diff --git a/examples/compare_batch_iterators.py b/examples/compare_batch_iterators.py
deleted file mode 100644
index ce57250f..00000000
--- a/examples/compare_batch_iterators.py
+++ /dev/null
@@ -1,75 +0,0 @@
-#!/usr/bin/env python
-from functools import partial
-from itertools import tee
-
-class Loader():
-    def example_batch_generator(self,n):
-        for batch in range(n):
-            yield batch
-
-class MPIModel():
-    def __init__(self,batch_generator):
-        self.batch_iterator = batch_generator
-
-    def train_epochs(self,M):
-        num_total = 8
-        for epoch in range(M):
-            num_so_far = 0
-            print ("Batch iter. summary: {}{}".format(self,self.batch_iterator))
-            for batch in self.batch_iterator():
-                num_so_far += 1
-
-                whatever=batch
-                print ("Next batch id: {}".format(batch))
-                if num_so_far > num_total: break
-            print "+++++++"
-
-
-class MPIModel_default():
-    def __init__(self,batch_generator):
-        self.batch_iterator = batch_generator
-
-    def train_epochs(self,M):
-        num_total = 8 #number of samples per epoch
-        batch_generator_func = self.batch_iterator()
-
-        for iepoch in range(M):
-            #print ("Batch iter. summary: {}{} epoch: {}".format(self,self.batch_iterator,iepoch))
-            num_so_far = 0
-
-            while num_so_far < num_total:
-                num_so_far += 1
-
-                try:
-                    batch = batch_generator_func.next()
-                except StopIteration:
-                    batch_generator_func = self.batch_iterator()
-                    batch = batch_generator_func.next()
-                print ("Next batch id: {}".format(batch))
-
-            print "+++++++"
-
-
-
-def main():
-    num_batches = 10
-    epochs = 3
-
-    loader = Loader()
-    batch_generator = partial(loader.example_batch_generator,n=num_batches)
-    my_example_class = MPIModel(batch_generator)
-    my_example_class.train_epochs(epochs)
-
-def main_default():
-    num_batches = 10
-    epochs = 3
-
-    loader = Loader()
-    batch_generator = partial(loader.example_batch_generator,n=num_batches)
-    my_example_class = MPIModel_default(batch_generator)
-    my_example_class.train_epochs(epochs)
-
-if __name__=='__main__':
-    import timeit
-    #print min(timeit.Timer(setup=main).repeat(7, 1000))
-    print min(timeit.Timer(setup=main_default).repeat(7, 1000))
diff --git a/examples/conf.yaml b/examples/conf.yaml
index 1b308cd8..a71947fb 100644
--- a/examples/conf.yaml
+++ b/examples/conf.yaml
@@ -10,7 +10,7 @@ paths:
     signal_prepath: '/signal_data/' #/signal_data/jet/
     shot_list_dir: '/shot_lists/'
     tensorboard_save_path: '/Graph/'
-    data: 'jet_data' #'d3d_to_jet_data' #'d3d_to_jet_data' # 'jet_to_d3d_data' #jet_data
+    data: jet_data #'d3d_to_jet_data' #'d3d_to_jet_data' # 'jet_to_d3d_data' #jet_data
     specific_signals: [] #['q95','li','ip','betan','energy','lm','pradcore','pradedge','pradtot','pin','torquein','tmamp1','tmamp2','tmfreq1','tmfreq2','pechin','energydt','ipdirect','etemp_profile','edens_profile'] #if left empty will use all valid signals defined on a machine. Only use if need a custom set
     executable: "mpi_learn.py"
     shallow_executable: "learn.py"
diff --git a/examples/guarantee_preprocessed.py b/examples/guarantee_preprocessed.py
index 3f005f0e..67826ad0 100644
--- a/examples/guarantee_preprocessed.py
+++ b/examples/guarantee_preprocessed.py
@@ -11,8 +11,6 @@
 pprint(conf)
 from plasma.preprocessor.preprocess import guarantee_preprocessed
 
