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hydpy-dev/hydpy
hydpy/models/lland/lland_model.py
pass_q_v1
def pass_q_v1(self): """Update the outlet link sequence. Required derived parameter: |QFactor| Required flux sequences: |lland_fluxes.Q| Calculated flux sequence: |lland_outlets.Q| Basic equation: :math:`Q_{outlets} = QFactor \\cdot Q_{fluxes}` """ der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess out = self.sequences.outlets.fastaccess out.q[0] += der.qfactor*flu.q
python
def pass_q_v1(self): """Update the outlet link sequence. Required derived parameter: |QFactor| Required flux sequences: |lland_fluxes.Q| Calculated flux sequence: |lland_outlets.Q| Basic equation: :math:`Q_{outlets} = QFactor \\cdot Q_{fluxes}` """ der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess out = self.sequences.outlets.fastaccess out.q[0] += der.qfactor*flu.q
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Update the outlet link sequence. Required derived parameter: |QFactor| Required flux sequences: |lland_fluxes.Q| Calculated flux sequence: |lland_outlets.Q| Basic equation: :math:`Q_{outlets} = QFactor \\cdot Q_{fluxes}`
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/lland/lland_model.py#L2005-L2023
train
hydpy-dev/hydpy
hydpy/models/hbranch/hbranch_model.py
calc_outputs_v1
def calc_outputs_v1(self): """Performs the actual interpolation or extrapolation. Required control parameters: |XPoints| |YPoints| Required derived parameter: |NmbPoints| |NmbBranches| Required flux sequence: |Input| Calculated flux sequence: |Outputs| Examples: As a simple example, assume a weir directing all discharge into `branch1` until the capacity limit of 2 m³/s is reached. The discharge exceeding this threshold is directed into `branch2`: >>> from hydpy.models.hbranch import * >>> parameterstep() >>> xpoints(0., 2., 4.) >>> ypoints(branch1=[0., 2., 2.], ... branch2=[0., 0., 2.]) >>> model.parameters.update() Low discharge example (linear interpolation between the first two supporting point pairs): >>> fluxes.input = 1. >>> model.calc_outputs_v1() >>> fluxes.outputs outputs(branch1=1.0, branch2=0.0) Medium discharge example (linear interpolation between the second two supporting point pairs): >>> fluxes.input = 3. >>> model.calc_outputs_v1() >>> print(fluxes.outputs) outputs(branch1=2.0, branch2=1.0) High discharge example (linear extrapolation beyond the second two supporting point pairs): >>> fluxes.input = 5. >>> model.calc_outputs_v1() >>> fluxes.outputs outputs(branch1=2.0, branch2=3.0) Non-monotonous relationships and balance violations are allowed, e.g.: >>> xpoints(0., 2., 4., 6.) >>> ypoints(branch1=[0., 2., 0., 0.], ... branch2=[0., 0., 2., 4.]) >>> model.parameters.update() >>> fluxes.input = 7. >>> model.calc_outputs_v1() >>> fluxes.outputs outputs(branch1=0.0, branch2=5.0) """ con = self.parameters.control.fastaccess der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess # Search for the index of the two relevant x points... for pdx in range(1, der.nmbpoints): if con.xpoints[pdx] > flu.input: break # ...and use it for linear interpolation (or extrapolation). for bdx in range(der.nmbbranches): flu.outputs[bdx] = ( (flu.input-con.xpoints[pdx-1]) * (con.ypoints[bdx, pdx]-con.ypoints[bdx, pdx-1]) / (con.xpoints[pdx]-con.xpoints[pdx-1]) + con.ypoints[bdx, pdx-1])
python
def calc_outputs_v1(self): """Performs the actual interpolation or extrapolation. Required control parameters: |XPoints| |YPoints| Required derived parameter: |NmbPoints| |NmbBranches| Required flux sequence: |Input| Calculated flux sequence: |Outputs| Examples: As a simple example, assume a weir directing all discharge into `branch1` until the capacity limit of 2 m³/s is reached. The discharge exceeding this threshold is directed into `branch2`: >>> from hydpy.models.hbranch import * >>> parameterstep() >>> xpoints(0., 2., 4.) >>> ypoints(branch1=[0., 2., 2.], ... branch2=[0., 0., 2.]) >>> model.parameters.update() Low discharge example (linear interpolation between the first two supporting point pairs): >>> fluxes.input = 1. >>> model.calc_outputs_v1() >>> fluxes.outputs outputs(branch1=1.0, branch2=0.0) Medium discharge example (linear interpolation between the second two supporting point pairs): >>> fluxes.input = 3. >>> model.calc_outputs_v1() >>> print(fluxes.outputs) outputs(branch1=2.0, branch2=1.0) High discharge example (linear extrapolation beyond the second two supporting point pairs): >>> fluxes.input = 5. >>> model.calc_outputs_v1() >>> fluxes.outputs outputs(branch1=2.0, branch2=3.0) Non-monotonous relationships and balance violations are allowed, e.g.: >>> xpoints(0., 2., 4., 6.) >>> ypoints(branch1=[0., 2., 0., 0.], ... branch2=[0., 0., 2., 4.]) >>> model.parameters.update() >>> fluxes.input = 7. >>> model.calc_outputs_v1() >>> fluxes.outputs outputs(branch1=0.0, branch2=5.0) """ con = self.parameters.control.fastaccess der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess # Search for the index of the two relevant x points... for pdx in range(1, der.nmbpoints): if con.xpoints[pdx] > flu.input: break # ...and use it for linear interpolation (or extrapolation). for bdx in range(der.nmbbranches): flu.outputs[bdx] = ( (flu.input-con.xpoints[pdx-1]) * (con.ypoints[bdx, pdx]-con.ypoints[bdx, pdx-1]) / (con.xpoints[pdx]-con.xpoints[pdx-1]) + con.ypoints[bdx, pdx-1])
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Performs the actual interpolation or extrapolation. Required control parameters: |XPoints| |YPoints| Required derived parameter: |NmbPoints| |NmbBranches| Required flux sequence: |Input| Calculated flux sequence: |Outputs| Examples: As a simple example, assume a weir directing all discharge into `branch1` until the capacity limit of 2 m³/s is reached. The discharge exceeding this threshold is directed into `branch2`: >>> from hydpy.models.hbranch import * >>> parameterstep() >>> xpoints(0., 2., 4.) >>> ypoints(branch1=[0., 2., 2.], ... branch2=[0., 0., 2.]) >>> model.parameters.update() Low discharge example (linear interpolation between the first two supporting point pairs): >>> fluxes.input = 1. >>> model.calc_outputs_v1() >>> fluxes.outputs outputs(branch1=1.0, branch2=0.0) Medium discharge example (linear interpolation between the second two supporting point pairs): >>> fluxes.input = 3. >>> model.calc_outputs_v1() >>> print(fluxes.outputs) outputs(branch1=2.0, branch2=1.0) High discharge example (linear extrapolation beyond the second two supporting point pairs): >>> fluxes.input = 5. >>> model.calc_outputs_v1() >>> fluxes.outputs outputs(branch1=2.0, branch2=3.0) Non-monotonous relationships and balance violations are allowed, e.g.: >>> xpoints(0., 2., 4., 6.) >>> ypoints(branch1=[0., 2., 0., 0.], ... branch2=[0., 0., 2., 4.]) >>> model.parameters.update() >>> fluxes.input = 7. >>> model.calc_outputs_v1() >>> fluxes.outputs outputs(branch1=0.0, branch2=5.0)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/hbranch/hbranch_model.py#L11-L94
train
hydpy-dev/hydpy
hydpy/models/hbranch/hbranch_model.py
pick_input_v1
def pick_input_v1(self): """Updates |Input| based on |Total|.""" flu = self.sequences.fluxes.fastaccess inl = self.sequences.inlets.fastaccess flu.input = 0. for idx in range(inl.len_total): flu.input += inl.total[idx][0]
python
def pick_input_v1(self): """Updates |Input| based on |Total|.""" flu = self.sequences.fluxes.fastaccess inl = self.sequences.inlets.fastaccess flu.input = 0. for idx in range(inl.len_total): flu.input += inl.total[idx][0]
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Updates |Input| based on |Total|.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/hbranch/hbranch_model.py#L97-L103
train
hydpy-dev/hydpy
hydpy/models/hbranch/hbranch_model.py
pass_outputs_v1
def pass_outputs_v1(self): """Updates |Branched| based on |Outputs|.""" der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess out = self.sequences.outlets.fastaccess for bdx in range(der.nmbbranches): out.branched[bdx][0] += flu.outputs[bdx]
python
def pass_outputs_v1(self): """Updates |Branched| based on |Outputs|.""" der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess out = self.sequences.outlets.fastaccess for bdx in range(der.nmbbranches): out.branched[bdx][0] += flu.outputs[bdx]
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Updates |Branched| based on |Outputs|.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/hbranch/hbranch_model.py#L106-L112
train
hydpy-dev/hydpy
hydpy/models/hbranch/hbranch_model.py
Model.connect
def connect(self): """Connect the |LinkSequence| instances handled by the actual model to the |NodeSequence| instances handled by one inlet node and multiple oulet nodes. The HydPy-H-Branch model passes multiple output values to different outlet nodes. This requires additional information regarding the `direction` of each output value. Therefore, node names are used as keywords. Assume the discharge values of both nodes `inflow1` and `inflow2` shall be branched to nodes `outflow1` and `outflow2` via element `branch`: >>> from hydpy import * >>> branch = Element('branch', ... inlets=['inflow1', 'inflow2'], ... outlets=['outflow1', 'outflow2']) Then parameter |YPoints| relates different supporting points via its keyword arguments to the respective nodes: >>> from hydpy.models.hbranch import * >>> parameterstep() >>> xpoints(0.0, 3.0) >>> ypoints(outflow1=[0.0, 1.0], outflow2=[0.0, 2.0]) >>> parameters.update() After connecting the model with its element the total discharge value of nodes `inflow1` and `inflow2` can be properly divided: >>> branch.model = model >>> branch.inlets.inflow1.sequences.sim = 1.0 >>> branch.inlets.inflow2.sequences.sim = 5.0 >>> model.doit(0) >>> print(branch.outlets.outflow1.sequences.sim) sim(2.0) >>> print(branch.outlets.outflow2.sequences.sim) sim(4.0) In case of missing (or misspelled) outlet nodes, the following error is raised: >>> branch.outlets.mutable = True >>> del branch.outlets.outflow1 >>> parameters.update() >>> model.connect() Traceback (most recent call last): ... RuntimeError: Model `hbranch` of element `branch` tried to connect \ to an outlet node named `outflow1`, which is not an available outlet node \ of element `branch`. """ nodes = self.element.inlets total = self.sequences.inlets.total if total.shape != (len(nodes),): total.shape = len(nodes) for idx, node in enumerate(nodes): double = node.get_double('inlets') total.set_pointer(double, idx) for (idx, name) in enumerate(self.nodenames): try: outlet = getattr(self.element.outlets, name) double = outlet.get_double('outlets') except AttributeError: raise RuntimeError( f'Model {objecttools.elementphrase(self)} tried ' f'to connect to an outlet node named `{name}`, ' f'which is not an available outlet node of element ' f'`{self.element.name}`.') self.sequences.outlets.branched.set_pointer(double, idx)
python
def connect(self): """Connect the |LinkSequence| instances handled by the actual model to the |NodeSequence| instances handled by one inlet node and multiple oulet nodes. The HydPy-H-Branch model passes multiple output values to different outlet nodes. This requires additional information regarding the `direction` of each output value. Therefore, node names are used as keywords. Assume the discharge values of both nodes `inflow1` and `inflow2` shall be branched to nodes `outflow1` and `outflow2` via element `branch`: >>> from hydpy import * >>> branch = Element('branch', ... inlets=['inflow1', 'inflow2'], ... outlets=['outflow1', 'outflow2']) Then parameter |YPoints| relates different supporting points via its keyword arguments to the respective nodes: >>> from hydpy.models.hbranch import * >>> parameterstep() >>> xpoints(0.0, 3.0) >>> ypoints(outflow1=[0.0, 1.0], outflow2=[0.0, 2.0]) >>> parameters.update() After connecting the model with its element the total discharge value of nodes `inflow1` and `inflow2` can be properly divided: >>> branch.model = model >>> branch.inlets.inflow1.sequences.sim = 1.0 >>> branch.inlets.inflow2.sequences.sim = 5.0 >>> model.doit(0) >>> print(branch.outlets.outflow1.sequences.sim) sim(2.0) >>> print(branch.outlets.outflow2.sequences.sim) sim(4.0) In case of missing (or misspelled) outlet nodes, the following error is raised: >>> branch.outlets.mutable = True >>> del branch.outlets.outflow1 >>> parameters.update() >>> model.connect() Traceback (most recent call last): ... RuntimeError: Model `hbranch` of element `branch` tried to connect \ to an outlet node named `outflow1`, which is not an available outlet node \ of element `branch`. """ nodes = self.element.inlets total = self.sequences.inlets.total if total.shape != (len(nodes),): total.shape = len(nodes) for idx, node in enumerate(nodes): double = node.get_double('inlets') total.set_pointer(double, idx) for (idx, name) in enumerate(self.nodenames): try: outlet = getattr(self.element.outlets, name) double = outlet.get_double('outlets') except AttributeError: raise RuntimeError( f'Model {objecttools.elementphrase(self)} tried ' f'to connect to an outlet node named `{name}`, ' f'which is not an available outlet node of element ' f'`{self.element.name}`.') self.sequences.outlets.branched.set_pointer(double, idx)
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Connect the |LinkSequence| instances handled by the actual model to the |NodeSequence| instances handled by one inlet node and multiple oulet nodes. The HydPy-H-Branch model passes multiple output values to different outlet nodes. This requires additional information regarding the `direction` of each output value. Therefore, node names are used as keywords. Assume the discharge values of both nodes `inflow1` and `inflow2` shall be branched to nodes `outflow1` and `outflow2` via element `branch`: >>> from hydpy import * >>> branch = Element('branch', ... inlets=['inflow1', 'inflow2'], ... outlets=['outflow1', 'outflow2']) Then parameter |YPoints| relates different supporting points via its keyword arguments to the respective nodes: >>> from hydpy.models.hbranch import * >>> parameterstep() >>> xpoints(0.0, 3.0) >>> ypoints(outflow1=[0.0, 1.0], outflow2=[0.0, 2.0]) >>> parameters.update() After connecting the model with its element the total discharge value of nodes `inflow1` and `inflow2` can be properly divided: >>> branch.model = model >>> branch.inlets.inflow1.sequences.sim = 1.0 >>> branch.inlets.inflow2.sequences.sim = 5.0 >>> model.doit(0) >>> print(branch.outlets.outflow1.sequences.sim) sim(2.0) >>> print(branch.outlets.outflow2.sequences.sim) sim(4.0) In case of missing (or misspelled) outlet nodes, the following error is raised: >>> branch.outlets.mutable = True >>> del branch.outlets.outflow1 >>> parameters.update() >>> model.connect() Traceback (most recent call last): ... RuntimeError: Model `hbranch` of element `branch` tried to connect \ to an outlet node named `outflow1`, which is not an available outlet node \ of element `branch`.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/hbranch/hbranch_model.py#L125-L193
train
hydpy-dev/hydpy
hydpy/models/arma/arma_derived.py
Nmb.update
def update(self): """Determine the number of response functions. >>> from hydpy.models.arma import * >>> parameterstep('1d') >>> responses(((1., 2.), (1.,)), th_3=((1.,), (1., 2., 3.))) >>> derived.nmb.update() >>> derived.nmb nmb(2) Note that updating parameter `nmb` sets the shape of the flux sequences |QPIn|, |QPOut|, |QMA|, and |QAR| automatically. >>> fluxes.qpin qpin(nan, nan) >>> fluxes.qpout qpout(nan, nan) >>> fluxes.qma qma(nan, nan) >>> fluxes.qar qar(nan, nan) """ pars = self.subpars.pars responses = pars.control.responses fluxes = pars.model.sequences.fluxes self(len(responses)) fluxes.qpin.shape = self.value fluxes.qpout.shape = self.value fluxes.qma.shape = self.value fluxes.qar.shape = self.value
python
def update(self): """Determine the number of response functions. >>> from hydpy.models.arma import * >>> parameterstep('1d') >>> responses(((1., 2.), (1.,)), th_3=((1.,), (1., 2., 3.))) >>> derived.nmb.update() >>> derived.nmb nmb(2) Note that updating parameter `nmb` sets the shape of the flux sequences |QPIn|, |QPOut|, |QMA|, and |QAR| automatically. >>> fluxes.qpin qpin(nan, nan) >>> fluxes.qpout qpout(nan, nan) >>> fluxes.qma qma(nan, nan) >>> fluxes.qar qar(nan, nan) """ pars = self.subpars.pars responses = pars.control.responses fluxes = pars.model.sequences.fluxes self(len(responses)) fluxes.qpin.shape = self.value fluxes.qpout.shape = self.value fluxes.qma.shape = self.value fluxes.qar.shape = self.value
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Determine the number of response functions. >>> from hydpy.models.arma import * >>> parameterstep('1d') >>> responses(((1., 2.), (1.,)), th_3=((1.,), (1., 2., 3.))) >>> derived.nmb.update() >>> derived.nmb nmb(2) Note that updating parameter `nmb` sets the shape of the flux sequences |QPIn|, |QPOut|, |QMA|, and |QAR| automatically. >>> fluxes.qpin qpin(nan, nan) >>> fluxes.qpout qpout(nan, nan) >>> fluxes.qma qma(nan, nan) >>> fluxes.qar qar(nan, nan)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/arma/arma_derived.py#L16-L45
train
hydpy-dev/hydpy
hydpy/models/arma/arma_derived.py
AR_Order.update
def update(self): """Determine the total number of AR coefficients. >>> from hydpy.models.arma import * >>> parameterstep('1d') >>> responses(((1., 2.), (1.,)), th_3=((1.,), (1., 2., 3.))) >>> derived.ar_order.update() >>> derived.ar_order ar_order(2, 1) """ responses = self.subpars.pars.control.responses self.shape = len(responses) self(responses.ar_orders)
python
def update(self): """Determine the total number of AR coefficients. >>> from hydpy.models.arma import * >>> parameterstep('1d') >>> responses(((1., 2.), (1.,)), th_3=((1.,), (1., 2., 3.))) >>> derived.ar_order.update() >>> derived.ar_order ar_order(2, 1) """ responses = self.subpars.pars.control.responses self.shape = len(responses) self(responses.ar_orders)
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Determine the total number of AR coefficients. >>> from hydpy.models.arma import * >>> parameterstep('1d') >>> responses(((1., 2.), (1.,)), th_3=((1.,), (1., 2., 3.))) >>> derived.ar_order.update() >>> derived.ar_order ar_order(2, 1)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/arma/arma_derived.py#L94-L106
train
hydpy-dev/hydpy
hydpy/models/arma/arma_derived.py
AR_Coefs.update
def update(self): """Determine all AR coefficients. >>> from hydpy.models.arma import * >>> parameterstep('1d') >>> responses(((1., 2.), (1.,)), th_3=((1.,), (1., 2., 3.))) >>> derived.ar_coefs.update() >>> derived.ar_coefs ar_coefs([[1.0, 2.0], [1.0, nan]]) Note that updating parameter `ar_coefs` sets the shape of the log sequence |LogOut| automatically. >>> logs.logout logout([[nan, nan], [nan, nan]]) """ pars = self.subpars.pars coefs = pars.control.responses.ar_coefs self.shape = coefs.shape self(coefs) pars.model.sequences.logs.logout.shape = self.shape
python
def update(self): """Determine all AR coefficients. >>> from hydpy.models.arma import * >>> parameterstep('1d') >>> responses(((1., 2.), (1.,)), th_3=((1.,), (1., 2., 3.))) >>> derived.ar_coefs.update() >>> derived.ar_coefs ar_coefs([[1.0, 2.0], [1.0, nan]]) Note that updating parameter `ar_coefs` sets the shape of the log sequence |LogOut| automatically. >>> logs.logout logout([[nan, nan], [nan, nan]]) """ pars = self.subpars.pars coefs = pars.control.responses.ar_coefs self.shape = coefs.shape self(coefs) pars.model.sequences.logs.logout.shape = self.shape
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Determine all AR coefficients. >>> from hydpy.models.arma import * >>> parameterstep('1d') >>> responses(((1., 2.), (1.,)), th_3=((1.,), (1., 2., 3.))) >>> derived.ar_coefs.update() >>> derived.ar_coefs ar_coefs([[1.0, 2.0], [1.0, nan]]) Note that updating parameter `ar_coefs` sets the shape of the log sequence |LogOut| automatically. >>> logs.logout logout([[nan, nan], [nan, nan]])
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/arma/arma_derived.py#L132-L154
train
hydpy-dev/hydpy
hydpy/models/arma/arma_derived.py
MA_Coefs.update
def update(self): """Determine all MA coefficients. >>> from hydpy.models.arma import * >>> parameterstep('1d') >>> responses(((1., 2.), (1.,)), th_3=((1.,), (1., 2., 3.))) >>> derived.ma_coefs.update() >>> derived.ma_coefs ma_coefs([[1.0, nan, nan], [1.0, 2.0, 3.0]]) Note that updating parameter `ar_coefs` sets the shape of the log sequence |LogIn| automatically. >>> logs.login login([[nan, nan, nan], [nan, nan, nan]]) """ pars = self.subpars.pars coefs = pars.control.responses.ma_coefs self.shape = coefs.shape self(coefs) pars.model.sequences.logs.login.shape = self.shape
python
def update(self): """Determine all MA coefficients. >>> from hydpy.models.arma import * >>> parameterstep('1d') >>> responses(((1., 2.), (1.,)), th_3=((1.,), (1., 2., 3.))) >>> derived.ma_coefs.update() >>> derived.ma_coefs ma_coefs([[1.0, nan, nan], [1.0, 2.0, 3.0]]) Note that updating parameter `ar_coefs` sets the shape of the log sequence |LogIn| automatically. >>> logs.login login([[nan, nan, nan], [nan, nan, nan]]) """ pars = self.subpars.pars coefs = pars.control.responses.ma_coefs self.shape = coefs.shape self(coefs) pars.model.sequences.logs.login.shape = self.shape
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Determine all MA coefficients. >>> from hydpy.models.arma import * >>> parameterstep('1d') >>> responses(((1., 2.), (1.,)), th_3=((1.,), (1., 2., 3.))) >>> derived.ma_coefs.update() >>> derived.ma_coefs ma_coefs([[1.0, nan, nan], [1.0, 2.0, 3.0]]) Note that updating parameter `ar_coefs` sets the shape of the log sequence |LogIn| automatically. >>> logs.login login([[nan, nan, nan], [nan, nan, nan]])
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/arma/arma_derived.py#L161-L183
train
hydpy-dev/hydpy
hydpy/core/optiontools.py
_prepare_docstrings
def _prepare_docstrings(): """Assign docstrings to the corresponding attributes of class `Options` to make them available in the interactive mode of Python.""" if config.USEAUTODOC: source = inspect.getsource(Options) docstrings = source.split('"""')[3::2] attributes = [line.strip().split()[0] for line in source.split('\n') if '_Option(' in line] for attribute, docstring in zip(attributes, docstrings): Options.__dict__[attribute].__doc__ = docstring
python
def _prepare_docstrings(): """Assign docstrings to the corresponding attributes of class `Options` to make them available in the interactive mode of Python.""" if config.USEAUTODOC: source = inspect.getsource(Options) docstrings = source.split('"""')[3::2] attributes = [line.strip().split()[0] for line in source.split('\n') if '_Option(' in line] for attribute, docstring in zip(attributes, docstrings): Options.__dict__[attribute].__doc__ = docstring
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/optiontools.py#L211-L220
train
hydpy-dev/hydpy
hydpy/core/selectiontools.py
Selections.nodes
def nodes(self) -> devicetools.Nodes: """A |set| containing the |Node| objects of all handled |Selection| objects. >>> from hydpy import Selection, Selections >>> selections = Selections( ... Selection('sel1', ['node1', 'node2'], ['element1']), ... Selection('sel2', ['node1', 'node3'], ['element2'])) >>> selections.nodes Nodes("node1", "node2", "node3") """ nodes = devicetools.Nodes() for selection in self: nodes += selection.nodes return nodes
python
def nodes(self) -> devicetools.Nodes: """A |set| containing the |Node| objects of all handled |Selection| objects. >>> from hydpy import Selection, Selections >>> selections = Selections( ... Selection('sel1', ['node1', 'node2'], ['element1']), ... Selection('sel2', ['node1', 'node3'], ['element2'])) >>> selections.nodes Nodes("node1", "node2", "node3") """ nodes = devicetools.Nodes() for selection in self: nodes += selection.nodes return nodes
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A |set| containing the |Node| objects of all handled |Selection| objects. >>> from hydpy import Selection, Selections >>> selections = Selections( ... Selection('sel1', ['node1', 'node2'], ['element1']), ... Selection('sel2', ['node1', 'node3'], ['element2'])) >>> selections.nodes Nodes("node1", "node2", "node3")
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/selectiontools.py#L160-L174
train
hydpy-dev/hydpy
hydpy/core/selectiontools.py
Selections.elements
def elements(self) -> devicetools.Elements: """A |set| containing the |Node| objects of all handled |Selection| objects. >>> from hydpy import Selection, Selections >>> selections = Selections( ... Selection('sel1', ['node1'], ['element1']), ... Selection('sel2', ['node1'], ['element2', 'element3'])) >>> selections.elements Elements("element1", "element2", "element3") """ elements = devicetools.Elements() for selection in self: elements += selection.elements return elements
python
def elements(self) -> devicetools.Elements: """A |set| containing the |Node| objects of all handled |Selection| objects. >>> from hydpy import Selection, Selections >>> selections = Selections( ... Selection('sel1', ['node1'], ['element1']), ... Selection('sel2', ['node1'], ['element2', 'element3'])) >>> selections.elements Elements("element1", "element2", "element3") """ elements = devicetools.Elements() for selection in self: elements += selection.elements return elements
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A |set| containing the |Node| objects of all handled |Selection| objects. >>> from hydpy import Selection, Selections >>> selections = Selections( ... Selection('sel1', ['node1'], ['element1']), ... Selection('sel2', ['node1'], ['element2', 'element3'])) >>> selections.elements Elements("element1", "element2", "element3")
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/selectiontools.py#L177-L191
train
hydpy-dev/hydpy
hydpy/core/selectiontools.py
Selections.__getiterable
def __getiterable(value): # ToDo: refactor """Try to convert the given argument to a |list| of |Selection| objects and return it. """ if isinstance(value, Selection): return [value] try: for selection in value: if not isinstance(selection, Selection): raise TypeError return list(value) except TypeError: raise TypeError( f'Binary operations on Selections objects are defined for ' f'other Selections objects, single Selection objects, or ' f'iterables containing `Selection` objects, but the type of ' f'the given argument is `{objecttools.classname(value)}`.')
python
def __getiterable(value): # ToDo: refactor """Try to convert the given argument to a |list| of |Selection| objects and return it. """ if isinstance(value, Selection): return [value] try: for selection in value: if not isinstance(selection, Selection): raise TypeError return list(value) except TypeError: raise TypeError( f'Binary operations on Selections objects are defined for ' f'other Selections objects, single Selection objects, or ' f'iterables containing `Selection` objects, but the type of ' f'the given argument is `{objecttools.classname(value)}`.')
