# HG changeset patch
# User mnhn65mo
# Date 1533216278 14400
# Node ID 8da8ec7da45f9b50d95047c4f36359d3f83175fe

Uploaded

diff -r 000000000000 -r 8da8ec7da45f netcdf_metadata_info.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/netcdf_metadata_info.xml	Thu Aug 02 09:24:38 2018 -0400
@@ -0,0 +1,77 @@
+<tool id="netcdf-metadata_info" name="Netcdf Metadata Info" version="0.1.0">
+    <description>summarize content of a nc file</description>
+    <requirements>
+        <requirement type="package" version="1.1.6">netcdf-metadata-info</requirement> 
+    </requirements>
+    <command detect_errors="exit_code"><![CDATA[
+        netcdf-metadata-info '$input'
+        &&
+        while read -r l;do
+            a=\$(echo \$l | cut -d' ' -f1);echo \$l>dimensions_\$a
+        ;done <variables.tabular
+        &&
+        rm dimensions_VariableName 
+        &&
+        for f in dimensions_*; do cat \$f | sed 's/ /\t\n/g' | sed '\$s/$/ /' >\$f.tabular ; done
+        &&
+        for f in dimensions_*.tabular;do
+            awk 'NR % 2 != 0' \$f > \$f.2
+            &&
+            sed 1d \$f.2 > \$f 
+            &&
+            rm \$f.2
+        ;done
+        &&
+        ncdump -h '$input' > '$info'
+    ]]></command>
+    <inputs>
+        <param type="data" name="input" label="Netcdf file" format="netcdf,h5" help="Netcdf file you need information about."/>
+    </inputs>
+    <outputs>
+<!--
+        <data name="var_tabs" format="tabular">
+            <discover_datasets pattern="__designation_and_ext__" visible="true"/>
+            <discover_datasets pattern="conda_activate.log" visible="false"/>
+        </data>
+-->
+        <data name="output" format="tabular" label="Metadata infos from ${input.name}.Variables.tab" from_work_dir="variables.tabular"/>
+        <data name="info" label="info file" format="txt"/>
+    </outputs>
+
+    <help><![CDATA[
+**What it does**
+
+First the tool will give general information about the input in a 'info file' output. (command $ncdump -h inputfile)
+
+Then, a general tabular 'variables' summarize dimensions details inside each available variable.
+
+
+The summary tabular file has the general structure :
+
+
+    Variable1    Var1_Number_of_Dim    Dim1    Dim1_size   ...    DimN    DimN_size    
+ 
+    VariableX    VarX_Number_of_Dim    DimX1   DimX1_size  ...    DimXN   DimXN_size   
+
+    ...                                                                              
+
+
+**Input**
+
+A netcdf file (xxx.nc).
+
+**Outputs**
+
+An Information file.
+
+A summary tabular file.
+
+
+--------------------------------
+ 
+The Netcdf Info tool use the netcdf functions : https://www.unidata.ucar.edu/software/netcdf/docs/index.html
+
+Run this tool before considering using Netcdf Read.