-os.environ["PYTHONHASHSEED"] = "0"
-
 #####################################################
 ####################PREPROCESSING####################
 #####################################################
diff --git a/examples/jenkins.sh b/examples/jenkins.sh
deleted file mode 100644
index 9d9087e6..00000000
--- a/examples/jenkins.sh
+++ /dev/null
@@ -1,16 +0,0 @@
-#!/bin/bash
-
-rm /tigress/alexeys/model_checkpoints/*
-
-ls ${PWD}
-
-module load anaconda
-module load cudatoolkit/8.0
-module load openmpi/intel-17.0/2.1.0/64 intel/17.0/64/17.0.4.196 intel-mkl/2017.3/4/64
-module load cudnn/cuda-8.0/6.0
-source activate PPPL
-
-export OMPI_MCA_btl="tcp,self,sm"
-
-echo $SLURM_NODELIST
-srun python mpi_learn.py
diff --git a/examples/mpi_learn.py b/examples/mpi_learn.py
index e6fd6503..a965a86b 100644
--- a/examples/mpi_learn.py
+++ b/examples/mpi_learn.py
@@ -24,8 +24,6 @@
 import random
 import numpy as np
 
-os.environ["PYTHONHASHSEED"] = "0"
-
 import matplotlib
 matplotlib.use('Agg')
 
diff --git a/Analyze Hyperparameter Tuning.ipynb b/examples/notebooks/Analyze Hyperparameter Tuning.ipynb
similarity index 100%
rename from Analyze Hyperparameter Tuning.ipynb
rename to examples/notebooks/Analyze Hyperparameter Tuning.ipynb
diff --git a/Signal Influence.ipynb b/examples/notebooks/Signal Influence.ipynb
similarity index 100%
rename from Signal Influence.ipynb
rename to examples/notebooks/Signal Influence.ipynb
diff --git a/examples/signal_influence.py b/examples/signal_influence.py
index 509af551..fc987177 100644
--- a/examples/signal_influence.py
+++ b/examples/signal_influence.py
@@ -26,8 +26,6 @@
 import copy
 from functools import partial
 
-os.environ["PYTHONHASHSEED"] = "0"
-
 import matplotlib
 matplotlib.use('Agg')
 
diff --git a/examples/simple_augmentation.py b/examples/simple_augmentation.py
index e6c05a78..3afd19c9 100644
--- a/examples/simple_augmentation.py
+++ b/examples/simple_augmentation.py
@@ -26,8 +26,6 @@
 import copy
 from functools import partial
 
-os.environ["PYTHONHASHSEED"] = "0"
-
 import matplotlib
 matplotlib.use('Agg')
 