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/selectiontools.py#L237-L253
train
hydpy-dev/hydpy
hydpy/core/selectiontools.py
Selections.assignrepr
def assignrepr(self, prefix='') -> str: """Return a |repr| string with a prefixed assignment.""" with objecttools.repr_.preserve_strings(True): with hydpy.pub.options.ellipsis(2, optional=True): prefix += '%s(' % objecttools.classname(self) repr_ = objecttools.assignrepr_values( sorted(self.names), prefix, 70) return repr_ + ')'
python
def assignrepr(self, prefix='') -> str: """Return a |repr| string with a prefixed assignment.""" with objecttools.repr_.preserve_strings(True): with hydpy.pub.options.ellipsis(2, optional=True): prefix += '%s(' % objecttools.classname(self) repr_ = objecttools.assignrepr_values( sorted(self.names), prefix, 70) return repr_ + ')'
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/selectiontools.py#L303-L310
train
hydpy-dev/hydpy
hydpy/core/selectiontools.py
Selection.search_upstream
def search_upstream(self, device: devicetools.Device, name: str = 'upstream') -> 'Selection': """Return the network upstream of the given starting point, including the starting point itself. >>> from hydpy.core.examples import prepare_full_example_2 >>> hp, pub, _ = prepare_full_example_2() You can pass both |Node| and |Element| objects and, optionally, the name of the newly created |Selection| object: >>> test = pub.selections.complete.copy('test') >>> test.search_upstream(hp.nodes.lahn_2) Selection("upstream", nodes=("dill", "lahn_1", "lahn_2"), elements=("land_dill", "land_lahn_1", "land_lahn_2", "stream_dill_lahn_2", "stream_lahn_1_lahn_2")) >>> test.search_upstream( ... hp.elements.stream_lahn_1_lahn_2, 'UPSTREAM') Selection("UPSTREAM", nodes="lahn_1", elements=("land_lahn_1", "stream_lahn_1_lahn_2")) Wrong device specifications result in errors like the following: >>> test.search_upstream(1) Traceback (most recent call last): ... TypeError: While trying to determine the upstream network of \ selection `test`, the following error occurred: Either a `Node` or \ an `Element` object is required as the "outlet device", but the given \ `device` value is of type `int`. >>> pub.selections.headwaters.search_upstream(hp.nodes.lahn_3) Traceback (most recent call last): ... KeyError: "While trying to determine the upstream network of \ selection `headwaters`, the following error occurred: 'No node named \ `lahn_3` available.'" Method |Selection.select_upstream| restricts the current selection to the one determined with the method |Selection.search_upstream|: >>> test.select_upstream(hp.nodes.lahn_2) Selection("test", nodes=("dill", "lahn_1", "lahn_2"), elements=("land_dill", "land_lahn_1", "land_lahn_2", "stream_dill_lahn_2", "stream_lahn_1_lahn_2")) On the contrary, the method |Selection.deselect_upstream| restricts the current selection to all devices not determined by method |Selection.search_upstream|: >>> complete = pub.selections.complete.deselect_upstream( ... hp.nodes.lahn_2) >>> complete Selection("complete", nodes="lahn_3", elements=("land_lahn_3", "stream_lahn_2_lahn_3")) If necessary, include the "outlet device" manually afterwards: >>> complete.nodes += hp.nodes.lahn_2 >>> complete Selection("complete", nodes=("lahn_2", "lahn_3"), elements=("land_lahn_3", "stream_lahn_2_lahn_3")) """ try: selection = Selection(name) if isinstance(device, devicetools.Node): node = self.nodes[device.name] return self.__get_nextnode(node, selection) if isinstance(device, devicetools.Element): element = self.elements[device.name] return self.__get_nextelement(element, selection) raise TypeError( f'Either a `Node` or an `Element` object is required ' f'as the "outlet device", but the given `device` value ' f'is of type `{objecttools.classname(device)}`.') except BaseException: objecttools.augment_excmessage( f'While trying to determine the upstream network of ' f'selection `{self.name}`')
python
def search_upstream(self, device: devicetools.Device, name: str = 'upstream') -> 'Selection': """Return the network upstream of the given starting point, including the starting point itself. >>> from hydpy.core.examples import prepare_full_example_2 >>> hp, pub, _ = prepare_full_example_2() You can pass both |Node| and |Element| objects and, optionally, the name of the newly created |Selection| object: >>> test = pub.selections.complete.copy('test') >>> test.search_upstream(hp.nodes.lahn_2) Selection("upstream", nodes=("dill", "lahn_1", "lahn_2"), elements=("land_dill", "land_lahn_1", "land_lahn_2", "stream_dill_lahn_2", "stream_lahn_1_lahn_2")) >>> test.search_upstream( ... hp.elements.stream_lahn_1_lahn_2, 'UPSTREAM') Selection("UPSTREAM", nodes="lahn_1", elements=("land_lahn_1", "stream_lahn_1_lahn_2")) Wrong device specifications result in errors like the following: >>> test.search_upstream(1) Traceback (most recent call last): ... TypeError: While trying to determine the upstream network of \ selection `test`, the following error occurred: Either a `Node` or \ an `Element` object is required as the "outlet device", but the given \ `device` value is of type `int`. >>> pub.selections.headwaters.search_upstream(hp.nodes.lahn_3) Traceback (most recent call last): ... KeyError: "While trying to determine the upstream network of \ selection `headwaters`, the following error occurred: 'No node named \ `lahn_3` available.'" Method |Selection.select_upstream| restricts the current selection to the one determined with the method |Selection.search_upstream|: >>> test.select_upstream(hp.nodes.lahn_2) Selection("test", nodes=("dill", "lahn_1", "lahn_2"), elements=("land_dill", "land_lahn_1", "land_lahn_2", "stream_dill_lahn_2", "stream_lahn_1_lahn_2")) On the contrary, the method |Selection.deselect_upstream| restricts the current selection to all devices not determined by method |Selection.search_upstream|: >>> complete = pub.selections.complete.deselect_upstream( ... hp.nodes.lahn_2) >>> complete Selection("complete", nodes="lahn_3", elements=("land_lahn_3", "stream_lahn_2_lahn_3")) If necessary, include the "outlet device" manually afterwards: >>> complete.nodes += hp.nodes.lahn_2 >>> complete Selection("complete", nodes=("lahn_2", "lahn_3"), elements=("land_lahn_3", "stream_lahn_2_lahn_3")) """ try: selection = Selection(name) if isinstance(device, devicetools.Node): node = self.nodes[device.name] return self.__get_nextnode(node, selection) if isinstance(device, devicetools.Element): element = self.elements[device.name] return self.__get_nextelement(element, selection) raise TypeError( f'Either a `Node` or an `Element` object is required ' f'as the "outlet device", but the given `device` value ' f'is of type `{objecttools.classname(device)}`.') except BaseException: objecttools.augment_excmessage( f'While trying to determine the upstream network of ' f'selection `{self.name}`')
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Return the network upstream of the given starting point, including the starting point itself. >>> from hydpy.core.examples import prepare_full_example_2 >>> hp, pub, _ = prepare_full_example_2() You can pass both |Node| and |Element| objects and, optionally, the name of the newly created |Selection| object: >>> test = pub.selections.complete.copy('test') >>> test.search_upstream(hp.nodes.lahn_2) Selection("upstream", nodes=("dill", "lahn_1", "lahn_2"), elements=("land_dill", "land_lahn_1", "land_lahn_2", "stream_dill_lahn_2", "stream_lahn_1_lahn_2")) >>> test.search_upstream( ... hp.elements.stream_lahn_1_lahn_2, 'UPSTREAM') Selection("UPSTREAM", nodes="lahn_1", elements=("land_lahn_1", "stream_lahn_1_lahn_2")) Wrong device specifications result in errors like the following: >>> test.search_upstream(1) Traceback (most recent call last): ... TypeError: While trying to determine the upstream network of \ selection `test`, the following error occurred: Either a `Node` or \ an `Element` object is required as the "outlet device", but the given \ `device` value is of type `int`. >>> pub.selections.headwaters.search_upstream(hp.nodes.lahn_3) Traceback (most recent call last): ... KeyError: "While trying to determine the upstream network of \ selection `headwaters`, the following error occurred: 'No node named \ `lahn_3` available.'" Method |Selection.select_upstream| restricts the current selection to the one determined with the method |Selection.search_upstream|: >>> test.select_upstream(hp.nodes.lahn_2) Selection("test", nodes=("dill", "lahn_1", "lahn_2"), elements=("land_dill", "land_lahn_1", "land_lahn_2", "stream_dill_lahn_2", "stream_lahn_1_lahn_2")) On the contrary, the method |Selection.deselect_upstream| restricts the current selection to all devices not determined by method |Selection.search_upstream|: >>> complete = pub.selections.complete.deselect_upstream( ... hp.nodes.lahn_2) >>> complete Selection("complete", nodes="lahn_3", elements=("land_lahn_3", "stream_lahn_2_lahn_3")) If necessary, include the "outlet device" manually afterwards: >>> complete.nodes += hp.nodes.lahn_2 >>> complete Selection("complete", nodes=("lahn_2", "lahn_3"), elements=("land_lahn_3", "stream_lahn_2_lahn_3"))
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/selectiontools.py#L434-L517
train
hydpy-dev/hydpy
hydpy/core/selectiontools.py
Selection.select_upstream
def select_upstream(self, device: devicetools.Device) -> 'Selection': """Restrict the current selection to the network upstream of the given starting point, including the starting point itself. See the documentation on method |Selection.search_upstream| for additional information. """ upstream = self.search_upstream(device) self.nodes = upstream.nodes self.elements = upstream.elements return self
python
def select_upstream(self, device: devicetools.Device) -> 'Selection': """Restrict the current selection to the network upstream of the given starting point, including the starting point itself. See the documentation on method |Selection.search_upstream| for additional information. """ upstream = self.search_upstream(device) self.nodes = upstream.nodes self.elements = upstream.elements return self
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Restrict the current selection to the network upstream of the given starting point, including the starting point itself. See the documentation on method |Selection.search_upstream| for additional information.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/selectiontools.py#L533-L543
train
hydpy-dev/hydpy
hydpy/core/selectiontools.py
Selection.search_modeltypes
def search_modeltypes(self, *models: ModelTypesArg, name: str = 'modeltypes') -> 'Selection': """Return a |Selection| object containing only the elements currently handling models of the given types. >>> from hydpy.core.examples import prepare_full_example_2 >>> hp, pub, _ = prepare_full_example_2() You can pass both |Model| objects and names and, as a keyword argument, the name of the newly created |Selection| object: >>> test = pub.selections.complete.copy('test') >>> from hydpy import prepare_model >>> hland_v1 = prepare_model('hland_v1') >>> test.search_modeltypes(hland_v1) Selection("modeltypes", nodes=(), elements=("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3")) >>> test.search_modeltypes( ... hland_v1, 'hstream_v1', 'lland_v1', name='MODELTYPES') Selection("MODELTYPES", nodes=(), elements=("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3", "stream_dill_lahn_2", "stream_lahn_1_lahn_2", "stream_lahn_2_lahn_3")) Wrong model specifications result in errors like the following: >>> test.search_modeltypes('wrong') Traceback (most recent call last): ... ModuleNotFoundError: While trying to determine the elements of \ selection `test` handling the model defined by the argument(s) `wrong` \ of type(s) `str`, the following error occurred: \ No module named 'hydpy.models.wrong' Method |Selection.select_modeltypes| restricts the current selection to the one determined with the method the |Selection.search_modeltypes|: >>> test.select_modeltypes(hland_v1) Selection("test", nodes=(), elements=("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3")) On the contrary, the method |Selection.deselect_upstream| restricts the current selection to all devices not determined by method the |Selection.search_upstream|: >>> pub.selections.complete.deselect_modeltypes(hland_v1) Selection("complete", nodes=(), elements=("stream_dill_lahn_2", "stream_lahn_1_lahn_2", "stream_lahn_2_lahn_3")) """ try: typelist = [] for model in models: if not isinstance(model, modeltools.Model): model = importtools.prepare_model(model) typelist.append(type(model)) typetuple = tuple(typelist) selection = Selection(name) for element in self.elements: if isinstance(element.model, typetuple): selection.elements += element return selection except BaseException: values = objecttools.enumeration(models) classes = objecttools.enumeration( objecttools.classname(model) for model in models) objecttools.augment_excmessage( f'While trying to determine the elements of selection ' f'`{self.name}` handling the model defined by the ' f'argument(s) `{values}` of type(s) `{classes}`')
python
def search_modeltypes(self, *models: ModelTypesArg, name: str = 'modeltypes') -> 'Selection': """Return a |Selection| object containing only the elements currently handling models of the given types. >>> from hydpy.core.examples import prepare_full_example_2 >>> hp, pub, _ = prepare_full_example_2() You can pass both |Model| objects and names and, as a keyword argument, the name of the newly created |Selection| object: >>> test = pub.selections.complete.copy('test') >>> from hydpy import prepare_model >>> hland_v1 = prepare_model('hland_v1') >>> test.search_modeltypes(hland_v1) Selection("modeltypes", nodes=(), elements=("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3")) >>> test.search_modeltypes( ... hland_v1, 'hstream_v1', 'lland_v1', name='MODELTYPES') Selection("MODELTYPES", nodes=(), elements=("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3", "stream_dill_lahn_2", "stream_lahn_1_lahn_2", "stream_lahn_2_lahn_3")) Wrong model specifications result in errors like the following: >>> test.search_modeltypes('wrong') Traceback (most recent call last): ... ModuleNotFoundError: While trying to determine the elements of \ selection `test` handling the model defined by the argument(s) `wrong` \ of type(s) `str`, the following error occurred: \ No module named 'hydpy.models.wrong' Method |Selection.select_modeltypes| restricts the current selection to the one determined with the method the |Selection.search_modeltypes|: >>> test.select_modeltypes(hland_v1) Selection("test", nodes=(), elements=("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3")) On the contrary, the method |Selection.deselect_upstream| restricts the current selection to all devices not determined by method the |Selection.search_upstream|: >>> pub.selections.complete.deselect_modeltypes(hland_v1) Selection("complete", nodes=(), elements=("stream_dill_lahn_2", "stream_lahn_1_lahn_2", "stream_lahn_2_lahn_3")) """ try: typelist = [] for model in models: if not isinstance(model, modeltools.Model): model = importtools.prepare_model(model) typelist.append(type(model)) typetuple = tuple(typelist) selection = Selection(name) for element in self.elements: if isinstance(element.model, typetuple): selection.elements += element return selection except BaseException: values = objecttools.enumeration(models) classes = objecttools.enumeration( objecttools.classname(model) for model in models) objecttools.augment_excmessage( f'While trying to determine the elements of selection ' f'`{self.name}` handling the model defined by the ' f'argument(s) `{values}` of type(s) `{classes}`')
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Return a |Selection| object containing only the elements currently handling models of the given types. >>> from hydpy.core.examples import prepare_full_example_2 >>> hp, pub, _ = prepare_full_example_2() You can pass both |Model| objects and names and, as a keyword argument, the name of the newly created |Selection| object: >>> test = pub.selections.complete.copy('test') >>> from hydpy import prepare_model >>> hland_v1 = prepare_model('hland_v1') >>> test.search_modeltypes(hland_v1) Selection("modeltypes", nodes=(), elements=("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3")) >>> test.search_modeltypes( ... hland_v1, 'hstream_v1', 'lland_v1', name='MODELTYPES') Selection("MODELTYPES", nodes=(), elements=("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3", "stream_dill_lahn_2", "stream_lahn_1_lahn_2", "stream_lahn_2_lahn_3")) Wrong model specifications result in errors like the following: >>> test.search_modeltypes('wrong') Traceback (most recent call last): ... ModuleNotFoundError: While trying to determine the elements of \ selection `test` handling the model defined by the argument(s) `wrong` \ of type(s) `str`, the following error occurred: \ No module named 'hydpy.models.wrong' Method |Selection.select_modeltypes| restricts the current selection to the one determined with the method the |Selection.search_modeltypes|: >>> test.select_modeltypes(hland_v1) Selection("test", nodes=(), elements=("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3")) On the contrary, the method |Selection.deselect_upstream| restricts the current selection to all devices not determined by method the |Selection.search_upstream|: >>> pub.selections.complete.deselect_modeltypes(hland_v1) Selection("complete", nodes=(), elements=("stream_dill_lahn_2", "stream_lahn_1_lahn_2", "stream_lahn_2_lahn_3"))
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/selectiontools.py#L559-L635
train
hydpy-dev/hydpy
hydpy/core/selectiontools.py
Selection.select_modeltypes
def select_modeltypes(self, *models: ModelTypesArg) -> 'Selection': """Restrict the current |Selection| object to all elements containing the given model types (removes all nodes). See the documentation on method |Selection.search_modeltypes| for additional information. """ self.nodes = devicetools.Nodes() self.elements = self.search_modeltypes(*models).elements return self
python
def select_modeltypes(self, *models: ModelTypesArg) -> 'Selection': """Restrict the current |Selection| object to all elements containing the given model types (removes all nodes). See the documentation on method |Selection.search_modeltypes| for additional information. """ self.nodes = devicetools.Nodes() self.elements = self.search_modeltypes(*models).elements return self
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Restrict the current |Selection| object to all elements containing the given model types (removes all nodes). See the documentation on method |Selection.search_modeltypes| for additional information.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/selectiontools.py#L637-L646
train
hydpy-dev/hydpy
hydpy/core/selectiontools.py
Selection.search_nodenames
def search_nodenames(self, *substrings: str, name: str = 'nodenames') -> \ 'Selection': """Return a new selection containing all nodes of the current selection with a name containing at least one of the given substrings. >>> from hydpy.core.examples import prepare_full_example_2 >>> hp, pub, _ = prepare_full_example_2() Pass the (sub)strings as positional arguments and, optionally, the name of the newly created |Selection| object as a keyword argument: >>> test = pub.selections.complete.copy('test') >>> from hydpy import prepare_model >>> test.search_nodenames('dill', 'lahn_1') Selection("nodenames", nodes=("dill", "lahn_1"), elements=()) Wrong string specifications result in errors like the following: >>> test.search_nodenames(['dill', 'lahn_1']) Traceback (most recent call last): ... TypeError: While trying to determine the nodes of selection \ `test` with names containing at least one of the given substrings \ `['dill', 'lahn_1']`, the following error occurred: 'in <string>' \ requires string as left operand, not list Method |Selection.select_nodenames| restricts the current selection to the one determined with the the method |Selection.search_nodenames|: >>> test.select_nodenames('dill', 'lahn_1') Selection("test", nodes=("dill", "lahn_1"), elements=("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3", "stream_dill_lahn_2", "stream_lahn_1_lahn_2", "stream_lahn_2_lahn_3")) On the contrary, the method |Selection.deselect_nodenames| restricts the current selection to all devices not determined by the method |Selection.search_nodenames|: >>> pub.selections.complete.deselect_nodenames('dill', 'lahn_1') Selection("complete", nodes=("lahn_2", "lahn_3"), elements=("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3", "stream_dill_lahn_2", "stream_lahn_1_lahn_2", "stream_lahn_2_lahn_3")) """ try: selection = Selection(name) for node in self.nodes: for substring in substrings: if substring in node.name: selection.nodes += node break return selection except BaseException: values = objecttools.enumeration(substrings) objecttools.augment_excmessage( f'While trying to determine the nodes of selection ' f'`{self.name}` with names containing at least one ' f'of the given substrings `{values}`')
python
def search_nodenames(self, *substrings: str, name: str = 'nodenames') -> \ 'Selection': """Return a new selection containing all nodes of the current selection with a name containing at least one of the given substrings. >>> from hydpy.core.examples import prepare_full_example_2 >>> hp, pub, _ = prepare_full_example_2() Pass the (sub)strings as positional arguments and, optionally, the name of the newly created |Selection| object as a keyword argument: >>> test = pub.selections.complete.copy('test') >>> from hydpy import prepare_model >>> test.search_nodenames('dill', 'lahn_1') Selection("nodenames", nodes=("dill", "lahn_1"), elements=()) Wrong string specifications result in errors like the following: >>> test.search_nodenames(['dill', 'lahn_1']) Traceback (most recent call last): ... TypeError: While trying to determine the nodes of selection \ `test` with names containing at least one of the given substrings \ `['dill', 'lahn_1']`, the following error occurred: 'in <string>' \ requires string as left operand, not list Method |Selection.select_nodenames| restricts the current selection to the one determined with the the method |Selection.search_nodenames|: >>> test.select_nodenames('dill', 'lahn_1') Selection("test", nodes=("dill", "lahn_1"), elements=("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3", "stream_dill_lahn_2", "stream_lahn_1_lahn_2", "stream_lahn_2_lahn_3")) On the contrary, the method |Selection.deselect_nodenames| restricts the current selection to all devices not determined by the method |Selection.search_nodenames|: >>> pub.selections.complete.deselect_nodenames('dill', 'lahn_1') Selection("complete", nodes=("lahn_2", "lahn_3"), elements=("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3", "stream_dill_lahn_2", "stream_lahn_1_lahn_2", "stream_lahn_2_lahn_3")) """ try: selection = Selection(name) for node in self.nodes: for substring in substrings: if substring in node.name: selection.nodes += node break return selection except BaseException: values = objecttools.enumeration(substrings) objecttools.augment_excmessage( f'While trying to determine the nodes of selection ' f'`{self.name}` with names containing at least one ' f'of the given substrings `{values}`')
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Return a new selection containing all nodes of the current selection with a name containing at least one of the given substrings. >>> from hydpy.core.examples import prepare_full_example_2 >>> hp, pub, _ = prepare_full_example_2() Pass the (sub)strings as positional arguments and, optionally, the name of the newly created |Selection| object as a keyword argument: >>> test = pub.selections.complete.copy('test') >>> from hydpy import prepare_model >>> test.search_nodenames('dill', 'lahn_1') Selection("nodenames", nodes=("dill", "lahn_1"), elements=()) Wrong string specifications result in errors like the following: >>> test.search_nodenames(['dill', 'lahn_1']) Traceback (most recent call last): ... TypeError: While trying to determine the nodes of selection \ `test` with names containing at least one of the given substrings \ `['dill', 'lahn_1']`, the following error occurred: 'in <string>' \ requires string as left operand, not list Method |Selection.select_nodenames| restricts the current selection to the one determined with the the method |Selection.search_nodenames|: >>> test.select_nodenames('dill', 'lahn_1') Selection("test", nodes=("dill", "lahn_1"), elements=("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3", "stream_dill_lahn_2", "stream_lahn_1_lahn_2", "stream_lahn_2_lahn_3")) On the contrary, the method |Selection.deselect_nodenames| restricts the current selection to all devices not determined by the method |Selection.search_nodenames|: >>> pub.selections.complete.deselect_nodenames('dill', 'lahn_1') Selection("complete", nodes=("lahn_2", "lahn_3"), elements=("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3", "stream_dill_lahn_2", "stream_lahn_1_lahn_2", "stream_lahn_2_lahn_3"))
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/selectiontools.py#L659-L721
train
hydpy-dev/hydpy
hydpy/core/selectiontools.py
Selection.select_nodenames
def select_nodenames(self, *substrings: str) -> 'Selection': """Restrict the current selection to all nodes with a name containing at least one of the given substrings (does not affect any elements). See the documentation on method |Selection.search_nodenames| for additional information. """ self.nodes = self.search_nodenames(*substrings).nodes return self
python
def select_nodenames(self, *substrings: str) -> 'Selection': """Restrict the current selection to all nodes with a name containing at least one of the given substrings (does not affect any elements). See the documentation on method |Selection.search_nodenames| for additional information. """ self.nodes = self.search_nodenames(*substrings).nodes return self
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Restrict the current selection to all nodes with a name containing at least one of the given substrings (does not affect any elements). See the documentation on method |Selection.search_nodenames| for additional information.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/selectiontools.py#L723-L732
train
hydpy-dev/hydpy
hydpy/core/selectiontools.py
Selection.deselect_nodenames
def deselect_nodenames(self, *substrings: str) -> 'Selection': """Restrict the current selection to all nodes with a name not containing at least one of the given substrings (does not affect any elements). See the documentation on method |Selection.search_nodenames| for additional information. """ self.nodes -= self.search_nodenames(*substrings).nodes return self
python
def deselect_nodenames(self, *substrings: str) -> 'Selection': """Restrict the current selection to all nodes with a name not containing at least one of the given substrings (does not affect any elements). See the documentation on method |Selection.search_nodenames| for additional information. """ self.nodes -= self.search_nodenames(*substrings).nodes return self
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Restrict the current selection to all nodes with a name not containing at least one of the given substrings (does not affect any elements). See the documentation on method |Selection.search_nodenames| for additional information.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/selectiontools.py#L734-L743
train
hydpy-dev/hydpy
hydpy/core/selectiontools.py
Selection.search_elementnames
def search_elementnames(self, *substrings: str, name: str = 'elementnames') -> 'Selection': """Return a new selection containing all elements of the current selection with a name containing at least one of the given substrings. >>> from hydpy.core.examples import prepare_full_example_2 >>> hp, pub, _ = prepare_full_example_2() Pass the (sub)strings as positional arguments and, optionally, the name of the newly created |Selection| object as a keyword argument: >>> test = pub.selections.complete.copy('test') >>> from hydpy import prepare_model >>> test.search_elementnames('dill', 'lahn_1') Selection("elementnames", nodes=(), elements=("land_dill", "land_lahn_1", "stream_dill_lahn_2", "stream_lahn_1_lahn_2")) Wrong string specifications result in errors like the following: >>> test.search_elementnames(['dill', 'lahn_1']) Traceback (most recent call last): ... TypeError: While trying to determine the elements of selection \ `test` with names containing at least one of the given substrings \ `['dill', 'lahn_1']`, the following error occurred: 'in <string>' \ requires string as left operand, not list Method |Selection.select_elementnames| restricts the current selection to the one determined with the method |Selection.search_elementnames|: >>> test.select_elementnames('dill', 'lahn_1') Selection("test", nodes=("dill", "lahn_1", "lahn_2", "lahn_3"), elements=("land_dill", "land_lahn_1", "stream_dill_lahn_2", "stream_lahn_1_lahn_2")) On the contrary, the method |Selection.deselect_elementnames| restricts the current selection to all devices not determined by the method |Selection.search_elementnames|: >>> pub.selections.complete.deselect_elementnames('dill', 'lahn_1') Selection("complete", nodes=("dill", "lahn_1", "lahn_2", "lahn_3"), elements=("land_lahn_2", "land_lahn_3", "stream_lahn_2_lahn_3")) """ try: selection = Selection(name) for element in self.elements: for substring in substrings: if substring in element.name: selection.elements += element break return selection except BaseException: values = objecttools.enumeration(substrings) objecttools.augment_excmessage( f'While trying to determine the elements of selection ' f'`{self.name}` with names containing at least one ' f'of the given substrings `{values}`')
python
def search_elementnames(self, *substrings: str, name: str = 'elementnames') -> 'Selection': """Return a new selection containing all elements of the current selection with a name containing at least one of the given substrings. >>> from hydpy.core.examples import prepare_full_example_2 >>> hp, pub, _ = prepare_full_example_2() Pass the (sub)strings as positional arguments and, optionally, the name of the newly created |Selection| object as a keyword argument: >>> test = pub.selections.complete.copy('test') >>> from hydpy import prepare_model >>> test.search_elementnames('dill', 'lahn_1') Selection("elementnames", nodes=(), elements=("land_dill", "land_lahn_1", "stream_dill_lahn_2", "stream_lahn_1_lahn_2")) Wrong string specifications result in errors like the following: >>> test.search_elementnames(['dill', 'lahn_1']) Traceback (most recent call last): ... TypeError: While trying to determine the elements of selection \ `test` with names containing at least one of the given substrings \ `['dill', 'lahn_1']`, the following error occurred: 'in <string>' \ requires string as left operand, not list Method |Selection.select_elementnames| restricts the current selection to the one determined with the method |Selection.search_elementnames|: >>> test.select_elementnames('dill', 'lahn_1') Selection("test", nodes=("dill", "lahn_1", "lahn_2", "lahn_3"), elements=("land_dill", "land_lahn_1", "stream_dill_lahn_2", "stream_lahn_1_lahn_2")) On the contrary, the method |Selection.deselect_elementnames| restricts the current selection to all devices not determined by the method |Selection.search_elementnames|: >>> pub.selections.complete.deselect_elementnames('dill', 'lahn_1') Selection("complete", nodes=("dill", "lahn_1", "lahn_2", "lahn_3"), elements=("land_lahn_2", "land_lahn_3", "stream_lahn_2_lahn_3")) """ try: selection = Selection(name) for element in self.elements: for substring in substrings: if substring in element.name: selection.elements += element break return selection except BaseException: values = objecttools.enumeration(substrings) objecttools.augment_excmessage( f'While trying to determine the elements of selection ' f'`{self.name}` with names containing at least one ' f'of the given substrings `{values}`')
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Return a new selection containing all elements of the current selection with a name containing at least one of the given substrings. >>> from hydpy.core.examples import prepare_full_example_2 >>> hp, pub, _ = prepare_full_example_2() Pass the (sub)strings as positional arguments and, optionally, the name of the newly created |Selection| object as a keyword argument: >>> test = pub.selections.complete.copy('test') >>> from hydpy import prepare_model >>> test.search_elementnames('dill', 'lahn_1') Selection("elementnames", nodes=(), elements=("land_dill", "land_lahn_1", "stream_dill_lahn_2", "stream_lahn_1_lahn_2")) Wrong string specifications result in errors like the following: >>> test.search_elementnames(['dill', 'lahn_1']) Traceback (most recent call last): ... TypeError: While trying to determine the elements of selection \ `test` with names containing at least one of the given substrings \ `['dill', 'lahn_1']`, the following error occurred: 'in <string>' \ requires string as left operand, not list Method |Selection.select_elementnames| restricts the current selection to the one determined with the method |Selection.search_elementnames|: >>> test.select_elementnames('dill', 'lahn_1') Selection("test", nodes=("dill", "lahn_1", "lahn_2", "lahn_3"), elements=("land_dill", "land_lahn_1", "stream_dill_lahn_2", "stream_lahn_1_lahn_2")) On the contrary, the method |Selection.deselect_elementnames| restricts the current selection to all devices not determined by the method |Selection.search_elementnames|: >>> pub.selections.complete.deselect_elementnames('dill', 'lahn_1') Selection("complete", nodes=("dill", "lahn_1", "lahn_2", "lahn_3"), elements=("land_lahn_2", "land_lahn_3", "stream_lahn_2_lahn_3"))
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/selectiontools.py#L745-L806
train
hydpy-dev/hydpy
hydpy/core/selectiontools.py
Selection.select_elementnames
def select_elementnames(self, *substrings: str) -> 'Selection': """Restrict the current selection to all elements with a name containing at least one of the given substrings (does not affect any nodes). See the documentation on method |Selection.search_elementnames| for additional information. """ self.elements = self.search_elementnames(*substrings).elements return self
python
def select_elementnames(self, *substrings: str) -> 'Selection': """Restrict the current selection to all elements with a name containing at least one of the given substrings (does not affect any nodes). See the documentation on method |Selection.search_elementnames| for additional information. """ self.elements = self.search_elementnames(*substrings).elements return self
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Restrict the current selection to all elements with a name containing at least one of the given substrings (does not affect any nodes). See the documentation on method |Selection.search_elementnames| for additional information.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/selectiontools.py#L808-L817
train
hydpy-dev/hydpy
hydpy/core/selectiontools.py
Selection.deselect_elementnames
def deselect_elementnames(self, *substrings: str) -> 'Selection': """Restrict the current selection to all elements with a name not containing at least one of the given substrings. (does not affect any nodes). See the documentation on method |Selection.search_elementnames| for additional information. """ self.elements -= self.search_elementnames(*substrings).elements return self
python
def deselect_elementnames(self, *substrings: str) -> 'Selection': """Restrict the current selection to all elements with a name not containing at least one of the given substrings. (does not affect any nodes). See the documentation on method |Selection.search_elementnames| for additional information. """ self.elements -= self.search_elementnames(*substrings).elements return self
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Restrict the current selection to all elements with a name not containing at least one of the given substrings. (does not affect any nodes). See the documentation on method |Selection.search_elementnames| for additional information.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/selectiontools.py#L819-L828
train
hydpy-dev/hydpy
hydpy/core/selectiontools.py
Selection.copy
def copy(self, name: str) -> 'Selection': """Return a new |Selection| object with the given name and copies of the handles |Nodes| and |Elements| objects based on method |Devices.copy|.""" return type(self)(name, copy.copy(self.nodes), copy.copy(self.elements))
python
def copy(self, name: str) -> 'Selection': """Return a new |Selection| object with the given name and copies of the handles |Nodes| and |Elements| objects based on method |Devices.copy|.""" return type(self)(name, copy.copy(self.nodes), copy.copy(self.elements))
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Return a new |Selection| object with the given name and copies of the handles |Nodes| and |Elements| objects based on method |Devices.copy|.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/selectiontools.py#L830-L834
train
hydpy-dev/hydpy
hydpy/core/selectiontools.py
Selection.save_networkfile
def save_networkfile(self, filepath: Union[str, None] = None, write_nodes: bool = True) -> None: """Save the selection as a network file. >>> from hydpy.core.examples import prepare_full_example_2 >>> _, pub, TestIO = prepare_full_example_2() In most cases, one should conveniently write network files via method |NetworkManager.save_files| of class |NetworkManager|. However, using the method |Selection.save_networkfile| allows for additional configuration via the arguments `filepath` and `write_nodes`: >>> with TestIO(): ... pub.selections.headwaters.save_networkfile() ... with open('headwaters.py') as networkfile: ... print(networkfile.read()) # -*- coding: utf-8 -*- <BLANKLINE> from hydpy import Node, Element <BLANKLINE> <BLANKLINE> Node("dill", variable="Q", keywords="gauge") <BLANKLINE> Node("lahn_1", variable="Q", keywords="gauge") <BLANKLINE> <BLANKLINE> Element("land_dill", outlets="dill", keywords="catchment") <BLANKLINE> Element("land_lahn_1", outlets="lahn_1", keywords="catchment") <BLANKLINE> >>> with TestIO(): ... pub.selections.headwaters.save_networkfile('test.py', False) ... with open('test.py') as networkfile: ... print(networkfile.read()) # -*- coding: utf-8 -*- <BLANKLINE> from hydpy import Node, Element <BLANKLINE> <BLANKLINE> Element("land_dill", outlets="dill", keywords="catchment") <BLANKLINE> Element("land_lahn_1", outlets="lahn_1", keywords="catchment") <BLANKLINE> """ if filepath is None: filepath = self.name + '.py' with open(filepath, 'w', encoding="utf-8") as file_: file_.write('# -*- coding: utf-8 -*-\n') file_.write('\nfrom hydpy import Node, Element\n\n') if write_nodes: for node in self.nodes: file_.write('\n' + repr(node) + '\n') file_.write('\n') for element in self.elements: file_.write('\n' + repr(element) + '\n')
python
def save_networkfile(self, filepath: Union[str, None] = None, write_nodes: bool = True) -> None: """Save the selection as a network file. >>> from hydpy.core.examples import prepare_full_example_2 >>> _, pub, TestIO = prepare_full_example_2() In most cases, one should conveniently write network files via method |NetworkManager.save_files| of class |NetworkManager|. However, using the method |Selection.save_networkfile| allows for additional configuration via the arguments `filepath` and `write_nodes`: >>> with TestIO(): ... pub.selections.headwaters.save_networkfile() ... with open('headwaters.py') as networkfile: ... print(networkfile.read()) # -*- coding: utf-8 -*- <BLANKLINE> from hydpy import Node, Element <BLANKLINE> <BLANKLINE> Node("dill", variable="Q", keywords="gauge") <BLANKLINE> Node("lahn_1", variable="Q", keywords="gauge") <BLANKLINE> <BLANKLINE> Element("land_dill", outlets="dill", keywords="catchment") <BLANKLINE> Element("land_lahn_1", outlets="lahn_1", keywords="catchment") <BLANKLINE> >>> with TestIO(): ... pub.selections.headwaters.save_networkfile('test.py', False) ... with open('test.py') as networkfile: ... print(networkfile.read()) # -*- coding: utf-8 -*- <BLANKLINE> from hydpy import Node, Element <BLANKLINE> <BLANKLINE> Element("land_dill", outlets="dill", keywords="catchment") <BLANKLINE> Element("land_lahn_1", outlets="lahn_1", keywords="catchment") <BLANKLINE> """ if filepath is None: filepath = self.name + '.py' with open(filepath, 'w', encoding="utf-8") as file_: file_.write('# -*- coding: utf-8 -*-\n') file_.write('\nfrom hydpy import Node, Element\n\n') if write_nodes: for node in self.nodes: file_.write('\n' + repr(node) + '\n') file_.write('\n') for element in self.elements: file_.write('\n' + repr(element) + '\n')
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Save the selection as a network file. >>> from hydpy.core.examples import prepare_full_example_2 >>> _, pub, TestIO = prepare_full_example_2() In most cases, one should conveniently write network files via method |NetworkManager.save_files| of class |NetworkManager|. However, using the method |Selection.save_networkfile| allows for additional configuration via the arguments `filepath` and `write_nodes`: >>> with TestIO(): ... pub.selections.headwaters.save_networkfile() ... with open('headwaters.py') as networkfile: ... print(networkfile.read()) # -*- coding: utf-8 -*- <BLANKLINE> from hydpy import Node, Element <BLANKLINE> <BLANKLINE> Node("dill", variable="Q", keywords="gauge") <BLANKLINE> Node("lahn_1", variable="Q", keywords="gauge") <BLANKLINE> <BLANKLINE> Element("land_dill", outlets="dill", keywords="catchment") <BLANKLINE> Element("land_lahn_1", outlets="lahn_1", keywords="catchment") <BLANKLINE> >>> with TestIO(): ... pub.selections.headwaters.save_networkfile('test.py', False) ... with open('test.py') as networkfile: ... print(networkfile.read()) # -*- coding: utf-8 -*- <BLANKLINE> from hydpy import Node, Element <BLANKLINE> <BLANKLINE> Element("land_dill", outlets="dill", keywords="catchment") <BLANKLINE> Element("land_lahn_1", outlets="lahn_1", keywords="catchment") <BLANKLINE>
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/selectiontools.py#L836-L901
train
hydpy-dev/hydpy
hydpy/core/selectiontools.py
Selection.assignrepr
def assignrepr(self, prefix: str) -> str: """Return a |repr| string with a prefixed assignment.""" with objecttools.repr_.preserve_strings(True): with hydpy.pub.options.ellipsis(2, optional=True): with objecttools.assignrepr_tuple.always_bracketed(False): classname = objecttools.classname(self) blanks = ' ' * (len(prefix+classname) + 1) nodestr = objecttools.assignrepr_tuple( self.nodes.names, blanks+'nodes=', 70) elementstr = objecttools.assignrepr_tuple( self.elements.names, blanks + 'elements=', 70) return (f'{prefix}{classname}("{self.name}",\n' f'{nodestr},\n' f'{elementstr})')
python
def assignrepr(self, prefix: str) -> str: """Return a |repr| string with a prefixed assignment.""" with objecttools.repr_.preserve_strings(True): with hydpy.pub.options.ellipsis(2, optional=True): with objecttools.assignrepr_tuple.always_bracketed(False): classname = objecttools.classname(self) blanks = ' ' * (len(prefix+classname) + 1) nodestr = objecttools.assignrepr_tuple( self.nodes.names, blanks+'nodes=', 70) elementstr = objecttools.assignrepr_tuple( self.elements.names, blanks + 'elements=', 70) return (f'{prefix}{classname}("{self.name}",\n' f'{nodestr},\n' f'{elementstr})')
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/selectiontools.py#L957-L970
train
hydpy-dev/hydpy
hydpy/models/arma/arma_model.py
calc_qpin_v1
def calc_qpin_v1(self): """Calculate the input discharge portions of the different response functions. Required derived parameters: |Nmb| |MaxQ| |DiffQ| Required flux sequence: |QIn| Calculated flux sequences: |QPIn| Examples: Initialize an arma model with three different response functions: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb = 3 >>> derived.maxq.shape = 3 >>> derived.diffq.shape = 2 >>> fluxes.qpin.shape = 3 Define the maximum discharge value of the respective response functions and their successive differences: >>> derived.maxq(0.0, 2.0, 6.0) >>> derived.diffq(2., 4.) The first six examples are performed for inflow values ranging from 0 to 12 m³/s: >>> from hydpy import UnitTest >>> test = UnitTest( ... model, model.calc_qpin_v1, ... last_example=6, ... parseqs=(fluxes.qin, fluxes.qpin)) >>> test.nexts.qin = 0., 1., 2., 4., 6., 12. >>> test() | ex. | qin | qpin | ------------------------------- | 1 | 0.0 | 0.0 0.0 0.0 | | 2 | 1.0 | 1.0 0.0 0.0 | | 3 | 2.0 | 2.0 0.0 0.0 | | 4 | 4.0 | 2.0 2.0 0.0 | | 5 | 6.0 | 2.0 4.0 0.0 | | 6 | 12.0 | 2.0 4.0 6.0 | The following two additional examples are just supposed to demonstrate method |calc_qpin_v1| also functions properly if there is only one response function, wherefore total discharge does not need to be divided: >>> derived.nmb = 1 >>> derived.maxq.shape = 1 >>> derived.diffq.shape = 0 >>> fluxes.qpin.shape = 1 >>> derived.maxq(0.) >>> test = UnitTest( ... model, model.calc_qpin_v1, ... first_example=7, last_example=8, ... parseqs=(fluxes.qin, ... fluxes.qpin)) >>> test.nexts.qin = 0., 12. >>> test() | ex. | qin | qpin | --------------------- | 7 | 0.0 | 0.0 | | 8 | 12.0 | 12.0 | """ der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess for idx in range(der.nmb-1): if flu.qin < der.maxq[idx]: flu.qpin[idx] = 0. elif flu.qin < der.maxq[idx+1]: flu.qpin[idx] = flu.qin-der.maxq[idx] else: flu.qpin[idx] = der.diffq[idx] flu.qpin[der.nmb-1] = max(flu.qin-der.maxq[der.nmb-1], 0.)