+    ]]></help>
+
+</tool>
diff -r 000000000000 -r 8da8ec7da45f netcdf_read.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/netcdf_read.py	Thu Aug 02 09:24:38 2018 -0400
@@ -0,0 +1,421 @@
+import netCDF4
+from netCDF4 import Dataset
+import numpy as np
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from pylab import *
+import sys
+import os
+from scipy import spatial
+from math import radians, cos, sin, asin, sqrt
+import itertools
+
+#####################
+#####################
+ 
+def checklist(dim_list, dim_name, filtre, threshold):
+    if not dim_list:
+        error="Error "+str(dim_name)+" has no value "+str(filtre)+" "+str(threshold)
+        sys.exit(error)
+
+
+#Return dist in km between two coord
+#Thx to : https://stackoverflow.com/questions/4913349/haversine-formula-in-python-bearing-and-distance-between-two-gps-points
+def haversine(lon1, lat1, lon2, lat2):
+    """
+    Calculate the great circle distance between two points 
+    on the earth (specified in decimal degrees)
+    """
+    # convert decimal degrees to radians 
+    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
+
+    # haversine formula 
+    dlon = lon2 - lon1 
+    dlat = lat2 - lat1 
+    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
+    c = 2 * asin(sqrt(a)) 
+    r = 6371 # Radius of earth in kilometers. Use 3956 for miles
+    return c * r
+
+
+#Comparison functions, return a list of indexes for the user conditions 
+def is_strict_inf(filename, dim_name, threshold):
+    list_dim=[]
+    for i in range(0,filename.variables[dim_name].size):
+        if filename.variables[dim_name][i] < threshold:
+            list_dim.append(i)
+    checklist(list_dim,dim_name,"<",threshold)
+    return list_dim
+
+def is_equal_inf(filename, dim_name, threshold):
+    list_dim=[]
+    for i in range(0,filename.variables[dim_name].size):
+        if filename.variables[dim_name][i] <= threshold:
+            list_dim.append(i)
+    checklist(list_dim,dim_name,"<=",threshold)
+    return list_dim
+
+def is_equal_sup(filename, dim_name, threshold):
+    list_dim=[]
+    for i in range(0,filename.variables[dim_name].size):
+        if filename.variables[dim_name][i] >= threshold:
+            list_dim.append(i)
+    checklist(list_dim,dim_name,">=",threshold)
+    return list_dim
+
+def is_strict_sup(filename, dim_name, threshold):
+    list_dim=[]
+    for i in range(0,filename.variables[dim_name].size):
+        if filename.variables[dim_name][i] > threshold:
+            list_dim.append(i)
+    checklist(list_dim,dim_name,">",threshold)
+    return list_dim
+
+def find_nearest(array,value):
+    index = (np.abs(array-value)).argmin()
+    return index
+
+def is_equal(filename, dim_name, value):
+    try:
+        index=filename.variables[dim_name][:].tolist().index(value)
+    except:
+        index=find_nearest(filename.variables[dim_name][:],value)
+    return index
+
+def is_between_include(filename, dim_name, threshold1, threshold2):
+    list_dim=[]
+    for i in range(0,filename.variables[dim_name].size):
+        if filename.variables[dim_name][i] >= threshold1 and filename.variables[dim_name][i] <= threshold2:
+            list_dim.append(i)
+    checklist(list_dim,dim_name,">=",threshold1)
+    checklist(list_dim,dim_name,"=<",threshold2)
+    return list_dim
+
+def is_between_exclude(filename, dim_name, threshold1, threshold2):
+    list_dim=[]
+    for i in range(0,filename.variables[dim_name].size):
+        if filename.variables[dim_name][i] > threshold1 and filename.variables[dim_name][i] < threshold2:
+            list_dim.append(i)
+    checklist(list_dim,dim_name,">",threshold1)
+    checklist(list_dim,dim_name,"<",threshold2)
+    return list_dim
+
+#######################
+#######################
+
+#Get args
+#Get Input file
+inputfile=Dataset(sys.argv[1])
+var_file_tab=sys.argv[2]
+var=sys.argv[3] #Var chosen by user
+
+Coord_bool=False
+
+
+######################
+######################
+#len_threshold=1000000
+len_threshold=7000
+x_percent=0.75
+threshold_latlon=100
+
+
+#Check if coord is passed as parameter
+arg_n=len(sys.argv)-1
+if(((arg_n-3)%3)!=0):
+    Coord_bool=True #Useful to get closest coord
+    arg_n=arg_n-4 #Number of arg minus lat & lon
+    name_dim_lat=str(sys.argv[-4])
+    name_dim_lon=str(sys.argv[-2])
+    value_dim_lat=float(sys.argv[-3])
+    value_dim_lon=float(sys.argv[-1])
+
+    #Get all lat & lon
+    #try:
+    if True:
+        latitude=np.ma.MaskedArray(inputfile.variables[name_dim_lat])
+        longitude=np.ma.MaskedArray(inputfile.variables[name_dim_lon])
+        lat=latitude;lon=longitude #Usefull to keep the originals lat/lon vect before potentially resize it bellow.
