20Long(-35.0:80.0),ansi(%221999-01-01T00:00%22:%221999-01-31T18:00%22)],%22csv%22)"
response = urllib2.urlopen(url)
html=response.read()
#print html
# ***
# * Retrieve **2m air temperature in Kelvin for Europe for 10 January 1999 at 12 UTC** encoded as **netCDF**
#
# http://earthserver.ecmwf.int/rasdaman/ows?service=WCS&version=2.0.1&request=ProcessCoverages&query=for c in (temp2m) return encode(c[Lat(-35.0:65.0), Long(35.0:80.0),ansi("1999-01-12T12:00")],"netcdf")
#
# **Note**: Both queries above do not require WCPS per se. Same data can be rerieved with WCS core requests.
# ***
# ## Conduct mathematical operations with WCPS
# Get the **minimum, maximum and mean temperature in degree celsius** of Berlin for the year 2003
#
# There are already predefined operators for retrieving the minimum, maximum and mean: min(), max() and avg(). We additionally convert the 2m air temperature in K into degC via a simple subtraction.
#
# * **Minimum**
#
# >for c in (temp2m) return encode (min ( c[Lat(52), Long(13),ansi("2003-01-01T00:00":"2003-12-31T18:00")] ) - 273.15,"csv")
# http://earthserver.ecmwf.int/rasdaman/ows?service=WCS&version=2.0.1&request=ProcessCoverages&query=for c in (temp2m) return encode(min (c[Lat(52), Long(13),ansi("2003-01-01T00:00":"2003-12-31T18:00")]) - 273.15,"csv")
# In[15]:
url = "http://earthserver.ecmwf.int/rasdaman/ows?service=WCS&version=2.0.1&request=ProcessCoverages&query=for%20c%20in%20(temp2m)%20return%20encode(%20min(c[Lat(52),%20Long(13),ansi(%222003-01-01T00:00%22:%222003-12-31T18:00%22)])%20-%20273.15,%22csv%22)"
response = urllib2.urlopen(url)
html = response.read()
print html
# ***
#
# * **Maximum**
#
# >for c in (temp2m) return encode (max ( c[Lat(52), Long(13),ansi("2003-01-01T00:00":"2003-12-31T18:00")] )- 273.15,"csv")
# http://earthserver.ecmwf.int/rasdaman/ows?service=WCS&version=2.0.1&request=ProcessCoverages&query=for c in (temp2m) return encode(max (c[Lat(52), Long(13),ansi("2003-01-01T00:00":"2003-12-31T18:00")]) - 273.15,"csv")
# In[12]:
url = "http://earthserver.ecmwf.int/rasdaman/ows?service=WCS&version=2.0.1&request=ProcessCoverages&query=for%20c%20in%20(temp2m)%20return%20encode(%20max(c[Lat(52),%20Long(13),ansi(%222003-01-01T00:00%22:%222003-12-31T18:00%22)])%20-%20273.15,%22csv%22)"
response = urllib2.urlopen(url)
html=response.read()
print html
# ***
#
# * **Average**
#
# >for c in (temp2m) return encode (avg ( c[Lat(52), Long(13),ansi("2003-01-01T00:00":"2003-12-31T18:00")] ) - 273.15,"csv")
# http://earthserver.ecmwf.int/rasdaman/ows?service=WCS&version=2.0.1&request=ProcessCoverages&query=for c in (temp2m) return encode(avg (c[Lat(52), Long(13),ansi("2003-01-01T00:00":"2003-12-31T18:00")]) - 273.15,"csv")
#
# In[14]:
url = "http://earthserver.ecmwf.int/rasdaman/ows?service=WCS&version=2.0.1&request=ProcessCoverages&query=for%20c%20in%20(temp2m)%20return%20encode(%20avg(c[Lat(52),%20Long(13),ansi(%222003-01-01T00:00%22:%222003-12-31T18:00%22)])%20-%20273.15,%22csv%22)"
response = urllib2.urlopen(url)
html = response.read()
print html
#
#
#
# With three simple queries, we retrieve the minimum, mamximum and average 2m air temperature of Berlin for the year 2003, which are -16 degC, 35.5 degC and 10.1 degC respectively.