python
def calc_qpin_v1(self): """Calculate the input discharge portions of the different response functions. Required derived parameters: |Nmb| |MaxQ| |DiffQ| Required flux sequence: |QIn| Calculated flux sequences: |QPIn| Examples: Initialize an arma model with three different response functions: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb = 3 >>> derived.maxq.shape = 3 >>> derived.diffq.shape = 2 >>> fluxes.qpin.shape = 3 Define the maximum discharge value of the respective response functions and their successive differences: >>> derived.maxq(0.0, 2.0, 6.0) >>> derived.diffq(2., 4.) The first six examples are performed for inflow values ranging from 0 to 12 m³/s: >>> from hydpy import UnitTest >>> test = UnitTest( ... model, model.calc_qpin_v1, ... last_example=6, ... parseqs=(fluxes.qin, fluxes.qpin)) >>> test.nexts.qin = 0., 1., 2., 4., 6., 12. >>> test() | ex. | qin | qpin | ------------------------------- | 1 | 0.0 | 0.0 0.0 0.0 | | 2 | 1.0 | 1.0 0.0 0.0 | | 3 | 2.0 | 2.0 0.0 0.0 | | 4 | 4.0 | 2.0 2.0 0.0 | | 5 | 6.0 | 2.0 4.0 0.0 | | 6 | 12.0 | 2.0 4.0 6.0 | The following two additional examples are just supposed to demonstrate method |calc_qpin_v1| also functions properly if there is only one response function, wherefore total discharge does not need to be divided: >>> derived.nmb = 1 >>> derived.maxq.shape = 1 >>> derived.diffq.shape = 0 >>> fluxes.qpin.shape = 1 >>> derived.maxq(0.) >>> test = UnitTest( ... model, model.calc_qpin_v1, ... first_example=7, last_example=8, ... parseqs=(fluxes.qin, ... fluxes.qpin)) >>> test.nexts.qin = 0., 12. >>> test() | ex. | qin | qpin | --------------------- | 7 | 0.0 | 0.0 | | 8 | 12.0 | 12.0 | """ der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess for idx in range(der.nmb-1): if flu.qin < der.maxq[idx]: flu.qpin[idx] = 0. elif flu.qin < der.maxq[idx+1]: flu.qpin[idx] = flu.qin-der.maxq[idx] else: flu.qpin[idx] = der.diffq[idx] flu.qpin[der.nmb-1] = max(flu.qin-der.maxq[der.nmb-1], 0.)
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Calculate the input discharge portions of the different response functions. Required derived parameters: |Nmb| |MaxQ| |DiffQ| Required flux sequence: |QIn| Calculated flux sequences: |QPIn| Examples: Initialize an arma model with three different response functions: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb = 3 >>> derived.maxq.shape = 3 >>> derived.diffq.shape = 2 >>> fluxes.qpin.shape = 3 Define the maximum discharge value of the respective response functions and their successive differences: >>> derived.maxq(0.0, 2.0, 6.0) >>> derived.diffq(2., 4.) The first six examples are performed for inflow values ranging from 0 to 12 m³/s: >>> from hydpy import UnitTest >>> test = UnitTest( ... model, model.calc_qpin_v1, ... last_example=6, ... parseqs=(fluxes.qin, fluxes.qpin)) >>> test.nexts.qin = 0., 1., 2., 4., 6., 12. >>> test() | ex. | qin | qpin | ------------------------------- | 1 | 0.0 | 0.0 0.0 0.0 | | 2 | 1.0 | 1.0 0.0 0.0 | | 3 | 2.0 | 2.0 0.0 0.0 | | 4 | 4.0 | 2.0 2.0 0.0 | | 5 | 6.0 | 2.0 4.0 0.0 | | 6 | 12.0 | 2.0 4.0 6.0 | The following two additional examples are just supposed to demonstrate method |calc_qpin_v1| also functions properly if there is only one response function, wherefore total discharge does not need to be divided: >>> derived.nmb = 1 >>> derived.maxq.shape = 1 >>> derived.diffq.shape = 0 >>> fluxes.qpin.shape = 1 >>> derived.maxq(0.) >>> test = UnitTest( ... model, model.calc_qpin_v1, ... first_example=7, last_example=8, ... parseqs=(fluxes.qin, ... fluxes.qpin)) >>> test.nexts.qin = 0., 12. >>> test() | ex. | qin | qpin | --------------------- | 7 | 0.0 | 0.0 | | 8 | 12.0 | 12.0 |
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/arma/arma_model.py#L10-L95
train
hydpy-dev/hydpy
hydpy/models/arma/arma_model.py
calc_login_v1
def calc_login_v1(self): """Refresh the input log sequence for the different MA processes. Required derived parameters: |Nmb| |MA_Order| Required flux sequence: |QPIn| Updated log sequence: |LogIn| Example: Assume there are three response functions, involving one, two and three MA coefficients respectively: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb(3) >>> derived.ma_order.shape = 3 >>> derived.ma_order = 1, 2, 3 >>> fluxes.qpin.shape = 3 >>> logs.login.shape = (3, 3) The "memory values" of the different MA processes are defined as follows (one row for each process): >>> logs.login = ((1.0, nan, nan), ... (2.0, 3.0, nan), ... (4.0, 5.0, 6.0)) These are the new inflow discharge portions to be included into the memories of the different processes: >>> fluxes.qpin = 7.0, 8.0, 9.0 Through applying method |calc_login_v1| all values already existing are shifted to the right ("into the past"). Values, which are no longer required due to the limited order or the different MA processes, are discarded. The new values are inserted in the first column: >>> model.calc_login_v1() >>> logs.login login([[7.0, nan, nan], [8.0, 2.0, nan], [9.0, 4.0, 5.0]]) """ der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess log = self.sequences.logs.fastaccess for idx in range(der.nmb): for jdx in range(der.ma_order[idx]-2, -1, -1): log.login[idx, jdx+1] = log.login[idx, jdx] for idx in range(der.nmb): log.login[idx, 0] = flu.qpin[idx]
python
def calc_login_v1(self): """Refresh the input log sequence for the different MA processes. Required derived parameters: |Nmb| |MA_Order| Required flux sequence: |QPIn| Updated log sequence: |LogIn| Example: Assume there are three response functions, involving one, two and three MA coefficients respectively: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb(3) >>> derived.ma_order.shape = 3 >>> derived.ma_order = 1, 2, 3 >>> fluxes.qpin.shape = 3 >>> logs.login.shape = (3, 3) The "memory values" of the different MA processes are defined as follows (one row for each process): >>> logs.login = ((1.0, nan, nan), ... (2.0, 3.0, nan), ... (4.0, 5.0, 6.0)) These are the new inflow discharge portions to be included into the memories of the different processes: >>> fluxes.qpin = 7.0, 8.0, 9.0 Through applying method |calc_login_v1| all values already existing are shifted to the right ("into the past"). Values, which are no longer required due to the limited order or the different MA processes, are discarded. The new values are inserted in the first column: >>> model.calc_login_v1() >>> logs.login login([[7.0, nan, nan], [8.0, 2.0, nan], [9.0, 4.0, 5.0]]) """ der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess log = self.sequences.logs.fastaccess for idx in range(der.nmb): for jdx in range(der.ma_order[idx]-2, -1, -1): log.login[idx, jdx+1] = log.login[idx, jdx] for idx in range(der.nmb): log.login[idx, 0] = flu.qpin[idx]
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Refresh the input log sequence for the different MA processes. Required derived parameters: |Nmb| |MA_Order| Required flux sequence: |QPIn| Updated log sequence: |LogIn| Example: Assume there are three response functions, involving one, two and three MA coefficients respectively: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb(3) >>> derived.ma_order.shape = 3 >>> derived.ma_order = 1, 2, 3 >>> fluxes.qpin.shape = 3 >>> logs.login.shape = (3, 3) The "memory values" of the different MA processes are defined as follows (one row for each process): >>> logs.login = ((1.0, nan, nan), ... (2.0, 3.0, nan), ... (4.0, 5.0, 6.0)) These are the new inflow discharge portions to be included into the memories of the different processes: >>> fluxes.qpin = 7.0, 8.0, 9.0 Through applying method |calc_login_v1| all values already existing are shifted to the right ("into the past"). Values, which are no longer required due to the limited order or the different MA processes, are discarded. The new values are inserted in the first column: >>> model.calc_login_v1() >>> logs.login login([[7.0, nan, nan], [8.0, 2.0, nan], [9.0, 4.0, 5.0]])
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/arma/arma_model.py#L98-L155
train
hydpy-dev/hydpy
hydpy/models/arma/arma_model.py
calc_qma_v1
def calc_qma_v1(self): """Calculate the discharge responses of the different MA processes. Required derived parameters: |Nmb| |MA_Order| |MA_Coefs| Required log sequence: |LogIn| Calculated flux sequence: |QMA| Examples: Assume there are three response functions, involving one, two and three MA coefficients respectively: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb(3) >>> derived.ma_order.shape = 3 >>> derived.ma_order = 1, 2, 3 >>> derived.ma_coefs.shape = (3, 3) >>> logs.login.shape = (3, 3) >>> fluxes.qma.shape = 3 The coefficients of the different MA processes are stored in separate rows of the 2-dimensional parameter `ma_coefs`: >>> derived.ma_coefs = ((1.0, nan, nan), ... (0.8, 0.2, nan), ... (0.5, 0.3, 0.2)) The "memory values" of the different MA processes are defined as follows (one row for each process). The current values are stored in first column, the values of the last time step in the second column, and so on: >>> logs.login = ((1.0, nan, nan), ... (2.0, 3.0, nan), ... (4.0, 5.0, 6.0)) Applying method |calc_qma_v1| is equivalent to calculating the inner product of the different rows of both matrices: >>> model.calc_qma_v1() >>> fluxes.qma qma(1.0, 2.2, 4.7) """ der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess log = self.sequences.logs.fastaccess for idx in range(der.nmb): flu.qma[idx] = 0. for jdx in range(der.ma_order[idx]): flu.qma[idx] += der.ma_coefs[idx, jdx] * log.login[idx, jdx]
python
def calc_qma_v1(self): """Calculate the discharge responses of the different MA processes. Required derived parameters: |Nmb| |MA_Order| |MA_Coefs| Required log sequence: |LogIn| Calculated flux sequence: |QMA| Examples: Assume there are three response functions, involving one, two and three MA coefficients respectively: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb(3) >>> derived.ma_order.shape = 3 >>> derived.ma_order = 1, 2, 3 >>> derived.ma_coefs.shape = (3, 3) >>> logs.login.shape = (3, 3) >>> fluxes.qma.shape = 3 The coefficients of the different MA processes are stored in separate rows of the 2-dimensional parameter `ma_coefs`: >>> derived.ma_coefs = ((1.0, nan, nan), ... (0.8, 0.2, nan), ... (0.5, 0.3, 0.2)) The "memory values" of the different MA processes are defined as follows (one row for each process). The current values are stored in first column, the values of the last time step in the second column, and so on: >>> logs.login = ((1.0, nan, nan), ... (2.0, 3.0, nan), ... (4.0, 5.0, 6.0)) Applying method |calc_qma_v1| is equivalent to calculating the inner product of the different rows of both matrices: >>> model.calc_qma_v1() >>> fluxes.qma qma(1.0, 2.2, 4.7) """ der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess log = self.sequences.logs.fastaccess for idx in range(der.nmb): flu.qma[idx] = 0. for jdx in range(der.ma_order[idx]): flu.qma[idx] += der.ma_coefs[idx, jdx] * log.login[idx, jdx]
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Calculate the discharge responses of the different MA processes. Required derived parameters: |Nmb| |MA_Order| |MA_Coefs| Required log sequence: |LogIn| Calculated flux sequence: |QMA| Examples: Assume there are three response functions, involving one, two and three MA coefficients respectively: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb(3) >>> derived.ma_order.shape = 3 >>> derived.ma_order = 1, 2, 3 >>> derived.ma_coefs.shape = (3, 3) >>> logs.login.shape = (3, 3) >>> fluxes.qma.shape = 3 The coefficients of the different MA processes are stored in separate rows of the 2-dimensional parameter `ma_coefs`: >>> derived.ma_coefs = ((1.0, nan, nan), ... (0.8, 0.2, nan), ... (0.5, 0.3, 0.2)) The "memory values" of the different MA processes are defined as follows (one row for each process). The current values are stored in first column, the values of the last time step in the second column, and so on: >>> logs.login = ((1.0, nan, nan), ... (2.0, 3.0, nan), ... (4.0, 5.0, 6.0)) Applying method |calc_qma_v1| is equivalent to calculating the inner product of the different rows of both matrices: >>> model.calc_qma_v1() >>> fluxes.qma qma(1.0, 2.2, 4.7)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/arma/arma_model.py#L158-L216
train
hydpy-dev/hydpy
hydpy/models/arma/arma_model.py
calc_qar_v1
def calc_qar_v1(self): """Calculate the discharge responses of the different AR processes. Required derived parameters: |Nmb| |AR_Order| |AR_Coefs| Required log sequence: |LogOut| Calculated flux sequence: |QAR| Examples: Assume there are four response functions, involving zero, one, two, and three AR coefficients respectively: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb(4) >>> derived.ar_order.shape = 4 >>> derived.ar_order = 0, 1, 2, 3 >>> derived.ar_coefs.shape = (4, 3) >>> logs.logout.shape = (4, 3) >>> fluxes.qar.shape = 4 The coefficients of the different AR processes are stored in separate rows of the 2-dimensional parameter `ma_coefs`. Note the special case of the first AR process of zero order (first row), which involves no autoregressive memory at all: >>> derived.ar_coefs = ((nan, nan, nan), ... (1.0, nan, nan), ... (0.8, 0.2, nan), ... (0.5, 0.3, 0.2)) The "memory values" of the different AR processes are defined as follows (one row for each process). The values of the last time step are stored in first column, the values of the last time step in the second column, and so on: >>> logs.logout = ((nan, nan, nan), ... (1.0, nan, nan), ... (2.0, 3.0, nan), ... (4.0, 5.0, 6.0)) Applying method |calc_qar_v1| is equivalent to calculating the inner product of the different rows of both matrices: >>> model.calc_qar_v1() >>> fluxes.qar qar(0.0, 1.0, 2.2, 4.7) """ der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess log = self.sequences.logs.fastaccess for idx in range(der.nmb): flu.qar[idx] = 0. for jdx in range(der.ar_order[idx]): flu.qar[idx] += der.ar_coefs[idx, jdx] * log.logout[idx, jdx]
python
def calc_qar_v1(self): """Calculate the discharge responses of the different AR processes. Required derived parameters: |Nmb| |AR_Order| |AR_Coefs| Required log sequence: |LogOut| Calculated flux sequence: |QAR| Examples: Assume there are four response functions, involving zero, one, two, and three AR coefficients respectively: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb(4) >>> derived.ar_order.shape = 4 >>> derived.ar_order = 0, 1, 2, 3 >>> derived.ar_coefs.shape = (4, 3) >>> logs.logout.shape = (4, 3) >>> fluxes.qar.shape = 4 The coefficients of the different AR processes are stored in separate rows of the 2-dimensional parameter `ma_coefs`. Note the special case of the first AR process of zero order (first row), which involves no autoregressive memory at all: >>> derived.ar_coefs = ((nan, nan, nan), ... (1.0, nan, nan), ... (0.8, 0.2, nan), ... (0.5, 0.3, 0.2)) The "memory values" of the different AR processes are defined as follows (one row for each process). The values of the last time step are stored in first column, the values of the last time step in the second column, and so on: >>> logs.logout = ((nan, nan, nan), ... (1.0, nan, nan), ... (2.0, 3.0, nan), ... (4.0, 5.0, 6.0)) Applying method |calc_qar_v1| is equivalent to calculating the inner product of the different rows of both matrices: >>> model.calc_qar_v1() >>> fluxes.qar qar(0.0, 1.0, 2.2, 4.7) """ der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess log = self.sequences.logs.fastaccess for idx in range(der.nmb): flu.qar[idx] = 0. for jdx in range(der.ar_order[idx]): flu.qar[idx] += der.ar_coefs[idx, jdx] * log.logout[idx, jdx]
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Calculate the discharge responses of the different AR processes. Required derived parameters: |Nmb| |AR_Order| |AR_Coefs| Required log sequence: |LogOut| Calculated flux sequence: |QAR| Examples: Assume there are four response functions, involving zero, one, two, and three AR coefficients respectively: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb(4) >>> derived.ar_order.shape = 4 >>> derived.ar_order = 0, 1, 2, 3 >>> derived.ar_coefs.shape = (4, 3) >>> logs.logout.shape = (4, 3) >>> fluxes.qar.shape = 4 The coefficients of the different AR processes are stored in separate rows of the 2-dimensional parameter `ma_coefs`. Note the special case of the first AR process of zero order (first row), which involves no autoregressive memory at all: >>> derived.ar_coefs = ((nan, nan, nan), ... (1.0, nan, nan), ... (0.8, 0.2, nan), ... (0.5, 0.3, 0.2)) The "memory values" of the different AR processes are defined as follows (one row for each process). The values of the last time step are stored in first column, the values of the last time step in the second column, and so on: >>> logs.logout = ((nan, nan, nan), ... (1.0, nan, nan), ... (2.0, 3.0, nan), ... (4.0, 5.0, 6.0)) Applying method |calc_qar_v1| is equivalent to calculating the inner product of the different rows of both matrices: >>> model.calc_qar_v1() >>> fluxes.qar qar(0.0, 1.0, 2.2, 4.7)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/arma/arma_model.py#L219-L281
train
hydpy-dev/hydpy
hydpy/models/arma/arma_model.py
calc_qpout_v1
def calc_qpout_v1(self): """Calculate the ARMA results for the different response functions. Required derived parameter: |Nmb| Required flux sequences: |QMA| |QAR| Calculated flux sequence: |QPOut| Examples: Initialize an arma model with three different response functions: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb(3) >>> fluxes.qma.shape = 3 >>> fluxes.qar.shape = 3 >>> fluxes.qpout.shape = 3 Define the output values of the MA and of the AR processes associated with the three response functions and apply method |calc_qpout_v1|: >>> fluxes.qar = 4.0, 5.0, 6.0 >>> fluxes.qma = 1.0, 2.0, 3.0 >>> model.calc_qpout_v1() >>> fluxes.qpout qpout(5.0, 7.0, 9.0) """ der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess for idx in range(der.nmb): flu.qpout[idx] = flu.qma[idx]+flu.qar[idx]
python
def calc_qpout_v1(self): """Calculate the ARMA results for the different response functions. Required derived parameter: |Nmb| Required flux sequences: |QMA| |QAR| Calculated flux sequence: |QPOut| Examples: Initialize an arma model with three different response functions: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb(3) >>> fluxes.qma.shape = 3 >>> fluxes.qar.shape = 3 >>> fluxes.qpout.shape = 3 Define the output values of the MA and of the AR processes associated with the three response functions and apply method |calc_qpout_v1|: >>> fluxes.qar = 4.0, 5.0, 6.0 >>> fluxes.qma = 1.0, 2.0, 3.0 >>> model.calc_qpout_v1() >>> fluxes.qpout qpout(5.0, 7.0, 9.0) """ der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess for idx in range(der.nmb): flu.qpout[idx] = flu.qma[idx]+flu.qar[idx]
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Calculate the ARMA results for the different response functions. Required derived parameter: |Nmb| Required flux sequences: |QMA| |QAR| Calculated flux sequence: |QPOut| Examples: Initialize an arma model with three different response functions: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb(3) >>> fluxes.qma.shape = 3 >>> fluxes.qar.shape = 3 >>> fluxes.qpout.shape = 3 Define the output values of the MA and of the AR processes associated with the three response functions and apply method |calc_qpout_v1|: >>> fluxes.qar = 4.0, 5.0, 6.0 >>> fluxes.qma = 1.0, 2.0, 3.0 >>> model.calc_qpout_v1() >>> fluxes.qpout qpout(5.0, 7.0, 9.0)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/arma/arma_model.py#L284-L321
train
hydpy-dev/hydpy
hydpy/models/arma/arma_model.py
calc_logout_v1
def calc_logout_v1(self): """Refresh the log sequence for the different AR processes. Required derived parameters: |Nmb| |AR_Order| Required flux sequence: |QPOut| Updated log sequence: |LogOut| Example: Assume there are four response functions, involving zero, one, two and three AR coefficients respectively: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb(4) >>> derived.ar_order.shape = 4 >>> derived.ar_order = 0, 1, 2, 3 >>> fluxes.qpout.shape = 4 >>> logs.logout.shape = (4, 3) The "memory values" of the different AR processes are defined as follows (one row for each process). Note the special case of the first AR process of zero order (first row), which is why there are no autoregressive memory values required: >>> logs.logout = ((nan, nan, nan), ... (0.0, nan, nan), ... (1.0, 2.0, nan), ... (3.0, 4.0, 5.0)) These are the new outflow discharge portions to be included into the memories of the different processes: >>> fluxes.qpout = 6.0, 7.0, 8.0, 9.0 Through applying method |calc_logout_v1| all values already existing are shifted to the right ("into the past"). Values, which are no longer required due to the limited order or the different AR processes, are discarded. The new values are inserted in the first column: >>> model.calc_logout_v1() >>> logs.logout logout([[nan, nan, nan], [7.0, nan, nan], [8.0, 1.0, nan], [9.0, 3.0, 4.0]]) """ der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess log = self.sequences.logs.fastaccess for idx in range(der.nmb): for jdx in range(der.ar_order[idx]-2, -1, -1): log.logout[idx, jdx+1] = log.logout[idx, jdx] for idx in range(der.nmb): if der.ar_order[idx] > 0: log.logout[idx, 0] = flu.qpout[idx]
python
def calc_logout_v1(self): """Refresh the log sequence for the different AR processes. Required derived parameters: |Nmb| |AR_Order| Required flux sequence: |QPOut| Updated log sequence: |LogOut| Example: Assume there are four response functions, involving zero, one, two and three AR coefficients respectively: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb(4) >>> derived.ar_order.shape = 4 >>> derived.ar_order = 0, 1, 2, 3 >>> fluxes.qpout.shape = 4 >>> logs.logout.shape = (4, 3) The "memory values" of the different AR processes are defined as follows (one row for each process). Note the special case of the first AR process of zero order (first row), which is why there are no autoregressive memory values required: >>> logs.logout = ((nan, nan, nan), ... (0.0, nan, nan), ... (1.0, 2.0, nan), ... (3.0, 4.0, 5.0)) These are the new outflow discharge portions to be included into the memories of the different processes: >>> fluxes.qpout = 6.0, 7.0, 8.0, 9.0 Through applying method |calc_logout_v1| all values already existing are shifted to the right ("into the past"). Values, which are no longer required due to the limited order or the different AR processes, are discarded. The new values are inserted in the first column: >>> model.calc_logout_v1() >>> logs.logout logout([[nan, nan, nan], [7.0, nan, nan], [8.0, 1.0, nan], [9.0, 3.0, 4.0]]) """ der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess log = self.sequences.logs.fastaccess for idx in range(der.nmb): for jdx in range(der.ar_order[idx]-2, -1, -1): log.logout[idx, jdx+1] = log.logout[idx, jdx] for idx in range(der.nmb): if der.ar_order[idx] > 0: log.logout[idx, 0] = flu.qpout[idx]