+        len_all_coord=len(lat)*len(lon)
+        
+        #print("len all coord "+str(len_all_coord)+" threshold "+str(len_threshold))
+
+        #To avoid case when all_coord is to big and need to much memory
+        #If the vector is too big, reduce it to its third in a loop until its < to the threshold
+        while len_all_coord > len_threshold:
+            
+            if len(lat)<threshold_latlon: #If lat and lon are very different and lon is >> than lat. This way only lon is reduce and not lat.
+                x_percent_len_lat=99999999
+            else:
+                x_percent_len_lat=int(x_percent*len(lat))
+
+            if len(lon)<threshold_latlon: #If lat and lon are very different and lat is >> than lon. This way only lat is reduce and not lon.
+                x_percent_len_lon=99999999
+            else:
+                x_percent_len_lon=int(x_percent*len(lon))
+
+            #print("len(lat) :"+str(len(lat))+" x_percent_len_lat "+str(x_percent_len_lat))
+            #print("len(lon) :"+str(len(lon))+" x_percent_len_lon "+str(x_percent_len_lon))
+
+ 
+            pos_lat_user=find_nearest(lat,value_dim_lat)
+            pos_lon_user=find_nearest(lon,value_dim_lon)
+
+              
+            #This part is to avoid having a vector that start bellow 0
+            lat_reduced=int(pos_lat_user-x_percent_len_lat/2-1)
+            if lat_reduced<0:
+                lat_reduced=0
+            lon_reduced=int(pos_lon_user-x_percent_len_lon/2-1)
+            if lon_reduced<0:
+                lon_reduced=0
+            #Opposite here to avoid having vector with len > to len(vector)
+            lat_extended=int(pos_lat_user+x_percent_len_lat/2-1)
+            if lat_extended>len(lat):
+                lat_extended=len(lat)
+            lon_extended=int(pos_lon_user+x_percent_len_lon/2-1)
+            if lon_extended>len(lon):
+                lon_extended=len(lon)
+
+            lat=lat[lat_reduced:lat_extended] #add a test to check if pos_lat_user-x_percent_len_lat/2-1 >0
+            lon=lon[lon_reduced:lon_extended]
+            #print("latreduced : "+str(lat_reduced)+" latextended "+str(lat_extended))
+            #print("lonreduced : "+str(lon_reduced)+" lonextended "+str(lon_extended))
+            #print("lat : "+str(lat))
+            #print("lon : "+str(lon))
+            len_all_coord=len(lat)*len(lon)
+
+            #print ("len_all_coord : "+str(len_all_coord)+". len_lat : "+str(len(lat))+" .len_lon : "+str(len(lon)))
+
+    else:
+    #except:
+        sys.exit("Latitude & Longitude not found") 
+
+    #Set all lat-lon pair avaible in list_coord
+    list_coord_dispo=[]
+    for i in lat:
+        for j in lon:
+            list_coord_dispo.append(i);list_coord_dispo.append(j)
+
+    #Reshape
+    all_coord=np.reshape(list_coord_dispo,(lat.size*lon.size,2))
+    #np.set_printoptions(threshold='nan')#to print full vec
+    #print(str(all_coord))
+    noval=True
+
+
+
+#########################
+#########################
+
+
+#Get the file of variables and number of dims : var.tab
+var_file=open(var_file_tab,"r") #read
+lines=var_file.readlines() #line
+dim_names=[]
+for line in lines: #for every lines
+    words=line.split()
+    if (words[0]==var): #When line match user input var
+        varndim=int(words[1])  #Get number of dim for the var
+        for dim in range(2,varndim*2+2,2): #Get dim names
+            dim_names.append(words[dim])
+        #print ("Chosen var : "+sys.argv[3]+". Number of dimensions : "+str(varndim)+". Dimensions : "+str(dim_names)) #Standard msg
+        
+
+########################
+########################
+
+
+#Use a dictionary to save every lists of indexes
+my_dic={} ##d["string{0}".format(x)]
+
+for i in range(4,arg_n,3):
+    #print("\nDimension name : "+sys.argv[i]+" action : "+sys.argv[i+1]+" .Value : "+sys.argv[i+2]+"\n") #Standard msg
+
+    #Check if the dim selected for filtering is present in the var dimensions.