diff --git a/jenkins-ci/jenkins.sh b/jenkins-ci/jenkins.sh
new file mode 100644
index 00000000..1fa10ffe
--- /dev/null
+++ b/jenkins-ci/jenkins.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+
+export OMPI_MCA_btl="tcp,self,sm"
+
+echo ${PWD}
+
+echo "Jenkins test Python3.6"
+rm /tigress/alexeys/model_checkpoints/*
+rm -rf /tigress/alexeys/processed_shots
+rm -rf /tigress/alexeys/processed_shotlists
+rm -rf /tigress/alexeys/normalization
+module load anaconda3/4.4.0
+source activate /tigress/alexeys/jenkins/.conda/envs/jenkins3
+module load cudatoolkit/8.0
+module load cudnn/cuda-8.0/6.0
+module load openmpi/cuda-8.0/intel-17.0/2.1.0/64
+module load intel/17.0/64/17.0.4.196
+
+echo ${PWD}
+cd /home/alexeys/jenkins/workspace/FRNM/PPPL
+echo ${PWD}
+python setup.py install
+
+echo $SLURM_NODELIST
+cd examples
+echo ${PWD}
+ls ${PWD}
+sed -i -e 's/num_epochs: 1000/num_epochs: 2/g' conf.yaml
+sed -i -e 's/data: jet_data/data: jenkins_jet/g' conf.yaml
+
+srun python mpi_learn.py
+
+echo "Jenkins test Python2.7"
+#rm /tigress/alexeys/model_checkpoints/*
+
+#source deactivate
+#module purge
+#module load anaconda/4.4.0
+#source activate /tigress/alexeys/jenkins/.conda/envs/jenkins2
+#module load cudatoolkit/8.0
+#module load cudnn/cuda-8.0/6.0
+#module load openmpi/cuda-8.0/intel-17.0/2.1.0/64
+#module load intel/17.0/64/17.0.4.196
+
+#cd ..
+#python setup.py install
+
+#echo $SLURM_NODELIST
+#cd examples
+#sed -i -e 's/data: jenkins_jet/data: jenkins_d3d/g' conf.yaml
+#srun python mpi_learn.py
diff --git a/jenkins-ci/run_jenkins.py b/jenkins-ci/run_jenkins.py
new file mode 100644
index 00000000..57be2675
--- /dev/null
+++ b/jenkins-ci/run_jenkins.py
@@ -0,0 +1,56 @@
+from plasma.utils.batch_jobs import generate_working_dirname,copy_files_to_environment,start_jenkins_job
+import yaml
+import sys,os,getpass
+import plasma.conf
+
+num_nodes = 4 #Set in the Jenkins project area!!
+test_matrix = [("Python3","jet_data"),("Python2","jet_data")]
+
+run_directory = "{}/{}/jenkins/".format(plasma.conf.conf['fs_path'],getpass.getuser())
+template_path = os.environ['PWD']
+conf_name = "conf.yaml"
+executable_name = "mpi_learn.py"
+
+def generate_conf_file(test_configuration,template_path = "../",save_path = "./",conf_name="conf.yaml"):
+    assert(template_path != save_path)
+    with open(os.path.join(template_path,conf_name), 'r') as yaml_file:
+        conf = yaml.load(yaml_file)
+    conf['training']['num_epochs'] = 2
+    conf['paths']['data'] = test_configuration[1]
+    if test_configuration[1] == "Python3":
+        conf['env']['name'] = "PPPL_dev3"
+        conf['env']['type'] = "anaconda3"
+    else:
+        conf['env']['name'] = "PPPL"
+        conf['env']['type'] = "anaconda"
+
+    with open(os.path.join(save_path,conf_name), 'w') as outfile:
+        yaml.dump(conf, outfile, default_flow_style=False)
+    return conf
+
+working_directory = generate_working_dirname(run_directory)
+os.makedirs(working_directory)
+
+os.system(" ".join(["cp -p",os.path.join(template_path,conf_name),working_directory]))
+os.system(" ".join(["cp -p",os.path.join(template_path,executable_name),working_directory]))
+
+#os.chdir(working_directory)
+#print("Going into {}".format(working_directory))
+
+for ci in test_matrix:
+    subdir = working_directory + "/{}/".format(ci[0]) 
+    os.makedirs(subdir)
+    copy_files_to_environment(subdir)
+    print("Making modified conf")
+    conf = generate_conf_file(ci,working_directory,subdir,conf_name)
+    print("Starting job")
+    if ci[1] == "Python3":
+        env_name = "PPPL_dev3"
+        env_type = "anaconda3"
+    else:
+        env_name = "PPPL"
+        env_type = "anaconda"
+    start_jenkins_job(subdir,num_nodes,executable_name,ci,env_name,env_type)
+
+
+print("submitted jobs.")
diff --git a/jenkins-ci/validate_jenkins.py b/jenkins-ci/validate_jenkins.py
new file mode 100644
index 00000000..6014fb2b
--- /dev/null
+++ b/jenkins-ci/validate_jenkins.py
@@ -0,0 +1,21 @@
+#!/usr/bin/env python
+
+import mpi4py as mmm
+print(mmm.__version__)
+
+import keras as kk
+print(kk.__version__)
+
+import tensorflow as tf
+print(tf.__version__)
+
+from mpi4py import MPI
+import sys
+
+size = MPI.COMM_WORLD.Get_size()
+rank = MPI.COMM_WORLD.Get_rank()
+name = MPI.Get_processor_name()
+
+sys.stdout.write(
+    "Hello, World! I am process %d of %d on %s.\n"
+    % (rank, size, name))
diff --git a/jenkins-ci/validate_jenkins.sh b/jenkins-ci/validate_jenkins.sh
new file mode 100644
index 00000000..f7d6b0d5
--- /dev/null
+++ b/jenkins-ci/validate_jenkins.sh
@@ -0,0 +1,25 @@
+#!/bin/bash
+
+export OMPI_MCA_btl="tcp,self,sm"
+
+module load anaconda3/4.4.0
+source activate /tigress/alexeys/jenkins/.conda/envs/jenkins3
+module load cudatoolkit/8.0
+module load cudnn/cuda-8.0/6.0
+module load openmpi/cuda-8.0/intel-17.0/2.1.0/64
+module load intel/17.0/64/17.0.4.196
+
+cd /home/alexeys/jenkins/workspace/FRNM/PPPL
+python setup.py install
+
+echo `which python`
+echo `which mpicc`
+
+echo ${PWD}
+echo $SLURM_NODELIST
+
+cd jenkins-ci
+echo ${PWD}
+ls ${PWD}
+
+srun python validate_jenkins.py
diff --git a/plasma/conf_parser.py b/plasma/conf_parser.py
index 1ded3a1a..15bf4dbe 100644
--- a/plasma/conf_parser.py
+++ b/plasma/conf_parser.py
@@ -6,6 +6,7 @@
 import uuid
 import yaml
 