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Refresh the log sequence for the different AR processes. Required derived parameters: |Nmb| |AR_Order| Required flux sequence: |QPOut| Updated log sequence: |LogOut| Example: Assume there are four response functions, involving zero, one, two and three AR coefficients respectively: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb(4) >>> derived.ar_order.shape = 4 >>> derived.ar_order = 0, 1, 2, 3 >>> fluxes.qpout.shape = 4 >>> logs.logout.shape = (4, 3) The "memory values" of the different AR processes are defined as follows (one row for each process). Note the special case of the first AR process of zero order (first row), which is why there are no autoregressive memory values required: >>> logs.logout = ((nan, nan, nan), ... (0.0, nan, nan), ... (1.0, 2.0, nan), ... (3.0, 4.0, 5.0)) These are the new outflow discharge portions to be included into the memories of the different processes: >>> fluxes.qpout = 6.0, 7.0, 8.0, 9.0 Through applying method |calc_logout_v1| all values already existing are shifted to the right ("into the past"). Values, which are no longer required due to the limited order or the different AR processes, are discarded. The new values are inserted in the first column: >>> model.calc_logout_v1() >>> logs.logout logout([[nan, nan, nan], [7.0, nan, nan], [8.0, 1.0, nan], [9.0, 3.0, 4.0]])
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/arma/arma_model.py#L324-L387
train
hydpy-dev/hydpy
hydpy/models/arma/arma_model.py
calc_qout_v1
def calc_qout_v1(self): """Sum up the results of the different response functions. Required derived parameter: |Nmb| Required flux sequences: |QPOut| Calculated flux sequence: |QOut| Examples: Initialize an arma model with three different response functions: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb(3) >>> fluxes.qpout.shape = 3 Define the output values of the three response functions and apply method |calc_qout_v1|: >>> fluxes.qpout = 1.0, 2.0, 3.0 >>> model.calc_qout_v1() >>> fluxes.qout qout(6.0) """ der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess flu.qout = 0. for idx in range(der.nmb): flu.qout += flu.qpout[idx]
python
def calc_qout_v1(self): """Sum up the results of the different response functions. Required derived parameter: |Nmb| Required flux sequences: |QPOut| Calculated flux sequence: |QOut| Examples: Initialize an arma model with three different response functions: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb(3) >>> fluxes.qpout.shape = 3 Define the output values of the three response functions and apply method |calc_qout_v1|: >>> fluxes.qpout = 1.0, 2.0, 3.0 >>> model.calc_qout_v1() >>> fluxes.qout qout(6.0) """ der = self.parameters.derived.fastaccess flu = self.sequences.fluxes.fastaccess flu.qout = 0. for idx in range(der.nmb): flu.qout += flu.qpout[idx]
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Sum up the results of the different response functions. Required derived parameter: |Nmb| Required flux sequences: |QPOut| Calculated flux sequence: |QOut| Examples: Initialize an arma model with three different response functions: >>> from hydpy.models.arma import * >>> parameterstep() >>> derived.nmb(3) >>> fluxes.qpout.shape = 3 Define the output values of the three response functions and apply method |calc_qout_v1|: >>> fluxes.qpout = 1.0, 2.0, 3.0 >>> model.calc_qout_v1() >>> fluxes.qout qout(6.0)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/arma/arma_model.py#L390-L423
train
hydpy-dev/hydpy
hydpy/models/arma/arma_model.py
pick_q_v1
def pick_q_v1(self): """Update inflow.""" flu = self.sequences.fluxes.fastaccess inl = self.sequences.inlets.fastaccess flu.qin = 0. for idx in range(inl.len_q): flu.qin += inl.q[idx][0]
python
def pick_q_v1(self): """Update inflow.""" flu = self.sequences.fluxes.fastaccess inl = self.sequences.inlets.fastaccess flu.qin = 0. for idx in range(inl.len_q): flu.qin += inl.q[idx][0]
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Update inflow.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/arma/arma_model.py#L426-L432
train
hydpy-dev/hydpy
hydpy/models/hbranch/hbranch_derived.py
NmbBranches.update
def update(self): """Determine the number of branches""" con = self.subpars.pars.control self(con.ypoints.shape[0])
python
def update(self): """Determine the number of branches""" con = self.subpars.pars.control self(con.ypoints.shape[0])
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Determine the number of branches
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/hbranch/hbranch_derived.py#L14-L17
train
hydpy-dev/hydpy
hydpy/models/lstream/lstream_derived.py
HV.update
def update(self): """Update value based on :math:`HV=BBV/BNV`. Required Parameters: |BBV| |BNV| Examples: >>> from hydpy.models.lstream import * >>> parameterstep('1d') >>> bbv(left=10., right=40.) >>> bnv(left=10., right=20.) >>> derived.hv.update() >>> derived.hv hv(left=1.0, right=2.0) >>> bbv(left=10., right=0.) >>> bnv(left=0., right=20.) >>> derived.hv.update() >>> derived.hv hv(0.0) """ con = self.subpars.pars.control self(0.) for idx in range(2): if (con.bbv[idx] > 0.) and (con.bnv[idx] > 0.): self.values[idx] = con.bbv[idx]/con.bnv[idx]
python
def update(self): """Update value based on :math:`HV=BBV/BNV`. Required Parameters: |BBV| |BNV| Examples: >>> from hydpy.models.lstream import * >>> parameterstep('1d') >>> bbv(left=10., right=40.) >>> bnv(left=10., right=20.) >>> derived.hv.update() >>> derived.hv hv(left=1.0, right=2.0) >>> bbv(left=10., right=0.) >>> bnv(left=0., right=20.) >>> derived.hv.update() >>> derived.hv hv(0.0) """ con = self.subpars.pars.control self(0.) for idx in range(2): if (con.bbv[idx] > 0.) and (con.bnv[idx] > 0.): self.values[idx] = con.bbv[idx]/con.bnv[idx]
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Update value based on :math:`HV=BBV/BNV`. Required Parameters: |BBV| |BNV| Examples: >>> from hydpy.models.lstream import * >>> parameterstep('1d') >>> bbv(left=10., right=40.) >>> bnv(left=10., right=20.) >>> derived.hv.update() >>> derived.hv hv(left=1.0, right=2.0) >>> bbv(left=10., right=0.) >>> bnv(left=0., right=20.) >>> derived.hv.update() >>> derived.hv hv(0.0)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/lstream/lstream_derived.py#L14-L39
train
hydpy-dev/hydpy
hydpy/models/lstream/lstream_derived.py
QM.update
def update(self): """Update value based on the actual |calc_qg_v1| method. Required derived parameter: |H| Note that the value of parameter |lstream_derived.QM| is directly related to the value of parameter |HM| and indirectly related to all parameters values relevant for method |calc_qg_v1|. Hence the complete paramter (and sequence) requirements might differ for various application models. For examples, see the documentation on method ToDo. """ mod = self.subpars.pars.model con = mod.parameters.control flu = mod.sequences.fluxes flu.h = con.hm mod.calc_qg() self(flu.qg)
python
def update(self): """Update value based on the actual |calc_qg_v1| method. Required derived parameter: |H| Note that the value of parameter |lstream_derived.QM| is directly related to the value of parameter |HM| and indirectly related to all parameters values relevant for method |calc_qg_v1|. Hence the complete paramter (and sequence) requirements might differ for various application models. For examples, see the documentation on method ToDo. """ mod = self.subpars.pars.model con = mod.parameters.control flu = mod.sequences.fluxes flu.h = con.hm mod.calc_qg() self(flu.qg)
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Update value based on the actual |calc_qg_v1| method. Required derived parameter: |H| Note that the value of parameter |lstream_derived.QM| is directly related to the value of parameter |HM| and indirectly related to all parameters values relevant for method |calc_qg_v1|. Hence the complete paramter (and sequence) requirements might differ for various application models. For examples, see the documentation on method ToDo.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/lstream/lstream_derived.py#L47-L66
train
hydpy-dev/hydpy
hydpy/models/hstream/hstream_derived.py
NmbSegments.update
def update(self): """Determines in how many segments the whole reach needs to be divided to approximate the desired lag time via integer rounding. Adjusts the shape of sequence |QJoints| additionally. Required control parameters: |Lag| Calculated derived parameters: |NmbSegments| Prepared state sequence: |QJoints| Examples: Define a lag time of 1.4 days and a simulation step size of 12 hours: >>> from hydpy.models.hstream import * >>> parameterstep('1d') >>> simulationstep('12h') >>> lag(1.4) Then the actual lag value for the simulation step size is 2.8 >>> lag lag(1.4) >>> lag.value 2.8 Through rounding the number of segments is determined: >>> derived.nmbsegments.update() >>> derived.nmbsegments nmbsegments(3) The number of joints is always the number of segments plus one: >>> states.qjoints.shape (4,) """ pars = self.subpars.pars self(int(round(pars.control.lag))) pars.model.sequences.states.qjoints.shape = self+1
python
def update(self): """Determines in how many segments the whole reach needs to be divided to approximate the desired lag time via integer rounding. Adjusts the shape of sequence |QJoints| additionally. Required control parameters: |Lag| Calculated derived parameters: |NmbSegments| Prepared state sequence: |QJoints| Examples: Define a lag time of 1.4 days and a simulation step size of 12 hours: >>> from hydpy.models.hstream import * >>> parameterstep('1d') >>> simulationstep('12h') >>> lag(1.4) Then the actual lag value for the simulation step size is 2.8 >>> lag lag(1.4) >>> lag.value 2.8 Through rounding the number of segments is determined: >>> derived.nmbsegments.update() >>> derived.nmbsegments nmbsegments(3) The number of joints is always the number of segments plus one: >>> states.qjoints.shape (4,) """ pars = self.subpars.pars self(int(round(pars.control.lag))) pars.model.sequences.states.qjoints.shape = self+1
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Determines in how many segments the whole reach needs to be divided to approximate the desired lag time via integer rounding. Adjusts the shape of sequence |QJoints| additionally. Required control parameters: |Lag| Calculated derived parameters: |NmbSegments| Prepared state sequence: |QJoints| Examples: Define a lag time of 1.4 days and a simulation step size of 12 hours: >>> from hydpy.models.hstream import * >>> parameterstep('1d') >>> simulationstep('12h') >>> lag(1.4) Then the actual lag value for the simulation step size is 2.8 >>> lag lag(1.4) >>> lag.value 2.8 Through rounding the number of segments is determined: >>> derived.nmbsegments.update() >>> derived.nmbsegments nmbsegments(3) The number of joints is always the number of segments plus one: >>> states.qjoints.shape (4,)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/hstream/hstream_derived.py#L16-L60
train
hydpy-dev/hydpy
hydpy/models/hstream/hstream_derived.py
C1.update
def update(self): """Update |C1| based on :math:`c_1 = \\frac{Damp}{1+Damp}`. Examples: The first examples show the calculated value of |C1| for the lowest possible value of |Lag|, the lowest possible value, and an intermediate value: >>> from hydpy.models.hstream import * >>> parameterstep('1d') >>> damp(0.0) >>> derived.c1.update() >>> derived.c1 c1(0.0) >>> damp(1.0) >>> derived.c1.update() >>> derived.c1 c1(0.5) >>> damp(0.25) >>> derived.c1.update() >>> derived.c1 c1(0.2) For to low and to high values of |Lag|, clipping is performed: >>> damp.value = -0.1 >>> derived.c1.update() >>> derived.c1 c1(0.0) >>> damp.value = 1.1 >>> derived.c1.update() >>> derived.c1 c1(0.5) """ damp = self.subpars.pars.control.damp self(numpy.clip(damp/(1.+damp), 0., .5))
python
def update(self): """Update |C1| based on :math:`c_1 = \\frac{Damp}{1+Damp}`. Examples: The first examples show the calculated value of |C1| for the lowest possible value of |Lag|, the lowest possible value, and an intermediate value: >>> from hydpy.models.hstream import * >>> parameterstep('1d') >>> damp(0.0) >>> derived.c1.update() >>> derived.c1 c1(0.0) >>> damp(1.0) >>> derived.c1.update() >>> derived.c1 c1(0.5) >>> damp(0.25) >>> derived.c1.update() >>> derived.c1 c1(0.2) For to low and to high values of |Lag|, clipping is performed: >>> damp.value = -0.1 >>> derived.c1.update() >>> derived.c1 c1(0.0) >>> damp.value = 1.1 >>> derived.c1.update() >>> derived.c1 c1(0.5) """ damp = self.subpars.pars.control.damp self(numpy.clip(damp/(1.+damp), 0., .5))
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Update |C1| based on :math:`c_1 = \\frac{Damp}{1+Damp}`. Examples: The first examples show the calculated value of |C1| for the lowest possible value of |Lag|, the lowest possible value, and an intermediate value: >>> from hydpy.models.hstream import * >>> parameterstep('1d') >>> damp(0.0) >>> derived.c1.update() >>> derived.c1 c1(0.0) >>> damp(1.0) >>> derived.c1.update() >>> derived.c1 c1(0.5) >>> damp(0.25) >>> derived.c1.update() >>> derived.c1 c1(0.2) For to low and to high values of |Lag|, clipping is performed: >>> damp.value = -0.1 >>> derived.c1.update() >>> derived.c1 c1(0.0) >>> damp.value = 1.1 >>> derived.c1.update() >>> derived.c1 c1(0.5)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/hstream/hstream_derived.py#L67-L102
train
hydpy-dev/hydpy
hydpy/models/hstream/hstream_derived.py
C2.update
def update(self): """Update |C2| based on :math:`c_2 = 1.-c_1-c_3`. Examples: The following examples show the calculated value of |C2| are clipped when to low or to high: >>> from hydpy.models.hstream import * >>> parameterstep('1d') >>> derived.c1 = 0.6 >>> derived.c3 = 0.1 >>> derived.c2.update() >>> derived.c2 c2(0.3) >>> derived.c1 = 1.6 >>> derived.c2.update() >>> derived.c2 c2(0.0) >>> derived.c1 = -1.6 >>> derived.c2.update() >>> derived.c2 c2(1.0) """ der = self.subpars self(numpy.clip(1. - der.c1 - der.c3, 0., 1.))
python
def update(self): """Update |C2| based on :math:`c_2 = 1.-c_1-c_3`. Examples: The following examples show the calculated value of |C2| are clipped when to low or to high: >>> from hydpy.models.hstream import * >>> parameterstep('1d') >>> derived.c1 = 0.6 >>> derived.c3 = 0.1 >>> derived.c2.update() >>> derived.c2 c2(0.3) >>> derived.c1 = 1.6 >>> derived.c2.update() >>> derived.c2 c2(0.0) >>> derived.c1 = -1.6 >>> derived.c2.update() >>> derived.c2 c2(1.0) """ der = self.subpars self(numpy.clip(1. - der.c1 - der.c3, 0., 1.))
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Update |C2| based on :math:`c_2 = 1.-c_1-c_3`. Examples: The following examples show the calculated value of |C2| are clipped when to low or to high: >>> from hydpy.models.hstream import * >>> parameterstep('1d') >>> derived.c1 = 0.6 >>> derived.c3 = 0.1 >>> derived.c2.update() >>> derived.c2 c2(0.3) >>> derived.c1 = 1.6 >>> derived.c2.update() >>> derived.c2 c2(0.0) >>> derived.c1 = -1.6 >>> derived.c2.update() >>> derived.c2 c2(1.0)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/models/hstream/hstream_derived.py#L109-L134
train
TabViewer/gtabview
gtabview/__init__.py
view
def view(data, enc=None, start_pos=None, delimiter=None, hdr_rows=None, idx_cols=None, sheet_index=0, transpose=False, wait=None, recycle=None, detach=None, metavar=None, title=None): """View the supplied data in an interactive, graphical table widget. data: When a valid path or IO object, read it as a tabular text file. When a valid URI, a Blaze object is constructed and visualized. Any other supported datatype is visualized directly and incrementally *without copying*. enc: File encoding (such as "utf-8", normally autodetected). delimiter: Text file delimiter (normally autodetected). hdr_rows: For files or lists of lists, specify the number of header rows. For files only, a default of one header line is assumed. idx_cols: For files or lists of lists, specify the number of index columns. By default, no index is assumed. sheet_index: For multi-table files (such as xls[x]), specify the sheet index to read, starting from 0. Defaults to the first. start_pos: A tuple of the form (y, x) specifying the initial cursor position. Negative offsets count from the end of the dataset. transpose: Transpose the resulting view. metavar: name of the variable being shown for display purposes (inferred automatically when possible). title: title of the data window. wait: Wait for the user to close the view before returning. By default, try to match the behavior of ``matplotlib.is_interactive()``. If matplotlib is not loaded, wait only if ``detach`` is also False. The default value can also be set through ``gtabview.WAIT``. recycle: Recycle the previous window instead of creating a new one. The default is True, and can also be set through ``gtabview.RECYCLE``. detach: Create a fully detached GUI thread for interactive use (note: this is *not* necessary if matplotlib is loaded). The default is False, and can also be set through ``gtabview.DETACH``. """ global WAIT, RECYCLE, DETACH, VIEW model = read_model(data, enc=enc, delimiter=delimiter, hdr_rows=hdr_rows, idx_cols=idx_cols, sheet_index=sheet_index, transpose=transpose) if model is None: warnings.warn("cannot visualize the supplied data type: {}".format(type(data)), category=RuntimeWarning) return None # setup defaults if wait is None: wait = WAIT if recycle is None: recycle = RECYCLE if detach is None: detach = DETACH if wait is None: if 'matplotlib' not in sys.modules: wait = not bool(detach) else: import matplotlib.pyplot as plt wait = not plt.isinteractive() # try to fetch the variable name in the upper stack if metavar is None: if isinstance(data, basestring): metavar = data else: metavar = _varname_in_stack(data, 1) # create a view controller if VIEW is None: if not detach: VIEW = ViewController() else: VIEW = DetachedViewController() VIEW.setDaemon(True) VIEW.start() if VIEW.is_detached(): atexit.register(VIEW.exit) else: VIEW = None return None # actually show the data view_kwargs = {'hdr_rows': hdr_rows, 'idx_cols': idx_cols, 'start_pos': start_pos, 'metavar': metavar, 'title': title} VIEW.view(model, view_kwargs, wait=wait, recycle=recycle) return VIEW
python
def view(data, enc=None, start_pos=None, delimiter=None, hdr_rows=None, idx_cols=None, sheet_index=0, transpose=False, wait=None, recycle=None, detach=None, metavar=None, title=None): """View the supplied data in an interactive, graphical table widget. data: When a valid path or IO object, read it as a tabular text file. When a valid URI, a Blaze object is constructed and visualized. Any other supported datatype is visualized directly and incrementally *without copying*. enc: File encoding (such as "utf-8", normally autodetected). delimiter: Text file delimiter (normally autodetected). hdr_rows: For files or lists of lists, specify the number of header rows. For files only, a default of one header line is assumed. idx_cols: For files or lists of lists, specify the number of index columns. By default, no index is assumed. sheet_index: For multi-table files (such as xls[x]), specify the sheet index to read, starting from 0. Defaults to the first. start_pos: A tuple of the form (y, x) specifying the initial cursor position. Negative offsets count from the end of the dataset. transpose: Transpose the resulting view. metavar: name of the variable being shown for display purposes (inferred automatically when possible). title: title of the data window. wait: Wait for the user to close the view before returning. By default, try to match the behavior of ``matplotlib.is_interactive()``. If matplotlib is not loaded, wait only if ``detach`` is also False. The default value can also be set through ``gtabview.WAIT``. recycle: Recycle the previous window instead of creating a new one. The default is True, and can also be set through ``gtabview.RECYCLE``. detach: Create a fully detached GUI thread for interactive use (note: this is *not* necessary if matplotlib is loaded). The default is False, and can also be set through ``gtabview.DETACH``. """ global WAIT, RECYCLE, DETACH, VIEW model = read_model(data, enc=enc, delimiter=delimiter, hdr_rows=hdr_rows, idx_cols=idx_cols, sheet_index=sheet_index, transpose=transpose) if model is None: warnings.warn("cannot visualize the supplied data type: {}".format(type(data)), category=RuntimeWarning) return None # setup defaults if wait is None: wait = WAIT if recycle is None: recycle = RECYCLE if detach is None: detach = DETACH if wait is None: if 'matplotlib' not in sys.modules: wait = not bool(detach) else: import matplotlib.pyplot as plt wait = not plt.isinteractive() # try to fetch the variable name in the upper stack if metavar is None: if isinstance(data, basestring): metavar = data else: metavar = _varname_in_stack(data, 1) # create a view controller if VIEW is None: if not detach: VIEW = ViewController() else: VIEW = DetachedViewController() VIEW.setDaemon(True) VIEW.start() if VIEW.is_detached(): atexit.register(VIEW.exit) else: VIEW = None return None # actually show the data view_kwargs = {'hdr_rows': hdr_rows, 'idx_cols': idx_cols, 'start_pos': start_pos, 'metavar': metavar, 'title': title} VIEW.view(model, view_kwargs, wait=wait, recycle=recycle) return VIEW
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View the supplied data in an interactive, graphical table widget. data: When a valid path or IO object, read it as a tabular text file. When a valid URI, a Blaze object is constructed and visualized. Any other supported datatype is visualized directly and incrementally *without copying*. enc: File encoding (such as "utf-8", normally autodetected). delimiter: Text file delimiter (normally autodetected). hdr_rows: For files or lists of lists, specify the number of header rows. For files only, a default of one header line is assumed. idx_cols: For files or lists of lists, specify the number of index columns. By default, no index is assumed. sheet_index: For multi-table files (such as xls[x]), specify the sheet index to read, starting from 0. Defaults to the first. start_pos: A tuple of the form (y, x) specifying the initial cursor position. Negative offsets count from the end of the dataset. transpose: Transpose the resulting view. metavar: name of the variable being shown for display purposes (inferred automatically when possible). title: title of the data window. wait: Wait for the user to close the view before returning. By default, try to match the behavior of ``matplotlib.is_interactive()``. If matplotlib is not loaded, wait only if ``detach`` is also False. The default value can also be set through ``gtabview.WAIT``. recycle: Recycle the previous window instead of creating a new one. The default is True, and can also be set through ``gtabview.RECYCLE``. detach: Create a fully detached GUI thread for interactive use (note: this is *not* necessary if matplotlib is loaded). The default is False, and can also be set through ``gtabview.DETACH``.