+    if (sys.argv[i] not in dim_names):
+        print("Warning ! "+sys.argv[i]+" is not a dimension of "+var+".\nThis filter will be skipped\nCheck in the file \"variables\" the dimensions available.\n\n")
+        pass
+
+    my_dic["string{0}".format(i)]="list_index_dim"
+    my_dic_index="list_index_dim"+str(sys.argv[i])   #Possible improvement: Check if lon/lat are not parsed again
+
+    #Apply every user filter. Call function and return list of index wich validate condition for every dim.
+    if (sys.argv[i+1]=="l"): #<
+        my_dic[my_dic_index]=is_strict_inf(inputfile, sys.argv[i], float(sys.argv[i+2]))
+    if (sys.argv[i+1]=="le"): #<=
+        my_dic[my_dic_index]=is_equal_inf(inputfile, sys.argv[i], float(sys.argv[i+2]))
+    if (sys.argv[i+1]=="g"): #>
+        my_dic[my_dic_index]=is_strict_sup(inputfile, sys.argv[i], float(sys.argv[i+2]))
+    if (sys.argv[i+1]=="ge"): #>=
+        my_dic[my_dic_index]=is_equal_sup(inputfile, sys.argv[i], float(sys.argv[i+2]))
+    if (sys.argv[i+1]=="e"): #==
+        my_dic[my_dic_index]=is_equal(inputfile, sys.argv[i], float(sys.argv[i+2]))
+    if (sys.argv[i+1]==":"): #all
+        my_dic[my_dic_index]=np.arange(inputfile.variables[sys.argv[i]].size)
+    if (sys.argv[i+1]=="be"): #between_exclude
+        #Get the 2 thresholds from the arg which looks like "threshold1-threshold2"
+        threshold1=sys.argv[i+2].split("-")[0] 
+        threshold2=sys.argv[i+2].split("-")[1] 
+        my_dic[my_dic_index]=is_between_exclude(inputfile, sys.argv[i], float(threshold1), float(threshold2))
+    if (sys.argv[i+1]=="bi"): #between_include
+        #Get the 2 thresholds from the arg which looks like "threshold1-threshold2"
+        threshold1=sys.argv[i+2].split("-")[0]
+        threshold2=sys.argv[i+2].split("-")[1]
+        my_dic[my_dic_index]=is_between_include(inputfile, sys.argv[i], float(threshold1), float(threshold2))
+
+#####################
+#####################
+
+
+#If precise coord given.
+if Coord_bool:
+    while noval: #While no closest coord with valid values is found
+        #Return closest coord avaible
+        tree=spatial.KDTree(all_coord)
+        closest_coord=(tree.query([(value_dim_lat,value_dim_lon)]))
+        cc_index=closest_coord[1]
+
+        closest_lat=float(all_coord[closest_coord[1]][0][0])
+        closest_lon=float(all_coord[closest_coord[1]][0][1])
+
+        #Get coord index into dictionary
+        my_dic_index="list_index_dim"+str(name_dim_lat)
+        my_dic[my_dic_index]=latitude.tolist().index(closest_lat)
+
+        my_dic_index="list_index_dim"+str(name_dim_lon)
+        my_dic[my_dic_index]=longitude.tolist().index(closest_lon)
+
+
+        #All dictionary are saved in the string exec2 which will be exec(). Value got are in vec2
+        exec2="vec2=inputfile.variables['"+var+"']["
+        first=True
+        for i in dim_names: #Every dim are in the right order
+            if not first:
+                exec2=exec2+","
+            dimension_indexes="my_dic[\"list_index_dim"+i+"\"]" #new dim, custom name dic
+            try:  #If some error or no specific user choices; every indexes are used for the selected dim.
