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Reviewer's GuideThis PR introduces a new experiment design feature in the Sequence diagram for compute_experiment_design command flowsequenceDiagram
participant Registry as "Device Registry"
participant Station as "DispensingStation"
participant Logger as "hardware_interface._logger"
Registry->>Station: Call compute_experiment_design(ratio, ...)
Station->>Logger: Log input ratio
alt Ratio is string
Station->>Station: Parse ratio (simplified or JSON)
Station->>Logger: Log parsed ratio
end
Station->>Station: Validate ratio and parameters
Station->>Station: Call _generate_experiment_design(...)
Station->>Logger: Log compound info
Station->>Station: Calculate solutions, titration, solvents, feeding_order
Station-->>Registry: Return result (solutions, titration, solvents, feeding_order, return_info)
ER diagram for compute_experiment_design result structureerDiagram
RESULT {
string return_info
}
SOLUTION {
string name
int order
float solid_mass
float solvent_volume
float concentration
float volume_needed
float molar_ratio
}
SOLID {
string name
int order
float mass
float molar_ratio
}
TITRATION {
string name
float main_portion
float titration_portion
float titration_solvent
}
SOLVENTS {
float additional_solvent
float total_liquid_volume
}
FEEDING_ORDER {
int step
string type
string name
float amount
int order
float titration_solvent
}
RESULT ||--o| SOLUTION : contains
RESULT ||--o| SOLID : contains
RESULT ||--o| TITRATION : contains
RESULT ||--o| SOLVENTS : contains
RESULT ||--o| FEEDING_ORDER : contains
Class diagram for updated DispensingStation with experiment designclassDiagram
class DispensingStation {
- order_completion_status: dict
- ureg: UnitRegistry
- compound_info: dict
+ compute_experiment_design(ratio, wt_percent, m_tot, titration_percent): dict
- _generate_experiment_design(ratio, wt_percent, m_tot, titration_percent): dict
}
DispensingStation --> UnitRegistry
DispensingStation --> BioyondException
File-Level Changes
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Hey there - I've reviewed your changes - here's some feedback:
- The compute_experiment_design method and _generate_experiment_design helper are very long—consider extracting parsing, calculation, and feeding-order logic into smaller, focused methods to improve readability and maintainability.
- The compound_info dictionary in init could be relocated to a module-level constant or external config to keep the class constructor lean and make updates easier.
- In _generate_experiment_design you mix ValueError and BioyondException—use a consistent exception type to standardize error handling across the compute_experiment_design workflow.
Prompt for AI Agents
Please address the comments from this code review:
## Overall Comments
- The compute_experiment_design method and _generate_experiment_design helper are very long—consider extracting parsing, calculation, and feeding-order logic into smaller, focused methods to improve readability and maintainability.
- The compound_info dictionary in __init__ could be relocated to a module-level constant or external config to keep the class constructor lean and make updates easier.
- In _generate_experiment_design you mix ValueError and BioyondException—use a consistent exception type to standardize error handling across the compute_experiment_design workflow.
## Individual Comments
### Comment 1
<location> `unilabos/devices/workstation/bioyond_studio/dispensing_station.py:286-289` </location>
<code_context>
+ compound_ratios = list(ratio.values())
+
+ # 验证所有化合物是否在 compound_info 中定义
+ undefined_compounds = [name for name in compound_names if name not in self.compound_info["MolWt"]]
+ if undefined_compounds:
+ available = list(self.compound_info["MolWt"].keys())
</code_context>
<issue_to_address>
**suggestion:** Raising ValueError instead of BioyondException may lead to inconsistent error handling.
Use BioyondException here for consistency, unless ValueError is required for a specific reason.