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14ba391f0b225a1bf32d52b640a47b580f8b1b28
https://github.com/TabViewer/gtabview/blob/14ba391f0b225a1bf32d52b640a47b580f8b1b28/gtabview/__init__.py#L120-L211
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
gather_registries
def gather_registries() -> Tuple[Dict, Mapping, Mapping]: """Get and clear the current |Node| and |Element| registries. Function |gather_registries| is thought to be used by class |Tester| only. """ id2devices = copy.copy(_id2devices) registry = copy.copy(_registry) selection = copy.copy(_selection) dict_ = globals() dict_['_id2devices'] = {} dict_['_registry'] = {Node: {}, Element: {}} dict_['_selection'] = {Node: {}, Element: {}} return id2devices, registry, selection
python
def gather_registries() -> Tuple[Dict, Mapping, Mapping]: """Get and clear the current |Node| and |Element| registries. Function |gather_registries| is thought to be used by class |Tester| only. """ id2devices = copy.copy(_id2devices) registry = copy.copy(_registry) selection = copy.copy(_selection) dict_ = globals() dict_['_id2devices'] = {} dict_['_registry'] = {Node: {}, Element: {}} dict_['_selection'] = {Node: {}, Element: {}} return id2devices, registry, selection
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L2287-L2299
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
reset_registries
def reset_registries(dicts: Tuple[Dict, Mapping, Mapping]): """Reset the current |Node| and |Element| registries. Function |reset_registries| is thought to be used by class |Tester| only. """ dict_ = globals() dict_['_id2devices'] = dicts[0] dict_['_registry'] = dicts[1] dict_['_selection'] = dicts[2]
python
def reset_registries(dicts: Tuple[Dict, Mapping, Mapping]): """Reset the current |Node| and |Element| registries. Function |reset_registries| is thought to be used by class |Tester| only. """ dict_ = globals() dict_['_id2devices'] = dicts[0] dict_['_registry'] = dicts[1] dict_['_selection'] = dicts[2]
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Reset the current |Node| and |Element| registries. Function |reset_registries| is thought to be used by class |Tester| only.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L2302-L2310
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
_get_pandasindex
def _get_pandasindex(): """ >>> from hydpy import pub >>> pub.timegrids = '2004.01.01', '2005.01.01', '1d' >>> from hydpy.core.devicetools import _get_pandasindex >>> _get_pandasindex() # doctest: +ELLIPSIS DatetimeIndex(['2004-01-01 12:00:00', '2004-01-02 12:00:00', ... '2004-12-30 12:00:00', '2004-12-31 12:00:00'], dtype='datetime64[ns]', length=366, freq=None) """ tg = hydpy.pub.timegrids.init shift = tg.stepsize / 2 index = pandas.date_range( (tg.firstdate + shift).datetime, (tg.lastdate - shift).datetime, (tg.lastdate - tg.firstdate - tg.stepsize) / tg.stepsize + 1) return index
python
def _get_pandasindex(): """ >>> from hydpy import pub >>> pub.timegrids = '2004.01.01', '2005.01.01', '1d' >>> from hydpy.core.devicetools import _get_pandasindex >>> _get_pandasindex() # doctest: +ELLIPSIS DatetimeIndex(['2004-01-01 12:00:00', '2004-01-02 12:00:00', ... '2004-12-30 12:00:00', '2004-12-31 12:00:00'], dtype='datetime64[ns]', length=366, freq=None) """ tg = hydpy.pub.timegrids.init shift = tg.stepsize / 2 index = pandas.date_range( (tg.firstdate + shift).datetime, (tg.lastdate - shift).datetime, (tg.lastdate - tg.firstdate - tg.stepsize) / tg.stepsize + 1) return index
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>>> from hydpy import pub >>> pub.timegrids = '2004.01.01', '2005.01.01', '1d' >>> from hydpy.core.devicetools import _get_pandasindex >>> _get_pandasindex() # doctest: +ELLIPSIS DatetimeIndex(['2004-01-01 12:00:00', '2004-01-02 12:00:00', ... '2004-12-30 12:00:00', '2004-12-31 12:00:00'], dtype='datetime64[ns]', length=366, freq=None)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L2313-L2330
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Keywords.startswith
def startswith(self, name: str) -> List[str]: """Return a list of all keywords starting with the given string. >>> from hydpy.core.devicetools import Keywords >>> keywords = Keywords('first_keyword', 'second_keyword', ... 'keyword_3', 'keyword_4', ... 'keyboard') >>> keywords.startswith('keyword') ['keyword_3', 'keyword_4'] """ return sorted(keyword for keyword in self if keyword.startswith(name))
python
def startswith(self, name: str) -> List[str]: """Return a list of all keywords starting with the given string. >>> from hydpy.core.devicetools import Keywords >>> keywords = Keywords('first_keyword', 'second_keyword', ... 'keyword_3', 'keyword_4', ... 'keyboard') >>> keywords.startswith('keyword') ['keyword_3', 'keyword_4'] """ return sorted(keyword for keyword in self if keyword.startswith(name))
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Return a list of all keywords starting with the given string. >>> from hydpy.core.devicetools import Keywords >>> keywords = Keywords('first_keyword', 'second_keyword', ... 'keyword_3', 'keyword_4', ... 'keyboard') >>> keywords.startswith('keyword') ['keyword_3', 'keyword_4']
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L122-L132
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Keywords.endswith
def endswith(self, name: str) -> List[str]: """Return a list of all keywords ending with the given string. >>> from hydpy.core.devicetools import Keywords >>> keywords = Keywords('first_keyword', 'second_keyword', ... 'keyword_3', 'keyword_4', ... 'keyboard') >>> keywords.endswith('keyword') ['first_keyword', 'second_keyword'] """ return sorted(keyword for keyword in self if keyword.endswith(name))
python
def endswith(self, name: str) -> List[str]: """Return a list of all keywords ending with the given string. >>> from hydpy.core.devicetools import Keywords >>> keywords = Keywords('first_keyword', 'second_keyword', ... 'keyword_3', 'keyword_4', ... 'keyboard') >>> keywords.endswith('keyword') ['first_keyword', 'second_keyword'] """ return sorted(keyword for keyword in self if keyword.endswith(name))
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Return a list of all keywords ending with the given string. >>> from hydpy.core.devicetools import Keywords >>> keywords = Keywords('first_keyword', 'second_keyword', ... 'keyword_3', 'keyword_4', ... 'keyboard') >>> keywords.endswith('keyword') ['first_keyword', 'second_keyword']
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L134-L144
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Keywords.contains
def contains(self, name: str) -> List[str]: """Return a list of all keywords containing the given string. >>> from hydpy.core.devicetools import Keywords >>> keywords = Keywords('first_keyword', 'second_keyword', ... 'keyword_3', 'keyword_4', ... 'keyboard') >>> keywords.contains('keyword') ['first_keyword', 'keyword_3', 'keyword_4', 'second_keyword'] """ return sorted(keyword for keyword in self if name in keyword)
python
def contains(self, name: str) -> List[str]: """Return a list of all keywords containing the given string. >>> from hydpy.core.devicetools import Keywords >>> keywords = Keywords('first_keyword', 'second_keyword', ... 'keyword_3', 'keyword_4', ... 'keyboard') >>> keywords.contains('keyword') ['first_keyword', 'keyword_3', 'keyword_4', 'second_keyword'] """ return sorted(keyword for keyword in self if name in keyword)
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Return a list of all keywords containing the given string. >>> from hydpy.core.devicetools import Keywords >>> keywords = Keywords('first_keyword', 'second_keyword', ... 'keyword_3', 'keyword_4', ... 'keyboard') >>> keywords.contains('keyword') ['first_keyword', 'keyword_3', 'keyword_4', 'second_keyword']
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L146-L156
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Keywords.update
def update(self, *names: Any) -> None: """Before updating, the given names are checked to be valid variable identifiers. >>> from hydpy.core.devicetools import Keywords >>> keywords = Keywords('first_keyword', 'second_keyword', ... 'keyword_3', 'keyword_4', ... 'keyboard') >>> keywords.update('test_1', 'test 2') # doctest: +ELLIPSIS Traceback (most recent call last): ... ValueError: While trying to add the keyword `test 2` to device ?, \ the following error occurred: The given name string `test 2` does not \ define a valid variable identifier. ... Note that even the first string (`test1`) is not added due to the second one (`test 2`) being invalid. >>> keywords Keywords("first_keyword", "keyboard", "keyword_3", "keyword_4", "second_keyword") After correcting the second string, everything works fine: >>> keywords.update('test_1', 'test_2') >>> keywords Keywords("first_keyword", "keyboard", "keyword_3", "keyword_4", "second_keyword", "test_1", "test_2") """ _names = [str(name) for name in names] self._check_keywords(_names) super().update(_names)
python
def update(self, *names: Any) -> None: """Before updating, the given names are checked to be valid variable identifiers. >>> from hydpy.core.devicetools import Keywords >>> keywords = Keywords('first_keyword', 'second_keyword', ... 'keyword_3', 'keyword_4', ... 'keyboard') >>> keywords.update('test_1', 'test 2') # doctest: +ELLIPSIS Traceback (most recent call last): ... ValueError: While trying to add the keyword `test 2` to device ?, \ the following error occurred: The given name string `test 2` does not \ define a valid variable identifier. ... Note that even the first string (`test1`) is not added due to the second one (`test 2`) being invalid. >>> keywords Keywords("first_keyword", "keyboard", "keyword_3", "keyword_4", "second_keyword") After correcting the second string, everything works fine: >>> keywords.update('test_1', 'test_2') >>> keywords Keywords("first_keyword", "keyboard", "keyword_3", "keyword_4", "second_keyword", "test_1", "test_2") """ _names = [str(name) for name in names] self._check_keywords(_names) super().update(_names)
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Before updating, the given names are checked to be valid variable identifiers. >>> from hydpy.core.devicetools import Keywords >>> keywords = Keywords('first_keyword', 'second_keyword', ... 'keyword_3', 'keyword_4', ... 'keyboard') >>> keywords.update('test_1', 'test 2') # doctest: +ELLIPSIS Traceback (most recent call last): ... ValueError: While trying to add the keyword `test 2` to device ?, \ the following error occurred: The given name string `test 2` does not \ define a valid variable identifier. ... Note that even the first string (`test1`) is not added due to the second one (`test 2`) being invalid. >>> keywords Keywords("first_keyword", "keyboard", "keyword_3", "keyword_4", "second_keyword") After correcting the second string, everything works fine: >>> keywords.update('test_1', 'test_2') >>> keywords Keywords("first_keyword", "keyboard", "keyword_3", "keyword_4", "second_keyword", "test_1", "test_2")
[ "Before", "updating", "the", "given", "names", "are", "checked", "to", "be", "valid", "variable", "identifiers", "." ]
1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L167-L198
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Keywords.add
def add(self, name: Any) -> None: """Before adding a new name, it is checked to be valid variable identifiers. >>> from hydpy.core.devicetools import Keywords >>> keywords = Keywords('first_keyword', 'second_keyword', ... 'keyword_3', 'keyword_4', ... 'keyboard') >>> keywords.add('1_test') # doctest: +ELLIPSIS Traceback (most recent call last): ... ValueError: While trying to add the keyword `1_test` to device ?, \ the following error occurred: The given name string `1_test` does not \ define a valid variable identifier. ... >>> keywords Keywords("first_keyword", "keyboard", "keyword_3", "keyword_4", "second_keyword") After correcting the string, everything works fine: >>> keywords.add('one_test') >>> keywords Keywords("first_keyword", "keyboard", "keyword_3", "keyword_4", "one_test", "second_keyword") """ self._check_keywords([str(name)]) super().add(str(name))
python
def add(self, name: Any) -> None: """Before adding a new name, it is checked to be valid variable identifiers. >>> from hydpy.core.devicetools import Keywords >>> keywords = Keywords('first_keyword', 'second_keyword', ... 'keyword_3', 'keyword_4', ... 'keyboard') >>> keywords.add('1_test') # doctest: +ELLIPSIS Traceback (most recent call last): ... ValueError: While trying to add the keyword `1_test` to device ?, \ the following error occurred: The given name string `1_test` does not \ define a valid variable identifier. ... >>> keywords Keywords("first_keyword", "keyboard", "keyword_3", "keyword_4", "second_keyword") After correcting the string, everything works fine: >>> keywords.add('one_test') >>> keywords Keywords("first_keyword", "keyboard", "keyword_3", "keyword_4", "one_test", "second_keyword") """ self._check_keywords([str(name)]) super().add(str(name))
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Before adding a new name, it is checked to be valid variable identifiers. >>> from hydpy.core.devicetools import Keywords >>> keywords = Keywords('first_keyword', 'second_keyword', ... 'keyword_3', 'keyword_4', ... 'keyboard') >>> keywords.add('1_test') # doctest: +ELLIPSIS Traceback (most recent call last): ... ValueError: While trying to add the keyword `1_test` to device ?, \ the following error occurred: The given name string `1_test` does not \ define a valid variable identifier. ... >>> keywords Keywords("first_keyword", "keyboard", "keyword_3", "keyword_4", "second_keyword") After correcting the string, everything works fine: >>> keywords.add('one_test') >>> keywords Keywords("first_keyword", "keyboard", "keyword_3", "keyword_4", "one_test", "second_keyword")
[ "Before", "adding", "a", "new", "name", "it", "is", "checked", "to", "be", "valid", "variable", "identifiers", "." ]
1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L200-L227
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Devices.add_device
def add_device(self, device: Union[DeviceType, str]) -> None: """Add the given |Node| or |Element| object to the actual |Nodes| or |Elements| object. You can pass either a string or a device: >>> from hydpy import Nodes >>> nodes = Nodes() >>> nodes.add_device('old_node') >>> nodes Nodes("old_node") >>> nodes.add_device('new_node') >>> nodes Nodes("new_node", "old_node") Method |Devices.add_device| is disabled for immutable |Nodes| and |Elements| objects: >>> nodes.mutable = False >>> nodes.add_device('newest_node') Traceback (most recent call last): ... RuntimeError: While trying to add the device `newest_node` to a \ Nodes object, the following error occurred: Adding devices to immutable \ Nodes objects is not allowed. """ try: if self.mutable: _device = self.get_contentclass()(device) self._name2device[_device.name] = _device _id2devices[_device][id(self)] = self else: raise RuntimeError( f'Adding devices to immutable ' f'{objecttools.classname(self)} objects is not allowed.') except BaseException: objecttools.augment_excmessage( f'While trying to add the device `{device}` to a ' f'{objecttools.classname(self)} object')
python
def add_device(self, device: Union[DeviceType, str]) -> None: """Add the given |Node| or |Element| object to the actual |Nodes| or |Elements| object. You can pass either a string or a device: >>> from hydpy import Nodes >>> nodes = Nodes() >>> nodes.add_device('old_node') >>> nodes Nodes("old_node") >>> nodes.add_device('new_node') >>> nodes Nodes("new_node", "old_node") Method |Devices.add_device| is disabled for immutable |Nodes| and |Elements| objects: >>> nodes.mutable = False >>> nodes.add_device('newest_node') Traceback (most recent call last): ... RuntimeError: While trying to add the device `newest_node` to a \ Nodes object, the following error occurred: Adding devices to immutable \ Nodes objects is not allowed. """ try: if self.mutable: _device = self.get_contentclass()(device) self._name2device[_device.name] = _device _id2devices[_device][id(self)] = self else: raise RuntimeError( f'Adding devices to immutable ' f'{objecttools.classname(self)} objects is not allowed.') except BaseException: objecttools.augment_excmessage( f'While trying to add the device `{device}` to a ' f'{objecttools.classname(self)} object')
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Add the given |Node| or |Element| object to the actual |Nodes| or |Elements| object. You can pass either a string or a device: >>> from hydpy import Nodes >>> nodes = Nodes() >>> nodes.add_device('old_node') >>> nodes Nodes("old_node") >>> nodes.add_device('new_node') >>> nodes Nodes("new_node", "old_node") Method |Devices.add_device| is disabled for immutable |Nodes| and |Elements| objects: >>> nodes.mutable = False >>> nodes.add_device('newest_node') Traceback (most recent call last): ... RuntimeError: While trying to add the device `newest_node` to a \ Nodes object, the following error occurred: Adding devices to immutable \ Nodes objects is not allowed.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L404-L442
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Devices.remove_device
def remove_device(self, device: Union[DeviceType, str]) -> None: """Remove the given |Node| or |Element| object from the actual |Nodes| or |Elements| object. You can pass either a string or a device: >>> from hydpy import Node, Nodes >>> nodes = Nodes('node_x', 'node_y') >>> node_x, node_y = nodes >>> nodes.remove_device(Node('node_y')) >>> nodes Nodes("node_x") >>> nodes.remove_device(Node('node_x')) >>> nodes Nodes() >>> nodes.remove_device(Node('node_z')) Traceback (most recent call last): ... ValueError: While trying to remove the device `node_z` from a \ Nodes object, the following error occurred: The actual Nodes object does \ not handle such a device. Method |Devices.remove_device| is disabled for immutable |Nodes| and |Elements| objects: >>> nodes.mutable = False >>> nodes.remove_device('node_z') Traceback (most recent call last): ... RuntimeError: While trying to remove the device `node_z` from a \ Nodes object, the following error occurred: Removing devices from \ immutable Nodes objects is not allowed. """ try: if self.mutable: _device = self.get_contentclass()(device) try: del self._name2device[_device.name] except KeyError: raise ValueError( f'The actual {objecttools.classname(self)} ' f'object does not handle such a device.') del _id2devices[_device][id(self)] else: raise RuntimeError( f'Removing devices from immutable ' f'{objecttools.classname(self)} objects is not allowed.') except BaseException: objecttools.augment_excmessage( f'While trying to remove the device `{device}` from a ' f'{objecttools.classname(self)} object')
python
def remove_device(self, device: Union[DeviceType, str]) -> None: """Remove the given |Node| or |Element| object from the actual |Nodes| or |Elements| object. You can pass either a string or a device: >>> from hydpy import Node, Nodes >>> nodes = Nodes('node_x', 'node_y') >>> node_x, node_y = nodes >>> nodes.remove_device(Node('node_y')) >>> nodes Nodes("node_x") >>> nodes.remove_device(Node('node_x')) >>> nodes Nodes() >>> nodes.remove_device(Node('node_z')) Traceback (most recent call last): ... ValueError: While trying to remove the device `node_z` from a \ Nodes object, the following error occurred: The actual Nodes object does \ not handle such a device. Method |Devices.remove_device| is disabled for immutable |Nodes| and |Elements| objects: >>> nodes.mutable = False >>> nodes.remove_device('node_z') Traceback (most recent call last): ... RuntimeError: While trying to remove the device `node_z` from a \ Nodes object, the following error occurred: Removing devices from \ immutable Nodes objects is not allowed. """ try: if self.mutable: _device = self.get_contentclass()(device) try: del self._name2device[_device.name] except KeyError: raise ValueError( f'The actual {objecttools.classname(self)} ' f'object does not handle such a device.') del _id2devices[_device][id(self)] else: raise RuntimeError( f'Removing devices from immutable ' f'{objecttools.classname(self)} objects is not allowed.') except BaseException: objecttools.augment_excmessage( f'While trying to remove the device `{device}` from a ' f'{objecttools.classname(self)} object')
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Remove the given |Node| or |Element| object from the actual |Nodes| or |Elements| object. You can pass either a string or a device: >>> from hydpy import Node, Nodes >>> nodes = Nodes('node_x', 'node_y') >>> node_x, node_y = nodes >>> nodes.remove_device(Node('node_y')) >>> nodes Nodes("node_x") >>> nodes.remove_device(Node('node_x')) >>> nodes Nodes() >>> nodes.remove_device(Node('node_z')) Traceback (most recent call last): ... ValueError: While trying to remove the device `node_z` from a \ Nodes object, the following error occurred: The actual Nodes object does \ not handle such a device. Method |Devices.remove_device| is disabled for immutable |Nodes| and |Elements| objects: >>> nodes.mutable = False >>> nodes.remove_device('node_z') Traceback (most recent call last): ... RuntimeError: While trying to remove the device `node_z` from a \ Nodes object, the following error occurred: Removing devices from \ immutable Nodes objects is not allowed.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L444-L494
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Devices.keywords
def keywords(self) -> Set[str]: """A set of all keywords of all handled devices. In addition to attribute access via device names, |Nodes| and |Elements| objects allow for attribute access via keywords, allowing for an efficient search of certain groups of devices. Let us use the example from above, where the nodes `na` and `nb` have no keywords, but each of the other three nodes both belongs to either `group_a` or `group_b` and `group_1` or `group_2`: >>> from hydpy import Node, Nodes >>> nodes = Nodes('na', ... Node('nb', variable='W'), ... Node('nc', keywords=('group_a', 'group_1')), ... Node('nd', keywords=('group_a', 'group_2')), ... Node('ne', keywords=('group_b', 'group_1'))) >>> nodes Nodes("na", "nb", "nc", "nd", "ne") >>> sorted(nodes.keywords) ['group_1', 'group_2', 'group_a', 'group_b'] If you are interested in inspecting all devices belonging to `group_a`, select them via this keyword: >>> subgroup = nodes.group_1 >>> subgroup Nodes("nc", "ne") You can further restrict the search by also selecting the devices belonging to `group_b`, which holds only for node "e", in the given example: >>> subsubgroup = subgroup.group_b >>> subsubgroup Node("ne", variable="Q", keywords=["group_1", "group_b"]) Note that the keywords already used for building a device subgroup are not informative anymore (as they hold for each device) and are thus not shown anymore: >>> sorted(subgroup.keywords) ['group_a', 'group_b'] The latter might be confusing if you intend to work with a device subgroup for a longer time. After copying the subgroup, all keywords of the contained devices are available again: >>> from copy import copy >>> newgroup = copy(subgroup) >>> sorted(newgroup.keywords) ['group_1', 'group_a', 'group_b'] """ return set(keyword for device in self for keyword in device.keywords if keyword not in self._shadowed_keywords)
python
def keywords(self) -> Set[str]: """A set of all keywords of all handled devices. In addition to attribute access via device names, |Nodes| and |Elements| objects allow for attribute access via keywords, allowing for an efficient search of certain groups of devices. Let us use the example from above, where the nodes `na` and `nb` have no keywords, but each of the other three nodes both belongs to either `group_a` or `group_b` and `group_1` or `group_2`: >>> from hydpy import Node, Nodes >>> nodes = Nodes('na', ... Node('nb', variable='W'), ... Node('nc', keywords=('group_a', 'group_1')), ... Node('nd', keywords=('group_a', 'group_2')), ... Node('ne', keywords=('group_b', 'group_1'))) >>> nodes Nodes("na", "nb", "nc", "nd", "ne") >>> sorted(nodes.keywords) ['group_1', 'group_2', 'group_a', 'group_b'] If you are interested in inspecting all devices belonging to `group_a`, select them via this keyword: >>> subgroup = nodes.group_1 >>> subgroup Nodes("nc", "ne") You can further restrict the search by also selecting the devices belonging to `group_b`, which holds only for node "e", in the given example: >>> subsubgroup = subgroup.group_b >>> subsubgroup Node("ne", variable="Q", keywords=["group_1", "group_b"]) Note that the keywords already used for building a device subgroup are not informative anymore (as they hold for each device) and are thus not shown anymore: >>> sorted(subgroup.keywords) ['group_a', 'group_b'] The latter might be confusing if you intend to work with a device subgroup for a longer time. After copying the subgroup, all keywords of the contained devices are available again: >>> from copy import copy >>> newgroup = copy(subgroup) >>> sorted(newgroup.keywords) ['group_1', 'group_a', 'group_b'] """ return set(keyword for device in self for keyword in device.keywords if keyword not in self._shadowed_keywords)
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A set of all keywords of all handled devices. In addition to attribute access via device names, |Nodes| and |Elements| objects allow for attribute access via keywords, allowing for an efficient search of certain groups of devices. Let us use the example from above, where the nodes `na` and `nb` have no keywords, but each of the other three nodes both belongs to either `group_a` or `group_b` and `group_1` or `group_2`: >>> from hydpy import Node, Nodes >>> nodes = Nodes('na', ... Node('nb', variable='W'), ... Node('nc', keywords=('group_a', 'group_1')), ... Node('nd', keywords=('group_a', 'group_2')), ... Node('ne', keywords=('group_b', 'group_1'))) >>> nodes Nodes("na", "nb", "nc", "nd", "ne") >>> sorted(nodes.keywords) ['group_1', 'group_2', 'group_a', 'group_b'] If you are interested in inspecting all devices belonging to `group_a`, select them via this keyword: >>> subgroup = nodes.group_1 >>> subgroup Nodes("nc", "ne") You can further restrict the search by also selecting the devices belonging to `group_b`, which holds only for node "e", in the given example: >>> subsubgroup = subgroup.group_b >>> subsubgroup Node("ne", variable="Q", keywords=["group_1", "group_b"]) Note that the keywords already used for building a device subgroup are not informative anymore (as they hold for each device) and are thus not shown anymore: >>> sorted(subgroup.keywords) ['group_a', 'group_b'] The latter might be confusing if you intend to work with a device subgroup for a longer time. After copying the subgroup, all keywords of the contained devices are available again: >>> from copy import copy >>> newgroup = copy(subgroup) >>> sorted(newgroup.keywords) ['group_1', 'group_a', 'group_b']
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L520-L575
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Devices.copy
def copy(self: DevicesTypeBound) -> DevicesTypeBound: """Return a shallow copy of the actual |Nodes| or |Elements| object. Method |Devices.copy| returns a semi-flat copy of |Nodes| or |Elements| objects, due to their devices being not copyable: >>> from hydpy import Nodes >>> old = Nodes('x', 'y') >>> import copy >>> new = copy.copy(old) >>> new == old True >>> new is old False >>> new.devices is old.devices False >>> new.x is new.x True Changing the |Device.name| of a device is recognised both by the original and the copied collection objects: >>> new.x.name = 'z' >>> old.z Node("z", variable="Q") >>> new.z Node("z", variable="Q") Deep copying is permitted due to the above reason: >>> copy.deepcopy(old) Traceback (most recent call last): ... NotImplementedError: Deep copying of Nodes objects is not supported, \ as it would require to make deep copies of the Node objects themselves, \ which is in conflict with using their names as identifiers. """ new = type(self)() vars(new).update(vars(self)) vars(new)['_name2device'] = copy.copy(self._name2device) vars(new)['_shadowed_keywords'].clear() for device in self: _id2devices[device][id(new)] = new return new
python
def copy(self: DevicesTypeBound) -> DevicesTypeBound: """Return a shallow copy of the actual |Nodes| or |Elements| object. Method |Devices.copy| returns a semi-flat copy of |Nodes| or |Elements| objects, due to their devices being not copyable: >>> from hydpy import Nodes >>> old = Nodes('x', 'y') >>> import copy >>> new = copy.copy(old) >>> new == old True >>> new is old False >>> new.devices is old.devices False >>> new.x is new.x True Changing the |Device.name| of a device is recognised both by the original and the copied collection objects: >>> new.x.name = 'z' >>> old.z Node("z", variable="Q") >>> new.z Node("z", variable="Q") Deep copying is permitted due to the above reason: >>> copy.deepcopy(old) Traceback (most recent call last): ... NotImplementedError: Deep copying of Nodes objects is not supported, \ as it would require to make deep copies of the Node objects themselves, \ which is in conflict with using their names as identifiers. """ new = type(self)() vars(new).update(vars(self)) vars(new)['_name2device'] = copy.copy(self._name2device) vars(new)['_shadowed_keywords'].clear() for device in self: _id2devices[device][id(new)] = new return new
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Return a shallow copy of the actual |Nodes| or |Elements| object. Method |Devices.copy| returns a semi-flat copy of |Nodes| or |Elements| objects, due to their devices being not copyable: >>> from hydpy import Nodes >>> old = Nodes('x', 'y') >>> import copy >>> new = copy.copy(old) >>> new == old True >>> new is old False >>> new.devices is old.devices False >>> new.x is new.x True Changing the |Device.name| of a device is recognised both by the original and the copied collection objects: >>> new.x.name = 'z' >>> old.z Node("z", variable="Q") >>> new.z Node("z", variable="Q") Deep copying is permitted due to the above reason: >>> copy.deepcopy(old) Traceback (most recent call last): ... NotImplementedError: Deep copying of Nodes objects is not supported, \ as it would require to make deep copies of the Node objects themselves, \ which is in conflict with using their names as identifiers.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L589-L632
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Nodes.prepare_allseries
def prepare_allseries(self, ramflag: bool = True) -> None: """Call methods |Node.prepare_simseries| and |Node.prepare_obsseries|.""" self.prepare_simseries(ramflag) self.prepare_obsseries(ramflag)
python
def prepare_allseries(self, ramflag: bool = True) -> None: """Call methods |Node.prepare_simseries| and |Node.prepare_obsseries|.""" self.prepare_simseries(ramflag) self.prepare_obsseries(ramflag)
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Call methods |Node.prepare_simseries| and |Node.prepare_obsseries|.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L814-L818
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Nodes.prepare_simseries
def prepare_simseries(self, ramflag: bool = True) -> None: """Call method |Node.prepare_simseries| of all handled |Node| objects.""" for node in printtools.progressbar(self): node.prepare_simseries(ramflag)
python
def prepare_simseries(self, ramflag: bool = True) -> None: """Call method |Node.prepare_simseries| of all handled |Node| objects.""" for node in printtools.progressbar(self): node.prepare_simseries(ramflag)
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Call method |Node.prepare_simseries| of all handled |Node| objects.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L821-L825
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Nodes.prepare_obsseries
def prepare_obsseries(self, ramflag: bool = True) -> None: """Call method |Node.prepare_obsseries| of all handled |Node| objects.""" for node in printtools.progressbar(self): node.prepare_obsseries(ramflag)
python
def prepare_obsseries(self, ramflag: bool = True) -> None: """Call method |Node.prepare_obsseries| of all handled |Node| objects.""" for node in printtools.progressbar(self): node.prepare_obsseries(ramflag)
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Call method |Node.prepare_obsseries| of all handled |Node| objects.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L828-L832
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Elements.init_models
def init_models(self) -> None: """Call method |Element.init_model| of all handle |Element| objects. We show, based the `LahnH` example project, that method |Element.init_model| prepares the |Model| objects of all elements, including building the required connections and updating the derived parameters: >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, pub, TestIO >>> with TestIO(): ... hp = HydPy('LahnH') ... pub.timegrids = '1996-01-01', '1996-02-01', '1d' ... hp.prepare_network() ... hp.init_models() >>> hp.elements.land_dill.model.parameters.derived.dt dt(0.000833) Wrong control files result in error messages like the following: >>> with TestIO(): ... with open('LahnH/control/default/land_dill.py', 'a') as file_: ... _ = file_.write('zonetype(-1)') ... hp.init_models() # doctest: +ELLIPSIS Traceback (most recent call last): ... ValueError: While trying to initialise the model object of element \ `land_dill`, the following error occurred: While trying to load the control \ file `...land_dill.py`, the following error occurred: At least one value of \ parameter `zonetype` of element `?` is not valid. By default, missing control files result in exceptions: >>> del hp.elements.land_dill.model >>> import os >>> with TestIO(): ... os.remove('LahnH/control/default/land_dill.py') ... hp.init_models() # doctest: +ELLIPSIS Traceback (most recent call last): ... FileNotFoundError: While trying to initialise the model object of \ element `land_dill`, the following error occurred: While trying to load the \ control file `...land_dill.py`, the following error occurred: ... >>> hasattr(hp.elements.land_dill, 'model') False When building new, still incomplete *HydPy* projects, this behaviour can be annoying. After setting the option |Options.warnmissingcontrolfile| to |False|, missing control files only result in a warning: >>> with TestIO(): ... with pub.options.warnmissingcontrolfile(True): ... hp.init_models() Traceback (most recent call last): ... UserWarning: Due to a missing or no accessible control file, \ no model could be initialised for element `land_dill` >>> hasattr(hp.elements.land_dill, 'model') False """ try: for element in printtools.progressbar(self): element.init_model(clear_registry=False) finally: hydpy.pub.controlmanager.clear_registry()
python
def init_models(self) -> None: """Call method |Element.init_model| of all handle |Element| objects. We show, based the `LahnH` example project, that method |Element.init_model| prepares the |Model| objects of all elements, including building the required connections and updating the derived parameters: >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, pub, TestIO >>> with TestIO(): ... hp = HydPy('LahnH') ... pub.timegrids = '1996-01-01', '1996-02-01', '1d' ... hp.prepare_network() ... hp.init_models() >>> hp.elements.land_dill.model.parameters.derived.dt dt(0.000833) Wrong control files result in error messages like the following: >>> with TestIO(): ... with open('LahnH/control/default/land_dill.py', 'a') as file_: ... _ = file_.write('zonetype(-1)') ... hp.init_models() # doctest: +ELLIPSIS Traceback (most recent call last): ... ValueError: While trying to initialise the model object of element \ `land_dill`, the following error occurred: While trying to load the control \ file `...land_dill.py`, the following error occurred: At least one value of \ parameter `zonetype` of element `?` is not valid. By default, missing control files result in exceptions: >>> del hp.elements.land_dill.model >>> import os >>> with TestIO(): ... os.remove('LahnH/control/default/land_dill.py') ... hp.init_models() # doctest: +ELLIPSIS Traceback (most recent call last): ... FileNotFoundError: While trying to initialise the model object of \ element `land_dill`, the following error occurred: While trying to load the \ control file `...land_dill.py`, the following error occurred: ... >>> hasattr(hp.elements.land_dill, 'model') False When building new, still incomplete *HydPy* projects, this behaviour can be annoying. After setting the option |Options.warnmissingcontrolfile| to |False|, missing control files only result in a warning: >>> with TestIO(): ... with pub.options.warnmissingcontrolfile(True): ... hp.init_models() Traceback (most recent call last): ... UserWarning: Due to a missing or no accessible control file, \ no model could be initialised for element `land_dill` >>> hasattr(hp.elements.land_dill, 'model') False """ try: for element in printtools.progressbar(self): element.init_model(clear_registry=False) finally: hydpy.pub.controlmanager.clear_registry()
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Call method |Element.init_model| of all handle |Element| objects. We show, based the `LahnH` example project, that method |Element.init_model| prepares the |Model| objects of all elements, including building the required connections and updating the derived parameters: >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, pub, TestIO >>> with TestIO(): ... hp = HydPy('LahnH') ... pub.timegrids = '1996-01-01', '1996-02-01', '1d' ... hp.prepare_network() ... hp.init_models() >>> hp.elements.land_dill.model.parameters.derived.dt dt(0.000833) Wrong control files result in error messages like the following: >>> with TestIO(): ... with open('LahnH/control/default/land_dill.py', 'a') as file_: ... _ = file_.write('zonetype(-1)') ... hp.init_models() # doctest: +ELLIPSIS Traceback (most recent call last): ... ValueError: While trying to initialise the model object of element \ `land_dill`, the following error occurred: While trying to load the control \ file `...land_dill.py`, the following error occurred: At least one value of \ parameter `zonetype` of element `?` is not valid. By default, missing control files result in exceptions: >>> del hp.elements.land_dill.model >>> import os >>> with TestIO(): ... os.remove('LahnH/control/default/land_dill.py') ... hp.init_models() # doctest: +ELLIPSIS Traceback (most recent call last): ... FileNotFoundError: While trying to initialise the model object of \ element `land_dill`, the following error occurred: While trying to load the \ control file `...land_dill.py`, the following error occurred: ... >>> hasattr(hp.elements.land_dill, 'model') False When building new, still incomplete *HydPy* projects, this behaviour can be annoying. After setting the option |Options.warnmissingcontrolfile| to |False|, missing control files only result in a warning: >>> with TestIO(): ... with pub.options.warnmissingcontrolfile(True): ... hp.init_models() Traceback (most recent call last): ... UserWarning: Due to a missing or no accessible control file, \ no model could be initialised for element `land_dill` >>> hasattr(hp.elements.land_dill, 'model') False
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L907-L973
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Elements.save_controls
def save_controls(self, parameterstep: 'timetools.PeriodConstrArg' = None, simulationstep: 'timetools.PeriodConstrArg' = None, auxfiler: 'Optional[auxfiletools.Auxfiler]' = None): """Save the control parameters of the |Model| object handled by each |Element| object and eventually the ones handled by the given |Auxfiler| object.""" if auxfiler: auxfiler.save(parameterstep, simulationstep) for element in printtools.progressbar(self): element.model.parameters.save_controls( parameterstep=parameterstep, simulationstep=simulationstep, auxfiler=auxfiler)
python
def save_controls(self, parameterstep: 'timetools.PeriodConstrArg' = None, simulationstep: 'timetools.PeriodConstrArg' = None, auxfiler: 'Optional[auxfiletools.Auxfiler]' = None): """Save the control parameters of the |Model| object handled by each |Element| object and eventually the ones handled by the given |Auxfiler| object.""" if auxfiler: auxfiler.save(parameterstep, simulationstep) for element in printtools.progressbar(self): element.model.parameters.save_controls( parameterstep=parameterstep, simulationstep=simulationstep, auxfiler=auxfiler)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L976-L988
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Elements.load_conditions
def load_conditions(self) -> None: """Save the initial conditions of the |Model| object handled by each |Element| object.""" for element in printtools.progressbar(self): element.model.sequences.load_conditions()
python
def load_conditions(self) -> None: """Save the initial conditions of the |Model| object handled by each |Element| object.""" for element in printtools.progressbar(self): element.model.sequences.load_conditions()
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L991-L995
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Elements.save_conditions
def save_conditions(self) -> None: """Save the calculated conditions of the |Model| object handled by each |Element| object.""" for element in printtools.progressbar(self): element.model.sequences.save_conditions()
python
def save_conditions(self) -> None: """Save the calculated conditions of the |Model| object handled by each |Element| object.""" for element in printtools.progressbar(self): element.model.sequences.save_conditions()
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Save the calculated conditions of the |Model| object handled by each |Element| object.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L998-L1002
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Elements.conditions
def conditions(self) -> \ Dict[str, Dict[str, Dict[str, Union[float, numpy.ndarray]]]]: """A nested dictionary containing the values of all |ConditionSequence| objects of all currently handled models. See the documentation on property |HydPy.conditions| for further information. """ return {element.name: element.model.sequences.conditions for element in self}
python
def conditions(self) -> \ Dict[str, Dict[str, Dict[str, Union[float, numpy.ndarray]]]]: """A nested dictionary containing the values of all |ConditionSequence| objects of all currently handled models. See the documentation on property |HydPy.conditions| for further information. """ return {element.name: element.model.sequences.conditions for element in self}
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A nested dictionary containing the values of all |ConditionSequence| objects of all currently handled models. See the documentation on property |HydPy.conditions| for further information.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L1017-L1026
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Elements.prepare_allseries
def prepare_allseries(self, ramflag: bool = True) -> None: """Call method |Element.prepare_allseries| of all handled |Element| objects.""" for element in printtools.progressbar(self): element.prepare_allseries(ramflag)
python
def prepare_allseries(self, ramflag: bool = True) -> None: """Call method |Element.prepare_allseries| of all handled |Element| objects.""" for element in printtools.progressbar(self): element.prepare_allseries(ramflag)
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Call method |Element.prepare_allseries| of all handled |Element| objects.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L1039-L1043
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Elements.prepare_inputseries
def prepare_inputseries(self, ramflag: bool = True) -> None: """Call method |Element.prepare_inputseries| of all handled |Element| objects.""" for element in printtools.progressbar(self): element.prepare_inputseries(ramflag)
python
def prepare_inputseries(self, ramflag: bool = True) -> None: """Call method |Element.prepare_inputseries| of all handled |Element| objects.""" for element in printtools.progressbar(self): element.prepare_inputseries(ramflag)
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Call method |Element.prepare_inputseries| of all handled |Element| objects.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L1046-L1050
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Elements.prepare_fluxseries
def prepare_fluxseries(self, ramflag: bool = True) -> None: """Call method |Element.prepare_fluxseries| of all handled |Element| objects.""" for element in printtools.progressbar(self): element.prepare_fluxseries(ramflag)
python
def prepare_fluxseries(self, ramflag: bool = True) -> None: """Call method |Element.prepare_fluxseries| of all handled |Element| objects.""" for element in printtools.progressbar(self): element.prepare_fluxseries(ramflag)
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Call method |Element.prepare_fluxseries| of all handled |Element| objects.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L1053-L1057
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Elements.prepare_stateseries
def prepare_stateseries(self, ramflag: bool = True) -> None: """Call method |Element.prepare_stateseries| of all handled |Element| objects.""" for element in printtools.progressbar(self): element.prepare_stateseries(ramflag)
python
def prepare_stateseries(self, ramflag: bool = True) -> None: """Call method |Element.prepare_stateseries| of all handled |Element| objects.""" for element in printtools.progressbar(self): element.prepare_stateseries(ramflag)
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Call method |Element.prepare_stateseries| of all handled |Element| objects.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L1060-L1064
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Device.extract_new
def extract_new(cls) -> DevicesTypeUnbound: """Gather all "new" |Node| or |Element| objects. See the main documentation on module |devicetools| for further information. """ devices = cls.get_handlerclass()(*_selection[cls]) _selection[cls].clear() return devices
python
def extract_new(cls) -> DevicesTypeUnbound: """Gather all "new" |Node| or |Element| objects. See the main documentation on module |devicetools| for further information. """ devices = cls.get_handlerclass()(*_selection[cls]) _selection[cls].clear() return devices
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Gather all "new" |Node| or |Element| objects. See the main documentation on module |devicetools| for further information.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L1172-L1180
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Node.get_double
def get_double(self, group: str) -> pointerutils.Double: """Return the |Double| object appropriate for the given |Element| input or output group and the actual |Node.deploymode|. Method |Node.get_double| should be of interest for framework developers only (and eventually for model developers). Let |Node| object `node1` handle different simulation and observation values: >>> from hydpy import Node >>> node = Node('node1') >>> node.sequences.sim = 1.0 >>> node.sequences.obs = 2.0 The following `test` function shows for a given |Node.deploymode| if method |Node.get_double| either returns the |Double| object handling the simulated value (1.0) or the |Double| object handling the observed value (2.0): >>> def test(deploymode): ... node.deploymode = deploymode ... for group in ('inlets', 'receivers', 'outlets', 'senders'): ... print(group, node.get_double(group)) In the default mode, nodes (passively) route simulated values through offering the |Double| object of sequence |Sim| to all |Element| input and output groups: >>> test('newsim') inlets 1.0 receivers 1.0 outlets 1.0 senders 1.0 Setting |Node.deploymode| to `obs` means that a node receives simulated values (from group `outlets` or `senders`), but provides observed values (to group `inlets` or `receivers`): >>> test('obs') inlets 2.0 receivers 2.0 outlets 1.0 senders 1.0 With |Node.deploymode| set to `oldsim`, the node provides (previously) simulated values (to group `inlets` or `receivers`) but does not receive any values. Method |Node.get_double| just returns a dummy |Double| object with value 0.0 in this case (for group `outlets` or `senders`): >>> test('oldsim') inlets 1.0 receivers 1.0 outlets 0.0 senders 0.0 Other |Element| input or output groups are not supported: >>> node.get_double('test') Traceback (most recent call last): ... ValueError: Function `get_double` of class `Node` does not support \ the given group name `test`. """ if group in ('inlets', 'receivers'): if self.deploymode != 'obs': return self.sequences.fastaccess.sim return self.sequences.fastaccess.obs if group in ('outlets', 'senders'): if self.deploymode != 'oldsim': return self.sequences.fastaccess.sim return self.__blackhole raise ValueError( f'Function `get_double` of class `Node` does not ' f'support the given group name `{group}`.')