+                exec(dimension_indexes)
+            except:
+                dimension_indexes=":"
+            exec2=exec2+dimension_indexes #Concatenate dim
+            first=False #Not the first element now
+        exec2=exec2+"]"
+        #print exec2 #To check integrity of the string
+        exec(exec2) #Execution, value are in vec2.
+        #print vec2 #Get the value, standard output
+
+        #Check integrity of vec2. We don't want  NA values
+        i=0 
+        #Check every value, if at least one non NA is found vec2 and the current closest coords are validated
+        vecsize=vec2.size
+        #print (str(vecsize))
+        if vecsize>1:
+            while i<vecsize:
+                #print (str(vec2))
+                if vec2[i]!="nan":
+                    break
+                else: 
+                    i=i+1
+        else:
+            if vec2!="nan":
+                break
+            else: 
+                i=i+1
+   
+        if i<vecsize: #There is at least 1 nonNA value
+            noval=False
+        else: #If only NA : pop the closest coord and search in the second closest coord in the next loop.
+            all_coord=np.delete(all_coord,cc_index,0)
+
+
+#Same as before, dictionary use in exec2. exec(exec2) give vec2 and the values wanted.
+else:
+    exec2="vec2=inputfile.variables['"+str(sys.argv[3])+"']["
+    first=True
+    for i in dim_names: #Respect order
+        if not first:
+            exec2=exec2+","
+        dimension_indexes="my_dic[\"list_index_dim"+i+"\"]"
+        try:  #Avoid error and exit
+            exec(dimension_indexes)
+        except:
+            dimension_indexes=":"
+        exec2=exec2+dimension_indexes
+        first=False
+    exec2=exec2+"]"
+    exec(exec2)
+   
+
+########################
+########################
+
+
+#This part create the header of every value. 
+#Values of every dim from the var is saved in a list : b[].
+#All the lists b are saved in the unique list a[]
+#All the combinations of the dim values inside a[] are the headers of the vec2 values 
+
+#Also write dim_name into a file to make clear header.
+fo=open("header_names",'w')
+
+a=[]
+for i in dim_names:
+    try: #If it doesn't work here its because my_dic= : so there is no size. Except will direcly take size of the dim.
+        size_dim=inputfile[i][my_dic['list_index_dim'+i]].size
+    except:
+        size_dim=inputfile[i].size 
+        my_dic['list_index_dim'+i]=range(size_dim)
+
+    #print (i,size_dim) #Standard msg
+    b=[]
+    #Check size is useful since b.append(inputfile[i][my_dic['list_index_dim'+i][0]])  won't work
+    if size_dim>1:
+        for s in range(0,size_dim):
+            b.append(inputfile[i][my_dic['list_index_dim'+i][s]])
+            #print (i,inputfile[i][my_dic['list_index_dim'+i][s]])
+    else:
+        b.append(inputfile[i][my_dic['list_index_dim'+i]])
+        #print (i,inputfile[i][my_dic['list_index_dim'+i]])
+
+    a.append(b) 
+    fo.write(i+"\t")
+if Coord_bool: 
+    fo.write("input_lat\t"+"input_lon\t")
+fo.write(var+"\n")
+fo.close()
+
+
+######################
+######################
+
+
+#Write header in file
+fo=open("header",'w')
+for combination in itertools.product(*a):
+    if Coord_bool:
+        fo.write(str(combination)+"_"+str(value_dim_lat)+"_"+str(value_dim_lon)+"\t")
+    else:
+        fo.write(str(combination)+"\t")
+fo.write("\n")
+fo.close()
+
+
+#Write vec2 in a tabular formated file
+fo=open("sortie.tabular",'w')
+#print(str(vec2))
+try:
+    vec2.tofile(fo,sep="\t",format="%s")
+except:
+    vec3=np.ma.filled(vec2,np.nan)
+    vec3.tofile(fo,sep="\t",format="%s")
+fo.close()
+
+
+######################
+######################
+
+
+#Final sweet msg
+print (var+" values successffuly extracted from "+sys.argv[1]+" !")