#
# ***
# ## Apply a colour scheme to a png output
# The switch statement allows to classify the png pixel values and to apply RGB colour codes to it. Below, there are two examples how predefined RGB colour codes for 2m air temperature and total precipitation can be applied on-the-fly.
#
# * **Colour scale for 2m air temperature on 18 June 2012 at 12 UTC in degrees Celsius**
#
# > for c in (temp2m) return encode (
# **switch**
# case -48 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 0; green: 0; blue: 128}
# case -44 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 0; green: 0; blue: 128}
# ...
# default return {red: 255; green: 0; blue:0},"png")
# http://earthserver.ecmwf.int/rasdaman/ows?service=WCS&version=2.0.1&request=ProcessCoverages&query=for c in (temp2m) return encode (switch case -48 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 0; green: 0; blue: 128} case -44 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 0; green: 0; blue: 128} case -40 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 0; green: 0; blue: 128} case -36 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 0; green: 0; blue: 128} case -32 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 0; green: 0; blue: 128} case -28 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 0; green: 0; blue: 217} case -24 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 64; green: 0; blue: 255} case -20 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 128; green: 0; blue: 255} case -16 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 0; green: 128; blue: 255} case -12 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 0; green: 255; blue: 255} case -8 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 0; green: 255; blue: 128} case -4 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 128; green: 255; blue: 0} case -0 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 218; green: 255; blue: 0} case 4 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 255; green: 255; blue: 0} case 8 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 255; green: 245; blue: 0} case 12 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 255; green: 218; blue: 0} case 16 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 255; green: 176; blue: 0} case 20 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 255; green: 164; blue: 0} case 24 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 255; green: 79; blue: 0} case 28 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 255; green: 37; blue: 0} case 32 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 255; green: 10; blue: 0} case 36 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 255; green: 0; blue: 0} case 40 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 255; green: 0; blue: 255} case 44 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 255; green: 0; blue: 255} case 48 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 255; green: 0; blue: 255} case 52 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 255; green: 0; blue: 255} case 56 > c[ansi("2012-06-18T12:00")] - 273.5 return {red: 255; green: 0; blue: 255} default return {red: 255; green: 0; blue:0},"png")
#
# In[2]:
get_ipython().run_cell_magic('html', '', '\n')
# ***
#
# * **Total precipitation for 18 June 2016 at 12 UTC in mm of water**
# By specifying alpha parameter of the RGB colour code, one can set specific values to transparent. In the case of total precipitation, all values with less than 0.5 mm of water are set to transparent.
#
# > for c in (precipitation) return encode (
# **switch**
# case 0.5 > c[ansi("2012-06-18T12:00")] * 1000 return {red: 255; green: 255; blue: 255; alpha: 0}
# case 2 > c[ansi("2012-06-18T12:00")] * 1000 return {red: 0; green: 255; blue: 255; alpha: 255}
# ...
# default return {red: 255; green: 0; blue:0; alpha: 255},"png")
# http://earthserver.ecmwf.int/rasdaman/ows?service=WCS&version=2.0.1&request=ProcessCoverages&query=for c in (precipitation) return encode ( switch case 0.5 > c[ansi("2012-06-18T12:00")] * 1000 return {red: 255; green: 255; blue: 255; alpha: 0} case 2 > c[ansi("2012-06-18T12:00")] * 1000 return {red: 0; green: 255; blue: 255; alpha: 255} case 4 > c[ansi("2012-06-18T12:00")] * 1000 return {red: 0; green: 128; blue: 255; alpha: 255} case 10 > c[ansi("2012-06-18T12:00")] * 1000 return {red: 0; green: 0; blue: 255; alpha: 255} case 25 > c[ansi("2012-06-18T12:00")] * 1000 return {red: 218; green: 0; blue: 255; alpha: 255} case 50 > c[ansi("2012-06-18T12:00")] * 1000 return {red: 255; green: 0; blue: 255; alpha: 255} case 100 > c[ansi("2012-06-18T12:00")] * 1000 return {red: 255; green: 164; blue: 0; alpha: 255} case 250 > c[ansi("2012-06-18T12:00")] * 1000 return {red: 255; green: 0; blue: 0; alpha: 255} case 250 <= c[ansi("2012-06-18T12:00")] * 1000 return {red: 51; green: 51; blue: 51; alpha: 255} default return {red: 255; green: 0; blue:0; alpha: 255},"png")
# In[37]:
get_ipython().run_cell_magic('html', '', '\n')
# ***
# ## WCPS examples for EO users
# ### Calculate a NDVI from a Landsat8 image
# The query below calculated on the fly the Normalized-Difference-Vegetation-Index (NDVI) from a Landsat8 satellite image for UTM Zone 31. Landsat 8 provides 11 bands and the NDVI is produced using the bands 4 (red) and 5 (near-infrared).
