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Merged
Jhsmit merged 8 commits into from
Mar 5, 2021
Merged

merging previous changes #181

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5d515f3
Testing RTD building with missing file entry
Jhsmit Mar 3, 2021
ec052d9
Merge remote-tracking branch 'upstream/PR_test'
sajetan Mar 5, 2021
bfcd2ab
Merge branch 'master' of https://github.com/Jhsmit/PyHDX into PR_test
sajetan Mar 5, 2021
2932d5c
merging dosctring fixes
sajetan Mar 5, 2021
ee2d617
Merge pull request #1 from sajetan/PR_test
sajetan Mar 5, 2021
bd88c6e
Update index.rst
Jhsmit Mar 5, 2021
e6e94f1
Update fitting.py
Jhsmit Mar 5, 2021
a489778
Update support.py
Jhsmit Mar 5, 2021
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8 changes: 4 additions & 4 deletions pyhdx/fileIO.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,11 +9,11 @@ def read_dynamx(*file_paths, intervals=('inclusive', 'inclusive'), time_unit='mi

Parameters
----------
file_paths: :obj:`iterable`
file_paths : :obj:`iterable`
File path of the .csv file or StringIO object
intervals: :obj:`tuple`
intervals : :obj:`tuple`
Format of how start and end intervals are specified.
time_unit :obj:`str`
time_unit : :obj:`str`
Not implemented

Returns
Expand Down Expand Up @@ -170,4 +170,4 @@ def _get_f_width(data, sign):
width = int(np.floor(w))
except OverflowError: # all zeros
width = 0
return width
return width
56 changes: 28 additions & 28 deletions pyhdx/fitting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,17 +47,17 @@ def __init__(self, bounds):

def make_parameter(self, name, value=None, min=None, max=None):
"""
Create a new :class:~symfit.Parameter.
Create a new :class:`~symfit.Parameter`.

Parameters
----------
name: :obj:`str`
name : :obj:`str`
Human-readable name for the parameter
value: :obj:`float`
value : :obj:`float`
Initial guess value
min: :obj:`float`
min : :obj:`float`
Lower bound value. If `None`, the value from `bounds` is used.
max: :obj:`float`
max : :obj:`float`
Lower bound value. If `None`, the value from `bounds` is used.

Returns
Expand All @@ -77,11 +77,11 @@ def make_parameter(self, name, value=None, min=None, max=None):

def make_variable(self, name):
"""
Create a new :class:~symfit.Variable.
Create a new :class:`~symfit.Variable`.

Parameters
----------
name: :obj:`str`
name : :obj:`str`
Human-readable name for the variable

Returns
Expand Down Expand Up @@ -258,9 +258,9 @@ def initial_guess(self, t, d):

Parameters
----------
t : :class:~`numpy.ndarray`
t : :class:`~numpy.ndarray`
Array with time points
d : :class:~`numpy.ndarray`
d : :class:`~numpy.ndarray`
Array with uptake values

"""
Expand Down Expand Up @@ -303,9 +303,9 @@ def initial_guess(self, t, d):

Parameters
----------
t : :class:~`numpy.ndarray`
t : :class:`~numpy.ndarray`
Array with time points
d : :class:~`numpy.ndarray`
d : :class:`~numpy.ndarray`
Array with uptake values

"""
Expand Down Expand Up @@ -409,9 +409,9 @@ def initial_guess(self, t, d):

Parameters
----------
t : :class:~`numpy.ndarray`
t : :class:`~numpy.ndarray`
Array with time points
d : :class:~`numpy.ndarray`
d : :class:`~numpy.ndarray`
Array with uptake values

"""
Expand Down Expand Up @@ -463,7 +463,7 @@ def func_long_dis(k, tt, A, k1):
Selected time point
A : :obj:`float`
Target amplitude
k1: : obj:`float`
k1 : :obj:`float`
Rate of fast time component

Returns
Expand Down Expand Up @@ -509,7 +509,7 @@ def func_long_ass(k, tt, A, k1):
Selected time point
A : :obj:`float`
Target amplitude
k1: : obj:`float`
k1 : :obj:`float`
Rate of fast time component

Returns
Expand All @@ -535,7 +535,7 @@ def fit_kinetics(t, d, model, chisq_thd):
Array of time points
d : :class:`~numpy.ndarray`
Array of uptake values
chisq_thd: :obj:`float`
chisq_thd : :obj:`float`
Threshold chi squared above which the fitting is repeated with the Differential Evolution algorithm.