diff -r 000000000000 -r 8da8ec7da45f netcdf_read.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/netcdf_read.xml	Thu Aug 02 09:24:38 2018 -0400
@@ -0,0 +1,256 @@
+<tool id="netcdf_read" name="Netcdf Reader" version="0.2.0">
+    <description>extracts variable values with custom conditions on dimensions</description>
+    <requirements>
+        <requirement type="package" version="2.1.0">matplotlib</requirement>
+        <requirement type="package" version="1.3.1">netCDF4</requirement>
+        <requirement type="package" version="1.0.0">scipy</requirement>
+        <requirement type="package" version="1.1.0">datamash</requirement>
+    </requirements>
+    <command detect_errors="exit_code"><![CDATA[
+    mkdir output_dir && 
+
+    #if $condi_source_coord.coord_source=="coord_from_file"
+        i=0 &&
+        re='^[-+]?[0-9]+\.?[0-9]*$' && 
+        while read line; do 
+            lat=\$(echo \$line | cut -d' ' -f1) 
+            lon=\$(echo \$line | cut -d' ' -f2)
+            && 
+            if ! [[ \$lat =~ \$re ]] || ! [[ \$lon =~ \$re ]] ; then continue ;fi
+            &&
+            i=\$((\$i+1)) &&
+            python '$__tool_directory__/netcdf_read.py' '$input' '$var_tab' $var
+            #for $i,$uc in enumerate($user_choice)
+                #if $uc.condi_between.comparator=="bi"
+                    ${uc.dim} ${uc.condi_between.comparator} ${uc.condi_between.t1}-${uc.condi_between.t2}
+                #elif $uc.condi_between.comparator=="be"
+                    ${uc.dim} ${uc.condi_between.comparator} ${uc.condi_between.t1}-${uc.condi_between.t2}
+                #else
+                    ${uc.dim} ${uc.condi_between.comparator} ${uc.condi_between.value}
+                #end if
+            #end for   
+            '$condi_source_coord.lat_dim'
+            \$lat
+            '$condi_source_coord.lon_dim'
+            \$lon
+        
+            &&
+            cat 'header' | sed 's/array(\[//g' | sed 's/], dtype=float32)//g'| sed 's/,\s/_/g' | sed 's/(//g' | sed 's/)//g' > 'header_cleaned'
+            &&
+            cat 'header_cleaned' 'sortie.tabular' > 'supersortie.tabular'
+            &&
+            datamash transpose < 'supersortie.tabular' > 'supersortie_transposed.tabular'
+            &&
+            sed -i 's/_/\t/g' 'supersortie_transposed.tabular'
+            &&
+            cat 'header_names' 'supersortie_transposed.tabular' | sed 's/\s/\t/g' > 'output_dir/coord'\$i'.tabular'; 
+        done<'$coord_tabular'
+
+    #else
+
+        python '$__tool_directory__/netcdf_read.py' '$input' '$var_tab' $var
+        #for $i,$uc in enumerate($user_choice)
+            #if $uc.condi_between.comparator=="bi"
+                ${uc.dim} ${uc.condi_between.comparator} ${uc.condi_between.t1}-${uc.condi_between.t2}
+            #elif $uc.condi_between.comparator=="be"
+                ${uc.dim} ${uc.condi_between.comparator} ${uc.condi_between.t1}-${uc.condi_between.t2}
+            #else
+                ${uc.dim} ${uc.condi_between.comparator} ${uc.condi_between.value}
+            #end if
+        #end for
+        #if $condi_source_coord.condi_coord.coord=='yes_cust_coord'
+            $condi_source_coord.condi_coord.lat_dim $condi_source_coord.condi_coord.lat_val $condi_source_coord.condi_coord.lon_dim $condi_source_coord.condi_coord.lon_val
+        #end if
+        &&
+        cat 'header' | sed 's/array(\[//g' | sed 's/], dtype=float32)//g'| sed 's/,\s/_/g' | sed 's/(//g' | sed 's/)//g' > 'header_cleaned'
+        &&
+        cat 'header_cleaned' 'sortie.tabular' > 'supersortie.tabular'
+        &&
+        datamash transpose < 'supersortie.tabular' > 'supersortie_transposed.tabular'
+        &&
+        sed -i 's/_/\t/g' 'supersortie_transposed.tabular'
+        &&
+        cat 'header_names' 'supersortie_transposed.tabular' | sed 's/\s/\t/g' > 'final.tabular'
+
+    #end if
+
+
+    ]]></command>
+    <inputs>
+        <param type="data" name="input" label="Input netcdf file" format="netcdf,h5"/>
