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Marea/local/Recon_rules.p Marea/marea.py Marea/marea.xml Marea/marea_cluster.py Marea/marea_cluster.xml Marea/marea_macros.xml |
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| diff -r 3d77287caf22 -r e96f3b85e5a0 Marea/local/Recon_rules.p |
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| Binary file Marea/local/Recon_rules.p has changed |
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| diff -r 3d77287caf22 -r e96f3b85e5a0 Marea/marea.py --- a/Marea/marea.py Wed Feb 13 05:08:25 2019 -0500 +++ b/Marea/marea.py Wed Feb 13 05:42:20 2019 -0500 |
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| @@ -456,7 +456,7 @@ elif l[0] == 'or': flag = False break - if flag == True: #se ci sono solo AND nella lista + if flag == True: #when there are only AND in list tmp.extend(tmpAnd) elif flag == False: tmp.append(tmpAnd) |
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| diff -r 3d77287caf22 -r e96f3b85e5a0 Marea/marea.xml --- a/Marea/marea.xml Wed Feb 13 05:08:25 2019 -0500 +++ b/Marea/marea.xml Wed Feb 13 05:42:20 2019 -0500 |
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| @@ -83,8 +83,8 @@ </when> </conditional> <param name="None" argument="--none" type="boolean" truevalue="true" falsevalue="false" checked="true" label="(A and NaN) solved as (A)?" /> - <param name="pValue" argument="--pValue" type="float" size="20" value="0.05" max="1" min="0" label="P-value threshold" help="min value 0" /> - <param name="fChange" argument="--fChange" type="float" size="20" value="1.5" min="1" label="Fold-Change threshold" help="min value 1" /> + <param name="pValue" argument="--pValue" type="float" size="20" value="0.05" max="1" min="0" label="P-value threshold:" help="min value 0" /> + <param name="fChange" argument="--fChange" type="float" size="20" value="1.5" min="1" label="Fold-Change threshold:" help="min value 1" /> </inputs> <outputs> |
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| diff -r 3d77287caf22 -r e96f3b85e5a0 Marea/marea_cluster.py --- a/Marea/marea_cluster.py Wed Feb 13 05:08:25 2019 -0500 +++ b/Marea/marea_cluster.py Wed Feb 13 05:42:20 2019 -0500 |
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| @@ -1,4 +1,3 @@ - from __future__ import division import os import sys @@ -7,6 +6,8 @@ import pickle as pk import argparse from sklearn.cluster import KMeans +import matplotlib +matplotlib.use('GTKAgg') import matplotlib.pyplot as plt ########################## argparse ########################################### @@ -540,13 +541,22 @@ def f_cluster(resolve_rules): os.makedirs('cluster_out') args = process_args(sys.argv) + k_min = args.k_min + k_max = args.k_max + if k_min > k_max: + warning('k range boundaries inverted.\n') + tmp = k_min + k_min = k_max + k_max = tmp + else: + warning('k range correct.\n') cluster_data = pd.DataFrame.from_dict(resolve_rules, orient = 'index') for i in cluster_data.columns: tmp = cluster_data[i][0] if tmp == None: cluster_data = cluster_data.drop(columns=[i]) distorsion = [] - for i in range(args.k_min, args.k_max+1): + for i in range(k_min, k_max+1): tmp_kmeans = KMeans(n_clusters = i, n_init = 100, max_iter = 300, @@ -559,7 +569,7 @@ classe.to_csv(dest, sep = '\t', index = False, header = ['Patient_ID', 'Class']) plt.figure(0) - plt.plot(range(args.k_min, args.k_max+1), distorsion, marker = 'o') + plt.plot(range(k_min, k_max+1), distorsion, marker = 'o') plt.xlabel('Number of cluster') plt.ylabel('Distorsion') plt.savefig(args.elbow, dpi = 240, format = 'pdf') @@ -576,8 +586,6 @@ def main(): args = process_args(sys.argv) - if args.k_min > args.k_max: - sys.exit('Execution aborted: max cluster > min cluster') if args.rules_selector == 'HMRcore': recon = pk.load(open(args.tool_dir + '/local/HMRcore_rules.p', 'rb')) elif args.rules_selector == 'Recon': |
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| diff -r 3d77287caf22 -r e96f3b85e5a0 Marea/marea_macros.xml --- a/Marea/marea_macros.xml Wed Feb 13 05:08:25 2019 -0500 +++ b/Marea/marea_macros.xml Wed Feb 13 05:42:20 2019 -0500 |
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| @@ -66,7 +66,7 @@ <token name="@REFERENCE@"> -This tool is developed by the `BIMIB`_ at the `Department of Informatics, Systems and Communications`_ of `University of Milan - Bicocca`_. Development team: Irene Sala, Luca Rosato, Davide Maspero, Chiara Damiani. +This tool is developed by the `BIMIB`_ at the `Department of Informatics, Systems and Communications`_ of `University of Milan - Bicocca`_. .. _BIMIB: http://sito di bio.org .. _Department of Informatics, Systems and Communications: http://www.disco.unimib.it/go/Home/English |