| Name | nasem_dairy JSON |
| Version |
1.0.1
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| Summary | Implementation of the NASEM Nutrient Requirements of Dairy Cattle 8th Edition in Python |
| upload_time | 2024-09-03 17:35:00 |
| maintainer | None |
| docs_url | None |
| author | Braeden Fieguth & David Innes |
| requires_python | <4.0,>=3.9 |
| license | MIT |
| keywords |
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# The NASEM 2021 Nutrient Requirements of Dairy Cattle Model in Python
[](https://coveralls.io/github/CNM-University-of-Guelph/NASEM-Model-Python?branch=main)
[](https://github.com/CNM-University-of-Guelph/NASEM-Model-Python/blob/main/LICENSE)
- [Installation](#installation)
- [Using the Model](#using-the-model)
- [Import package](#import-package)
- [Get input data](#get-input-data)
- [Execute Model](#execute-model)
This is a python version of the Nutrient Requirments of Dairy Cattle 8th
revisied edition (NASEM
2021)\[https://nap.nationalacademies.org/catalog/25806/nutrient-requirements-of-dairy-cattle-eighth-revised-edition\]
model. The equations have been copied from the R functions released with the official NASEM software:
https://nap.nationalacademies.org/resource/25806/Installation_Instructions_NASEM_Dairy8.pdf
## License
This project is distributed under the terms of the [MIT License](./LICENSE).
### Code Attribution
The NASEM equations were coppied from the R code that was distributed under different licensing terms. The original R code is distributed under a [separate license](./LICENSE-R_Code).
Please refer to both the [MIT License](./LICENSE) and the [R Code License](./LICENSE-R_Code) for detailed terms.
## Contributing
We welcome contributions! Please read our [Contributing Guidelines](./CONTRIBUTING.md) to get started.
## Installation
This package can be installed through a terminal using:
pip install nasem_dairy
The environment that this is installed into will require
`python >= 3.10`. It will also install `pandas` and `numpy`
automatically.
The suggested way to install this is inside a virtual environment. For
example, by using [Miniconda](https://docs.conda.io/en/latest/miniconda.html).
1. Open a terminal window, e.g., Anaconda Prompt.
2. Create a new conda environment with Python installed:
conda create --name myenvironment python>=3.10
conda activate myenvironment
3. Install this package via pip:
pip install nasem_dairy
## Using the Model
### Import package
First, import `nasem_dairy`. `pandas` is also required if you want to modify the user_diet input.
``` python
import nasem_dairy as nd
import pandas as pd
```
### Get input data
There are several demo scenarios built into the app which can be used as input to the
model. These can be acessed using `nd.demo()`:
``` python
user_diet, animal_input, equation_selection, infusion_input = nd.demo("input")
```
These 4 inputs can be modified to simulate different scenarios:
- user_diet (pd.DataFrame): The name and kg/d to feed for each ingredient in the diet.
- animal_input (dict): Parameters to descibe the animal being modelled.
- equation_selection (dict): Select which equation to use when there are multiple options.
- infusion_input (dict): If simulating infusions specify this here. This is optional.
### Execute Model
To run the model use `nd.nasem` and pass the input data. This will return an instance of the nd.ModelOutput.
Printing this will provide a snapshot of the results.
``` python
## RUN MODEL ###
output = nd.nasem(
user_diet = user_diet_in,
animal_input = animal_input_in,
equation_selection = equation_selection_in,
infusion_input = infusion_input)
print(output)
```
=====================
Model Output Snapshot
=====================
Milk production kg (Mlk_Prod_comp): 34.197
Milk fat g/g (MlkFat_Milk): 0.053
Milk protein g/g (MlkNP_Milk): 0.037
Milk Production - MP allowable kg (Mlk_Prod_MPalow): 35.906
Milk Production - NE allowable kg (Mlk_Prod_NEalow): 31.261
Animal ME intake Mcal/d (An_MEIn): 59.976
Target ME use Mcal/d (Trg_MEuse): 52.195
Animal MP intake g/d (An_MPIn_g): 2565.214
Animal MP use g/d (An_MPuse_g_Trg): 1989.985
Animal RDP intake g/d (An_RDPIn_g): 3556.058
Diet DCAD meq (An_DCADmeq): 96.453
This is a `ModelOutput` object with methods to access all model outputs. See help(ModelOutput).
### Exploring the Results
The `nd.ModelOutput` class provides several methods to help you explore the results.