+        <param type="data" label="Tabular of variables" name="var_tab" format="tabular" help="Select the tabular file which summarize the available variables and dimensions."/>
+
+        <param name="var" type="select" label="Chose the variable to extract">
+            <options from_dataset="var_tab">
+                <column name="name" index="0"/>
+                <column name="value" index="0"/>
+                <column name="n_dim" index="1"/>
+            </options>
+        </param>
+
+        <conditional name="condi_source_coord">
+            <param name="coord_source" type="select" label="Source of coordinates">
+                <option value="coord_from_file">Use coordinates from input file</option>
+                <option value="coord_from_stdin">Manually enter coordinate</option>
+            </param>
+
+            <when value="coord_from_file">
+                <param type="data" label="Tabular of coord" name="coord_tabular" format="tabular" help="Format : Latitude	Longitude"/>
+                <param name="lat_dim" type="select" label="Select latitude" >
+                    <options from_dataset="var_tab">
+                        <column name="value" index="0"/>
+                    </options>
+                </param>
+                <param name="lon_dim" type="select" label="Select longitude" >
+                    <options from_dataset="var_tab">
+                        <column name="value" index="0"/>
+                    </options>
+                </param>
+            </when>
+
+            <when value="coord_from_stdin">
+                <conditional name="condi_coord">
+                    <param name="coord" type="boolean" label="Search values for custom coordinates" truevalue="yes_cust_coord" checked="true" falsevalue="nope" help="Use this option to get valid values at your custom coordinates. If only NA values are availables the tool will search for the next closest coordinate until valid values."/>
+                    <when value="yes_cust_coord">
+                        <param name="lat_dim" type="select" label="Select latitude" >
+                            <options from_dataset="var_tab">
+                                <column name="value" index="0"/>
+                            </options>
+                        </param>
+                        <param name="lat_val" type="float" value="0" label="Latitude"/>
+                        <param name="lon_dim" type="select" label="Select longitude" >
+                            <options from_dataset="var_tab">
+                                <column name="value" index="0"/>
+                            </options>
+                        </param>
+                        <param name="lon_val" type="float" value="0" label="Longitude"/>
+                    </when>
+
+                    <when value="nope"></when>
+                </conditional>
+            </when>
+
+        </conditional>
+
+        <repeat name="user_choice" title="Filter">
+            <param name="dim" type="select" label="Dimensions">
+                <options from_dataset="var_tab">
+                    <column name="value" index="0"/>
+                </options>
+            </param>
+            <conditional name="condi_between">
+                <param name="comparator" type="select" label="Comparator">
+                    <option value="e">Equal</option>
+                    <option value="g">Greater</option>
+                    <option value="ge">Greater or equal</option>
+                    <option value="l">Less</option>
+                    <option value="le">Less or equal</option>
+                    <option value="be">Between-exclude ]threshold1,threshold2[</option>
+                    <option value="bi">Between-include [threshold1,threshold2]</option>
+                </param>
+                <when value="bi">
+                    <param name="t1" type="float" value="0" label="Inferior threshold"/>
+                    <param name="t2" type="float" value="0" label="Superior threshold"/>