```suggestion
raise BioyondException(
f"以下化合物未在 compound_info 中定义: {undefined_compounds}。"
f"可用的化合物: {available}"
)
```
</issue_to_address>
### Comment 2
<location> `unilabos/devices/workstation/bioyond_studio/dispensing_station.py:304-309` </location>
<code_context>
+ diamine_compounds = [(name, ratio_val, mw, i) for i, (name, ratio_val, mw, fg) in enumerate(ordered_compounds) if fg == "Amine"]
+ anhydride_compounds = [(name, ratio_val, mw, i) for i, (name, ratio_val, mw, fg) in enumerate(ordered_compounds) if fg == "Anhydride"]
+
+ if not diamine_compounds or not anhydride_compounds:
+ raise ValueError(
+ f"需要同时包含二胺(Amine)和二酐(Anhydride)化合物。"
</code_context>
<issue_to_address>
**suggestion:** Error message for missing compound types could be more actionable.
Including the input ratio in the error message will make it easier for users to identify which input caused the issue.
```suggestion
if not diamine_compounds or not anhydride_compounds:
diamine_info = [(c[0], c[1]) for c in diamine_compounds]
anhydride_info = [(c[0], c[1]) for c in anhydride_compounds]
raise ValueError(
f"需要同时包含二胺(Amine)和二酐(Anhydride)化合物。\n"
f"当前二胺: {diamine_info} (格式: 名称, 摩尔比), "
f"当前二酐: {anhydride_info} (格式: 名称, 摩尔比)\n"
f"输入的原始摩尔比分别为: {compound_ratios}"
)
```
</issue_to_address>
### Comment 3
<location> `unilabos/devices/workstation/bioyond_studio/dispensing_station.py:444-447` </location>
<code_context>
+ })
+
+ # 4. 补加溶剂
+ if m_solvent_add > 0:
+ feeding_order.append({
+ "step": len(feeding_order) + 1,
</code_context>
<issue_to_address>
**suggestion (bug_risk):** Potential floating point comparison issue with m_solvent_add > 0.
Use an epsilon value when comparing floating point numbers to zero to prevent precision errors.
```suggestion
# 4. 补加溶剂
epsilon = 1e-8
if m_solvent_add > epsilon:
feeding_order.append({
"step": len(feeding_order) + 1,
```
</issue_to_address>
### Comment 4
<location> `unilabos/devices/workstation/bioyond_studio/dispensing_station.py:484` </location>
<code_context>
+ "minimum_required_mass": m_tot_min.magnitude
+ }
+
+ return results
+
# 90%10%小瓶投料任务创建方法
</code_context>
<issue_to_address>
**suggestion (bug_risk):** Unit magnitudes are extracted for output, but input units are not validated.
Validate input units to ensure consistency and avoid errors from unexpected unit types.
Suggested implementation:
```python
return results
# 90%10%小瓶投料任务创建方法
def create_90_10_vial_feeding_task(self,
order_name: str = None,
m_solvent_titration=None,
m_solvent_add=None,
total_liquid_volume=None,
m_tot_min=None,
*args, **kwargs):
# Validate input units
from pint import Quantity
expected_units = {
"m_solvent_titration": "gram",
"m_solvent_add": "gram",
"total_liquid_volume": "milliliter",
"m_tot_min": "gram"
}
for var_name, expected_unit in expected_units.items():
var = locals()[var_name]
if not isinstance(var, Quantity):
raise ValueError(f"{var_name} must be a Pint Quantity with units of {expected_unit}.")
if not var.check(expected_unit):
raise ValueError(f"{var_name} must have units of {expected_unit}, got {var.units}.")
```
You may need to adjust the function signature to ensure all relevant inputs are explicitly passed and validated. If these variables are set elsewhere, move the validation to where they are first assigned.
If you use a different units library or have a custom Quantity class, replace the Pint-specific checks accordingly.