# > for r in (L8_B6_32631_30), g in (L8_B5_32631_30), b in (L8_B4_32631_30)
# return
# encode ( {
# red: ( (r * 0.00002) - 0.1 ) * 255;
# green: ( (g * 0.00002) - 0.1 ) * 255;
# blue: ( (b * 0.00002) - 0.1 ) * 255
# }
# [E(377983:390000),N(4902991:4917275),unix(1433068497)]
# ,"png")
# In[36]:
get_ipython().run_cell_magic('html', '', '\n')
# ### Generate a RGB image and apply a mask on-the-fly
# Following query generates on-the-fly a RGB image from a Landsat 8 satellite image and at the same applies a a mask based on the BQA (band-quality-assessment) band from the Landsat8 satellite. Each pixel with a value smaller than the band-quality value "24576" is considered as valid, all others are masked.
# > for r in (L8_B6_32631_30), g in (L8_B5_32631_30), b in (L8_B4_32631_30), bq in (L8_BQA_32631_30)
# return
# encode ( {
# red: ( (r*(bq < 24576) * 0.00002) - 0.1 ) * 255;
# green: ( (g*(bq < 24576) * 0.00002) - 0.1 ) * 255;
# blue: ( (b*(bq < 24576) * 0.00002) - 0.1 ) * 255
# }
# [E(377983:390000),N(4902991:4917275),unix(1433068497)]
# ,"png")
# In[35]:
get_ipython().run_cell_magic('html', '', '\n')
# ***
# ## WCPS examples from PlanetServer
# ### Data description
# The **Compact Reconnaissance Imaging Spectrometer for Mars (CRISM)** is a visible and infrared spectrometer on board of the **Mars Reconnaissance Orbiter (MRO)**. The main purpose of CRISM is to characterize the mineralogy of Mars that may have hosted water. CRISM contains two spectrographs, one with a range of 400 to 830 nm (visible light) and one that ranges from 830 to 4050 nm (infrared light).
#
# In order to detect the minerals the normal procedure is to produce images using band math to highlight certain materials that have a particular spectral signal. Once the mineral has been detected, one should analyze the whole spectra at those locations pixel-wise or with a kernel.
# ### Retrieve a false color composite
# > for data in (frt0000805f_07_if166l_trr3) return encode({
# red: (int)(255 / (max((data.band_233 != 65535) * data.band_233) - min(data.band_233))) * (data.band_233 - min(data.band_233));
# green: (int)(255 / (max((data.band_13 != 65535) * data.band_13) - min(data.band_13))) * (data.band_13 - min(data.band_13));
# blue: (int)(255 / (max((data.band_78 != 65535) * data.band_78) - min(data.band_78))) * (data.band_78 - min(data.band_78));
# alpha: (data.band_100 != 65535) * 255},
# "png", "nodata=null")
# http://access.planetserver.eu:8080/rasdaman/ows?service=WCS&version=2.0.1&request=ProcessCoverages&query=
# for data in (frt0000805f_07_if166l_trr3) return encode({red: (int)(255 / (max((data.band_233 != 65535) * data.band_233) - min(data.band_233))) * (data.band_233 - min(data.band_233));green: (int)(255 / (max((data.band_13 != 65535) * data.band_13) - min(data.band_13))) * (data.band_13 - min(data.band_13)); blue: (int)(255 / (max((data.band_78 != 65535) * data.band_78) - min(data.band_78))) * (data.band_78 - min(data.band_78)); alpha: (data.band_100 != 65535) * 255},"png", "nodata=null")
# In[34]:
get_ipython().run_cell_magic('html', '', '\n')
# ### RGB combination with a customised WCPS query
# The query is used to look for phyllosilicates with Aluminium content. It is translated from the products created by Vivano-beck found in [1](http://onlinelibrary.wiley.com/doi/10.1002/2014JE004627/abstract). The following query is a WCPS query with a WCPS query in each channel.