If you need a specific value you can used `get_value()`.
```python
Du_MiCP = output.get_value("Du_MiCP")
print(Du_MiCP)
```
2.0524052134583415
If you don't know the exact name of the variable you can use the `search()` method.
For example if you want to see values related to microbial crude protein (MiCP) you
could search for "MiCP".
```python
print(output.search("MiCP"))
```
Name Value Category Level 1 Level 2
0 Du_EndCP 0.280988 Production MiCP Du_EndCP
1 Du_EndCP_g 280.98834 Production MiCP Du_EndCP_g
2 Du_EndN 0.04507 Production MiCP Du_EndN
3 Du_EndN_g 45.07041 Production MiCP Du_EndN_g
4 Du_IdEAAMic 767.796581 Production MiCP Du_IdEAAMic
5 Du_MiCP 2.052405 Production MiCP Du_MiCP
6 Du_MiCP_g 2052.405213 Production MiCP Du_MiCP_g
7 Du_MiN_NRC2001_g 334.939405 Production MiCP Du_MiN_NRC2001_g
8 Du_MiN_g 328.384834 Production MiCP Du_MiN_g
9 Du_MiTP 1.691182 Production MiCP Du_MiTP
10 Du_MiTP_g 1691.181896 Production MiCP Du_MiTP_g
11 Du_NANMN_g 302.751473 Production MiCP Du_NANMN_g
12 Du_NAN_g 631.136307 Production MiCP Du_NAN_g
13 Du_idMiCP 1.641924 Production MiCP Du_idMiCP
14 Du_idMiCP_g 1641.924171 Production MiCP Du_idMiCP_g
15 Du_idMiTP 1.352946 Production MiCP Du_idMiTP
16 Du_idMiTP_g 1352.945517 Production MiCP Du_idMiTP_g
17 EndAAProf [4.61, 2.9, 4.09, 7.67, 6.23, 1.26, 3.98, 5.18... Production MiCP EndAAProf
18 Fe_DEMiCPend 2.319218 Excretion fecal Fe_DEMiCPend
19 Fe_RumMiCP 0.410481 Excretion fecal Fe_RumMiCP
20 MiCP Dictionary Production MiCP
21 MiN_Vm 340.791931 Production MiCP MiN_Vm
22 MiTPAAProf [5.47, 2.21, 6.99, 9.23, 9.44, 2.63, 6.3, 6.23... Production MiCP MiTPAAProf
23 RDPIn_MiNmax 2.94252 Production MiCP RDPIn_MiNmax
24 Rum_MiCP_DigCHO 0.326383 Production MiCP Rum_MiCP_DigCHO
25 SI_dcMiCP 80.0 Inputs coeff_dict SI_dcMiCP
26 fMiTP_MiCP 0.824 Inputs coeff_dict fMiTP_MiCP
This will print a table with all the variables related to the search string and their value.
The "Category" and "Level" columns show where the values are stored within ModelOutput.
In this case "MiCP" is the name of a dictionary containg values related to microbial crude protein
production, so all values in this dictionary are displayed even if "MiCP" is not in the variable name.
The `nasem_dairy` package also includes all of the reports from the R version. These can be
accessed through the `get_report()` method by specifying the report name.
```python
print(output.get_report("table1_1"))
```
Description Values
0 Animal Type Lactating Cow
1 Animal Breed Holstein
2 Body Weight, kg 624.795
3 Empty BW, kg 512.395561
4 Birth Weight, kg 44.1
5 Mature Weight, kg 700.0
6 Mature Empty BW, kg 574.0
7 Age, d 820.8
8 Condition Score, 1 to 5 3.0
9 Percent First Parity 100.0
10 Days in Milk 100
11 Age At First Calving, d
12 Days Pregnant 46
13 Temperature, C 22.0
14 Use In vitro NDF digest 0
15 Feeding Monensin, 0=No, 1=Yes 0
16 Grazing 0
17 Topography 0.0
18 Dist. (Pasture to Parlor, m) 0.0
19 One-Way Trips to the Parlor, m 0
### The NASEM Directed Acyclic Graph (DAG)
`nasem_dairy` comes with an optional dag subpackage. This subpackage requires you install
the `graph-tool` package. This package has several requirements depending on your operating system.
For instructions on how to install visit their [website](https://graph-tool.skewed.de/installation.html)
This subpackage parses the equations used to build the NASEM model and uses the function arguments to
build a DAG. This can then be used to explore how varaibles in the model are connected.