+import hashlib
 
 def parameters(input_file):
     """Parse yaml file of configuration parameters."""
@@ -69,12 +70,17 @@ def parameters(input_file):
         #shot lists
         jet_carbon_wall = ShotListFiles(jet,params['paths']['shot_list_dir'],['CWall_clear.txt','CFC_unint.txt'],'jet carbon wall data')
         jet_iterlike_wall = ShotListFiles(jet,params['paths']['shot_list_dir'],['ILW_unint.txt','BeWall_clear.txt'],'jet iter like wall data')
+
+        jenkins_jet_carbon_wall = ShotListFiles(jet,params['paths']['shot_list_dir'],['jenkins_CWall_clear.txt','jenkins_CFC_unint.txt'],'Subset of jet carbon wall data for Jenkins tests')
+        jenkins_jet_iterlike_wall = ShotListFiles(jet,params['paths']['shot_list_dir'],['jenkins_ILW_unint.txt','jenkins_BeWall_clear.txt'],'Subset of jet iter like wall data for Jenkins tests')
+
         jet_full = ShotListFiles(jet,params['paths']['shot_list_dir'],['ILW_unint.txt','BeWall_clear.txt','CWall_clear.txt','CFC_unint.txt'],'jet full data')
 
         d3d_10000 = ShotListFiles(d3d,params['paths']['shot_list_dir'],['d3d_clear_10000.txt','d3d_disrupt_10000.txt'],'d3d data 10000 ND and D shots')
         d3d_1000 = ShotListFiles(d3d,params['paths']['shot_list_dir'],['d3d_clear_1000.txt','d3d_disrupt_1000.txt'],'d3d data 1000 ND and D shots')
         d3d_100 = ShotListFiles(d3d,params['paths']['shot_list_dir'],['d3d_clear_100.txt','d3d_disrupt_100.txt'],'d3d data 100 ND and D shots')
         d3d_full = ShotListFiles(d3d,params['paths']['shot_list_dir'],['d3d_clear_data_avail.txt','d3d_disrupt_data_avail.txt'],'d3d data since shot 125500')
+        d3d_jenkins = ShotListFiles(d3d,params['paths']['shot_list_dir'],['jenkins_d3d_clear.txt','jenkins_d3d_disrupt.txt'],'Subset of d3d data for Jenkins test')
         d3d_jb_full = ShotListFiles(d3d,params['paths']['shot_list_dir'],['shotlist_JaysonBarr_clear.txt','shotlist_JaysonBarr_disrupt.txt'],'d3d shots since 160000-170000')
 