# > for data in ( frt00009971_07_if166l_trr3 ) return encode( {
# **red**:(int)( 255 / ( max((1 - (data.band_185 / ((1 - (0.538461538)) * data.band_178 (0.538461538) * data.band_191)))) - min((1 - (data.band_185 / ((1 - (0.538461538)) * data.band_178 (0.538461538) * data.band_191)))) )) * ( ((1 - (data.band_185 / ((1 - (0.538461538)) * data.band_178 (0.538461538) * data.band_191)))) - min((1 - (data.band_185 / ((1 - (0.538461538)) * data.band_178 (0.538461538) * data.band_191)))) );
# **green**:(int)( 255 / ( max((1 - (data.band_181 / ((1 - (0.49988651)) * data.band_171 (0.49988651) * data.band_191)))) - min((1 - (data.band_181 / ((1 - (0.49988651)) * data.band_171 (0.49988651) * data.band_191)))) )) * ( ((1 - (data.band_181 / ((1 - (0.49988651)) * data.band_171 (0.49988651) * data.band_191)))) - min((1 - (data.band_181 / ((1 - (0.49988651)) * data.band_171 (0.49988651) * data.band_191)))) );
# **blue**:(int)( 255 / ( max((1 - (data.band_178 / ((1 - (0.374881786)) * data.band_171 (0.374881786) * data.band_188)))) - min((1 - (data.band_178 / ((1 - (0.374881786)) * data.band_171 (0.374881786) * data.band_188)))) )) * ( ((1 - (data.band_178 / ((1 - (0.374881786)) * data.band_171 (0.374881786) * data.band_188)))) - min((1 - (data.band_178 / ((1 - (0.374881786)) * data.band_171 (0.374881786) * data.band_188)))) );
# alpha: (data.band_100 != 65535) * 255 }, "tiff", "nodata=null")
# In[33]:
get_ipython().run_cell_magic('html', '', '\n')
# ### Single pixel query
# Single pixel spectral analysis is pursued by querying all the channels of one of the observations for one pixel and compare the signal's absorption bands with a laboratory sample. While this will give us a first idea of possible materials, the established procedure consists on creating a kernel and average the signal. This allows us to avoid possible sub-pixel errors since normally materials are mixed in different concentrations with other materials.
#
# In this example we show a single pixel query. The description of the variables is as follows:
#
# * x_axis: We use a fixed X axis in order to avoid possible rounding errors if this is created with a function.
# * data: It will contain the spectral signal stored as a numpy array which has been queried to the data endpoint. Note that the data has to be cleaned from null_values and nan.