python
def get_double(self, group: str) -> pointerutils.Double: """Return the |Double| object appropriate for the given |Element| input or output group and the actual |Node.deploymode|. Method |Node.get_double| should be of interest for framework developers only (and eventually for model developers). Let |Node| object `node1` handle different simulation and observation values: >>> from hydpy import Node >>> node = Node('node1') >>> node.sequences.sim = 1.0 >>> node.sequences.obs = 2.0 The following `test` function shows for a given |Node.deploymode| if method |Node.get_double| either returns the |Double| object handling the simulated value (1.0) or the |Double| object handling the observed value (2.0): >>> def test(deploymode): ... node.deploymode = deploymode ... for group in ('inlets', 'receivers', 'outlets', 'senders'): ... print(group, node.get_double(group)) In the default mode, nodes (passively) route simulated values through offering the |Double| object of sequence |Sim| to all |Element| input and output groups: >>> test('newsim') inlets 1.0 receivers 1.0 outlets 1.0 senders 1.0 Setting |Node.deploymode| to `obs` means that a node receives simulated values (from group `outlets` or `senders`), but provides observed values (to group `inlets` or `receivers`): >>> test('obs') inlets 2.0 receivers 2.0 outlets 1.0 senders 1.0 With |Node.deploymode| set to `oldsim`, the node provides (previously) simulated values (to group `inlets` or `receivers`) but does not receive any values. Method |Node.get_double| just returns a dummy |Double| object with value 0.0 in this case (for group `outlets` or `senders`): >>> test('oldsim') inlets 1.0 receivers 1.0 outlets 0.0 senders 0.0 Other |Element| input or output groups are not supported: >>> node.get_double('test') Traceback (most recent call last): ... ValueError: Function `get_double` of class `Node` does not support \ the given group name `test`. """ if group in ('inlets', 'receivers'): if self.deploymode != 'obs': return self.sequences.fastaccess.sim return self.sequences.fastaccess.obs if group in ('outlets', 'senders'): if self.deploymode != 'oldsim': return self.sequences.fastaccess.sim return self.__blackhole raise ValueError( f'Function `get_double` of class `Node` does not ' f'support the given group name `{group}`.')
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Return the |Double| object appropriate for the given |Element| input or output group and the actual |Node.deploymode|. Method |Node.get_double| should be of interest for framework developers only (and eventually for model developers). Let |Node| object `node1` handle different simulation and observation values: >>> from hydpy import Node >>> node = Node('node1') >>> node.sequences.sim = 1.0 >>> node.sequences.obs = 2.0 The following `test` function shows for a given |Node.deploymode| if method |Node.get_double| either returns the |Double| object handling the simulated value (1.0) or the |Double| object handling the observed value (2.0): >>> def test(deploymode): ... node.deploymode = deploymode ... for group in ('inlets', 'receivers', 'outlets', 'senders'): ... print(group, node.get_double(group)) In the default mode, nodes (passively) route simulated values through offering the |Double| object of sequence |Sim| to all |Element| input and output groups: >>> test('newsim') inlets 1.0 receivers 1.0 outlets 1.0 senders 1.0 Setting |Node.deploymode| to `obs` means that a node receives simulated values (from group `outlets` or `senders`), but provides observed values (to group `inlets` or `receivers`): >>> test('obs') inlets 2.0 receivers 2.0 outlets 1.0 senders 1.0 With |Node.deploymode| set to `oldsim`, the node provides (previously) simulated values (to group `inlets` or `receivers`) but does not receive any values. Method |Node.get_double| just returns a dummy |Double| object with value 0.0 in this case (for group `outlets` or `senders`): >>> test('oldsim') inlets 1.0 receivers 1.0 outlets 0.0 senders 0.0 Other |Element| input or output groups are not supported: >>> node.get_double('test') Traceback (most recent call last): ... ValueError: Function `get_double` of class `Node` does not support \ the given group name `test`.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L1483-L1558
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Node.plot_simseries
def plot_simseries(self, **kwargs: Any) -> None: """Plot the |IOSequence.series| of the |Sim| sequence object. See method |Node.plot_allseries| for further information. """ self.__plot_series([self.sequences.sim], kwargs)
python
def plot_simseries(self, **kwargs: Any) -> None: """Plot the |IOSequence.series| of the |Sim| sequence object. See method |Node.plot_allseries| for further information. """ self.__plot_series([self.sequences.sim], kwargs)
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Plot the |IOSequence.series| of the |Sim| sequence object. See method |Node.plot_allseries| for further information.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L1668-L1673
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Node.plot_obsseries
def plot_obsseries(self, **kwargs: Any) -> None: """Plot the |IOSequence.series| of the |Obs| sequence object. See method |Node.plot_allseries| for further information. """ self.__plot_series([self.sequences.obs], kwargs)
python
def plot_obsseries(self, **kwargs: Any) -> None: """Plot the |IOSequence.series| of the |Obs| sequence object. See method |Node.plot_allseries| for further information. """ self.__plot_series([self.sequences.obs], kwargs)
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Plot the |IOSequence.series| of the |Obs| sequence object. See method |Node.plot_allseries| for further information.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L1675-L1680
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Node.assignrepr
def assignrepr(self, prefix: str = '') -> str: """Return a |repr| string with a prefixed assignment.""" lines = ['%sNode("%s", variable="%s",' % (prefix, self.name, self.variable)] if self.keywords: subprefix = '%skeywords=' % (' '*(len(prefix)+5)) with objecttools.repr_.preserve_strings(True): with objecttools.assignrepr_tuple.always_bracketed(False): line = objecttools.assignrepr_list( sorted(self.keywords), subprefix, width=70) lines.append(line + ',') lines[-1] = lines[-1][:-1]+')' return '\n'.join(lines)
python
def assignrepr(self, prefix: str = '') -> str: """Return a |repr| string with a prefixed assignment.""" lines = ['%sNode("%s", variable="%s",' % (prefix, self.name, self.variable)] if self.keywords: subprefix = '%skeywords=' % (' '*(len(prefix)+5)) with objecttools.repr_.preserve_strings(True): with objecttools.assignrepr_tuple.always_bracketed(False): line = objecttools.assignrepr_list( sorted(self.keywords), subprefix, width=70) lines.append(line + ',') lines[-1] = lines[-1][:-1]+')' return '\n'.join(lines)
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Return a |repr| string with a prefixed assignment.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L1703-L1715
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Element.model
def model(self) -> 'modeltools.Model': """The |Model| object handled by the actual |Element| object. Directly after their initialisation, elements do not know which model they require: >>> from hydpy import Element >>> hland = Element('hland', outlets='outlet') >>> hland.model Traceback (most recent call last): ... AttributeError: The model object of element `hland` has been \ requested but not been prepared so far. During scripting and when working interactively in the Python shell, it is often convenient to assign a |model| directly. >>> from hydpy.models.hland_v1 import * >>> parameterstep('1d') >>> hland.model = model >>> hland.model.name 'hland_v1' >>> del hland.model >>> hasattr(hland, 'model') False For the "usual" approach to prepare models, please see the method |Element.init_model|. The following examples show that assigning |Model| objects to property |Element.model| creates some connection required by the respective model type automatically . These examples should be relevant for developers only. The following |hbranch| model branches a single input value (from to node `inp`) to multiple outputs (nodes `out1` and `out2`): >>> from hydpy import Element, Node, reverse_model_wildcard_import >>> reverse_model_wildcard_import() >>> element = Element('a_branch', ... inlets='branch_input', ... outlets=('branch_output_1', 'branch_output_2')) >>> inp = element.inlets.branch_input >>> out1, out2 = element.outlets >>> from hydpy.models.hbranch import * >>> parameterstep() >>> xpoints(0.0, 3.0) >>> ypoints(branch_output_1=[0.0, 1.0], branch_output_2=[0.0, 2.0]) >>> parameters.update() >>> element.model = model To show that the inlet and outlet connections are built properly, we assign a new value to the inlet node `inp` and verify that the suitable fractions of this value are passed to the outlet nodes out1` and `out2` by calling method |Model.doit|: >>> inp.sequences.sim = 999.0 >>> model.doit(0) >>> fluxes.input input(999.0) >>> out1.sequences.sim sim(333.0) >>> out2.sequences.sim sim(666.0) """ model = vars(self).get('model') if model: return model raise AttributeError( f'The model object of element `{self.name}` has ' f'been requested but not been prepared so far.')
python
def model(self) -> 'modeltools.Model': """The |Model| object handled by the actual |Element| object. Directly after their initialisation, elements do not know which model they require: >>> from hydpy import Element >>> hland = Element('hland', outlets='outlet') >>> hland.model Traceback (most recent call last): ... AttributeError: The model object of element `hland` has been \ requested but not been prepared so far. During scripting and when working interactively in the Python shell, it is often convenient to assign a |model| directly. >>> from hydpy.models.hland_v1 import * >>> parameterstep('1d') >>> hland.model = model >>> hland.model.name 'hland_v1' >>> del hland.model >>> hasattr(hland, 'model') False For the "usual" approach to prepare models, please see the method |Element.init_model|. The following examples show that assigning |Model| objects to property |Element.model| creates some connection required by the respective model type automatically . These examples should be relevant for developers only. The following |hbranch| model branches a single input value (from to node `inp`) to multiple outputs (nodes `out1` and `out2`): >>> from hydpy import Element, Node, reverse_model_wildcard_import >>> reverse_model_wildcard_import() >>> element = Element('a_branch', ... inlets='branch_input', ... outlets=('branch_output_1', 'branch_output_2')) >>> inp = element.inlets.branch_input >>> out1, out2 = element.outlets >>> from hydpy.models.hbranch import * >>> parameterstep() >>> xpoints(0.0, 3.0) >>> ypoints(branch_output_1=[0.0, 1.0], branch_output_2=[0.0, 2.0]) >>> parameters.update() >>> element.model = model To show that the inlet and outlet connections are built properly, we assign a new value to the inlet node `inp` and verify that the suitable fractions of this value are passed to the outlet nodes out1` and `out2` by calling method |Model.doit|: >>> inp.sequences.sim = 999.0 >>> model.doit(0) >>> fluxes.input input(999.0) >>> out1.sequences.sim sim(333.0) >>> out2.sequences.sim sim(666.0) """ model = vars(self).get('model') if model: return model raise AttributeError( f'The model object of element `{self.name}` has ' f'been requested but not been prepared so far.')
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The |Model| object handled by the actual |Element| object. Directly after their initialisation, elements do not know which model they require: >>> from hydpy import Element >>> hland = Element('hland', outlets='outlet') >>> hland.model Traceback (most recent call last): ... AttributeError: The model object of element `hland` has been \ requested but not been prepared so far. During scripting and when working interactively in the Python shell, it is often convenient to assign a |model| directly. >>> from hydpy.models.hland_v1 import * >>> parameterstep('1d') >>> hland.model = model >>> hland.model.name 'hland_v1' >>> del hland.model >>> hasattr(hland, 'model') False For the "usual" approach to prepare models, please see the method |Element.init_model|. The following examples show that assigning |Model| objects to property |Element.model| creates some connection required by the respective model type automatically . These examples should be relevant for developers only. The following |hbranch| model branches a single input value (from to node `inp`) to multiple outputs (nodes `out1` and `out2`): >>> from hydpy import Element, Node, reverse_model_wildcard_import >>> reverse_model_wildcard_import() >>> element = Element('a_branch', ... inlets='branch_input', ... outlets=('branch_output_1', 'branch_output_2')) >>> inp = element.inlets.branch_input >>> out1, out2 = element.outlets >>> from hydpy.models.hbranch import * >>> parameterstep() >>> xpoints(0.0, 3.0) >>> ypoints(branch_output_1=[0.0, 1.0], branch_output_2=[0.0, 2.0]) >>> parameters.update() >>> element.model = model To show that the inlet and outlet connections are built properly, we assign a new value to the inlet node `inp` and verify that the suitable fractions of this value are passed to the outlet nodes out1` and `out2` by calling method |Model.doit|: >>> inp.sequences.sim = 999.0 >>> model.doit(0) >>> fluxes.input input(999.0) >>> out1.sequences.sim sim(333.0) >>> out2.sequences.sim sim(666.0)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L1921-L1992
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Element.init_model
def init_model(self, clear_registry: bool = True) -> None: """Load the control file of the actual |Element| object, initialise its |Model| object, build the required connections via (an eventually overridden version of) method |Model.connect| of class |Model|, and update its derived parameter values via calling (an eventually overridden version) of method |Parameters.update| of class |Parameters|. See method |HydPy.init_models| of class |HydPy| and property |model| of class |Element| fur further information. """ try: with hydpy.pub.options.warnsimulationstep(False): info = hydpy.pub.controlmanager.load_file( element=self, clear_registry=clear_registry) self.model = info['model'] self.model.parameters.update() except OSError: if hydpy.pub.options.warnmissingcontrolfile: warnings.warn( f'Due to a missing or no accessible control file, no ' f'model could be initialised for element `{self.name}`') else: objecttools.augment_excmessage( f'While trying to initialise the model ' f'object of element `{self.name}`') except BaseException: objecttools.augment_excmessage( f'While trying to initialise the model ' f'object of element `{self.name}`')
python
def init_model(self, clear_registry: bool = True) -> None: """Load the control file of the actual |Element| object, initialise its |Model| object, build the required connections via (an eventually overridden version of) method |Model.connect| of class |Model|, and update its derived parameter values via calling (an eventually overridden version) of method |Parameters.update| of class |Parameters|. See method |HydPy.init_models| of class |HydPy| and property |model| of class |Element| fur further information. """ try: with hydpy.pub.options.warnsimulationstep(False): info = hydpy.pub.controlmanager.load_file( element=self, clear_registry=clear_registry) self.model = info['model'] self.model.parameters.update() except OSError: if hydpy.pub.options.warnmissingcontrolfile: warnings.warn( f'Due to a missing or no accessible control file, no ' f'model could be initialised for element `{self.name}`') else: objecttools.augment_excmessage( f'While trying to initialise the model ' f'object of element `{self.name}`') except BaseException: objecttools.augment_excmessage( f'While trying to initialise the model ' f'object of element `{self.name}`')
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L2004-L2033
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Element.variables
def variables(self) -> Set[str]: """A set of all different |Node.variable| values of the |Node| objects directly connected to the actual |Element| object. Suppose there is an element connected to five nodes, which (partly) represent different variables: >>> from hydpy import Element, Node >>> element = Element('Test', ... inlets=(Node('N1', 'X'), Node('N2', 'Y1')), ... outlets=(Node('N3', 'X'), Node('N4', 'Y2')), ... receivers=(Node('N5', 'X'), Node('N6', 'Y3')), ... senders=(Node('N7', 'X'), Node('N8', 'Y4'))) Property |Element.variables| puts all the different variables of these nodes together: >>> sorted(element.variables) ['X', 'Y1', 'Y2', 'Y3', 'Y4'] """ variables: Set[str] = set() for connection in self.__connections: variables.update(connection.variables) return variables
python
def variables(self) -> Set[str]: """A set of all different |Node.variable| values of the |Node| objects directly connected to the actual |Element| object. Suppose there is an element connected to five nodes, which (partly) represent different variables: >>> from hydpy import Element, Node >>> element = Element('Test', ... inlets=(Node('N1', 'X'), Node('N2', 'Y1')), ... outlets=(Node('N3', 'X'), Node('N4', 'Y2')), ... receivers=(Node('N5', 'X'), Node('N6', 'Y3')), ... senders=(Node('N7', 'X'), Node('N8', 'Y4'))) Property |Element.variables| puts all the different variables of these nodes together: >>> sorted(element.variables) ['X', 'Y1', 'Y2', 'Y3', 'Y4'] """ variables: Set[str] = set() for connection in self.__connections: variables.update(connection.variables) return variables
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A set of all different |Node.variable| values of the |Node| objects directly connected to the actual |Element| object. Suppose there is an element connected to five nodes, which (partly) represent different variables: >>> from hydpy import Element, Node >>> element = Element('Test', ... inlets=(Node('N1', 'X'), Node('N2', 'Y1')), ... outlets=(Node('N3', 'X'), Node('N4', 'Y2')), ... receivers=(Node('N5', 'X'), Node('N6', 'Y3')), ... senders=(Node('N7', 'X'), Node('N8', 'Y4'))) Property |Element.variables| puts all the different variables of these nodes together: >>> sorted(element.variables) ['X', 'Y1', 'Y2', 'Y3', 'Y4']
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L2036-L2059
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Element.prepare_allseries
def prepare_allseries(self, ramflag: bool = True) -> None: """Prepare the |IOSequence.series| objects of all `input`, `flux` and `state` sequences of the model handled by this element. Call this method before a simulation run, if you need access to (nearly) all simulated series of the handled model after the simulation run is finished. By default, the time series are stored in RAM, which is the faster option. If your RAM is limited, pass |False| to function argument `ramflag` to store the series on disk. """ self.prepare_inputseries(ramflag) self.prepare_fluxseries(ramflag) self.prepare_stateseries(ramflag)
python
def prepare_allseries(self, ramflag: bool = True) -> None: """Prepare the |IOSequence.series| objects of all `input`, `flux` and `state` sequences of the model handled by this element. Call this method before a simulation run, if you need access to (nearly) all simulated series of the handled model after the simulation run is finished. By default, the time series are stored in RAM, which is the faster option. If your RAM is limited, pass |False| to function argument `ramflag` to store the series on disk. """ self.prepare_inputseries(ramflag) self.prepare_fluxseries(ramflag) self.prepare_stateseries(ramflag)
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Prepare the |IOSequence.series| objects of all `input`, `flux` and `state` sequences of the model handled by this element. Call this method before a simulation run, if you need access to (nearly) all simulated series of the handled model after the simulation run is finished. By default, the time series are stored in RAM, which is the faster option. If your RAM is limited, pass |False| to function argument `ramflag` to store the series on disk.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L2071-L2085
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Element.plot_inputseries
def plot_inputseries( self, names: Optional[Iterable[str]] = None, average: bool = False, **kwargs: Any) \ -> None: """Plot (the selected) |InputSequence| |IOSequence.series| values. We demonstrate the functionalities of method |Element.plot_inputseries| based on the `Lahn` example project: >>> from hydpy.core.examples import prepare_full_example_2 >>> hp, _, _ = prepare_full_example_2(lastdate='1997-01-01') Without any arguments, |Element.plot_inputseries| prints the time series of all input sequences handled by its |Model| object directly to the screen (in the given example, |hland_inputs.P|, |hland_inputs.T|, |hland_inputs.TN|, and |hland_inputs.EPN| of application model |hland_v1|): >>> land = hp.elements.land_dill >>> land.plot_inputseries() You can use the `pyplot` API of `matplotlib` to modify the figure or to save it to disk (or print it to the screen, in case the interactive mode of `matplotlib` is disabled): >>> from matplotlib import pyplot >>> from hydpy.docs import figs >>> pyplot.savefig(figs.__path__[0] + '/Element_plot_inputseries.png') >>> pyplot.close() .. image:: Element_plot_inputseries.png Methods |Element.plot_fluxseries| and |Element.plot_stateseries| work in the same manner. Before applying them, one has at first to calculate the time series of the |FluxSequence| and |StateSequence| objects: >>> hp.doit() All three methods allow to select certain sequences by passing their names (here, flux sequences |hland_fluxes.Q0| and |hland_fluxes.Q1| of |hland_v1|). Additionally, you can pass the keyword arguments supported by `matplotlib` for modifying the line style: >>> land.plot_fluxseries(['q0', 'q1'], linewidth=2) >>> pyplot.savefig(figs.__path__[0] + '/Element_plot_fluxseries.png') >>> pyplot.close() .. image:: Element_plot_fluxseries.png For 1-dimensional |IOSequence| objects, all three methods plot the individual time series in the same colour (here, from the state sequences |hland_states.SP| and |hland_states.WC| of |hland_v1|): >>> land.plot_stateseries(['sp', 'wc']) >>> pyplot.savefig(figs.__path__[0] + '/Element_plot_stateseries1.png') >>> pyplot.close() .. image:: Element_plot_stateseries1.png Alternatively, you can print the averaged time series through passing |True| to the method `average` argument (demonstrated for the state sequence |hland_states.SM|): >>> land.plot_stateseries(['sm'], color='grey') >>> land.plot_stateseries( ... ['sm'], average=True, color='black', linewidth=3) >>> pyplot.savefig(figs.__path__[0] + '/Element_plot_stateseries2.png') >>> pyplot.close() .. image:: Element_plot_stateseries2.png """ self.__plot(self.model.sequences.inputs, names, average, kwargs)
python
def plot_inputseries( self, names: Optional[Iterable[str]] = None, average: bool = False, **kwargs: Any) \ -> None: """Plot (the selected) |InputSequence| |IOSequence.series| values. We demonstrate the functionalities of method |Element.plot_inputseries| based on the `Lahn` example project: >>> from hydpy.core.examples import prepare_full_example_2 >>> hp, _, _ = prepare_full_example_2(lastdate='1997-01-01') Without any arguments, |Element.plot_inputseries| prints the time series of all input sequences handled by its |Model| object directly to the screen (in the given example, |hland_inputs.P|, |hland_inputs.T|, |hland_inputs.TN|, and |hland_inputs.EPN| of application model |hland_v1|): >>> land = hp.elements.land_dill >>> land.plot_inputseries() You can use the `pyplot` API of `matplotlib` to modify the figure or to save it to disk (or print it to the screen, in case the interactive mode of `matplotlib` is disabled): >>> from matplotlib import pyplot >>> from hydpy.docs import figs >>> pyplot.savefig(figs.__path__[0] + '/Element_plot_inputseries.png') >>> pyplot.close() .. image:: Element_plot_inputseries.png Methods |Element.plot_fluxseries| and |Element.plot_stateseries| work in the same manner. Before applying them, one has at first to calculate the time series of the |FluxSequence| and |StateSequence| objects: >>> hp.doit() All three methods allow to select certain sequences by passing their names (here, flux sequences |hland_fluxes.Q0| and |hland_fluxes.Q1| of |hland_v1|). Additionally, you can pass the keyword arguments supported by `matplotlib` for modifying the line style: >>> land.plot_fluxseries(['q0', 'q1'], linewidth=2) >>> pyplot.savefig(figs.__path__[0] + '/Element_plot_fluxseries.png') >>> pyplot.close() .. image:: Element_plot_fluxseries.png For 1-dimensional |IOSequence| objects, all three methods plot the individual time series in the same colour (here, from the state sequences |hland_states.SP| and |hland_states.WC| of |hland_v1|): >>> land.plot_stateseries(['sp', 'wc']) >>> pyplot.savefig(figs.__path__[0] + '/Element_plot_stateseries1.png') >>> pyplot.close() .. image:: Element_plot_stateseries1.png Alternatively, you can print the averaged time series through passing |True| to the method `average` argument (demonstrated for the state sequence |hland_states.SM|): >>> land.plot_stateseries(['sm'], color='grey') >>> land.plot_stateseries( ... ['sm'], average=True, color='black', linewidth=3) >>> pyplot.savefig(figs.__path__[0] + '/Element_plot_stateseries2.png') >>> pyplot.close() .. image:: Element_plot_stateseries2.png """ self.__plot(self.model.sequences.inputs, names, average, kwargs)
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Plot (the selected) |InputSequence| |IOSequence.series| values. We demonstrate the functionalities of method |Element.plot_inputseries| based on the `Lahn` example project: >>> from hydpy.core.examples import prepare_full_example_2 >>> hp, _, _ = prepare_full_example_2(lastdate='1997-01-01') Without any arguments, |Element.plot_inputseries| prints the time series of all input sequences handled by its |Model| object directly to the screen (in the given example, |hland_inputs.P|, |hland_inputs.T|, |hland_inputs.TN|, and |hland_inputs.EPN| of application model |hland_v1|): >>> land = hp.elements.land_dill >>> land.plot_inputseries() You can use the `pyplot` API of `matplotlib` to modify the figure or to save it to disk (or print it to the screen, in case the interactive mode of `matplotlib` is disabled): >>> from matplotlib import pyplot >>> from hydpy.docs import figs >>> pyplot.savefig(figs.__path__[0] + '/Element_plot_inputseries.png') >>> pyplot.close() .. image:: Element_plot_inputseries.png Methods |Element.plot_fluxseries| and |Element.plot_stateseries| work in the same manner. Before applying them, one has at first to calculate the time series of the |FluxSequence| and |StateSequence| objects: >>> hp.doit() All three methods allow to select certain sequences by passing their names (here, flux sequences |hland_fluxes.Q0| and |hland_fluxes.Q1| of |hland_v1|). Additionally, you can pass the keyword arguments supported by `matplotlib` for modifying the line style: >>> land.plot_fluxseries(['q0', 'q1'], linewidth=2) >>> pyplot.savefig(figs.__path__[0] + '/Element_plot_fluxseries.png') >>> pyplot.close() .. image:: Element_plot_fluxseries.png For 1-dimensional |IOSequence| objects, all three methods plot the individual time series in the same colour (here, from the state sequences |hland_states.SP| and |hland_states.WC| of |hland_v1|): >>> land.plot_stateseries(['sp', 'wc']) >>> pyplot.savefig(figs.__path__[0] + '/Element_plot_stateseries1.png') >>> pyplot.close() .. image:: Element_plot_stateseries1.png Alternatively, you can print the averaged time series through passing |True| to the method `average` argument (demonstrated for the state sequence |hland_states.SM|): >>> land.plot_stateseries(['sm'], color='grey') >>> land.plot_stateseries( ... ['sm'], average=True, color='black', linewidth=3) >>> pyplot.savefig(figs.__path__[0] + '/Element_plot_stateseries2.png') >>> pyplot.close() .. image:: Element_plot_stateseries2.png
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L2154-L2229
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Element.plot_fluxseries
def plot_fluxseries( self, names: Optional[Iterable[str]] = None, average: bool = False, **kwargs: Any) \ -> None: """Plot the `flux` series of the handled model. See the documentation on method |Element.plot_inputseries| for additional information. """ self.__plot(self.model.sequences.fluxes, names, average, kwargs)
python
def plot_fluxseries( self, names: Optional[Iterable[str]] = None, average: bool = False, **kwargs: Any) \ -> None: """Plot the `flux` series of the handled model. See the documentation on method |Element.plot_inputseries| for additional information. """ self.__plot(self.model.sequences.fluxes, names, average, kwargs)
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Plot the `flux` series of the handled model. See the documentation on method |Element.plot_inputseries| for additional information.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L2231-L2240
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Element.plot_stateseries
def plot_stateseries( self, names: Optional[Iterable[str]] = None, average: bool = False, **kwargs: Any) \ -> None: """Plot the `state` series of the handled model. See the documentation on method |Element.plot_inputseries| for additional information. """ self.__plot(self.model.sequences.states, names, average, kwargs)
python
def plot_stateseries( self, names: Optional[Iterable[str]] = None, average: bool = False, **kwargs: Any) \ -> None: """Plot the `state` series of the handled model. See the documentation on method |Element.plot_inputseries| for additional information. """ self.__plot(self.model.sequences.states, names, average, kwargs)