         nstx_full = ShotListFiles(nstx,params['paths']['shot_list_dir'],['disrupt_nstx.txt'],'nstx shots (all are disruptive')
@@ -95,6 +101,10 @@ def parameters(input_file):
             params['paths']['shot_files'] = [jet_full]
             params['paths']['shot_files_test'] = []
             params['paths']['use_signals_dict'] = jet_signals
+        elif params['paths']['data'] == 'jenkins_jet':
+            params['paths']['shot_files'] = [jenkins_jet_carbon_wall]
+            params['paths']['shot_files_test'] = [jenkins_jet_iterlike_wall]
+            params['paths']['use_signals_dict'] = jet_signals
         elif params['paths']['data'] == 'd3d_data':
             params['paths']['shot_files'] = [d3d_full]
             params['paths']['shot_files_test'] = [] 
@@ -116,6 +126,12 @@ def parameters(input_file):
             params['paths']['shot_files'] = [d3d_full]
             params['paths']['shot_files_test'] = [] 
             params['paths']['use_signals_dict'] = d3d_signals
+        elif params['paths']['data'] == 'jenkins_d3d':
+            params['paths']['shot_files'] = [d3d_jenkins]
+            params['paths']['shot_files_test'] = []
+            params['paths']['use_signals_dict'] = {'q95':q95,'li':li,'ip':ip,'lm':lm,'betan':betan,'energy':energy,'dens':dens,'pradcore':pradcore,'pradedge':pradedge,'pin':pin,'torquein':torquein,'ipdirect':ipdirect,'iptarget':iptarget,'iperr':iperr,
+'etemp_profile':etemp_profile ,'edens_profile':edens_profile}
+
 
         #cross-machine
         elif params['paths']['data'] == 'jet_to_d3d_data':
@@ -167,7 +183,7 @@ def parameters(input_file):
     return params
 
 def get_unique_signal_hash(signals):
-    return hash(tuple(sorted(signals)))
+    return int(hashlib.md5(''.join(tuple(map(lambda x: x.description, sorted(signals)))).encode('utf-8')).hexdigest(),16)
 
 #make sure 1D signals come last! This is necessary for model builder.
 def sort_by_channels(list_of_signals):
diff --git a/plasma/models/builder.py b/plasma/models/builder.py
index 2e0fc0c0..0b223356 100644
--- a/plasma/models/builder.py
+++ b/plasma/models/builder.py
@@ -1,3 +1,4 @@
+from __future__ import division
 import keras
 from keras.models import Sequential, Model
 from keras.layers import Input
@@ -21,6 +22,7 @@
 from copy import deepcopy
 from plasma.utils.downloading import makedirs_process_safe
 
+import hashlib
 
 class LossHistory(Callback):
     def on_train_begin(self, logs=None):
@@ -39,7 +41,7 @@ def get_unique_id(self):
         this_conf = deepcopy(self.conf)
         #don't make hash dependent on number of epochs.
         this_conf['training']['num_epochs'] = 0
-        unique_id =  hash(dill.dumps(this_conf))
+        unique_id = int(hashlib.md5((dill.dumps(this_conf).decode('unicode_escape')).encode('utf-8')).hexdigest(),16)
         return unique_id
 
     def get_0D_1D_indices(self):
@@ -139,20 +141,20 @@ def slicer_output_shape(input_shape,indices):
             