# > for c in (frt000050f2_07_if165l_trr3) return encode(c[N(893017.359289:893017.359289),E(4278699.1105:4278699.1105)],"csv")
# http://access.planetserver.eu:8080/rasdaman/ows?service=WCS&version=2.0.1&request=ProcessCoverages&query=
# for c in (frt000050f2_07_if165l_trr3) return encode(c[N(893017.359289:893017.359289),E(4278699.1105:4278699.1105)],"csv")
# In[18]:
get_ipython().run_line_magic('matplotlib', 'inline')
import math
import numpy as np
import matplotlib.pyplot as plt
x_axis = [1.00135, 1.0079, 1.0144500000000001, 1.0209999999999999, 1.02755, 1.0341, 1.0406500000000001, 1.0471999999999999, 1.05375, 1.0603, 1.0668500000000001, 1.07341, 1.07996, 1.0865100000000001, 1.09307, 1.09962, 1.1061700000000001, 1.11273, 1.1192800000000001, 1.12584, 1.13239, 1.1389499999999999, 1.14551, 1.1520600000000001, 1.15862, 1.1651800000000001, 1.1717299999999999, 1.1782900000000001, 1.18485, 1.1914100000000001, 1.19797, 1.2045300000000001, 1.21109, 1.2176499999999999, 1.22421, 1.2307699999999999, 1.23733, 1.2438899999999999, 1.2504500000000001, 1.25701, 1.2635700000000001, 1.27014, 1.2766999999999999, 1.2832600000000001, 1.28983, 1.2963899999999999, 1.3029500000000001, 1.30952, 1.3160799999999999, 1.3226500000000001, 1.32921, 1.33578, 1.3423400000000001, 1.3489100000000001, 1.35548, 1.36205, 1.3686100000000001, 1.3751800000000001, 1.38175, 1.38832, 1.39489, 1.4014500000000001, 1.40802, 1.41459, 1.42116, 1.4277299999999999, 1.43431, 1.4408799999999999, 1.4474499999999999, 1.4540200000000001, 1.4605900000000001, 1.46716, 1.47374, 1.48031, 1.48688, 1.49346, 1.50003, 1.50661, 1.51318, 1.51976, 1.52633, 1.53291, 1.53948, 1.54606, 1.55264, 1.55921, 1.56579, 1.57237, 1.5789500000000001, 1.58552, 1.5921000000000001, 1.5986800000000001, 1.6052599999999999, 1.6118399999999999, 1.61842, 1.625, 1.63158, 1.6381600000000001, 1.6447400000000001, 1.65133, 1.65791, 1.66449, 1.6710700000000001, 1.6776599999999999, 1.68424, 1.69082, 1.6974100000000001, 1.7039899999999999, 1.71058, 1.71716, 1.7237499999999999, 1.7303299999999999, 1.73692, 1.7435099999999999, 1.7500899999999999, 1.75668, 1.7632699999999999, 1.7698499999999999, 1.77644, 1.7830299999999999, 1.78962, 1.7962100000000001, 1.8028, 1.8093900000000001, 1.8159799999999999, 1.82257, 1.8291599999999999, 1.83575, 1.8423400000000001, 1.84893, 1.8555200000000001, 1.86212, 1.8687100000000001, 1.8753, 1.8818999999999999, 1.88849, 1.8950800000000001, 1.90168, 1.9082699999999999, 1.9148700000000001, 1.9214599999999999, 1.9280600000000001, 1.93465, 1.9412499999999999, 1.9478500000000001, 1.95444, 1.9610399999999999, 1.9676400000000001, 1.97424, 1.9808399999999999, 1.98743, 1.99403, 2.0006300000000001, 2.0072299999999998, 2.01383, 2.0204300000000002, 2.0270299999999999, 2.03363, 2.0402399999999998, 2.04684, 2.0534400000000002, 2.0600399999999999, 2.06664, 2.0732499999999998, 2.07985, 2.0864500000000001, 2.0930599999999999, 2.0996600000000001, 2.1062699999999999, 2.11287, 2.1194799999999998, 2.12608, 2.1326900000000002, 2.1393, 2.1459000000000001, 2.1525099999999999, 2.1591200000000002, 2.1657199999999999, 2.1723300000000001, 2.1789399999999999, 2.1855500000000001, 