Once you have `graph-tool` installed you can create a DAG.
```python
dag = nd.ModelDAG()
```
Once you have created the DAG you can explore how functions are connected using `dag.get_calculation_order()`.
```python
dag.get_calculation_order("GrUter_Wt")
```
Requirements for Calculating GrUter_Wt
Order of Functions to Call:
1. calculate_Uter_Wtpart
2. calculate_Uter_Wt
3. calculate_GrUter_Wtpart
4. calculate_GrUter_Wt
Required User Inputs:
animal_input:
- An_AgeDay
- An_GestLength
- An_Parity_rl
- An_GestDay
- Fet_BWbrth
- An_LactDay
Required Constants:
coeff_dict:
- GrUter_Ksyn
- GrUterWt_FetBWbrth
- Uter_KsynDecay
- GrUter_KsynDecay
- Uter_Ksyn
- Uter_Kdeg
- Uter_Wt_coeff
- UterWt_FetBWbrth
This will display the functions that need to be called in order to calulate the specified value.
It also provides all of the input values that are involved in the calculation.
If you are only interested in calcualting a specific value you can use the `create_function()` method.
This will return a function that takes the required inputs and will call all of the functions
required to calculate the specified value. This returns a ModelOuptut just like the `nd.nasem()` function.
```
GrUter_Wt_function = dag.create_function("GrUter_Wt")
print(inspect.signature(GrUter_Wt_function))
```
(animal_input, coeff_dict)
By inspecting the signature of the created function we see it requires animal_input and coeff_dict.
The animal_input can be the same as the one used to run `nd.nasem()`, though only the specified keys are required.
The coeff_dict contains default coefficients. The default can be accessed as `nd.coeff_dict`.
Raw data
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"description": "# The NASEM 2021 Nutrient Requirements of Dairy Cattle Model in Python\n\n\n[](https://coveralls.io/github/CNM-University-of-Guelph/NASEM-Model-Python?branch=main)\n[](https://github.com/CNM-University-of-Guelph/NASEM-Model-Python/blob/main/LICENSE)\n\n\n- [Installation](#installation)\n- [Using the Model](#using-the-model)\n - [Import package](#import-package)\n - [Get input data](#get-input-data)\n - [Execute Model](#execute-model)\n\nThis is a python version of the Nutrient Requirments of Dairy Cattle 8th\nrevisied edition (NASEM\n2021)\\[https://nap.nationalacademies.org/catalog/25806/nutrient-requirements-of-dairy-cattle-eighth-revised-edition\\]\nmodel. The equations have been copied from the R functions released with the official NASEM software:\nhttps://nap.nationalacademies.org/resource/25806/Installation_Instructions_NASEM_Dairy8.pdf\n\n## License\n\nThis project is distributed under the terms of the [MIT License](./LICENSE).\n\n### Code Attribution\n\nThe NASEM equations were coppied from the R code that was distributed under different licensing terms. The original R code is distributed under a [separate license](./LICENSE-R_Code).\n\nPlease refer to both the [MIT License](./LICENSE) and the [R Code License](./LICENSE-R_Code) for detailed terms.\n\n## Contributing\n\nWe welcome contributions! Please read our [Contributing Guidelines](./CONTRIBUTING.md) to get started.\n\n## Installation\n\nThis package can be installed through a terminal using:\n\n pip install nasem_dairy\n\nThe environment that this is installed into will require\n`python >= 3.10`. It will also install `pandas` and `numpy`\nautomatically.\n\nThe suggested way to install this is inside a virtual environment. For\nexample, by using [Miniconda](https://docs.conda.io/en/latest/miniconda.html).\n\n1. Open a terminal window, e.g., Anaconda Prompt.\n\n2. Create a new conda environment with Python installed:\n\n conda create --name myenvironment python>=3.10\n\n conda activate myenvironment\n\n3. Install this package via pip:\n\n pip install nasem_dairy\n\n## Using the Model\n\n### Import package\n\nFirst, import `nasem_dairy`. `pandas` is also required if you want to modify the user_diet input.