             for i in range(model_conf['num_conv_layers']):
                 div_fac = 2**i
-                pre_rnn_1D = Convolution1D(num_conv_filters/div_fac,size_conv_filters,padding='valid',activation='relu') (pre_rnn_1D)
-                pre_rnn_1D = Convolution1D(num_conv_filters/div_fac,1,padding='valid',activation='relu') (pre_rnn_1D)
+                pre_rnn_1D = Convolution1D(num_conv_filters//div_fac,size_conv_filters,padding='valid',activation='relu') (pre_rnn_1D)
+                pre_rnn_1D = Convolution1D(num_conv_filters//div_fac,1,padding='valid',activation='relu') (pre_rnn_1D)
                 pre_rnn_1D = MaxPooling1D(pool_size) (pre_rnn_1D)
             pre_rnn_1D = Flatten() (pre_rnn_1D)
             pre_rnn_1D = Dense(dense_size,activation='relu',kernel_regularizer=l2(dense_regularization),bias_regularizer=l2(dense_regularization),activity_regularizer=l2(dense_regularization)) (pre_rnn_1D)
-            pre_rnn_1D = Dense(dense_size/4,activation='relu',kernel_regularizer=l2(dense_regularization),bias_regularizer=l2(dense_regularization),activity_regularizer=l2(dense_regularization)) (pre_rnn_1D)
+            pre_rnn_1D = Dense(dense_size//4,activation='relu',kernel_regularizer=l2(dense_regularization),bias_regularizer=l2(dense_regularization),activity_regularizer=l2(dense_regularization)) (pre_rnn_1D)
             pre_rnn = Concatenate() ([pre_rnn_0D,pre_rnn_1D])
         else:
             pre_rnn = pre_rnn_input        
 
         if model_conf['rnn_layers'] == 0 or model_conf['extra_dense_input']:
             pre_rnn = Dense(dense_size,activation='relu',kernel_regularizer=l2(dense_regularization),bias_regularizer=l2(dense_regularization),activity_regularizer=l2(dense_regularization)) (pre_rnn)
-            pre_rnn = Dense(dense_size/2,activation='relu',kernel_regularizer=l2(dense_regularization),bias_regularizer=l2(dense_regularization),activity_regularizer=l2(dense_regularization)) (pre_rnn)
-            pre_rnn = Dense(dense_size/4,activation='relu',kernel_regularizer=l2(dense_regularization),bias_regularizer=l2(dense_regularization),activity_regularizer=l2(dense_regularization)) (pre_rnn)
+            pre_rnn = Dense(dense_size//2,activation='relu',kernel_regularizer=l2(dense_regularization),bias_regularizer=l2(dense_regularization),activity_regularizer=l2(dense_regularization)) (pre_rnn)
+            pre_rnn = Dense(dense_size//4,activation='relu',kernel_regularizer=l2(dense_regularization),bias_regularizer=l2(dense_regularization),activity_regularizer=l2(dense_regularization)) (pre_rnn)
         
         pre_rnn_model = Model(inputs = pre_rnn_input,outputs=pre_rnn)
         x_input = Input(batch_shape = batch_input_shape)
diff --git a/plasma/models/shallow_runner.py b/plasma/models/shallow_runner.py
index 140454cc..b9ccf883 100644
--- a/plasma/models/shallow_runner.py
+++ b/plasma/models/shallow_runner.py
@@ -29,6 +29,8 @@
 
 from keras.utils.generic_utils import Progbar 
 
+import hashlib
+
 debug_use_shots = 100000
 model_filename = "saved_model.pkl"
 dataset_path = "dataset.npz"
@@ -85,8 +87,8 @@ def load_shots(self,shot_list,is_inference=False,as_list=False,num_samples=np.In
     def get_save_prepath(self):
         prepath = self.loader.conf['paths']['processed_prepath']
         use_signals = self.loader.conf['paths']['use_signals']
-        identifying_tuple = tuple(sorted(use_signals))
-        save_prepath = prepath + "shallow/use_signals_{}/".format(hash(identifying_tuple))
+        identifying_tuple = ''.join(tuple(map(lambda x: x.description, sorted(use_signals)))).encode('utf-8')
+        save_prepath = prepath + "shallow/use_signals_{}/".format(int(hashlib.md5(identifying_tuple).hexdigest(),16))
         return save_prepath
 
     def process(self,shot): 
diff --git a/plasma/primitives/data.py b/plasma/primitives/data.py
index d6139f66..1354f506 100644
--- a/plasma/primitives/data.py
+++ b/plasma/primitives/data.py
@@ -1,3 +1,4 @@
+from __future__ import division
 import numpy as np
 import time
 import sys,os
@@ -121,8 +122,8 @@ def load_data(self,prepath,shot,dtype='float32'):
 