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Plot the `state` series of the handled model. See the documentation on method |Element.plot_inputseries| for additional information.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L2242-L2251
train
hydpy-dev/hydpy
hydpy/core/devicetools.py
Element.assignrepr
def assignrepr(self, prefix: str) -> str: """Return a |repr| string with a prefixed assignment.""" with objecttools.repr_.preserve_strings(True): with objecttools.assignrepr_tuple.always_bracketed(False): blanks = ' ' * (len(prefix) + 8) lines = ['%sElement("%s",' % (prefix, self.name)] for groupname in ('inlets', 'outlets', 'receivers', 'senders'): group = getattr(self, groupname, Node) if group: subprefix = '%s%s=' % (blanks, groupname) # pylint: disable=not-an-iterable # because pylint is wrong nodes = [str(node) for node in group] # pylint: enable=not-an-iterable line = objecttools.assignrepr_list( nodes, subprefix, width=70) lines.append(line + ',') if self.keywords: subprefix = '%skeywords=' % blanks line = objecttools.assignrepr_list( sorted(self.keywords), subprefix, width=70) lines.append(line + ',') lines[-1] = lines[-1][:-1]+')' return '\n'.join(lines)
python
def assignrepr(self, prefix: str) -> str: """Return a |repr| string with a prefixed assignment.""" with objecttools.repr_.preserve_strings(True): with objecttools.assignrepr_tuple.always_bracketed(False): blanks = ' ' * (len(prefix) + 8) lines = ['%sElement("%s",' % (prefix, self.name)] for groupname in ('inlets', 'outlets', 'receivers', 'senders'): group = getattr(self, groupname, Node) if group: subprefix = '%s%s=' % (blanks, groupname) # pylint: disable=not-an-iterable # because pylint is wrong nodes = [str(node) for node in group] # pylint: enable=not-an-iterable line = objecttools.assignrepr_list( nodes, subprefix, width=70) lines.append(line + ',') if self.keywords: subprefix = '%skeywords=' % blanks line = objecttools.assignrepr_list( sorted(self.keywords), subprefix, width=70) lines.append(line + ',') lines[-1] = lines[-1][:-1]+')' return '\n'.join(lines)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/devicetools.py#L2253-L2276
train
hydpy-dev/hydpy
hydpy/core/modeltools.py
Model._init_methods
def _init_methods(self): """Convert all pure Python calculation functions of the model class to methods and assign them to the model instance. """ for name_group in self._METHOD_GROUPS: functions = getattr(self, name_group, ()) uniques = {} for func in functions: name_func = func.__name__ method = types.MethodType(func, self) setattr(self, name_func, method) shortname = '_'.join(name_func.split('_')[:-1]) if shortname in uniques: uniques[shortname] = None else: uniques[shortname] = method for (shortname, method) in uniques.items(): if method is not None: setattr(self, shortname, method)
python
def _init_methods(self): """Convert all pure Python calculation functions of the model class to methods and assign them to the model instance. """ for name_group in self._METHOD_GROUPS: functions = getattr(self, name_group, ()) uniques = {} for func in functions: name_func = func.__name__ method = types.MethodType(func, self) setattr(self, name_func, method) shortname = '_'.join(name_func.split('_')[:-1]) if shortname in uniques: uniques[shortname] = None else: uniques[shortname] = method for (shortname, method) in uniques.items(): if method is not None: setattr(self, shortname, method)
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Convert all pure Python calculation functions of the model class to methods and assign them to the model instance.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/modeltools.py#L47-L65
train
hydpy-dev/hydpy
hydpy/core/modeltools.py
Model.name
def name(self): """Name of the model type. For base models, |Model.name| corresponds to the package name: >>> from hydpy import prepare_model >>> hland = prepare_model('hland') >>> hland.name 'hland' For application models, |Model.name| corresponds the module name: >>> hland_v1 = prepare_model('hland_v1') >>> hland_v1.name 'hland_v1' This last example has only technical reasons: >>> hland.name 'hland' """ name = self.__name if name: return name subs = self.__module__.split('.') if len(subs) == 2: type(self).__name = subs[1] else: type(self).__name = subs[2] return self.__name
python
def name(self): """Name of the model type. For base models, |Model.name| corresponds to the package name: >>> from hydpy import prepare_model >>> hland = prepare_model('hland') >>> hland.name 'hland' For application models, |Model.name| corresponds the module name: >>> hland_v1 = prepare_model('hland_v1') >>> hland_v1.name 'hland_v1' This last example has only technical reasons: >>> hland.name 'hland' """ name = self.__name if name: return name subs = self.__module__.split('.') if len(subs) == 2: type(self).__name = subs[1] else: type(self).__name = subs[2] return self.__name
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Name of the model type. For base models, |Model.name| corresponds to the package name: >>> from hydpy import prepare_model >>> hland = prepare_model('hland') >>> hland.name 'hland' For application models, |Model.name| corresponds the module name: >>> hland_v1 = prepare_model('hland_v1') >>> hland_v1.name 'hland_v1' This last example has only technical reasons: >>> hland.name 'hland'
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/modeltools.py#L68-L97
train
hydpy-dev/hydpy
hydpy/core/modeltools.py
Model.connect
def connect(self): """Connect the link sequences of the actual model.""" try: for group in ('inlets', 'receivers', 'outlets', 'senders'): self._connect_subgroup(group) except BaseException: objecttools.augment_excmessage( 'While trying to build the node connection of the `%s` ' 'sequences of the model handled by element `%s`' % (group[:-1], objecttools.devicename(self)))
python
def connect(self): """Connect the link sequences of the actual model.""" try: for group in ('inlets', 'receivers', 'outlets', 'senders'): self._connect_subgroup(group) except BaseException: objecttools.augment_excmessage( 'While trying to build the node connection of the `%s` ' 'sequences of the model handled by element `%s`' % (group[:-1], objecttools.devicename(self)))
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Connect the link sequences of the actual model.
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/modeltools.py#L99-L108
train
hydpy-dev/hydpy
hydpy/core/modeltools.py
ModelELS.calculate_single_terms
def calculate_single_terms(self): """Apply all methods stored in the hidden attribute `PART_ODE_METHODS`. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> k(0.25) >>> states.s = 1.0 >>> model.calculate_single_terms() >>> fluxes.q q(0.25) """ self.numvars.nmb_calls = self.numvars.nmb_calls+1 for method in self.PART_ODE_METHODS: method(self)
python
def calculate_single_terms(self): """Apply all methods stored in the hidden attribute `PART_ODE_METHODS`. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> k(0.25) >>> states.s = 1.0 >>> model.calculate_single_terms() >>> fluxes.q q(0.25) """ self.numvars.nmb_calls = self.numvars.nmb_calls+1 for method in self.PART_ODE_METHODS: method(self)
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Apply all methods stored in the hidden attribute `PART_ODE_METHODS`. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> k(0.25) >>> states.s = 1.0 >>> model.calculate_single_terms() >>> fluxes.q q(0.25)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/modeltools.py#L515-L529
train
hydpy-dev/hydpy
hydpy/core/modeltools.py
ModelELS.get_sum_fluxes
def get_sum_fluxes(self): """Get the sum of the fluxes calculated so far. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> fluxes.q = 0.0 >>> fluxes.fastaccess._q_sum = 1.0 >>> model.get_sum_fluxes() >>> fluxes.q q(1.0) """ fluxes = self.sequences.fluxes for flux in fluxes.numerics: flux(getattr(fluxes.fastaccess, '_%s_sum' % flux.name))
python
def get_sum_fluxes(self): """Get the sum of the fluxes calculated so far. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> fluxes.q = 0.0 >>> fluxes.fastaccess._q_sum = 1.0 >>> model.get_sum_fluxes() >>> fluxes.q q(1.0) """ fluxes = self.sequences.fluxes for flux in fluxes.numerics: flux(getattr(fluxes.fastaccess, '_%s_sum' % flux.name))
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Get the sum of the fluxes calculated so far. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> fluxes.q = 0.0 >>> fluxes.fastaccess._q_sum = 1.0 >>> model.get_sum_fluxes() >>> fluxes.q q(1.0)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/modeltools.py#L611-L624
train
hydpy-dev/hydpy
hydpy/core/modeltools.py
ModelELS.integrate_fluxes
def integrate_fluxes(self): """Perform a dot multiplication between the fluxes and the A coefficients associated with the different stages of the actual method. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> model.numvars.idx_method = 2 >>> model.numvars.idx_stage = 1 >>> model.numvars.dt = 0.5 >>> points = numpy.asarray(fluxes.fastaccess._q_points) >>> points[:4] = 15., 2., -999., 0. >>> model.integrate_fluxes() >>> from hydpy import round_ >>> from hydpy import pub >>> round_(numpy.asarray(model.numconsts.a_coefs)[1, 1, :2]) 0.375, 0.125 >>> fluxes.q q(2.9375) """ fluxes = self.sequences.fluxes for flux in fluxes.numerics: points = getattr(fluxes.fastaccess, '_%s_points' % flux.name) coefs = self.numconsts.a_coefs[self.numvars.idx_method-1, self.numvars.idx_stage, :self.numvars.idx_method] flux(self.numvars.dt * numpy.dot(coefs, points[:self.numvars.idx_method]))
python
def integrate_fluxes(self): """Perform a dot multiplication between the fluxes and the A coefficients associated with the different stages of the actual method. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> model.numvars.idx_method = 2 >>> model.numvars.idx_stage = 1 >>> model.numvars.dt = 0.5 >>> points = numpy.asarray(fluxes.fastaccess._q_points) >>> points[:4] = 15., 2., -999., 0. >>> model.integrate_fluxes() >>> from hydpy import round_ >>> from hydpy import pub >>> round_(numpy.asarray(model.numconsts.a_coefs)[1, 1, :2]) 0.375, 0.125 >>> fluxes.q q(2.9375) """ fluxes = self.sequences.fluxes for flux in fluxes.numerics: points = getattr(fluxes.fastaccess, '_%s_points' % flux.name) coefs = self.numconsts.a_coefs[self.numvars.idx_method-1, self.numvars.idx_stage, :self.numvars.idx_method] flux(self.numvars.dt * numpy.dot(coefs, points[:self.numvars.idx_method]))
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Perform a dot multiplication between the fluxes and the A coefficients associated with the different stages of the actual method. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> model.numvars.idx_method = 2 >>> model.numvars.idx_stage = 1 >>> model.numvars.dt = 0.5 >>> points = numpy.asarray(fluxes.fastaccess._q_points) >>> points[:4] = 15., 2., -999., 0. >>> model.integrate_fluxes() >>> from hydpy import round_ >>> from hydpy import pub >>> round_(numpy.asarray(model.numconsts.a_coefs)[1, 1, :2]) 0.375, 0.125 >>> fluxes.q q(2.9375)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/modeltools.py#L664-L691
train
hydpy-dev/hydpy
hydpy/core/modeltools.py
ModelELS.reset_sum_fluxes
def reset_sum_fluxes(self): """Set the sum of the fluxes calculated so far to zero. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> fluxes.fastaccess._q_sum = 5. >>> model.reset_sum_fluxes() >>> fluxes.fastaccess._q_sum 0.0 """ fluxes = self.sequences.fluxes for flux in fluxes.numerics: if flux.NDIM == 0: setattr(fluxes.fastaccess, '_%s_sum' % flux.name, 0.) else: getattr(fluxes.fastaccess, '_%s_sum' % flux.name)[:] = 0.
python
def reset_sum_fluxes(self): """Set the sum of the fluxes calculated so far to zero. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> fluxes.fastaccess._q_sum = 5. >>> model.reset_sum_fluxes() >>> fluxes.fastaccess._q_sum 0.0 """ fluxes = self.sequences.fluxes for flux in fluxes.numerics: if flux.NDIM == 0: setattr(fluxes.fastaccess, '_%s_sum' % flux.name, 0.) else: getattr(fluxes.fastaccess, '_%s_sum' % flux.name)[:] = 0.
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Set the sum of the fluxes calculated so far to zero. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> fluxes.fastaccess._q_sum = 5. >>> model.reset_sum_fluxes() >>> fluxes.fastaccess._q_sum 0.0
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/modeltools.py#L693-L708
train
hydpy-dev/hydpy
hydpy/core/modeltools.py
ModelELS.addup_fluxes
def addup_fluxes(self): """Add up the sum of the fluxes calculated so far. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> fluxes.fastaccess._q_sum = 1.0 >>> fluxes.q(2.0) >>> model.addup_fluxes() >>> fluxes.fastaccess._q_sum 3.0 """ fluxes = self.sequences.fluxes for flux in fluxes.numerics: sum_ = getattr(fluxes.fastaccess, '_%s_sum' % flux.name) sum_ += flux if flux.NDIM == 0: setattr(fluxes.fastaccess, '_%s_sum' % flux.name, sum_)
python
def addup_fluxes(self): """Add up the sum of the fluxes calculated so far. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> fluxes.fastaccess._q_sum = 1.0 >>> fluxes.q(2.0) >>> model.addup_fluxes() >>> fluxes.fastaccess._q_sum 3.0 """ fluxes = self.sequences.fluxes for flux in fluxes.numerics: sum_ = getattr(fluxes.fastaccess, '_%s_sum' % flux.name) sum_ += flux if flux.NDIM == 0: setattr(fluxes.fastaccess, '_%s_sum' % flux.name, sum_)
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Add up the sum of the fluxes calculated so far. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> fluxes.fastaccess._q_sum = 1.0 >>> fluxes.q(2.0) >>> model.addup_fluxes() >>> fluxes.fastaccess._q_sum 3.0
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/modeltools.py#L710-L726
train
hydpy-dev/hydpy
hydpy/core/modeltools.py
ModelELS.calculate_error
def calculate_error(self): """Estimate the numerical error based on the fluxes calculated by the current and the last method. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> model.numvars.idx_method = 2 >>> results = numpy.asarray(fluxes.fastaccess._q_results) >>> results[:4] = 0., 3., 4., 0. >>> model.calculate_error() >>> from hydpy import round_ >>> round_(model.numvars.error) 1.0 """ self.numvars.error = 0. fluxes = self.sequences.fluxes for flux in fluxes.numerics: results = getattr(fluxes.fastaccess, '_%s_results' % flux.name) diff = (results[self.numvars.idx_method] - results[self.numvars.idx_method-1]) self.numvars.error = max(self.numvars.error, numpy.max(numpy.abs(diff)))
python
def calculate_error(self): """Estimate the numerical error based on the fluxes calculated by the current and the last method. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> model.numvars.idx_method = 2 >>> results = numpy.asarray(fluxes.fastaccess._q_results) >>> results[:4] = 0., 3., 4., 0. >>> model.calculate_error() >>> from hydpy import round_ >>> round_(model.numvars.error) 1.0 """ self.numvars.error = 0. fluxes = self.sequences.fluxes for flux in fluxes.numerics: results = getattr(fluxes.fastaccess, '_%s_results' % flux.name) diff = (results[self.numvars.idx_method] - results[self.numvars.idx_method-1]) self.numvars.error = max(self.numvars.error, numpy.max(numpy.abs(diff)))
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Estimate the numerical error based on the fluxes calculated by the current and the last method. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> model.numvars.idx_method = 2 >>> results = numpy.asarray(fluxes.fastaccess._q_results) >>> results[:4] = 0., 3., 4., 0. >>> model.calculate_error() >>> from hydpy import round_ >>> round_(model.numvars.error) 1.0
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/modeltools.py#L728-L749
train
hydpy-dev/hydpy
hydpy/core/modeltools.py
ModelELS.extrapolate_error
def extrapolate_error(self): """Estimate the numerical error to be expected when applying all methods available based on the results of the current and the last method. Note that this expolation strategy cannot be applied on the first method. If the current method is the first one, `-999.9` is returned. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> model.numvars.error = 1e-2 >>> model.numvars.last_error = 1e-1 >>> model.numvars.idx_method = 10 >>> model.extrapolate_error() >>> from hydpy import round_ >>> round_(model.numvars.extrapolated_error) 0.01 >>> model.numvars.idx_method = 9 >>> model.extrapolate_error() >>> round_(model.numvars.extrapolated_error) 0.001 """ if self.numvars.idx_method > 2: self.numvars.extrapolated_error = modelutils.exp( modelutils.log(self.numvars.error) + (modelutils.log(self.numvars.error) - modelutils.log(self.numvars.last_error)) * (self.numconsts.nmb_methods-self.numvars.idx_method)) else: self.numvars.extrapolated_error = -999.9
python
def extrapolate_error(self): """Estimate the numerical error to be expected when applying all methods available based on the results of the current and the last method. Note that this expolation strategy cannot be applied on the first method. If the current method is the first one, `-999.9` is returned. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> model.numvars.error = 1e-2 >>> model.numvars.last_error = 1e-1 >>> model.numvars.idx_method = 10 >>> model.extrapolate_error() >>> from hydpy import round_ >>> round_(model.numvars.extrapolated_error) 0.01 >>> model.numvars.idx_method = 9 >>> model.extrapolate_error() >>> round_(model.numvars.extrapolated_error) 0.001 """ if self.numvars.idx_method > 2: self.numvars.extrapolated_error = modelutils.exp( modelutils.log(self.numvars.error) + (modelutils.log(self.numvars.error) - modelutils.log(self.numvars.last_error)) * (self.numconsts.nmb_methods-self.numvars.idx_method)) else: self.numvars.extrapolated_error = -999.9
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Estimate the numerical error to be expected when applying all methods available based on the results of the current and the last method. Note that this expolation strategy cannot be applied on the first method. If the current method is the first one, `-999.9` is returned. >>> from hydpy.models.test_v1 import * >>> parameterstep() >>> model.numvars.error = 1e-2 >>> model.numvars.last_error = 1e-1 >>> model.numvars.idx_method = 10 >>> model.extrapolate_error() >>> from hydpy import round_ >>> round_(model.numvars.extrapolated_error) 0.01 >>> model.numvars.idx_method = 9 >>> model.extrapolate_error() >>> round_(model.numvars.extrapolated_error) 0.001
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/core/modeltools.py#L751-L780
train
hydpy-dev/hydpy
hydpy/auxs/xmltools.py
run_simulation
def run_simulation(projectname: str, xmlfile: str): """Perform a HydPy workflow in agreement with the given XML configuration file available in the directory of the given project. ToDo Function |run_simulation| is a "script function" and is normally used as explained in the main documentation on module |xmltools|. """ write = commandtools.print_textandtime hydpy.pub.options.printprogress = False write(f'Start HydPy project `{projectname}`') hp = hydpytools.HydPy(projectname) write(f'Read configuration file `{xmlfile}`') interface = XMLInterface(xmlfile) write('Interpret the defined options') interface.update_options() hydpy.pub.options.printprogress = False write('Interpret the defined period') interface.update_timegrids() write('Read all network files') hp.prepare_network() write('Activate the selected network') hp.update_devices(interface.fullselection) write('Read the required control files') hp.init_models() write('Read the required condition files') interface.conditions_io.load_conditions() write('Read the required time series files') interface.series_io.prepare_series() interface.series_io.load_series() write('Perform the simulation run') hp.doit() write('Write the desired condition files') interface.conditions_io.save_conditions() write('Write the desired time series files') interface.series_io.save_series()
python
def run_simulation(projectname: str, xmlfile: str): """Perform a HydPy workflow in agreement with the given XML configuration file available in the directory of the given project. ToDo Function |run_simulation| is a "script function" and is normally used as explained in the main documentation on module |xmltools|. """ write = commandtools.print_textandtime hydpy.pub.options.printprogress = False write(f'Start HydPy project `{projectname}`') hp = hydpytools.HydPy(projectname) write(f'Read configuration file `{xmlfile}`') interface = XMLInterface(xmlfile) write('Interpret the defined options') interface.update_options() hydpy.pub.options.printprogress = False write('Interpret the defined period') interface.update_timegrids() write('Read all network files') hp.prepare_network() write('Activate the selected network') hp.update_devices(interface.fullselection) write('Read the required control files') hp.init_models() write('Read the required condition files') interface.conditions_io.load_conditions() write('Read the required time series files') interface.series_io.prepare_series() interface.series_io.load_series() write('Perform the simulation run') hp.doit() write('Write the desired condition files') interface.conditions_io.save_conditions() write('Write the desired time series files') interface.series_io.save_series()
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/auxs/xmltools.py#L188-L222
train
hydpy-dev/hydpy
hydpy/auxs/xmltools.py
XMLInterface.validate_xml
def validate_xml(self) -> None: """Raise an error if the actual XML does not agree with one of the available schema files. # ToDo: should it be accompanied by a script function? The first example relies on a distorted version of the configuration file `single_run.xml`: >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import TestIO, xml_replace >>> from hydpy.auxs.xmltools import XMLInterface >>> import os >>> with TestIO(): # doctest: +ELLIPSIS ... xml_replace('LahnH/single_run', ... firstdate='1996-01-32T00:00:00') template file: LahnH/single_run.xmlt target file: LahnH/single_run.xml replacements: config_start --> <...HydPyConfigBase.xsd" ...HydPyConfigSingleRun.xsd"> (default argument) firstdate --> 1996-01-32T00:00:00 (given argument) zip_ --> false (default argument) zip_ --> false (default argument) config_end --> </hpcsr:config> (default argument) >>> with TestIO(): ... interface = XMLInterface('single_run.xml', 'LahnH') >>> interface.validate_xml() # doctest: +ELLIPSIS Traceback (most recent call last): ... hydpy.core.objecttools.xmlschema.validators.exceptions.\ XMLSchemaDecodeError: While trying to validate XML file `...single_run.xml`, \ the following error occurred: failed validating '1996-01-32T00:00:00' with \ XsdAtomicBuiltin(name='xs:dateTime'). ... Reason: day is out of range for month ... Schema: ... Instance: ... <firstdate xmlns="https://github.com/hydpy-dev/hydpy/releases/\ download/your-hydpy-version/HydPyConfigBase.xsd">1996-01-32T00:00:00</firstdate> ... Path: /hpcsr:config/timegrid/firstdate ... In the second example, we examine a correct configuration file: >>> with TestIO(): # doctest: +ELLIPSIS ... xml_replace('LahnH/single_run') ... interface = XMLInterface('single_run.xml', 'LahnH') template file: LahnH/single_run.xmlt target file: LahnH/single_run.xml replacements: config_start --> <...HydPyConfigBase.xsd" ...HydPyConfigSingleRun.xsd"> (default argument) firstdate --> 1996-01-01T00:00:00 (default argument) zip_ --> false (default argument) zip_ --> false (default argument) config_end --> </hpcsr:config> (default argument) >>> interface.validate_xml() The XML configuration file must correctly refer to the corresponding schema file: >>> with TestIO(): # doctest: +ELLIPSIS ... xml_replace('LahnH/single_run', ... config_start='<config>', ... config_end='</config>') ... interface = XMLInterface('single_run.xml', 'LahnH') template file: LahnH/single_run.xmlt target file: LahnH/single_run.xml replacements: config_start --> <config> (given argument) firstdate --> 1996-01-01T00:00:00 (default argument) zip_ --> false (default argument) zip_ --> false (default argument) config_end --> </config> (given argument) >>> interface.validate_xml() # doctest: +ELLIPSIS Traceback (most recent call last): ... RuntimeError: While trying to validate XML file `...single_run.xml`, \ the following error occurred: Configuration file `single_run.xml` does not \ correctly refer to one of the available XML schema files \ (HydPyConfigSingleRun.xsd and HydPyConfigMultipleRuns.xsd). XML files based on `HydPyConfigMultipleRuns.xsd` can be validated as well: >>> with TestIO(): ... interface = XMLInterface('multiple_runs.xml', 'LahnH') >>> interface.validate_xml() # doctest: +ELLIPSIS """ try: filenames = ('HydPyConfigSingleRun.xsd', 'HydPyConfigMultipleRuns.xsd') for name in filenames: if name in self.root.tag: schemafile = name break else: raise RuntimeError( f'Configuration file `{os.path.split(self.filepath)[-1]}` ' f'does not correctly refer to one of the available XML ' f'schema files ({objecttools.enumeration(filenames)}).') schemapath = os.path.join(conf.__path__[0], schemafile) schema = xmlschema.XMLSchema(schemapath) schema.validate(self.filepath) except BaseException: objecttools.augment_excmessage( f'While trying to validate XML file `{self.filepath}`')
python
def validate_xml(self) -> None: """Raise an error if the actual XML does not agree with one of the available schema files. # ToDo: should it be accompanied by a script function? The first example relies on a distorted version of the configuration file `single_run.xml`: >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import TestIO, xml_replace >>> from hydpy.auxs.xmltools import XMLInterface >>> import os >>> with TestIO(): # doctest: +ELLIPSIS ... xml_replace('LahnH/single_run', ... firstdate='1996-01-32T00:00:00') template file: LahnH/single_run.xmlt target file: LahnH/single_run.xml replacements: config_start --> <...HydPyConfigBase.xsd" ...HydPyConfigSingleRun.xsd"> (default argument) firstdate --> 1996-01-32T00:00:00 (given argument) zip_ --> false (default argument) zip_ --> false (default argument) config_end --> </hpcsr:config> (default argument) >>> with TestIO(): ... interface = XMLInterface('single_run.xml', 'LahnH') >>> interface.validate_xml() # doctest: +ELLIPSIS Traceback (most recent call last): ... hydpy.core.objecttools.xmlschema.validators.exceptions.\ XMLSchemaDecodeError: While trying to validate XML file `...single_run.xml`, \ the following error occurred: failed validating '1996-01-32T00:00:00' with \ XsdAtomicBuiltin(name='xs:dateTime'). ... Reason: day is out of range for month ... Schema: ... Instance: ... <firstdate xmlns="https://github.com/hydpy-dev/hydpy/releases/\ download/your-hydpy-version/HydPyConfigBase.xsd">1996-01-32T00:00:00</firstdate> ... Path: /hpcsr:config/timegrid/firstdate ... In the second example, we examine a correct configuration file: >>> with TestIO(): # doctest: +ELLIPSIS ... xml_replace('LahnH/single_run') ... interface = XMLInterface('single_run.xml', 'LahnH') template file: LahnH/single_run.xmlt target file: LahnH/single_run.xml replacements: config_start --> <...HydPyConfigBase.xsd" ...HydPyConfigSingleRun.xsd"> (default argument) firstdate --> 1996-01-01T00:00:00 (default argument) zip_ --> false (default argument) zip_ --> false (default argument) config_end --> </hpcsr:config> (default argument) >>> interface.validate_xml() The XML configuration file must correctly refer to the corresponding schema file: >>> with TestIO(): # doctest: +ELLIPSIS ... xml_replace('LahnH/single_run', ... config_start='<config>', ... config_end='</config>') ... interface = XMLInterface('single_run.xml', 'LahnH') template file: LahnH/single_run.xmlt target file: LahnH/single_run.xml replacements: config_start --> <config> (given argument) firstdate --> 1996-01-01T00:00:00 (default argument) zip_ --> false (default argument) zip_ --> false (default argument) config_end --> </config> (given argument) >>> interface.validate_xml() # doctest: +ELLIPSIS Traceback (most recent call last): ... RuntimeError: While trying to validate XML file `...single_run.xml`, \ the following error occurred: Configuration file `single_run.xml` does not \ correctly refer to one of the available XML schema files \ (HydPyConfigSingleRun.xsd and HydPyConfigMultipleRuns.xsd). XML files based on `HydPyConfigMultipleRuns.xsd` can be validated as well: >>> with TestIO(): ... interface = XMLInterface('multiple_runs.xml', 'LahnH') >>> interface.validate_xml() # doctest: +ELLIPSIS """ try: filenames = ('HydPyConfigSingleRun.xsd', 'HydPyConfigMultipleRuns.xsd') for name in filenames: if name in self.root.tag: schemafile = name break else: raise RuntimeError( f'Configuration file `{os.path.split(self.filepath)[-1]}` ' f'does not correctly refer to one of the available XML ' f'schema files ({objecttools.enumeration(filenames)}).') schemapath = os.path.join(conf.__path__[0], schemafile) schema = xmlschema.XMLSchema(schemapath) schema.validate(self.filepath) except BaseException: objecttools.augment_excmessage( f'While trying to validate XML file `{self.filepath}`')