\n\n``` python\nimport nasem_dairy as nd\nimport pandas as pd\n```\n\n### Get input data\n\nThere are several demo scenarios built into the app which can be used as input to the\nmodel. These can be acessed using `nd.demo()`:\n\n``` python\nuser_diet, animal_input, equation_selection, infusion_input = nd.demo(\"input\")\n```\nThese 4 inputs can be modified to simulate different scenarios:\n- user_diet (pd.DataFrame): The name and kg/d to feed for each ingredient in the diet.\n- animal_input (dict): Parameters to descibe the animal being modelled. \n- equation_selection (dict): Select which equation to use when there are multiple options.\n- infusion_input (dict): If simulating infusions specify this here. This is optional.\n\n### Execute Model\n\nTo run the model use `nd.nasem` and pass the input data. This will return an instance of the nd.ModelOutput. \nPrinting this will provide a snapshot of the results. \n\n``` python\n## RUN MODEL ###\noutput = nd.nasem(\n user_diet = user_diet_in, \n animal_input = animal_input_in, \n equation_selection = equation_selection_in, \n infusion_input = infusion_input)\n\nprint(output)\n```\n\n =====================\n Model Output Snapshot\n =====================\n Milk production kg (Mlk_Prod_comp): 34.197\n Milk fat g/g (MlkFat_Milk): 0.053\n Milk protein g/g (MlkNP_Milk): 0.037\n Milk Production - MP allowable kg (Mlk_Prod_MPalow): 35.906\n Milk Production - NE allowable kg (Mlk_Prod_NEalow): 31.261\n Animal ME intake Mcal/d (An_MEIn): 59.976\n Target ME use Mcal/d (Trg_MEuse): 52.195\n Animal MP intake g/d (An_MPIn_g): 2565.214\n Animal MP use g/d (An_MPuse_g_Trg): 1989.985\n Animal RDP intake g/d (An_RDPIn_g): 3556.058\n Diet DCAD meq (An_DCADmeq): 96.453\n\n This is a `ModelOutput` object with methods to access all model outputs. See help(ModelOutput).\n\n\n### Exploring the Results\n\nThe `nd.ModelOutput` class provides several methods to help you explore the results.\n\nIf you need a specific value you can used `get_value()`.\n\n```python\nDu_MiCP = output.get_value(\"Du_MiCP\")\nprint(Du_MiCP)\n```\n\n 2.0524052134583415\n\nIf you don't know the exact name of the variable you can use the `search()` method.\nFor example if you want to see values related to microbial crude protein (MiCP) you\ncould search for \"MiCP\".\n\n```python\nprint(output.search(\"MiCP\"))\n```\n Name Value Category Level 1 Level 2\n 0 Du_EndCP 0.280988 Production MiCP Du_EndCP\n 1 Du_EndCP_g 280.98834 Production MiCP Du_EndCP_g\n 2 Du_EndN 0.04507 Production MiCP Du_EndN\n 3 Du_EndN_g 45.07041 Production MiCP Du_EndN_g\n 4 Du_IdEAAMic 767.796581 Production MiCP Du_IdEAAMic\n 5 Du_MiCP 2.052405 Production MiCP Du_MiCP\n 6 Du_MiCP_g 2052.405213 Production MiCP Du_MiCP_g\n 7 Du_MiN_NRC2001_g 334.939405 Production MiCP Du_MiN_NRC2001_g\n 8 Du_MiN_g 328.384834 Production MiCP Du_MiN_g\n 9 Du_MiTP 1.691182 Production MiCP Du_MiTP\n 10 Du_MiTP_g 1691.181896 Production MiCP Du_MiTP_g\n 11 Du_NANMN_g 302.751473 Production MiCP Du_NANMN_g\n 12 Du_NAN_g 631.136307 Production MiCP Du_NAN_g\n 13 Du_idMiCP 1.641924 Production MiCP Du_idMiCP\n 14 Du_idMiCP_g 1641.924171 Production MiCP Du_idMiCP_g\n 15 Du_idMiTP 1.352946 Production MiCP Du_idMiTP\n 16 Du_idMiTP_g 1352.945517 Production MiCP Du_idMiTP_g\n 17 EndAAProf [4.61, 2.9, 4.09, 7.67, 6.23, 1.26, 3.98, 5.18... Production MiCP EndAAProf\n 18 Fe_DEMiCPend 2.319218 Excretion fecal Fe_DEMiCPend\n 19 Fe_RumMiCP 0.410481 Excretion fecal Fe_RumMiCP\n 20 MiCP Dictionary Production MiCP \n 21 MiN_Vm 340.791931 Production MiCP MiN_Vm\n 22 MiTPAAProf [5.47, 2.21, 6.99, 9.23, 9.44, 2.63, 6.3, 6.23... Production