 
     def fetch_data_basic(self,machine,shot_num,c,path=None):
-	if path is None:
-	    path = self.get_path(machine)
+        if path is None:
+            path = self.get_path(machine)
         success = False
         mapping = None
         try:
@@ -183,7 +184,8 @@ def __lt__(self,other):
         return self.description.__lt__(other.description)
     
     def __hash__(self):
-        return self.description.__hash__()
+       import hashlib
+       return int(hashlib.md5(self.description.encode('utf-8')).hexdigest(),16)
 
     def __str__(self):
         return self.description
@@ -205,7 +207,7 @@ def load_data(self,prepath,shot,dtype='float32'):
         if np.ndim(data) == 1:
             data = np.expand_dims(data,axis=0)
             #_ = data[0,0]
-        T = data.shape[0]/2 #time is stored twice, once for mapping and once for signal
+        T = data.shape[0]//2 #time is stored twice, once for mapping and once for signal
         mapping = data[:T,1:]
         remapping = np.linspace(self.mapping_range[0],self.mapping_range[1],self.num_channels)
         t = data[:T,0] 
@@ -224,8 +226,12 @@ def load_data(self,prepath,shot,dtype='float32'):
         sig_interp = np.zeros((timesteps,self.num_channels))
         for i in range(timesteps):
             _,order = np.unique(mapping[i,:],return_index=True) #make sure the mapping is ordered and unique
-            f = UnivariateSpline(mapping[i,order],sig[i,order],s=0,k=1,ext=0)
-            sig_interp[i,:] = f(remapping)
+            if sig[i,order].shape[0] > 2:
+                f = UnivariateSpline(mapping[i,order],sig[i,order],s=0,k=1,ext=0)
+                sig_interp[i,:] = f(remapping)
+            else:
+                print('Signal {}, shot {} has not enough points for linear interpolation. dfitpack.error: (m>k) failed for hidden m: fpcurf0:m=1'.format(self.description,shot.number))
+                return None,None,False
 
         return t,sig_interp,True
 
@@ -283,7 +289,7 @@ def get_channel_num(self,machine):
 
     def fetch_data(self,machine,shot_num,c):
         time,data,mapping,success = self.fetch_data_basic(machine,shot_num,c)
-	mapping = None #we are not interested in the whole profile
+        mapping = None #we are not interested in the whole profile
         channel_num = self.get_channel_num(machine)
         if channel_num is not None and success:
             if np.ndim(data) != 2:
diff --git a/plasma/primitives/shots.py b/plasma/primitives/shots.py
index 383ed7e6..09685093 100644
--- a/plasma/primitives/shots.py
+++ b/plasma/primitives/shots.py
@@ -323,8 +323,8 @@ def __eq__(self,other):
         return self.get_id_str().__eq__(other.get_id_str())
 
     def __hash__(self):
-        return self.get_id_str().__hash__()
-
+       import hashlib
+       return int(hashlib.md5(self.get_id_str().encode('utf-8')).hexdigest(),16)
 
     def __str__(self):
         string = 'number: {}\n'.format(self.number)
diff --git a/plasma/utils/batch_jobs.py b/plasma/utils/batch_jobs.py
index f0bcea7f..56203b97 100644
--- a/plasma/utils/batch_jobs.py
+++ b/plasma/utils/batch_jobs.py
@@ -1,3 +1,4 @@
+from __future__ import division 
 from pprint import pprint
 import yaml
 import datetime
@@ -11,8 +12,6 @@ def generate_working_dirname(run_directory):
     s += "_{}".format(uuid.uuid4())
     return run_directory + s
 
-
-
 def get_executable_name(conf):
     shallow = conf['model']['shallow']
     if shallow:
@@ -31,6 +30,36 @@ def start_slurm_job(subdir,num_nodes,i,conf,shallow,env_name="frnn",env_type="an
     sp.Popen("sbatch "+script,shell=True)
 