Returns
Expand Down Expand Up @@ -793,7 +793,7 @@ def global_fit_torch(self, initial_result, regularizer=2, epochs=100000, patienc

optimizer_klass = getattr(torch.optim, optimizer)
optimizer_obj = optimizer_klass(model.parameters(), **kwargs)

mse_loss = [torch.tensor(np.inf)] # Mean squared loss only
reg_loss = [torch.tensor(np.inf)] # Loss including regularization loss
stop = 0
Expand Down Expand Up @@ -829,8 +829,6 @@ def closure():

for pname, param in model.named_parameters():
loss = loss + regularizer * torch.mean(torch.abs(param[:-1] - param[1:]))


reg_loss.append(loss)
diff = reg_loss[-2] - loss
if diff < stop_loss:
Expand All @@ -842,10 +840,10 @@ def closure():

loss.backward()
optimizer_obj.step()

mse_loss = np.array([val.detach().numpy() for val in mse_loss])
reg_loss = np.array([val.detach().numpy() for val in reg_loss])

result = TorchFitResult(self.series, model, temperature=temperature,
mse_loss=mse_loss, reg_loss=reg_loss)

Expand All @@ -859,7 +857,7 @@ def weighted_avg_t50(self):
Returns
-------

output: :~class:np.ndarray
output: :class:`~np.ndarray`
array with fields r_number, rate

"""
Expand Down Expand Up @@ -1252,6 +1250,7 @@ def setup_fit(self):
timepoints[i, -Nti:] = kf.series.timepoints
D[i, 0: Npi, -Nti:] = kf.series.uptake_corrected.T


# Create pytorch tensors from input data, assign final shapes for matrix batch multiplication by tf.matmul
dtype = torch.float64
temperature_T = torch.tensor(temperature, dtype=dtype).unsqueeze(-1).unsqueeze(-1) # Ns x 1 x 1
Expand Down Expand Up @@ -1289,7 +1288,7 @@ def global_fit(self, r1=2, r2=5, epochs=100000, patience=50, stop_loss=0.05,
optimizer_obj = optimizer_klass(model.parameters(), **kwargs)

criterion = torch.nn.MSELoss(reduction='sum')

mse_loss = [torch.tensor(np.inf)] # Mean squared loss only
reg_loss = [torch.tensor(np.inf)] # Loss including regularization loss
stop = 0
Expand Down Expand Up @@ -1317,7 +1316,7 @@ def global_fit(self, r1=2, r2=5, epochs=100000, patience=50, stop_loss=0.05,

loss.backward()
optimizer_obj.step()

mse_loss = np.array([val.detach().numpy() for val in mse_loss])
reg_loss = np.array([val.detach().numpy() for val in reg_loss])

Expand All @@ -1344,8 +1343,8 @@ def global_fit_aligned(self, alignment_array, r1=2, r2=5, epochs=100000, patienc
-------

"""
r_numbers = np.cumsum(alignment_array != '-', axis=1) # residue numbers in alignment array
aligned_bool = np.all(alignment_array != '-', axis=0) # Array True where residues align
r_numbers = np.cumsum(alignment_array != '-', axis=1) #residue numbers in alignment array
aligned_bool = np.all(alignment_array != '-', axis=0) # Array True where residues align
aligned_residues = np.array([row[aligned_bool] for row in r_numbers]) # Residue numbers of aligned residues

try:
Expand Down Expand Up @@ -1412,9 +1411,10 @@ def global_fit_aligned(self, alignment_array, r1=2, r2=5, epochs=100000, patienc

loss.backward()
optimizer_obj.step()



mse_loss = np.array([val.detach().numpy() for val in mse_loss])
reg_loss = np.array([val.detach().numpy() for val in reg_loss])
reg_loss = np.array([val.detach().numpy() for val in reg_loss])

result = TorchBatchFitResult(self, model, mse_loss=mse_loss, reg_loss=reg_loss)
return result
Expand Down
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