MiCP MiTPAAProf\n 23 RDPIn_MiNmax 2.94252 Production MiCP RDPIn_MiNmax\n 24 Rum_MiCP_DigCHO 0.326383 Production MiCP Rum_MiCP_DigCHO\n 25 SI_dcMiCP 80.0 Inputs coeff_dict SI_dcMiCP\n 26 fMiTP_MiCP 0.824 Inputs coeff_dict fMiTP_MiCP\n\nThis will print a table with all the variables related to the search string and their value. \nThe \"Category\" and \"Level\" columns show where the values are stored within ModelOutput. \nIn this case \"MiCP\" is the name of a dictionary containg values related to microbial crude protein\nproduction, so all values in this dictionary are displayed even if \"MiCP\" is not in the variable name.\n\nThe `nasem_dairy` package also includes all of the reports from the R version. These can be\naccessed through the `get_report()` method by specifying the report name.\n\n```python\nprint(output.get_report(\"table1_1\"))\n```\n Description Values\n 0 Animal Type Lactating Cow\n 1 Animal Breed Holstein\n 2 Body Weight, kg 624.795\n 3 Empty BW, kg 512.395561\n 4 Birth Weight, kg 44.1\n 5 Mature Weight, kg 700.0\n 6 Mature Empty BW, kg 574.0\n 7 Age, d 820.8\n 8 Condition Score, 1 to 5 3.0\n 9 Percent First Parity 100.0\n 10 Days in Milk 100\n 11 Age At First Calving, d \n 12 Days Pregnant 46\n 13 Temperature, C 22.0\n 14 Use In vitro NDF digest 0\n 15 Feeding Monensin, 0=No, 1=Yes 0\n 16 Grazing 0\n 17 Topography 0.0\n 18 Dist. (Pasture to Parlor, m) 0.0\n 19 One-Way Trips to the Parlor, m 0\n\n### The NASEM Directed Acyclic Graph (DAG)\n\n`nasem_dairy` comes with an optional dag subpackage. This subpackage requires you install\nthe `graph-tool` package. This package has several requirements depending on your operating system.\nFor instructions on how to install visit their [website](https://graph-tool.skewed.de/installation.html)\n\nThis subpackage parses the equations used to build the NASEM model and uses the function arguments to \nbuild a DAG. This can then be used to explore how varaibles in the model are connected.\n\nOnce you have `graph-tool` installed you can create a DAG.\n\n```python\ndag = nd.ModelDAG()\n```\n\nOnce you have created the DAG you can explore how functions are connected using `dag.get_calculation_order()`.\n\n```python\ndag.get_calculation_order(\"GrUter_Wt\")\n```\n\n Requirements for Calculating GrUter_Wt\n\n Order of Functions to Call:\n 1. calculate_Uter_Wtpart\n 2. calculate_Uter_Wt\n 3. calculate_GrUter_Wtpart\n 4. calculate_GrUter_Wt\n\n Required User Inputs:\n animal_input:\n - An_AgeDay\n - An_GestLength\n - An_Parity_rl\n - An_GestDay\n - Fet_BWbrth\n - An_LactDay\n\n Required Constants:\n coeff_dict:\n - GrUter_Ksyn\n - GrUterWt_FetBWbrth\n - Uter_KsynDecay\n - GrUter_KsynDecay\n - Uter_Ksyn\n - Uter_Kdeg\n - Uter_Wt_coeff\n - UterWt_FetBWbrth\n\nThis will display the functions that need to be called in order to calulate the specified value.\nIt also provides all of the input values that are involved in the calculation.\n\nIf you are only interested in calcualting a specific value you can use the `create_function()` method.\nThis will return a function that takes the required inputs and will call all of the functions\nrequired to calculate the specified value. This returns a ModelOuptut just like the `nd.nasem()` function.\n\n```\nGrUter_Wt_function = dag.create_function(\"GrUter_Wt\")\nprint(inspect.signature(GrUter_Wt_function))\n```\n (animal_input, coeff_dict)\n\nBy inspecting the signature of the created function we see it requires animal_input and coeff_dict.\nThe animal_input can be the same as the one used to run `nd.nasem()`, though only the specified keys are required.\nThe coeff_dict contains default coefficients. The default can be accessed as `nd.coeff_dict`.\n",
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