2.1921599999999999, 2.1987700000000001, 2.2053799999999999, 2.2119900000000001, 2.2185999999999999, 2.2252100000000001, 2.2318199999999999, 2.2384300000000001, 2.2450399999999999, 2.2516500000000002, 2.25827, 2.2648799999999998, 2.27149, 2.2780999999999998, 2.2847200000000001, 2.2913299999999999, 2.2979500000000002, 2.3045599999999999, 2.3111799999999998, 2.31779, 2.3244099999999999, 2.3310200000000001, 2.3376399999999999, 2.3442599999999998, 2.35087, 2.3574899999999999, 2.3641100000000002, 2.3707199999999999, 2.3773399999999998, 2.3839600000000001, 2.3905799999999999, 2.3972000000000002, 2.4038200000000001, 2.4104399999999999, 2.4170600000000002, 2.4236800000000001, 2.4302999999999999, 2.4369200000000002, 2.44354, 2.45017, 2.4567899999999998, 2.4634100000000001, 2.4700299999999999, 2.4766599999999999, 2.4832800000000002, 2.4899, 2.4965299999999999, 2.50312, 2.5097200000000002, 2.5163199999999999, 2.5229200000000001, 2.5295100000000001, 2.5361099999999999, 2.54271, 2.5493100000000002, 2.5559099999999999, 2.5625100000000001, 2.5691099999999998, 2.5757099999999999, 2.5823100000000001, 2.5889099999999998, 2.59551, 2.6021200000000002, 2.6087199999999999, 2.6153200000000001, 2.6219199999999998, 2.62853, 2.6351300000000002, 2.64174, 2.6483400000000001, 2.6549499999999999, 2.6615500000000001, 2.6681599999999999, 2.67476, 2.6813699999999998, 2.68798, 2.6945800000000002, 2.70119, 2.7606799999999998, 2.76729, 2.7738999999999998, 2.7805200000000001, 2.7871299999999999, 2.7937400000000001, 2.8003499999999999, 2.8069700000000002, 2.81358, 2.8201999999999998, 2.82681, 2.8334299999999999, 2.8400400000000001, 2.84666, 2.8532799999999998, 2.85989, 2.8665099999999999, 2.8731300000000002, 2.87975, 2.8863599999999998, 2.8929800000000001, 2.8996, 2.9062199999999998, 2.9128400000000001, 2.9194599999999999, 2.9260799999999998, 2.9327000000000001, 2.9393199999999999, 2.9459499999999998, 2.9525700000000001, 2.95919, 2.9658099999999998, 2.9724400000000002, 2.97906, 2.9856799999999999, 2.9923099999999998, 2.9989300000000001, 3.00556, 3.0121799999999999, 3.0188100000000002, 3.0254400000000001, 3.03206, 3.0386899999999999, 3.0453199999999998, 3.0519500000000002, 3.05857, 3.0651999999999999, 3.0718299999999998, 3.0784600000000002, 3.0850900000000001, 3.09172, 3.0983499999999999, 3.1049799999999999, 3.1116100000000002, 3.1182500000000002, 3.1248800000000001, 3.13151, 3.1381399999999999, 3.1447799999999999, 3.1514099999999998, 3.1580400000000002, 3.1646800000000002, 3.1713100000000001, 3.1779500000000001, 3.18458, 3.1912199999999999, 3.1978499999999999, 3.2044899999999998, 3.2111299999999998, 3.2177600000000002, 3.2244000000000002, 3.2310400000000001, 3.2376800000000001, 3.2443200000000001, 3.2509600000000001, 3.2576000000000001, 3.26424, 3.27088, 3.27752, 3.28416, 3.2907999999999999, 3.2974399999999999, 3.3040799999999999, 3.31073, 3.3173699999999999, 3.3240099999999999, 3.33066, 3.3372999999999999, 3.34395, 3.35059, 3.35724, 3.36388, 3.37053, 3.37717, 3.3838200000000001, 3.3904700000000001, 3.3971200000000001, 3.4037600000000001, 3.4104100000000002, 3.4170600000000002, 3.4237099999999998, 3.4303599999999999, 