</issue_to_address>
### Comment 5
<location> `unilabos/devices/workstation/bioyond_studio/dispensing_station.py:250` </location>
<code_context>
def _generate_experiment_design(
self,
ratio: dict,
wt_percent: float = 0.25,
m_tot: float = 70,
titration_percent: float = 0.03,
) -> dict:
"""内部方法：生成实验设计
根据FuncGroup自动区分二胺和二酐，每种二胺单独配溶液，严格按照ratio顺序投料。
参数:
ratio: 化合物配比字典，格式: {"compound_name": ratio_value}
wt_percent: 固体重量百分比
m_tot: 反应混合物总质量(g)
titration_percent: 滴定溶液百分比
返回:
包含实验设计详细参数的字典
"""
# 溶剂密度
ρ_solvent = 1.03 * self.ureg.g / self.ureg.ml
# 二酐溶解度
solubility = 0.02 * self.ureg.g / self.ureg.ml
# 投入固体时最小溶剂体积
V_min = 30 * self.ureg.ml
m_tot = m_tot * self.ureg.g
# 保持ratio中的顺序
compound_names = list(ratio.keys())
compound_ratios = list(ratio.values())
# 验证所有化合物是否在 compound_info 中定义
undefined_compounds = [name for name in compound_names if name not in self.compound_info["MolWt"]]
if undefined_compounds:
available = list(self.compound_info["MolWt"].keys())
raise ValueError(
f"以下化合物未在 compound_info 中定义: {undefined_compounds}。"
f"可用的化合物: {available}"
)
# 获取各化合物的分子量和官能团类型
molecular_weights = [self.compound_info["MolWt"][name] for name in compound_names]
func_groups = [self.compound_info["FuncGroup"][name] for name in compound_names]
# 记录化合物信息用于调试
self.hardware_interface._logger.info(f"化合物名称: {compound_names}")
self.hardware_interface._logger.info(f"官能团类型: {func_groups}")
# 按原始顺序分离二胺和二酐
ordered_compounds = list(zip(compound_names, compound_ratios, molecular_weights, func_groups))
diamine_compounds = [(name, ratio_val, mw, i) for i, (name, ratio_val, mw, fg) in enumerate(ordered_compounds) if fg == "Amine"]
anhydride_compounds = [(name, ratio_val, mw, i) for i, (name, ratio_val, mw, fg) in enumerate(ordered_compounds) if fg == "Anhydride"]
if not diamine_compounds or not anhydride_compounds:
raise ValueError(
f"需要同时包含二胺(Amine)和二酐(Anhydride)化合物。"
f"当前二胺: {[c[0] for c in diamine_compounds]}, "
f"当前二酐: {[c[0] for c in anhydride_compounds]}"
)
# 计算加权平均分子量 (基于摩尔比)
total_molar_ratio = sum(compound_ratios)
weighted_molecular_weight = sum(ratio_val * mw for ratio_val, mw in zip(compound_ratios, molecular_weights))
# 取最后一个二酐用于滴定
titration_anhydride = anhydride_compounds[-1]
solid_anhydrides = anhydride_compounds[:-1] if len(anhydride_compounds) > 1 else []
# 二胺溶液配制参数 - 每种二胺单独配制
diamine_solutions = []
total_diamine_volume = 0 * self.ureg.ml
# 计算反应物的总摩尔量
n_reactant = m_tot * wt_percent / weighted_molecular_weight
for name, ratio_val, mw, order_index in diamine_compounds:
# 跳过 SIDA
if name == "SIDA":
continue
# 计算该二胺需要的摩尔数
n_diamine_needed = n_reactant * ratio_val
# 二胺溶液配制参数 (每种二胺固定配制参数)
m_diamine_solid = 5.0 * self.ureg.g # 每种二胺固体质量
V_solvent_for_this = 20 * self.ureg.ml # 每种二胺溶剂体积
m_solvent_for_this = ρ_solvent * V_solvent_for_this
# 计算该二胺溶液的浓度
c_diamine = (m_diamine_solid / mw) / V_solvent_for_this
# 计算需要移取的溶液体积
V_diamine_needed = n_diamine_needed / c_diamine
diamine_solutions.append({
"name": name,
"order": order_index,
"solid_mass": m_diamine_solid.magnitude,
"solvent_volume": V_solvent_for_this.magnitude,
"concentration": c_diamine.magnitude,
"volume_needed": V_diamine_needed.magnitude,
"molar_ratio": ratio_val
})
total_diamine_volume += V_diamine_needed
# 按原始顺序排序
diamine_solutions.sort(key=lambda x: x["order"])
# 计算滴定二酐的质量
titration_name, titration_ratio, titration_mw, _ = titration_anhydride
m_titration_anhydride = n_reactant * titration_ratio * titration_mw
m_titration_90 = m_titration_anhydride * (1 - titration_percent)
m_titration_10 = m_titration_anhydride * titration_percent
# 计算其他固体二酐的质量 (按顺序)
solid_anhydride_masses = []
for name, ratio_val, mw, order_index in solid_anhydrides:
mass = n_reactant * ratio_val * mw
solid_anhydride_masses.append({
"name": name,
"order": order_index,
"mass": mass.magnitude,
"molar_ratio": ratio_val
})
# 按原始顺序排序
solid_anhydride_masses.sort(key=lambda x: x["order"])
# 计算溶剂用量
total_diamine_solution_mass = sum(
sol["volume_needed"] * ρ_solvent for sol in diamine_solutions
) * self.ureg.ml
# 预估滴定溶剂量、计算补加溶剂量
m_solvent_titration = m_titration_10 / solubility * ρ_solvent
m_solvent_add = m_tot * (1 - wt_percent) - total_diamine_solution_mass - m_solvent_titration
# 检查最小溶剂体积要求
total_liquid_volume = (total_diamine_solution_mass + m_solvent_add) / ρ_solvent
m_tot_min = V_min / total_liquid_volume * m_tot
# 如果需要，按比例放大
scale_factor = 1.0
if m_tot_min > m_tot:
scale_factor = (m_tot_min / m_tot).magnitude
m_titration_90 *= scale_factor
m_titration_10 *= scale_factor
m_solvent_add *= scale_factor
m_solvent_titration *= scale_factor
# 更新二胺溶液用量
for sol in diamine_solutions:
sol["volume_needed"] *= scale_factor
# 更新固体二酐用量
for anhydride in solid_anhydride_masses:
anhydride["mass"] *= scale_factor
m_tot = m_tot_min
# 生成投料顺序
feeding_order = []
# 1. 固体二酐 (按顺序)
for anhydride in solid_anhydride_masses:
feeding_order.append({
"step": len(feeding_order) + 1,
"type": "solid_anhydride",
"name": anhydride["name"],
"amount": anhydride["mass"],
"order": anhydride["order"]
})
# 2. 二胺溶液 (按顺序)
for sol in diamine_solutions:
feeding_order.append({
"step": len(feeding_order) + 1,
"type": "diamine_solution",
"name": sol["name"],
"amount": sol["volume_needed"],
"order": sol["order"]
})
# 3. 主要二酐粉末
feeding_order.append({
"step": len(feeding_order) + 1,
"type": "main_anhydride",
"name": titration_name,
"amount": m_titration_90.magnitude,
"order": titration_anhydride[3]
})
# 4. 补加溶剂
if m_solvent_add > 0:
feeding_order.append({
"step": len(feeding_order) + 1,
"type": "additional_solvent",
"name": "溶剂",
"amount": m_solvent_add.magnitude,
"order": 999
})
# 5. 滴定二酐溶液
feeding_order.append({
"step": len(feeding_order) + 1,
"type": "titration_anhydride",
"name": f"{titration_name} 滴定液",
"amount": m_titration_10.magnitude,
"titration_solvent": m_solvent_titration.magnitude,
"order": titration_anhydride[3]
})
# 返回实验设计结果
results = {
"total_mass": m_tot.magnitude,
"scale_factor": scale_factor,
"solutions": diamine_solutions,
"solids": solid_anhydride_masses,
"titration": {
"name": titration_name,
"main_portion": m_titration_90.magnitude,
"titration_portion": m_titration_10.magnitude,
"titration_solvent": m_solvent_titration.magnitude,
},
"solvents": {
"additional_solvent": m_solvent_add.magnitude,
"total_liquid_volume": total_liquid_volume.magnitude
},
"feeding_order": feeding_order,
"minimum_required_mass": m_tot_min.magnitude
}
return results
</code_context>
<issue_to_address>
**issue (code-quality):** We've found these issues:
- Use named expression to simplify assignment and conditional ([`use-named-expression`](https://docs.sourcery.ai/Reference/Default-Rules/refactorings/use-named-expression/))
- Replace assignment with augmented assignment ([`aug-assign`](https://docs.sourcery.ai/Reference/Default-Rules/refactorings/aug-assign/))
- Inline variable that is immediately returned ([`inline-immediately-returned-variable`](https://docs.sourcery.ai/Reference/Default-Rules/refactorings/inline-immediately-returned-variable/))
- Low code quality found in BioyondDispensingStation.\_generate\_experiment\_design - 22% ([`low-code-quality`](https://docs.sourcery.ai/Reference/Default-Rules/comments/low-code-quality/))
<br/><details><summary>Explanation</summary>
The quality score for this function is below the quality threshold of 25%.