 
+def create_jenkins_script(subdir,num_nodes,executable_name,test_configuration,env_name="frnn",env_type="anaconda"):
+    filename = "jenkins_{}_{}.cmd".format(test_configuration[0],test_configuration[1]) #version of Python and the dataset
+    filepath = os.path.join(subdir,filename)
+    user = getpass.getuser()
+    with open(filepath,"w") as f:
+        f.write('#!/usr/bin/bash\n')
+        f.write('export OMPI_MCA_btl=\"tcp,self,sm\"\n')
+        f.write('echo \"Jenkins test {}\"\n'.format(test_configuration[1]))
+        f.write('rm /tigress/alexeys/model_checkpoints/*\n')
+        f.write('rm -rf /tigress/alexeys/processed_shots\n')
+        f.write('rm -rf /tigress/alexeys/processed_shotlists\n')
+        f.write('rm -rf /tigress/alexeys/normalization\n')
+        f.write('module load {}\n'.format(env_type))
+        f.write('source activate {}\n'.format(env_name))
+        f.write('module load cudatoolkit/8.0\n')
+        f.write('module load cudnn/cuda-8.0/6.0\n')
+        f.write('module load openmpi/cuda-8.0/intel-17.0/2.1.0/64\n')
+        f.write('module load intel/17.0/64/17.0.4.196\n')
+        f.write('cd /home/alexeys/jenkins/workspace/FRNM/PPPL\n') 
+        f.write('python setup.py install\n')
+        f.write('cd {}\n'.format(subdir))
+        f.write('srun -N {} -n {} python {}\n'.format(num_nodes//2, num_nodes//2*4, executable_name))
+
+    return filepath
+
+def start_jenkins_job(subdir,num_nodes,executable_name,test_configuration,env_name,env_type):
+    os.system(" ".join(["cp -p",executable_name,subdir]))
+    script = create_jenkins_script(subdir,num_nodes,executable_name,test_configuration,env_name,env_type)
+    sp.Popen("sh "+script,shell=True)
+
 def start_pbs_job(subdir,num_nodes,i,conf,shallow,env_name="frnn",env_type="anaconda"):
     executable_name,use_mpi = get_executable_name(conf)
     os.system(" ".join(["cp -p",executable_name,subdir]))
@@ -53,7 +82,7 @@ def create_slurm_script(subdir,num_nodes,idx,executable_name,use_mpi,env_name="f
         # f.write('rm -f /tigress/{}/model_checkpoints/*.h5\n'.format(user))
         f.write('cd {}\n'.format(subdir))
         f.write('export OMPI_MCA_btl=\"tcp,self,sm\"\n')
-        f.write('srun env PYTHONHASHSEED=0 python {}\n'.format(executable_name))
+        f.write('srun python {}\n'.format(executable_name))
         f.write('echo "done."')
 
     return filepath
@@ -72,7 +101,7 @@ def create_pbs_script(subdir,num_nodes,idx,executable_name,use_mpi,env_name="frn
         f.write('module load tensorflow\n')
         # f.write('rm $HOME/tigress/alexeys/model_checkpoints/*\n')
         f.write('cd {}\n'.format(subdir))
-        f.write('aprun -n {} -N1 env PYTHONHASHSEED=0 env KERAS_HOME={} singularity exec $TENSORFLOW_CONTAINER python3 {}\n'.format(str(num_nodes),subdir,executable_name))
+        f.write('aprun -n {} -N1 env KERAS_HOME={} singularity exec $TENSORFLOW_CONTAINER python3 {}\n'.format(str(num_nodes),subdir,executable_name))
         f.write('echo "done."')
 
     return filepath
diff --git a/plasma/version.py b/plasma/version.py
index 78235354..758ad1a2 100644
--- a/plasma/version.py
+++ b/plasma/version.py
@@ -1,6 +1,6 @@
 import re
 
-__version__ = "0.3.0"
+__version__ = "1.0.0"
 
 version = __version__