3.4370099999999999, 3.4436599999999999, 3.45031, 3.45696, 3.4636100000000001, 3.4702600000000001, 3.4769199999999998, 3.4835699999999998, 3.4902199999999999, 3.4968699999999999, 3.50353, 3.5101800000000001, 3.5168400000000002, 3.5234899999999998, 3.5301499999999999, 3.5367999999999999, 3.5434600000000001, 3.5501100000000001, 3.5567700000000002, 3.5634299999999999, 3.5700799999999999, 3.57674, 3.5834000000000001, 3.5900599999999998, 3.5967199999999999, 3.60338, 3.6100400000000001, 3.6166999999999998, 3.6233599999999999, 3.63002, 3.6366800000000001, 3.6433399999999998, 3.6499999999999999, 3.6566700000000001, 3.6633300000000002, 3.6699899999999999, 3.67665, 3.6833200000000001, 3.6899799999999998, 3.69665, 3.7033100000000001, 3.7099799999999998, 3.7166399999999999, 3.7233100000000001, 3.7299799999999999, 3.73664, 3.7433100000000001, 3.7499799999999999, 3.75665, 3.7633100000000002, 3.7699799999999999, 3.7766500000000001, 3.7833199999999998, 3.78999, 3.7966600000000001, 3.8033299999999999, 3.8100000000000001, 3.8166699999999998, 3.82335, 3.8300200000000002, 3.8366899999999999, 3.8433600000000001, 3.8500399999999999, 3.8567100000000001, 3.8633899999999999, 3.8700600000000001, 3.8767299999999998, 3.88341, 3.8900800000000002, 3.89676, 3.9034399999999998, 3.91011, 3.9167900000000002, 3.92347, 3.9301499999999998, 3.93682, 4.0]
#query = "http://access.planetserver.eu:8080/rasdaman/ows?query=for%20c%20in%20(frt000050f2_07_if165l_trr3)%20return%20encode(c[%20N(893017.359289:893017.359289),%20E(4278699.1105:4278699.1105)%20],%20%22csv%22)"
data_single = np.genfromtxt('http://access.planetserver.eu:8080/rasdaman/ows?query=for%20c%20in%20(frt000050f2_07_if165l_trr3)%20return%20encode(c[%20N(893017.359289:893017.359289),%20E(4278699.1105:4278699.1105)%20],%20%22csv%22)',delimiter=' ', dtype = float)
for x in range (0,len(data_single)):
if data_single[x] == 65535 or math.isnan(data_single[x]):
data_single[x] = 0
plt.xlabel( 'Wavelength (' + u"\u03BC" + ')')
plt.ylabel( 'Reflectance' )
plt.plot(x_axis,data_single)
plt.show()
# ***
# ## WCPS examples for Marine Science Data
# ### Apply a colour scheme to a png output
# > for a in (CCI_V2_monthly_chlor_a) return encode(
# switch case 0.05 > a[Lat(30:70),Long(-30:10),ansi("2009-09-30T23:59:00Z")] return {red: 255; green: 255; blue: 255; alpha: 0}
# case 0.1 > a[Lat(30:70),Long(-30:10),ansi("2009-09-30T23:59:00Z")] return {red: 0; green: 255; blue: 255; alpha: 255}
# case 0.2 > a[Lat(30:70),Long(-30:10),ansi("2009-09-30T23:59:00Z")] return {red: 0; green: 128; blue: 255; alpha: 255}
# case 0.5 > a[Lat(30:70),Long(-30:10),ansi("2009-09-30T23:59:00Z")] return {red: 0; green: 0; blue: 255; alpha: 255}
# case 1.5 > a[Lat(30:70),Long(-30:10),ansi("2009-09-30T23:59:00Z")] return {red: 218; green: 0; blue: 255; alpha: 255}
# case 3.0 > a[Lat(30:70),Long(-30:10),ansi("2009-09-30T23:59:00Z")] return {red: 255; green: 0; blue: 255; alpha: 255}
# case 4.5 > a[Lat(30:70),Long(-30:10),ansi("2009-09-30T23:59:00Z")] return {red: 255; green: 164; blue: 0; alpha: 255}
# case 6.2 > a[Lat(30:70),Long(-30:10),ansi("2009-09-30T23:59:00Z")] return {red: 255; green: 250; blue: 0; alpha: 255}
# case 20 > a[Lat(30:70),Long(-30:10),ansi("2009-09-30T23:59:00Z")] return {red: 255; green: 0; blue: 0; alpha: 255}
# default return {red: 255; green: 255; blue:255; alpha: 0} ,"png" )
# http://earthserver.pml.ac.uk/rasdaman/ows?service=WCS&version=2.0.1&request=ProcessCoverages&query=for a in (CCI_V2_monthly_chlor_a) return encode (switch case 0.05 > a[Lat(30:70),Long(-30:10),ansi("2009-09-30T23:59:00Z")] return {red: 255; green: 255; blue: 255; alpha: 0} case 0.1 > a[Lat(30:70),Long(-30:10),ansi("2009-09-30T23:59:00Z")] return {red: 0; green: 255; blue: 255; alpha: 255} case 0.2 > a[Lat(30:70),Long(-30:10),ansi("2009-09-30T23:59:00Z")] return {red: 0; green: 128; blue: 255; alpha: 255} case 0.5 > a[Lat(30:70),Long(-30:10),ansi("2009-09-30T23:59:00Z")] return {red: 0; green: 0; blue: 255; alpha: 255}
# case 1.5 > a[Lat(30:70),Long(-30:10),ansi("2009-09-30T23:59:00Z")] return {red: 218; green: 0; blue: 255; alpha: 255} case 3.0 > a[Lat(30:70),Long(-30:10),ansi("2009-09-30T23:59:00Z")] return {red: 255; green: 0; blue: 255; alpha: 255} case 4.5 > a[Lat(30:70),Long(-30:10),ansi("2009-09-30T23:59:00Z")] return {red: 255; green: 164; blue: 0; alpha: 255} case 6.2 > a[Lat(30:70),Long(-30:10),ansi("2009-09-30T23:59:00Z")] return {red: 255; green: 250; blue: 0; alpha: 255} case 20 > a[Lat(30:70),Long(-30:10),ansi("2009-09-30T23:59:00Z")] return {red: 255; green: 0; blue: 0; alpha: 255} default return {red: 255; green: 255; blue:255; alpha: 0} ,"png")
# In[30]:
get_ipython().run_cell_magic('html', '', '\n')
# ### Retrieve a time-series of daily chlorophyll values averaged over a given bounding box
# > for c in (CCI_V2_release_daily_chlor_a) return encode((float)
# coverage histogram over
# $px x( 0 : 0 ),
# $py y( 0 : 0 ),
# $pt ansi( 0 : 364 )
# values (
# add( (c[Long(-50:-40), Lat(45:55),ansi:"CRS:1"($pt)] < 100000 ) * c[Long(-50:-40), Lat(45:55),ansi:"CRS:1"($ pt)])
# /
# count(c[Long(-50:-40), Lat(45:55),ansi:"CRS:1"($pt)] < 100000)),
# "csv")
# In[31]:
url = "http://earthserver.pml.ac.uk/rasdaman/ows?service=WCS&version=2.0.1&request=ProcessCoverages&query=for%20c%20in%20%28CCI_V2_release_daily_chlor_a%29%20return%20encode%28%28float%29%20coverage%20histogram%20over%20$px%20x%28%200%20:%200%20%29,%20$py%20y%28%200%20:%200%20%29,%20$pt%20ansi%28%200%20:%20364%20%29%20values%20%20%28add%28%20%28c[Long%28-50:-40%29,%20Lat%2845:55%29,ansi:%22CRS:1%22%28$pt%29]%20%3C%20100000%20%29%20*%20c[Long%28-50:-40%29,%20Lat%2845:55%29,ansi:%22CRS:1%22%28$pt%29]%29/count%28c[Long%28-50:-40%29,%20Lat%2845:55%29,ansi:%22CRS:1%22%28$pt%29]%20%3C%20100000%20%29%29,%20%22csv%22%29"
response = urllib2.urlopen(url)
html = response.read()
print html
# ***
# The examples above shall give you an idea of what data processing and retrieval with a Web Coverage Processing Service is alike. WCPS can deal with far more complex data processing requests. For more information about the variety of data processing possible with WCPS, have a look to the two WCPS webinars available (see [06-Ressources](./06-Ressources.ipynb)).
# The [next tutorial chapter](./05-xWCPS_intro.ipynb) gives an introduction to xWCPS.
# ***
# 