+                </when>
+                <when value="be">
+                    <param name="t1" type="float" value="0" label="Inferior threshold"/>
+                    <param name="t2" type="float" value="0" label="Superior threshold"/>
+                </when>
+                <when value="e">
+                    <param name="value" type="float" value="0" label="Value"/>
+                </when>
+                <when value="g">
+                    <param name="value" type="float" value="0" label="Value"/>
+                </when>
+                <when value="ge">
+                    <param name="value" type="float" value="0" label="Value"/>
+                </when>
+                <when value="l">
+                    <param name="value" type="float" value="0" label="Value"/>
+                </when>
+                <when value="le">
+                    <param name="value" type="float" value="0" label="Value"/>
+                </when>
+            </conditional>
+        </repeat>
+
+    </inputs>        
+
+    <outputs>
+        <collection type="list" name="output">
+            <discover_datasets pattern="__designation_and_ext__" visible="false" format="tabular" directory="output_dir"/>
+            <filter>condi_source_coord['coord_source'] == 'coord_from_file'</filter>
+        </collection>
+        <data name="simpleoutput" from_work_dir="final.tabular" format="tabular">
+            <filter>condi_source_coord['coord_source'] == 'coord_from_stdin'</filter>
+        </data>
+    </outputs>
+
+
+    <tests>
+        <test>
+             <param name="input" value="dataset-ibi-reanalysis-bio-005-003-monthly-regulargrid_1510914389133.nc"/>
+             <param name="var_tab" value="var_tab_dataset-ibi"/>
+             <param name="var" value="phy"/>
+             <param name="dim_tab" value="tab_dim_phy_dataset-ibi"/>
+             <param name="coord" value="yes_cut_coord"/>
+             <param name="lat_dim" value="latitude"/>
+             <param name="lat_val" value="44.0"/>
+             <param name="lon_dim" value="longitude"/>
+             <param name="lon_val" value="-2.0"/>
+             <param name="output" value="Test1.tabular"/>
+        </test>
+        <test>
+             <param name="input" value="dataset-ibi-reanalysis-bio-005-003-monthly-regulargrid_1510914389133.nc"/>
+             <param name="var_tab" value="var_tab_dataset-ibi"/>
+             <param name="var" value="nh4"/>
+             <param name="dim_tab" value="tab_dim_nh4_dataset-ibi"/>
+             <param name="coord" value="nope"/>
+             <param name="dim" value="time"/>
+             <param name="comparator" value="e"/>
+             <param name="value" value="7272.0"/>
+             <param name="dim" value="latitude"/>
+             <param name="comparator" value="ge"/>
+             <param name="value" value="45.0"/>
+             <param name="output" value="Test2.tabular"/>
+        </test>
+
+
+
+
+    </tests>
+
+    <help><![CDATA[
+**What it does**
+
+This tool extracts variable values with custom conditions on dimensions.
+
+It can use manualy given coordinates or automaticaly take them from a tabular file to filter informations.
+
+If no values are availables at a coordinate X, the tool will search the closest coordinate with a non NA value.
+
+Filter can be set on every dimension. Available filtering operations are : =, >, <, >=, <=, [interval], ]interval[.
+
+
+
+**Input**
+
+A netcdf file (.nc).
+
+Variable tabular file from 'Netcdf Metadate Info'.
+
+Tabular file with coordinates and the following structure : 'lat'	'lon'.
+
+
+**Outputs**
+
+A single output with values for the wanted variable if there is only one coordinate.
+
+A data collection where one file is created for every coordinate, if multiple coordinates from tabular file.
+
+
+------------------------------------------------- 
+
+The Netcdf Read tool can be used after the Netcdf Info. 
+    ]]></help>
+</tool>