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Raise an error if the actual XML does not agree with one of the available schema files. # ToDo: should it be accompanied by a script function? The first example relies on a distorted version of the configuration file `single_run.xml`: >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import TestIO, xml_replace >>> from hydpy.auxs.xmltools import XMLInterface >>> import os >>> with TestIO(): # doctest: +ELLIPSIS ... xml_replace('LahnH/single_run', ... firstdate='1996-01-32T00:00:00') template file: LahnH/single_run.xmlt target file: LahnH/single_run.xml replacements: config_start --> <...HydPyConfigBase.xsd" ...HydPyConfigSingleRun.xsd"> (default argument) firstdate --> 1996-01-32T00:00:00 (given argument) zip_ --> false (default argument) zip_ --> false (default argument) config_end --> </hpcsr:config> (default argument) >>> with TestIO(): ... interface = XMLInterface('single_run.xml', 'LahnH') >>> interface.validate_xml() # doctest: +ELLIPSIS Traceback (most recent call last): ... hydpy.core.objecttools.xmlschema.validators.exceptions.\ XMLSchemaDecodeError: While trying to validate XML file `...single_run.xml`, \ the following error occurred: failed validating '1996-01-32T00:00:00' with \ XsdAtomicBuiltin(name='xs:dateTime'). ... Reason: day is out of range for month ... Schema: ... Instance: ... <firstdate xmlns="https://github.com/hydpy-dev/hydpy/releases/\ download/your-hydpy-version/HydPyConfigBase.xsd">1996-01-32T00:00:00</firstdate> ... Path: /hpcsr:config/timegrid/firstdate ... In the second example, we examine a correct configuration file: >>> with TestIO(): # doctest: +ELLIPSIS ... xml_replace('LahnH/single_run') ... interface = XMLInterface('single_run.xml', 'LahnH') template file: LahnH/single_run.xmlt target file: LahnH/single_run.xml replacements: config_start --> <...HydPyConfigBase.xsd" ...HydPyConfigSingleRun.xsd"> (default argument) firstdate --> 1996-01-01T00:00:00 (default argument) zip_ --> false (default argument) zip_ --> false (default argument) config_end --> </hpcsr:config> (default argument) >>> interface.validate_xml() The XML configuration file must correctly refer to the corresponding schema file: >>> with TestIO(): # doctest: +ELLIPSIS ... xml_replace('LahnH/single_run', ... config_start='<config>', ... config_end='</config>') ... interface = XMLInterface('single_run.xml', 'LahnH') template file: LahnH/single_run.xmlt target file: LahnH/single_run.xml replacements: config_start --> <config> (given argument) firstdate --> 1996-01-01T00:00:00 (default argument) zip_ --> false (default argument) zip_ --> false (default argument) config_end --> </config> (given argument) >>> interface.validate_xml() # doctest: +ELLIPSIS Traceback (most recent call last): ... RuntimeError: While trying to validate XML file `...single_run.xml`, \ the following error occurred: Configuration file `single_run.xml` does not \ correctly refer to one of the available XML schema files \ (HydPyConfigSingleRun.xsd and HydPyConfigMultipleRuns.xsd). XML files based on `HydPyConfigMultipleRuns.xsd` can be validated as well: >>> with TestIO(): ... interface = XMLInterface('multiple_runs.xml', 'LahnH') >>> interface.validate_xml() # doctest: +ELLIPSIS
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/auxs/xmltools.py#L315-L427
train
hydpy-dev/hydpy
hydpy/auxs/xmltools.py
XMLInterface.update_options
def update_options(self) -> None: """Update the |Options| object available in module |pub| with the values defined in the `options` XML element. >>> from hydpy.auxs.xmltools import XMLInterface >>> from hydpy import data, pub >>> interface = XMLInterface('single_run.xml', data.get_path('LahnH')) >>> pub.options.printprogress = True >>> pub.options.printincolor = True >>> pub.options.reprdigits = -1 >>> pub.options.utcoffset = -60 >>> pub.options.ellipsis = 0 >>> pub.options.warnsimulationstep = 0 >>> interface.update_options() >>> pub.options Options( autocompile -> 1 checkseries -> 1 dirverbose -> 0 ellipsis -> 0 forcecompiling -> 0 printprogress -> 0 printincolor -> 0 reprcomments -> 0 reprdigits -> 6 skipdoctests -> 0 trimvariables -> 1 usecython -> 1 usedefaultvalues -> 0 utcoffset -> 60 warnmissingcontrolfile -> 0 warnmissingobsfile -> 1 warnmissingsimfile -> 1 warnsimulationstep -> 0 warntrim -> 1 flattennetcdf -> True isolatenetcdf -> True timeaxisnetcdf -> 0 ) >>> pub.options.printprogress = False >>> pub.options.reprdigits = 6 """ options = hydpy.pub.options for option in self.find('options'): value = option.text if value in ('true', 'false'): value = value == 'true' setattr(options, strip(option.tag), value) options.printprogress = False options.printincolor = False
python
def update_options(self) -> None: """Update the |Options| object available in module |pub| with the values defined in the `options` XML element. >>> from hydpy.auxs.xmltools import XMLInterface >>> from hydpy import data, pub >>> interface = XMLInterface('single_run.xml', data.get_path('LahnH')) >>> pub.options.printprogress = True >>> pub.options.printincolor = True >>> pub.options.reprdigits = -1 >>> pub.options.utcoffset = -60 >>> pub.options.ellipsis = 0 >>> pub.options.warnsimulationstep = 0 >>> interface.update_options() >>> pub.options Options( autocompile -> 1 checkseries -> 1 dirverbose -> 0 ellipsis -> 0 forcecompiling -> 0 printprogress -> 0 printincolor -> 0 reprcomments -> 0 reprdigits -> 6 skipdoctests -> 0 trimvariables -> 1 usecython -> 1 usedefaultvalues -> 0 utcoffset -> 60 warnmissingcontrolfile -> 0 warnmissingobsfile -> 1 warnmissingsimfile -> 1 warnsimulationstep -> 0 warntrim -> 1 flattennetcdf -> True isolatenetcdf -> True timeaxisnetcdf -> 0 ) >>> pub.options.printprogress = False >>> pub.options.reprdigits = 6 """ options = hydpy.pub.options for option in self.find('options'): value = option.text if value in ('true', 'false'): value = value == 'true' setattr(options, strip(option.tag), value) options.printprogress = False options.printincolor = False
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Update the |Options| object available in module |pub| with the values defined in the `options` XML element. >>> from hydpy.auxs.xmltools import XMLInterface >>> from hydpy import data, pub >>> interface = XMLInterface('single_run.xml', data.get_path('LahnH')) >>> pub.options.printprogress = True >>> pub.options.printincolor = True >>> pub.options.reprdigits = -1 >>> pub.options.utcoffset = -60 >>> pub.options.ellipsis = 0 >>> pub.options.warnsimulationstep = 0 >>> interface.update_options() >>> pub.options Options( autocompile -> 1 checkseries -> 1 dirverbose -> 0 ellipsis -> 0 forcecompiling -> 0 printprogress -> 0 printincolor -> 0 reprcomments -> 0 reprdigits -> 6 skipdoctests -> 0 trimvariables -> 1 usecython -> 1 usedefaultvalues -> 0 utcoffset -> 60 warnmissingcontrolfile -> 0 warnmissingobsfile -> 1 warnmissingsimfile -> 1 warnsimulationstep -> 0 warntrim -> 1 flattennetcdf -> True isolatenetcdf -> True timeaxisnetcdf -> 0 ) >>> pub.options.printprogress = False >>> pub.options.reprdigits = 6
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/auxs/xmltools.py#L429-L478
train
hydpy-dev/hydpy
hydpy/auxs/xmltools.py
XMLInterface.update_timegrids
def update_timegrids(self) -> None: """Update the |Timegrids| object available in module |pub| with the values defined in the `timegrid` XML element. Usually, one would prefer to define `firstdate`, `lastdate`, and `stepsize` elements as in the XML configuration file of the `LahnH` example project: >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, pub, TestIO >>> from hydpy.auxs.xmltools import XMLInterface >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... XMLInterface('single_run.xml').update_timegrids() >>> pub.timegrids Timegrids(Timegrid('1996-01-01T00:00:00', '1996-01-06T00:00:00', '1d')) Alternatively, one can provide the file path to a `seriesfile`, which must be a valid NetCDF file. The |XMLInterface| object then interprets the file's time information: >>> name = 'LahnH/series/input/hland_v1_input_p.nc' >>> with TestIO(): ... with open('LahnH/single_run.xml') as file_: ... lines = file_.readlines() ... for idx, line in enumerate(lines): ... if '<timegrid>' in line: ... break ... with open('LahnH/single_run.xml', 'w') as file_: ... _ = file_.write(''.join(lines[:idx+1])) ... _ = file_.write( ... f' <seriesfile>{name}</seriesfile>\\n') ... _ = file_.write(''.join(lines[idx+4:])) ... XMLInterface('single_run.xml').update_timegrids() >>> pub.timegrids Timegrids(Timegrid('1996-01-01 00:00:00', '2007-01-01 00:00:00', '1d')) """ timegrid_xml = self.find('timegrid') try: timegrid = timetools.Timegrid( *(timegrid_xml[idx].text for idx in range(3))) hydpy.pub.timegrids = timetools.Timegrids(timegrid) except IndexError: seriesfile = find(timegrid_xml, 'seriesfile').text with netcdf4.Dataset(seriesfile) as ncfile: hydpy.pub.timegrids = timetools.Timegrids( netcdftools.query_timegrid(ncfile))
python
def update_timegrids(self) -> None: """Update the |Timegrids| object available in module |pub| with the values defined in the `timegrid` XML element. Usually, one would prefer to define `firstdate`, `lastdate`, and `stepsize` elements as in the XML configuration file of the `LahnH` example project: >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, pub, TestIO >>> from hydpy.auxs.xmltools import XMLInterface >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... XMLInterface('single_run.xml').update_timegrids() >>> pub.timegrids Timegrids(Timegrid('1996-01-01T00:00:00', '1996-01-06T00:00:00', '1d')) Alternatively, one can provide the file path to a `seriesfile`, which must be a valid NetCDF file. The |XMLInterface| object then interprets the file's time information: >>> name = 'LahnH/series/input/hland_v1_input_p.nc' >>> with TestIO(): ... with open('LahnH/single_run.xml') as file_: ... lines = file_.readlines() ... for idx, line in enumerate(lines): ... if '<timegrid>' in line: ... break ... with open('LahnH/single_run.xml', 'w') as file_: ... _ = file_.write(''.join(lines[:idx+1])) ... _ = file_.write( ... f' <seriesfile>{name}</seriesfile>\\n') ... _ = file_.write(''.join(lines[idx+4:])) ... XMLInterface('single_run.xml').update_timegrids() >>> pub.timegrids Timegrids(Timegrid('1996-01-01 00:00:00', '2007-01-01 00:00:00', '1d')) """ timegrid_xml = self.find('timegrid') try: timegrid = timetools.Timegrid( *(timegrid_xml[idx].text for idx in range(3))) hydpy.pub.timegrids = timetools.Timegrids(timegrid) except IndexError: seriesfile = find(timegrid_xml, 'seriesfile').text with netcdf4.Dataset(seriesfile) as ncfile: hydpy.pub.timegrids = timetools.Timegrids( netcdftools.query_timegrid(ncfile))
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Update the |Timegrids| object available in module |pub| with the values defined in the `timegrid` XML element. Usually, one would prefer to define `firstdate`, `lastdate`, and `stepsize` elements as in the XML configuration file of the `LahnH` example project: >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, pub, TestIO >>> from hydpy.auxs.xmltools import XMLInterface >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... XMLInterface('single_run.xml').update_timegrids() >>> pub.timegrids Timegrids(Timegrid('1996-01-01T00:00:00', '1996-01-06T00:00:00', '1d')) Alternatively, one can provide the file path to a `seriesfile`, which must be a valid NetCDF file. The |XMLInterface| object then interprets the file's time information: >>> name = 'LahnH/series/input/hland_v1_input_p.nc' >>> with TestIO(): ... with open('LahnH/single_run.xml') as file_: ... lines = file_.readlines() ... for idx, line in enumerate(lines): ... if '<timegrid>' in line: ... break ... with open('LahnH/single_run.xml', 'w') as file_: ... _ = file_.write(''.join(lines[:idx+1])) ... _ = file_.write( ... f' <seriesfile>{name}</seriesfile>\\n') ... _ = file_.write(''.join(lines[idx+4:])) ... XMLInterface('single_run.xml').update_timegrids() >>> pub.timegrids Timegrids(Timegrid('1996-01-01 00:00:00', '2007-01-01 00:00:00', '1d'))
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/auxs/xmltools.py#L480-L533
train
hydpy-dev/hydpy
hydpy/auxs/xmltools.py
XMLInterface.elements
def elements(self) -> Iterator[devicetools.Element]: """Yield all |Element| objects returned by |XMLInterface.selections| and |XMLInterface.devices| without duplicates. >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, TestIO, XMLInterface >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... interface = XMLInterface('single_run.xml') >>> interface.find('selections').text = 'headwaters streams' >>> for element in interface.elements: ... print(element.name) land_dill land_lahn_1 stream_dill_lahn_2 stream_lahn_1_lahn_2 stream_lahn_2_lahn_3 """ selections = copy.copy(self.selections) selections += self.devices elements = set() for selection in selections: for element in selection.elements: if element not in elements: elements.add(element) yield element
python
def elements(self) -> Iterator[devicetools.Element]: """Yield all |Element| objects returned by |XMLInterface.selections| and |XMLInterface.devices| without duplicates. >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, TestIO, XMLInterface >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... interface = XMLInterface('single_run.xml') >>> interface.find('selections').text = 'headwaters streams' >>> for element in interface.elements: ... print(element.name) land_dill land_lahn_1 stream_dill_lahn_2 stream_lahn_1_lahn_2 stream_lahn_2_lahn_3 """ selections = copy.copy(self.selections) selections += self.devices elements = set() for selection in selections: for element in selection.elements: if element not in elements: elements.add(element) yield element
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Yield all |Element| objects returned by |XMLInterface.selections| and |XMLInterface.devices| without duplicates. >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, TestIO, XMLInterface >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... interface = XMLInterface('single_run.xml') >>> interface.find('selections').text = 'headwaters streams' >>> for element in interface.elements: ... print(element.name) land_dill land_lahn_1 stream_dill_lahn_2 stream_lahn_1_lahn_2 stream_lahn_2_lahn_3
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/auxs/xmltools.py#L598-L626
train
hydpy-dev/hydpy
hydpy/auxs/xmltools.py
XMLInterface.fullselection
def fullselection(self) -> selectiontools.Selection: """A |Selection| object containing all |Element| and |Node| objects defined by |XMLInterface.selections| and |XMLInterface.devices|. >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, TestIO, XMLInterface >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... interface = XMLInterface('single_run.xml') >>> interface.find('selections').text = 'nonheadwaters' >>> interface.fullselection Selection("fullselection", nodes=("dill", "lahn_2", "lahn_3"), elements=("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3")) """ fullselection = selectiontools.Selection('fullselection') for selection in self.selections: fullselection += selection fullselection += self.devices return fullselection
python
def fullselection(self) -> selectiontools.Selection: """A |Selection| object containing all |Element| and |Node| objects defined by |XMLInterface.selections| and |XMLInterface.devices|. >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, TestIO, XMLInterface >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... interface = XMLInterface('single_run.xml') >>> interface.find('selections').text = 'nonheadwaters' >>> interface.fullselection Selection("fullselection", nodes=("dill", "lahn_2", "lahn_3"), elements=("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3")) """ fullselection = selectiontools.Selection('fullselection') for selection in self.selections: fullselection += selection fullselection += self.devices return fullselection
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A |Selection| object containing all |Element| and |Node| objects defined by |XMLInterface.selections| and |XMLInterface.devices|. >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, TestIO, XMLInterface >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... interface = XMLInterface('single_run.xml') >>> interface.find('selections').text = 'nonheadwaters' >>> interface.fullselection Selection("fullselection", nodes=("dill", "lahn_2", "lahn_3"), elements=("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3"))
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/auxs/xmltools.py#L629-L652
train
hydpy-dev/hydpy
hydpy/auxs/xmltools.py
XMLConditions.load_conditions
def load_conditions(self) -> None: """Load the condition files of the |Model| objects of all |Element| objects returned by |XMLInterface.elements|: >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, TestIO, XMLInterface >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... hp.init_models() ... interface = XMLInterface('single_run.xml') ... interface.update_timegrids() ... interface.find('selections').text = 'headwaters' ... interface.conditions_io.load_conditions() >>> hp.elements.land_lahn_1.model.sequences.states.lz lz(8.18711) >>> hp.elements.land_lahn_2.model.sequences.states.lz lz(nan) """ hydpy.pub.conditionmanager.currentdir = strip( self.find('inputdir').text) for element in self.master.elements: element.model.sequences.load_conditions()
python
def load_conditions(self) -> None: """Load the condition files of the |Model| objects of all |Element| objects returned by |XMLInterface.elements|: >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, TestIO, XMLInterface >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... hp.init_models() ... interface = XMLInterface('single_run.xml') ... interface.update_timegrids() ... interface.find('selections').text = 'headwaters' ... interface.conditions_io.load_conditions() >>> hp.elements.land_lahn_1.model.sequences.states.lz lz(8.18711) >>> hp.elements.land_lahn_2.model.sequences.states.lz lz(nan) """ hydpy.pub.conditionmanager.currentdir = strip( self.find('inputdir').text) for element in self.master.elements: element.model.sequences.load_conditions()
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Load the condition files of the |Model| objects of all |Element| objects returned by |XMLInterface.elements|: >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, TestIO, XMLInterface >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... hp.init_models() ... interface = XMLInterface('single_run.xml') ... interface.update_timegrids() ... interface.find('selections').text = 'headwaters' ... interface.conditions_io.load_conditions() >>> hp.elements.land_lahn_1.model.sequences.states.lz lz(8.18711) >>> hp.elements.land_lahn_2.model.sequences.states.lz lz(nan)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/auxs/xmltools.py#L700-L724
train
hydpy-dev/hydpy
hydpy/auxs/xmltools.py
XMLConditions.save_conditions
def save_conditions(self) -> None: """Save the condition files of the |Model| objects of all |Element| objects returned by |XMLInterface.elements|: >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> import os >>> from hydpy import HydPy, TestIO, XMLInterface, pub >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... hp.init_models() ... hp.elements.land_dill.model.sequences.states.lz = 999.0 ... interface = XMLInterface('single_run.xml') ... interface.update_timegrids() ... interface.find('selections').text = 'headwaters' ... interface.conditions_io.save_conditions() ... dirpath = 'LahnH/conditions/init_1996_01_06' ... with open(os.path.join(dirpath, 'land_dill.py')) as file_: ... print(file_.readlines()[11].strip()) ... os.path.exists(os.path.join(dirpath, 'land_lahn_2.py')) lz(999.0) False """ hydpy.pub.conditionmanager.currentdir = strip( self.find('outputdir').text) for element in self.master.elements: element.model.sequences.save_conditions() if strip(self.find('zip').text) == 'true': hydpy.pub.conditionmanager.zip_currentdir()
python
def save_conditions(self) -> None: """Save the condition files of the |Model| objects of all |Element| objects returned by |XMLInterface.elements|: >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> import os >>> from hydpy import HydPy, TestIO, XMLInterface, pub >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... hp.init_models() ... hp.elements.land_dill.model.sequences.states.lz = 999.0 ... interface = XMLInterface('single_run.xml') ... interface.update_timegrids() ... interface.find('selections').text = 'headwaters' ... interface.conditions_io.save_conditions() ... dirpath = 'LahnH/conditions/init_1996_01_06' ... with open(os.path.join(dirpath, 'land_dill.py')) as file_: ... print(file_.readlines()[11].strip()) ... os.path.exists(os.path.join(dirpath, 'land_lahn_2.py')) lz(999.0) False """ hydpy.pub.conditionmanager.currentdir = strip( self.find('outputdir').text) for element in self.master.elements: element.model.sequences.save_conditions() if strip(self.find('zip').text) == 'true': hydpy.pub.conditionmanager.zip_currentdir()
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Save the condition files of the |Model| objects of all |Element| objects returned by |XMLInterface.elements|: >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> import os >>> from hydpy import HydPy, TestIO, XMLInterface, pub >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... hp.init_models() ... hp.elements.land_dill.model.sequences.states.lz = 999.0 ... interface = XMLInterface('single_run.xml') ... interface.update_timegrids() ... interface.find('selections').text = 'headwaters' ... interface.conditions_io.save_conditions() ... dirpath = 'LahnH/conditions/init_1996_01_06' ... with open(os.path.join(dirpath, 'land_dill.py')) as file_: ... print(file_.readlines()[11].strip()) ... os.path.exists(os.path.join(dirpath, 'land_lahn_2.py')) lz(999.0) False
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/auxs/xmltools.py#L726-L756
train
hydpy-dev/hydpy
hydpy/auxs/xmltools.py
XMLSeries.prepare_series
def prepare_series(self) -> None: # noinspection PyUnresolvedReferences """Call |XMLSubseries.prepare_series| of all |XMLSubseries| objects with the same memory |set| object. >>> from hydpy.auxs.xmltools import XMLInterface, XMLSubseries >>> from hydpy import data >>> interface = XMLInterface('single_run.xml', data.get_path('LahnH')) >>> series_io = interface.series_io >>> from unittest import mock >>> prepare_series = XMLSubseries.prepare_series >>> XMLSubseries.prepare_series = mock.MagicMock() >>> series_io.prepare_series() >>> args = XMLSubseries.prepare_series.call_args_list >>> len(args) == len(series_io.readers) + len(series_io.writers) True >>> args[0][0][0] set() >>> args[0][0][0] is args[-1][0][0] True >>> XMLSubseries.prepare_series = prepare_series """ memory = set() for output in itertools.chain(self.readers, self.writers): output.prepare_series(memory)
python
def prepare_series(self) -> None: # noinspection PyUnresolvedReferences """Call |XMLSubseries.prepare_series| of all |XMLSubseries| objects with the same memory |set| object. >>> from hydpy.auxs.xmltools import XMLInterface, XMLSubseries >>> from hydpy import data >>> interface = XMLInterface('single_run.xml', data.get_path('LahnH')) >>> series_io = interface.series_io >>> from unittest import mock >>> prepare_series = XMLSubseries.prepare_series >>> XMLSubseries.prepare_series = mock.MagicMock() >>> series_io.prepare_series() >>> args = XMLSubseries.prepare_series.call_args_list >>> len(args) == len(series_io.readers) + len(series_io.writers) True >>> args[0][0][0] set() >>> args[0][0][0] is args[-1][0][0] True >>> XMLSubseries.prepare_series = prepare_series """ memory = set() for output in itertools.chain(self.readers, self.writers): output.prepare_series(memory)
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Call |XMLSubseries.prepare_series| of all |XMLSubseries| objects with the same memory |set| object. >>> from hydpy.auxs.xmltools import XMLInterface, XMLSubseries >>> from hydpy import data >>> interface = XMLInterface('single_run.xml', data.get_path('LahnH')) >>> series_io = interface.series_io >>> from unittest import mock >>> prepare_series = XMLSubseries.prepare_series >>> XMLSubseries.prepare_series = mock.MagicMock() >>> series_io.prepare_series() >>> args = XMLSubseries.prepare_series.call_args_list >>> len(args) == len(series_io.readers) + len(series_io.writers) True >>> args[0][0][0] set() >>> args[0][0][0] is args[-1][0][0] True >>> XMLSubseries.prepare_series = prepare_series
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/auxs/xmltools.py#L796-L820
train
hydpy-dev/hydpy
hydpy/auxs/xmltools.py
XMLSelector.selections
def selections(self) -> selectiontools.Selections: """The |Selections| object defined for the respective `reader` or `writer` element of the actual XML file. ToDo If the `reader` or `writer` element does not define a special selections element, the general |XMLInterface.selections| element of |XMLInterface| is used. >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, TestIO, XMLInterface >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... hp.init_models() ... interface = XMLInterface('single_run.xml') >>> series_io = interface.series_io >>> for seq in (series_io.readers + series_io.writers): ... print(seq.info, seq.selections.names) all input data () precipitation ('headwaters',) soilmoisture ('complete',) averaged ('complete',) """ selections = self.find('selections') master = self while selections is None: master = master.master selections = master.find('selections') return _query_selections(selections)
python
def selections(self) -> selectiontools.Selections: """The |Selections| object defined for the respective `reader` or `writer` element of the actual XML file. ToDo If the `reader` or `writer` element does not define a special selections element, the general |XMLInterface.selections| element of |XMLInterface| is used. >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, TestIO, XMLInterface >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... hp.init_models() ... interface = XMLInterface('single_run.xml') >>> series_io = interface.series_io >>> for seq in (series_io.readers + series_io.writers): ... print(seq.info, seq.selections.names) all input data () precipitation ('headwaters',) soilmoisture ('complete',) averaged ('complete',) """ selections = self.find('selections') master = self while selections is None: master = master.master selections = master.find('selections') return _query_selections(selections)
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The |Selections| object defined for the respective `reader` or `writer` element of the actual XML file. ToDo If the `reader` or `writer` element does not define a special selections element, the general |XMLInterface.selections| element of |XMLInterface| is used. >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, TestIO, XMLInterface >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... hp.init_models() ... interface = XMLInterface('single_run.xml') >>> series_io = interface.series_io >>> for seq in (series_io.readers + series_io.writers): ... print(seq.info, seq.selections.names) all input data () precipitation ('headwaters',) soilmoisture ('complete',) averaged ('complete',)
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/auxs/xmltools.py#L842-L872
train
hydpy-dev/hydpy
hydpy/auxs/xmltools.py
XMLSelector.devices
def devices(self) -> selectiontools.Selection: """The additional devices defined for the respective `reader` or `writer` element contained within a |Selection| object. ToDo If the `reader` or `writer` element does not define its own additional devices, |XMLInterface.devices| of |XMLInterface| is used. >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, TestIO, XMLInterface >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... interface = XMLInterface('single_run.xml') >>> series_io = interface.series_io >>> for seq in (series_io.readers + series_io.writers): ... print(seq.info, seq.devices.nodes, seq.devices.elements) all input data Nodes() \ Elements("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3") precipitation Nodes() Elements("land_lahn_1", "land_lahn_2") soilmoisture Nodes("dill") Elements("land_dill", "land_lahn_1") averaged Nodes() Elements() """ devices = self.find('devices') master = self while devices is None: master = master.master devices = master.find('devices') return _query_devices(devices)
python
def devices(self) -> selectiontools.Selection: """The additional devices defined for the respective `reader` or `writer` element contained within a |Selection| object. ToDo If the `reader` or `writer` element does not define its own additional devices, |XMLInterface.devices| of |XMLInterface| is used. >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, TestIO, XMLInterface >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... interface = XMLInterface('single_run.xml') >>> series_io = interface.series_io >>> for seq in (series_io.readers + series_io.writers): ... print(seq.info, seq.devices.nodes, seq.devices.elements) all input data Nodes() \ Elements("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3") precipitation Nodes() Elements("land_lahn_1", "land_lahn_2") soilmoisture Nodes("dill") Elements("land_dill", "land_lahn_1") averaged Nodes() Elements() """ devices = self.find('devices') master = self while devices is None: master = master.master devices = master.find('devices') return _query_devices(devices)
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The additional devices defined for the respective `reader` or `writer` element contained within a |Selection| object. ToDo If the `reader` or `writer` element does not define its own additional devices, |XMLInterface.devices| of |XMLInterface| is used. >>> from hydpy.core.examples import prepare_full_example_1 >>> prepare_full_example_1() >>> from hydpy import HydPy, TestIO, XMLInterface >>> hp = HydPy('LahnH') >>> with TestIO(): ... hp.prepare_network() ... interface = XMLInterface('single_run.xml') >>> series_io = interface.series_io >>> for seq in (series_io.readers + series_io.writers): ... print(seq.info, seq.devices.nodes, seq.devices.elements) all input data Nodes() \ Elements("land_dill", "land_lahn_1", "land_lahn_2", "land_lahn_3") precipitation Nodes() Elements("land_lahn_1", "land_lahn_2") soilmoisture Nodes("dill") Elements("land_dill", "land_lahn_1") averaged Nodes() Elements()
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1bc6a82cf30786521d86b36e27900c6717d3348d
https://github.com/hydpy-dev/hydpy/blob/1bc6a82cf30786521d86b36e27900c6717d3348d/hydpy/auxs/xmltools.py#L875-L904
train