This score is a combination of the method length, cognitive complexity and working memory.
How can you solve this?
It might be worth refactoring this function to make it shorter and more readable.
- Reduce the function length by extracting pieces of functionality out into
their own functions. This is the most important thing you can do - ideally a
function should be less than 10 lines.
- Reduce nesting, perhaps by introducing guard clauses to return early.
- Ensure that variables are tightly scoped, so that code using related concepts
sits together within the function rather than being scattered.</details>
</issue_to_address>Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews.
| "minimum_required_mass": m_tot_min.magnitude | ||
| } | ||
|
|
||
| return results |
There was a problem hiding this comment.
suggestion (bug_risk): Unit magnitudes are extracted for output, but input units are not validated.
Validate input units to ensure consistency and avoid errors from unexpected unit types.
Suggested implementation:
return results
# 90%10%小瓶投料任务创建方法
def create_90_10_vial_feeding_task(self,
order_name: str = None,
m_solvent_titration=None,
m_solvent_add=None,
total_liquid_volume=None,
m_tot_min=None,
*args, **kwargs):
# Validate input units
from pint import Quantity
expected_units = {
"m_solvent_titration": "gram",
"m_solvent_add": "gram",
"total_liquid_volume": "milliliter",
"m_tot_min": "gram"
}
for var_name, expected_unit in expected_units.items():
var = locals()[var_name]
if not isinstance(var, Quantity):
raise ValueError(f"{var_name} must be a Pint Quantity with units of {expected_unit}.")
if not var.check(expected_unit):
raise ValueError(f"{var_name} must have units of {expected_unit}, got {var.units}.")
You may need to adjust the function signature to ensure all relevant inputs are explicitly passed and validated. If these variables are set elsewhere, move the validation to where they are first assigned.
If you use a different units library or have a custom Quantity class, replace the Pint-specific checks accordingly.
改进节点ID解析逻辑以支持多种格式，包括字符串和数字标识符 添加数据类型转换处理，确保写入值时类型匹配 优化错误提示信息，便于调试节点连接问题
添加后处理站的YAML配置文件，包含动作映射、状态类型和设备描述
… configurations - Removed redundant action value mappings from bioyond_dispensing_station. - Updated goal properties in bioyond_dispensing_station to use enums for target_stack and other parameters. - Changed data types for end_point and start_point in reaction_station_bioyond to use string enums (Start, End). - Simplified descriptions and updated measurement units from μL to mL where applicable. - Removed unused commands from reaction_station_bioyond to streamline the configuration.
# Conflicts: # unilabos/device_comms/opcua_client/client.py # unilabos/device_comms/opcua_client/node/uniopcua.py # unilabos/registry/devices/post_process_station.yaml
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Summary by Sourcery
Introduce a unit-aware experiment design workflow by adding compute_experiment_design and its internal logic, enabling flexible ratio parsing, compound metadata lookup, and automated feeding order generation.
New Features:
Enhancements: